From b1fec6453f82e0dab717fb161711a8d33345e570 Mon Sep 17 00:00:00 2001
From: Iacopo Masi <iacopo.masi@gmail.com>
Date: Tue, 30 Apr 2024 12:19:06 +0200
Subject: [PATCH] decision trees

---
 .../09_decision_trees/09_decision_trees.css   |    87 +
 .../09_decision_trees/09_decision_trees.html  | 19339 ++++++++++++++++
 .../09_decision_trees/09_decision_trees.ipynb |  4249 ++++
 .../09_decision_trees/09_decision_trees.pdf   |   Bin 0 -> 3795758 bytes
 .../09_decision_trees/figs/bias_variance.png  |   Bin 0 -> 82479 bytes
 .../course/09_decision_trees/figs/data_01.png |   Bin 0 -> 37534 bytes
 .../course/09_decision_trees/figs/data_02.png |   Bin 0 -> 38812 bytes
 .../course/09_decision_trees/figs/data_03.png |   Bin 0 -> 20452 bytes
 .../course/09_decision_trees/figs/data_04.png |   Bin 0 -> 32148 bytes
 AA2324/course/09_decision_trees/figs/dice.png |   Bin 0 -> 145200 bytes
 .../09_decision_trees/figs/ensemble.png       |   Bin 0 -> 108699 bytes
 .../course/09_decision_trees/figs/ent_a.png   |   Bin 0 -> 8517 bytes
 .../course/09_decision_trees/figs/ent_b.png   |   Bin 0 -> 7685 bytes
 .../09_decision_trees/figs/impurity_00.png    |   Bin 0 -> 10992 bytes
 .../09_decision_trees/figs/impurity_01.png    |   Bin 0 -> 12490 bytes
 .../09_decision_trees/figs/impurity_02.png    |   Bin 0 -> 12542 bytes
 .../09_decision_trees/figs/impurity_03.png    |   Bin 0 -> 13919 bytes
 .../09_decision_trees/figs/impurity_04.png    |   Bin 0 -> 13919 bytes
 .../09_decision_trees/figs/impurity_05.png    |   Bin 0 -> 15900 bytes
 .../09_decision_trees/figs/impurity_06.png    |   Bin 0 -> 18874 bytes
 .../09_decision_trees/figs/impurity_07.png    |   Bin 0 -> 25744 bytes
 .../09_decision_trees/figs/impurity_08.png    |   Bin 0 -> 25744 bytes
 .../09_decision_trees/figs/impurity_09.png    |   Bin 0 -> 19038 bytes
 AA2324/course/09_decision_trees/figs/mdi.png  |   Bin 0 -> 16413 bytes
 .../figs/missclass_split.png                  |   Bin 0 -> 24742 bytes
 .../figs/missclass_split2.png                 |   Bin 0 -> 15314 bytes
 AA2324/course/09_decision_trees/figs/nn.png   |   Bin 0 -> 58062 bytes
 .../09_decision_trees/figs/over_under_fit.png |   Bin 0 -> 266187 bytes
 .../09_decision_trees/figs/split_00.png       |   Bin 0 -> 33078 bytes
 .../09_decision_trees/figs/split_01.png       |   Bin 0 -> 25251 bytes
 AA2324/course/09_decision_trees/figs/tree.png |   Bin 0 -> 25169 bytes
 .../course/09_decision_trees/figs/tree_02.png |   Bin 0 -> 25169 bytes
 AA2324/course/09_decision_trees/figs/xbox.png |   Bin 0 -> 169842 bytes
 .../course/09_decision_trees/figs/xbox2.png   |   Bin 0 -> 184858 bytes
 .../course/09_decision_trees/figs/xbox3.png   |   Bin 0 -> 271328 bytes
 .../course/09_decision_trees/figs/xbox4.png   |   Bin 0 -> 233382 bytes
 36 files changed, 23675 insertions(+)
 create mode 100644 AA2324/course/09_decision_trees/09_decision_trees.css
 create mode 100644 AA2324/course/09_decision_trees/09_decision_trees.html
 create mode 100644 AA2324/course/09_decision_trees/09_decision_trees.ipynb
 create mode 100644 AA2324/course/09_decision_trees/09_decision_trees.pdf
 create mode 100644 AA2324/course/09_decision_trees/figs/bias_variance.png
 create mode 100644 AA2324/course/09_decision_trees/figs/data_01.png
 create mode 100644 AA2324/course/09_decision_trees/figs/data_02.png
 create mode 100644 AA2324/course/09_decision_trees/figs/data_03.png
 create mode 100644 AA2324/course/09_decision_trees/figs/data_04.png
 create mode 100644 AA2324/course/09_decision_trees/figs/dice.png
 create mode 100644 AA2324/course/09_decision_trees/figs/ensemble.png
 create mode 100644 AA2324/course/09_decision_trees/figs/ent_a.png
 create mode 100644 AA2324/course/09_decision_trees/figs/ent_b.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_00.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_01.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_02.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_03.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_04.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_05.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_06.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_07.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_08.png
 create mode 100644 AA2324/course/09_decision_trees/figs/impurity_09.png
 create mode 100644 AA2324/course/09_decision_trees/figs/mdi.png
 create mode 100644 AA2324/course/09_decision_trees/figs/missclass_split.png
 create mode 100644 AA2324/course/09_decision_trees/figs/missclass_split2.png
 create mode 100644 AA2324/course/09_decision_trees/figs/nn.png
 create mode 100644 AA2324/course/09_decision_trees/figs/over_under_fit.png
 create mode 100644 AA2324/course/09_decision_trees/figs/split_00.png
 create mode 100644 AA2324/course/09_decision_trees/figs/split_01.png
 create mode 100644 AA2324/course/09_decision_trees/figs/tree.png
 create mode 100644 AA2324/course/09_decision_trees/figs/tree_02.png
 create mode 100644 AA2324/course/09_decision_trees/figs/xbox.png
 create mode 100644 AA2324/course/09_decision_trees/figs/xbox2.png
 create mode 100644 AA2324/course/09_decision_trees/figs/xbox3.png
 create mode 100644 AA2324/course/09_decision_trees/figs/xbox4.png

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+++ b/AA2324/course/09_decision_trees/09_decision_trees.css
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diff --git a/AA2324/course/09_decision_trees/09_decision_trees.html b/AA2324/course/09_decision_trees/09_decision_trees.html
new file mode 100644
index 0000000..d652ac9
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@@ -0,0 +1,19339 @@
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+	border:1px solid transparent;
+  background-color: transparent;
+  position: absolute;
+	z-index:1;
+	right:3%;
+	top: 0;
+	bottom: 0;
+	margin: auto;
+	padding: 7px 0;
+	display: none;
+	vertical-align: middle;
+  outline: 0;
+  cursor: pointer;
+}
+.lm-close-icon:after {
+	content: "X";
+	display: block;
+	width: 15px;
+	height: 15px;
+	text-align: center;
+	color:#000;
+	font-weight: normal;
+	font-size: 12px;
+	cursor: pointer;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-DockPanel, /* </DEPRECATED> */
+.lm-DockPanel {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-widget, /* </DEPRECATED> */
+.lm-DockPanel-widget {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-tabBar, /* </DEPRECATED> */
+.lm-DockPanel-tabBar {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle, /* </DEPRECATED> */
+.lm-DockPanel-handle {
+  z-index: 2;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-handle:after, /* </DEPRECATED> */
+.lm-DockPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal'] {
+  cursor: ew-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical'] {
+  cursor: ns-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='horizontal']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='horizontal']:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+
+/* <DEPRECATED> */
+.p-DockPanel-handle[data-orientation='vertical']:after,
+/* </DEPRECATED> */
+.lm-DockPanel-handle[data-orientation='vertical']:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay, /* </DEPRECATED> */
+.lm-DockPanel-overlay {
+  z-index: 3;
+  box-sizing: border-box;
+  pointer-events: none;
+}
+
+
+/* <DEPRECATED> */ .p-DockPanel-overlay.p-mod-hidden, /* </DEPRECATED> */
+.lm-DockPanel-overlay.lm-mod-hidden {
+  display: none !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-Menu, /* </DEPRECATED> */
+.lm-Menu {
+  z-index: 10000;
+  position: absolute;
+  white-space: nowrap;
+  overflow-x: hidden;
+  overflow-y: auto;
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-content, /* </DEPRECATED> */
+.lm-Menu-content {
+  margin: 0;
+  padding: 0;
+  display: table;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-item, /* </DEPRECATED> */
+.lm-Menu-item {
+  display: table-row;
+}
+
+
+/* <DEPRECATED> */
+.p-Menu-item.p-mod-hidden,
+.p-Menu-item.p-mod-collapsed,
+/* </DEPRECATED> */
+.lm-Menu-item.lm-mod-hidden,
+.lm-Menu-item.lm-mod-collapsed {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */
+.p-Menu-itemIcon,
+.p-Menu-itemSubmenuIcon,
+/* </DEPRECATED> */
+.lm-Menu-itemIcon,
+.lm-Menu-itemSubmenuIcon {
+  display: table-cell;
+  text-align: center;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-itemLabel, /* </DEPRECATED> */
+.lm-Menu-itemLabel {
+  display: table-cell;
+  text-align: left;
+}
+
+
+/* <DEPRECATED> */ .p-Menu-itemShortcut, /* </DEPRECATED> */
+.lm-Menu-itemShortcut {
+  display: table-cell;
+  text-align: right;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-MenuBar, /* </DEPRECATED> */
+.lm-MenuBar {
+  outline: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-MenuBar-content, /* </DEPRECATED> */
+.lm-MenuBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex-direction: row;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */ .p--MenuBar-item, /* </DEPRECATED> */
+.lm-MenuBar-item {
+  box-sizing: border-box;
+}
+
+
+/* <DEPRECATED> */
+.p-MenuBar-itemIcon,
+.p-MenuBar-itemLabel,
+/* </DEPRECATED> */
+.lm-MenuBar-itemIcon,
+.lm-MenuBar-itemLabel {
+  display: inline-block;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-ScrollBar, /* </DEPRECATED> */
+.lm-ScrollBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='horizontal'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='horizontal'] {
+  flex-direction: row;
+}
+
+
+/* <DEPRECATED> */
+.p-ScrollBar[data-orientation='vertical'],
+/* </DEPRECATED> */
+.lm-ScrollBar[data-orientation='vertical'] {
+  flex-direction: column;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-button, /* </DEPRECATED> */
+.lm-ScrollBar-button {
+  box-sizing: border-box;
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-track, /* </DEPRECATED> */
+.lm-ScrollBar-track {
+  box-sizing: border-box;
+  position: relative;
+  overflow: hidden;
+  flex: 1 1 auto;
+}
+
+
+/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
+.lm-ScrollBar-thumb {
+  box-sizing: border-box;
+  position: absolute;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-SplitPanel-child, /* </DEPRECATED> */
+.lm-SplitPanel-child {
+  z-index: 0;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle, /* </DEPRECATED> */
+.lm-SplitPanel-handle {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle.p-mod-hidden, /* </DEPRECATED> */
+.lm-SplitPanel-handle.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-SplitPanel-handle:after, /* </DEPRECATED> */
+.lm-SplitPanel-handle:after {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  content: '';
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
+  cursor: ew-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
+  cursor: ns-resize;
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
+  left: 50%;
+  min-width: 8px;
+  transform: translateX(-50%);
+}
+
+
+/* <DEPRECATED> */
+.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle:after,
+/* </DEPRECATED> */
+.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
+  top: 50%;
+  min-height: 8px;
+  transform: translateY(-50%);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-TabBar, /* </DEPRECATED> */
+.lm-TabBar {
+  display: flex;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] {
+  flex-direction: row;
+  align-items: flex-end;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] {
+  flex-direction: column;
+  align-items: flex-end;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
+.lm-TabBar-content {
+  margin: 0;
+  padding: 0;
+  display: flex;
+  flex: 1 1 auto;
+  list-style-type: none;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
+  flex-direction: row;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
+/* </DEPRECATED> */
+.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
+  flex-direction: column;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
+.lm-TabBar-tab {
+  display: flex;
+  flex-direction: row;
+  box-sizing: border-box;
+  overflow: hidden;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar-tabIcon,
+.p-TabBar-tabCloseIcon,
+/* </DEPRECATED> */
+.lm-TabBar-tabIcon,
+.lm-TabBar-tabCloseIcon {
+  flex: 0 0 auto;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tabLabel, /* </DEPRECATED> */
+.lm-TabBar-tabLabel {
+  flex: 1 1 auto;
+  overflow: hidden;
+  white-space: nowrap;
+}
+
+
+.lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing : border-box;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */
+.lm-TabBar-tab.lm-mod-hidden {
+  display: none !important;
+}
+
+
+.lm-TabBar-addButton.lm-mod-hidden {
+  display: none !important;
+}
+
+
+/* <DEPRECATED> */ .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
+  position: relative;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
+  left: 0;
+  transition: left 150ms ease;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
+  top: 0;
+  transition: top 150ms ease;
+}
+
+
+/* <DEPRECATED> */
+.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging,
+/* </DEPRECATED> */
+.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
+  transition: none;
+}
+
+.lm-TabBar-tabLabel .lm-TabBar-tabInput {
+  user-select: all;
+  width: 100%;
+  box-sizing : border-box;
+  background: inherit;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ .p-TabPanel-tabBar, /* </DEPRECATED> */
+.lm-TabPanel-tabBar {
+  z-index: 1;
+}
+
+
+/* <DEPRECATED> */ .p-TabPanel-stackedPanel, /* </DEPRECATED> */
+.lm-TabPanel-stackedPanel {
+  z-index: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+@charset "UTF-8";
+html{
+  -webkit-box-sizing:border-box;
+          box-sizing:border-box; }
+
+*,
+*::before,
+*::after{
+  -webkit-box-sizing:inherit;
+          box-sizing:inherit; }
+
+body{
+  font-size:14px;
+  font-weight:400;
+  letter-spacing:0;
+  line-height:1.28581;
+  text-transform:none;
+  color:#182026;
+  font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; }
+
+p{
+  margin-bottom:10px;
+  margin-top:0; }
+
+small{
+  font-size:12px; }
+
+strong{
+  font-weight:600; }
+
+::-moz-selection{
+  background:rgba(125, 188, 255, 0.6); }
+
+::selection{
+  background:rgba(125, 188, 255, 0.6); }
+.bp3-heading{
+  color:#182026;
+  font-weight:600;
+  margin:0 0 10px;
+  padding:0; }
+  .bp3-dark .bp3-heading{
+    color:#f5f8fa; }
+
+h1.bp3-heading, .bp3-running-text h1{
+  font-size:36px;
+  line-height:40px; }
+
+h2.bp3-heading, .bp3-running-text h2{
+  font-size:28px;
+  line-height:32px; }
+
+h3.bp3-heading, .bp3-running-text h3{
+  font-size:22px;
+  line-height:25px; }
+
+h4.bp3-heading, .bp3-running-text h4{
+  font-size:18px;
+  line-height:21px; }
+
+h5.bp3-heading, .bp3-running-text h5{
+  font-size:16px;
+  line-height:19px; }
+
+h6.bp3-heading, .bp3-running-text h6{
+  font-size:14px;
+  line-height:16px; }
+.bp3-ui-text{
+  font-size:14px;
+  font-weight:400;
+  letter-spacing:0;
+  line-height:1.28581;
+  text-transform:none; }
+
+.bp3-monospace-text{
+  font-family:monospace;
+  text-transform:none; }
+
+.bp3-text-muted{
+  color:#5c7080; }
+  .bp3-dark .bp3-text-muted{
+    color:#a7b6c2; }
+
+.bp3-text-disabled{
+  color:rgba(92, 112, 128, 0.6); }
+  .bp3-dark .bp3-text-disabled{
+    color:rgba(167, 182, 194, 0.6); }
+
+.bp3-text-overflow-ellipsis{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal; }
+.bp3-running-text{
+  font-size:14px;
+  line-height:1.5; }
+  .bp3-running-text h1{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h1{
+      color:#f5f8fa; }
+  .bp3-running-text h2{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h2{
+      color:#f5f8fa; }
+  .bp3-running-text h3{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h3{
+      color:#f5f8fa; }
+  .bp3-running-text h4{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h4{
+      color:#f5f8fa; }
+  .bp3-running-text h5{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h5{
+      color:#f5f8fa; }
+  .bp3-running-text h6{
+    color:#182026;
+    font-weight:600;
+    margin-bottom:20px;
+    margin-top:40px; }
+    .bp3-dark .bp3-running-text h6{
+      color:#f5f8fa; }
+  .bp3-running-text hr{
+    border:none;
+    border-bottom:1px solid rgba(16, 22, 26, 0.15);
+    margin:20px 0; }
+    .bp3-dark .bp3-running-text hr{
+      border-color:rgba(255, 255, 255, 0.15); }
+  .bp3-running-text p{
+    margin:0 0 10px;
+    padding:0; }
+
+.bp3-text-large{
+  font-size:16px; }
+
+.bp3-text-small{
+  font-size:12px; }
+a{
+  color:#106ba3;
+  text-decoration:none; }
+  a:hover{
+    color:#106ba3;
+    cursor:pointer;
+    text-decoration:underline; }
+  a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{
+    color:inherit; }
+  a code,
+  .bp3-dark a code{
+    color:inherit; }
+  .bp3-dark a,
+  .bp3-dark a:hover{
+    color:#48aff0; }
+    .bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large,
+    .bp3-dark a:hover .bp3-icon,
+    .bp3-dark a:hover .bp3-icon-standard,
+    .bp3-dark a:hover .bp3-icon-large{
+      color:inherit; }
+.bp3-running-text code, .bp3-code{
+  font-family:monospace;
+  text-transform:none;
+  background:rgba(255, 255, 255, 0.7);
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);
+  color:#5c7080;
+  font-size:smaller;
+  padding:2px 5px; }
+  .bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#a7b6c2; }
+  .bp3-running-text a > code, a > .bp3-code{
+    color:#137cbd; }
+    .bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{
+      color:inherit; }
+
+.bp3-running-text pre, .bp3-code-block{
+  font-family:monospace;
+  text-transform:none;
+  background:rgba(255, 255, 255, 0.7);
+  border-radius:3px;
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+  color:#182026;
+  display:block;
+  font-size:13px;
+  line-height:1.4;
+  margin:10px 0;
+  padding:13px 15px 12px;
+  word-break:break-all;
+  word-wrap:break-word; }
+  .bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+  .bp3-running-text pre > code, .bp3-code-block > code{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:inherit;
+    font-size:inherit;
+    padding:0; }
+
+.bp3-running-text kbd, .bp3-key{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#5c7080;
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  font-family:inherit;
+  font-size:12px;
+  height:24px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  line-height:24px;
+  min-width:24px;
+  padding:3px 6px;
+  vertical-align:middle; }
+  .bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{
+    margin-right:5px; }
+  .bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{
+    background:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#a7b6c2; }
+.bp3-running-text blockquote, .bp3-blockquote{
+  border-left:solid 4px rgba(167, 182, 194, 0.5);
+  margin:0 0 10px;
+  padding:0 20px; }
+  .bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{
+    border-color:rgba(115, 134, 148, 0.5); }
+.bp3-running-text ul,
+.bp3-running-text ol, .bp3-list{
+  margin:10px 0;
+  padding-left:30px; }
+  .bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){
+    margin-bottom:5px; }
+  .bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol,
+  .bp3-running-text ul ul,
+  .bp3-running-text ol ul,
+  .bp3-list ul{
+    margin-top:5px; }
+
+.bp3-list-unstyled{
+  list-style:none;
+  margin:0;
+  padding:0; }
+  .bp3-list-unstyled li{
+    padding:0; }
+.bp3-rtl{
+  text-align:right; }
+
+.bp3-dark{
+  color:#f5f8fa; }
+
+:focus{
+  outline:rgba(19, 124, 189, 0.6) auto 2px;
+  outline-offset:2px;
+  -moz-outline-radius:6px; }
+
+.bp3-focus-disabled :focus{
+  outline:none !important; }
+  .bp3-focus-disabled :focus ~ .bp3-control-indicator{
+    outline:none !important; }
+
+.bp3-alert{
+  max-width:400px;
+  padding:20px; }
+
+.bp3-alert-body{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-alert-body .bp3-icon{
+    font-size:40px;
+    margin-right:20px;
+    margin-top:0; }
+
+.bp3-alert-contents{
+  word-break:break-word; }
+
+.bp3-alert-footer{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:reverse;
+      -ms-flex-direction:row-reverse;
+          flex-direction:row-reverse;
+  margin-top:10px; }
+  .bp3-alert-footer .bp3-button{
+    margin-left:10px; }
+.bp3-breadcrumbs{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  cursor:default;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:wrap;
+      flex-wrap:wrap;
+  height:30px;
+  list-style:none;
+  margin:0;
+  padding:0; }
+  .bp3-breadcrumbs > li{
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex; }
+    .bp3-breadcrumbs > li::after{
+      background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 00-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 001.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e");
+      content:"";
+      display:block;
+      height:16px;
+      margin:0 5px;
+      width:16px; }
+    .bp3-breadcrumbs > li:last-of-type::after{
+      display:none; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumb-current,
+.bp3-breadcrumbs-collapsed{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  font-size:16px; }
+
+.bp3-breadcrumb,
+.bp3-breadcrumbs-collapsed{
+  color:#5c7080; }
+
+.bp3-breadcrumb:hover{
+  text-decoration:none; }
+
+.bp3-breadcrumb.bp3-disabled{
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-breadcrumb .bp3-icon{
+  margin-right:5px; }
+
+.bp3-breadcrumb-current{
+  color:inherit;
+  font-weight:600; }
+  .bp3-breadcrumb-current .bp3-input{
+    font-size:inherit;
+    font-weight:inherit;
+    vertical-align:baseline; }
+
+.bp3-breadcrumbs-collapsed{
+  background:#ced9e0;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  margin-right:2px;
+  padding:1px 5px;
+  vertical-align:text-bottom; }
+  .bp3-breadcrumbs-collapsed::before{
+    background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cg fill='%235C7080'%3e%3ccircle cx='2' cy='8.03' r='2'/%3e%3ccircle cx='14' cy='8.03' r='2'/%3e%3ccircle cx='8' cy='8.03' r='2'/%3e%3c/g%3e%3c/svg%3e") center no-repeat;
+    content:"";
+    display:block;
+    height:16px;
+    width:16px; }
+  .bp3-breadcrumbs-collapsed:hover{
+    background:#bfccd6;
+    color:#182026;
+    text-decoration:none; }
+
+.bp3-dark .bp3-breadcrumb,
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumbs > li::after{
+  color:#a7b6c2; }
+
+.bp3-dark .bp3-breadcrumb.bp3-disabled{
+  color:rgba(167, 182, 194, 0.6); }
+
+.bp3-dark .bp3-breadcrumb-current{
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-breadcrumbs-collapsed{
+  background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-breadcrumbs-collapsed:hover{
+    background:rgba(16, 22, 26, 0.6);
+    color:#f5f8fa; }
+.bp3-button{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  font-size:14px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  padding:5px 10px;
+  text-align:left;
+  vertical-align:middle;
+  min-height:30px;
+  min-width:30px; }
+  .bp3-button > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-button > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-button::before,
+  .bp3-button > *{
+    margin-right:7px; }
+  .bp3-button:empty::before,
+  .bp3-button > :last-child{
+    margin-right:0; }
+  .bp3-button:empty{
+    padding:0 !important; }
+  .bp3-button:disabled, .bp3-button.bp3-disabled{
+    cursor:not-allowed; }
+  .bp3-button.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button.bp3-align-right,
+  .bp3-align-right .bp3-button{
+    text-align:right; }
+  .bp3-button.bp3-align-left,
+  .bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-button:not([class*="bp3-intent-"]){
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    color:#182026; }
+    .bp3-button:not([class*="bp3-intent-"]):hover{
+      background-clip:padding-box;
+      background-color:#ebf1f5;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+    .bp3-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      background-color:#d8e1e8;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      outline:none; }
+      .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active:hover, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-button.bp3-intent-primary{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-primary:hover{
+      background-color:#106ba3;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{
+      background-color:#0e5a8a;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
+      background-color:rgba(19, 124, 189, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-success{
+    background-color:#0f9960;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-success:hover{
+      background-color:#0d8050;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{
+      background-color:#0a6640;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
+      background-color:rgba(15, 153, 96, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-warning{
+    background-color:#d9822b;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-warning:hover{
+      background-color:#bf7326;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{
+      background-color:#a66321;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
+      background-color:rgba(217, 130, 43, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button.bp3-intent-danger{
+    background-color:#db3737;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      color:#ffffff; }
+    .bp3-button.bp3-intent-danger:hover{
+      background-color:#c23030;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{
+      background-color:#a82a2a;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
+      background-color:rgba(219, 55, 55, 0.5);
+      background-image:none;
+      border-color:transparent;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.6); }
+  .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+    stroke:#ffffff; }
+  .bp3-button.bp3-large,
+  .bp3-large .bp3-button{
+    min-height:40px;
+    min-width:40px;
+    font-size:16px;
+    padding:5px 15px; }
+    .bp3-button.bp3-large::before,
+    .bp3-button.bp3-large > *,
+    .bp3-large .bp3-button::before,
+    .bp3-large .bp3-button > *{
+      margin-right:10px; }
+    .bp3-button.bp3-large:empty::before,
+    .bp3-button.bp3-large > :last-child,
+    .bp3-large .bp3-button:empty::before,
+    .bp3-large .bp3-button > :last-child{
+      margin-right:0; }
+  .bp3-button.bp3-small,
+  .bp3-small .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    padding:0 7px; }
+  .bp3-button.bp3-loading{
+    position:relative; }
+    .bp3-button.bp3-loading[class*="bp3-icon-"]::before{
+      visibility:hidden; }
+    .bp3-button.bp3-loading .bp3-button-spinner{
+      margin:0;
+      position:absolute; }
+    .bp3-button.bp3-loading > :not(.bp3-button-spinner){
+      visibility:hidden; }
+  .bp3-button[class*="bp3-icon-"]::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    color:#5c7080; }
+  .bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{
+    color:#5c7080; }
+    .bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{
+      margin-left:7px; }
+  .bp3-button .bp3-icon:first-child:last-child,
+  .bp3-button .bp3-spinner + .bp3-icon:last-child{
+    margin:0 -7px; }
+  .bp3-dark .bp3-button:not([class*="bp3-intent-"]){
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover, .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"])[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+    .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-large{
+      color:#a7b6c2; }
+  .bp3-dark .bp3-button[class*="bp3-intent-"]{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:hover{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:active, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-active{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"]:disabled, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-disabled{
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(255, 255, 255, 0.3); }
+    .bp3-dark .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{
+      stroke:#8a9ba8; }
+  .bp3-button:disabled::before,
+  .bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before,
+  .bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*="bp3-intent-"]::before,
+  .bp3-button[class*="bp3-intent-"] .bp3-icon, .bp3-button[class*="bp3-intent-"] .bp3-icon-standard, .bp3-button[class*="bp3-intent-"] .bp3-icon-large{
+    color:inherit !important; }
+  .bp3-button.bp3-minimal{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-button.bp3-minimal:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button.bp3-minimal{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button.bp3-minimal:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button.bp3-minimal.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+  .bp3-button.bp3-outlined{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    border:1px solid rgba(24, 32, 38, 0.2);
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box; }
+    .bp3-button.bp3-outlined:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button.bp3-outlined:active, .bp3-button.bp3-outlined.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button.bp3-outlined{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button.bp3-outlined:hover, .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button.bp3-outlined:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:hover, .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:hover, .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:hover, .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button.bp3-outlined.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:hover, .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button.bp3-outlined.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+    .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled:hover{
+      border-color:rgba(92, 112, 128, 0.1); }
+    .bp3-dark .bp3-button.bp3-outlined{
+      border-color:rgba(255, 255, 255, 0.4); }
+      .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{
+        border-color:rgba(255, 255, 255, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-primary{
+      border-color:rgba(16, 107, 163, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+        border-color:rgba(16, 107, 163, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{
+        border-color:rgba(72, 175, 240, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{
+          border-color:rgba(72, 175, 240, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-success{
+      border-color:rgba(13, 128, 80, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+        border-color:rgba(13, 128, 80, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{
+        border-color:rgba(61, 204, 145, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{
+          border-color:rgba(61, 204, 145, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-warning{
+      border-color:rgba(191, 115, 38, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+        border-color:rgba(191, 115, 38, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{
+        border-color:rgba(255, 179, 102, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{
+          border-color:rgba(255, 179, 102, 0.2); }
+    .bp3-button.bp3-outlined.bp3-intent-danger{
+      border-color:rgba(194, 48, 48, 0.6); }
+      .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+        border-color:rgba(194, 48, 48, 0.2); }
+      .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{
+        border-color:rgba(255, 115, 115, 0.6); }
+        .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{
+          border-color:rgba(255, 115, 115, 0.2); }
+
+a.bp3-button{
+  text-align:center;
+  text-decoration:none;
+  -webkit-transition:none;
+  transition:none; }
+  a.bp3-button, a.bp3-button:hover, a.bp3-button:active{
+    color:#182026; }
+  a.bp3-button.bp3-disabled{
+    color:rgba(92, 112, 128, 0.6); }
+
+.bp3-button-text{
+  -webkit-box-flex:0;
+      -ms-flex:0 1 auto;
+          flex:0 1 auto; }
+
+.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
+.bp3-button-group.bp3-align-left .bp3-button-text,
+.bp3-button-group.bp3-align-right .bp3-button-text{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto; }
+.bp3-button-group{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex; }
+  .bp3-button-group .bp3-button{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    position:relative;
+    z-index:4; }
+    .bp3-button-group .bp3-button:focus{
+      z-index:5; }
+    .bp3-button-group .bp3-button:hover{
+      z-index:6; }
+    .bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{
+      z-index:7; }
+    .bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{
+      z-index:3; }
+    .bp3-button-group .bp3-button[class*="bp3-intent-"]{
+      z-index:9; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:focus{
+        z-index:10; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:hover{
+        z-index:11; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:active, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-active{
+        z-index:12; }
+      .bp3-button-group .bp3-button[class*="bp3-intent-"]:disabled, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-disabled{
+        z-index:8; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){
+    border-bottom-left-radius:0;
+    border-top-left-radius:0; }
+  .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    border-bottom-right-radius:0;
+    border-top-right-radius:0;
+    margin-right:-1px; }
+  .bp3-button-group.bp3-minimal .bp3-button{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-button-group.bp3-minimal .bp3-button:hover{
+      background:rgba(167, 182, 194, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026;
+      text-decoration:none; }
+    .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+      background:rgba(115, 134, 148, 0.3);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#182026; }
+    .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+      background:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed; }
+      .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+        background:rgba(115, 134, 148, 0.3); }
+    .bp3-dark .bp3-button-group.bp3-minimal .bp3-button{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:inherit; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{
+        background:rgba(138, 155, 168, 0.15); }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
+        background:rgba(138, 155, 168, 0.3);
+        color:#f5f8fa; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{
+        background:none;
+        color:rgba(167, 182, 194, 0.6);
+        cursor:not-allowed; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{
+          background:rgba(138, 155, 168, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+      color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.15);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#106ba3; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(16, 107, 163, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+        stroke:#106ba3; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
+        color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
+          background:rgba(19, 124, 189, 0.2);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
+          background:rgba(19, 124, 189, 0.3);
+          color:#48aff0; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
+          background:none;
+          color:rgba(72, 175, 240, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
+            background:rgba(19, 124, 189, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+      color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.15);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#0d8050; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(13, 128, 80, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+        stroke:#0d8050; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
+        color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
+          background:rgba(15, 153, 96, 0.2);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
+          background:rgba(15, 153, 96, 0.3);
+          color:#3dcc91; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
+          background:none;
+          color:rgba(61, 204, 145, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
+            background:rgba(15, 153, 96, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+      color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.15);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#bf7326; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(191, 115, 38, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+        stroke:#bf7326; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
+        color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
+          background:rgba(217, 130, 43, 0.2);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
+          background:rgba(217, 130, 43, 0.3);
+          color:#ffb366; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
+          background:none;
+          color:rgba(255, 179, 102, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
+            background:rgba(217, 130, 43, 0.3); }
+    .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+      color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        background:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.15);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#c23030; }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(194, 48, 48, 0.5); }
+        .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+      .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+        stroke:#c23030; }
+      .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
+        color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
+          background:rgba(219, 55, 55, 0.2);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
+          background:rgba(219, 55, 55, 0.3);
+          color:#ff7373; }
+        .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
+          background:none;
+          color:rgba(255, 115, 115, 0.5); }
+          .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
+            background:rgba(219, 55, 55, 0.3); }
+  .bp3-button-group .bp3-popover-wrapper,
+  .bp3-button-group .bp3-popover-target{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-button-group .bp3-button.bp3-fill,
+  .bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-button-group.bp3-vertical{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column;
+    vertical-align:top; }
+    .bp3-button-group.bp3-vertical.bp3-fill{
+      height:100%;
+      width:unset; }
+    .bp3-button-group.bp3-vertical .bp3-button{
+      margin-right:0 !important;
+      width:100%; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{
+      border-radius:3px 3px 0 0; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{
+      border-radius:0 0 3px 3px; }
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+    .bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){
+      margin-bottom:-1px; }
+  .bp3-button-group.bp3-align-left .bp3-button{
+    text-align:left; }
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
+    margin-right:1px; }
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button,
+  .bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){
+    margin-bottom:1px; }
+.bp3-callout{
+  font-size:14px;
+  line-height:1.5;
+  background-color:rgba(138, 155, 168, 0.15);
+  border-radius:3px;
+  padding:10px 12px 9px;
+  position:relative;
+  width:100%; }
+  .bp3-callout[class*="bp3-icon-"]{
+    padding-left:40px; }
+    .bp3-callout[class*="bp3-icon-"]::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      color:#5c7080;
+      left:10px;
+      position:absolute;
+      top:10px; }
+  .bp3-callout.bp3-callout-icon{
+    padding-left:40px; }
+    .bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
+      color:#5c7080;
+      left:10px;
+      position:absolute;
+      top:10px; }
+  .bp3-callout .bp3-heading{
+    line-height:20px;
+    margin-bottom:5px;
+    margin-top:0; }
+    .bp3-callout .bp3-heading:last-child{
+      margin-bottom:0; }
+  .bp3-dark .bp3-callout{
+    background-color:rgba(138, 155, 168, 0.2); }
+    .bp3-dark .bp3-callout[class*="bp3-icon-"]::before{
+      color:#a7b6c2; }
+  .bp3-callout.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15); }
+    .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-primary .bp3-heading{
+      color:#106ba3; }
+    .bp3-dark .bp3-callout.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{
+        color:#48aff0; }
+  .bp3-callout.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15); }
+    .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-success .bp3-heading{
+      color:#0d8050; }
+    .bp3-dark .bp3-callout.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{
+        color:#3dcc91; }
+  .bp3-callout.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15); }
+    .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-warning .bp3-heading{
+      color:#bf7326; }
+    .bp3-dark .bp3-callout.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{
+        color:#ffb366; }
+  .bp3-callout.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15); }
+    .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+    .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+    .bp3-callout.bp3-intent-danger .bp3-heading{
+      color:#c23030; }
+    .bp3-dark .bp3-callout.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-dark .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
+      .bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
+      .bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{
+        color:#ff7373; }
+  .bp3-running-text .bp3-callout{
+    margin:20px 0; }
+.bp3-card{
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+  padding:20px;
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-card.bp3-dark,
+  .bp3-dark .bp3-card{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+
+.bp3-elevation-0{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+  .bp3-elevation-0.bp3-dark,
+  .bp3-dark .bp3-elevation-0{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }
+
+.bp3-elevation-1{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-1.bp3-dark,
+  .bp3-dark .bp3-elevation-1{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-2{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-2.bp3-dark,
+  .bp3-dark .bp3-elevation-2{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-3{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-3.bp3-dark,
+  .bp3-dark .bp3-elevation-3{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-elevation-4{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-elevation-4.bp3-dark,
+  .bp3-dark .bp3-elevation-4{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:hover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  cursor:pointer; }
+  .bp3-card.bp3-interactive:hover.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:hover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+
+.bp3-card.bp3-interactive:active{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  opacity:0.9;
+  -webkit-transition-duration:0;
+          transition-duration:0; }
+  .bp3-card.bp3-interactive:active.bp3-dark,
+  .bp3-dark .bp3-card.bp3-interactive:active{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-collapse{
+  height:0;
+  overflow-y:hidden;
+  -webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-collapse .bp3-collapse-body{
+    -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-collapse .bp3-collapse-body[aria-hidden="true"]{
+      display:none; }
+
+.bp3-context-menu .bp3-popover-target{
+  display:block; }
+
+.bp3-context-menu-popover-target{
+  position:fixed; }
+
+.bp3-divider{
+  border-bottom:1px solid rgba(16, 22, 26, 0.15);
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  margin:5px; }
+  .bp3-dark .bp3-divider{
+    border-color:rgba(16, 22, 26, 0.4); }
+.bp3-dialog-container{
+  opacity:1;
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  min-height:100%;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none;
+  width:100%; }
+  .bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5); }
+  .bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{
+    opacity:1;
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{
+    opacity:0;
+    -webkit-transform:scale(0.5);
+            transform:scale(0.5);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-transform;
+    transition-property:opacity, -webkit-transform;
+    transition-property:opacity, transform;
+    transition-property:opacity, transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+
+.bp3-dialog{
+  background:#ebf1f5;
+  border-radius:6px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:30px 0;
+  padding-bottom:20px;
+  pointer-events:all;
+  -webkit-user-select:text;
+     -moz-user-select:text;
+      -ms-user-select:text;
+          user-select:text;
+  width:500px; }
+  .bp3-dialog:focus{
+    outline:0; }
+  .bp3-dialog.bp3-dark,
+  .bp3-dark .bp3-dialog{
+    background:#293742;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+
+.bp3-dialog-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background:#ffffff;
+  border-radius:6px 6px 0 0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  min-height:40px;
+  padding-left:20px;
+  padding-right:5px;
+  z-index:30; }
+  .bp3-dialog-header .bp3-icon-large,
+  .bp3-dialog-header .bp3-icon{
+    color:#5c7080;
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px; }
+  .bp3-dialog-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:inherit;
+    margin:0; }
+    .bp3-dialog-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-dialog-header{
+    background:#30404d;
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-dialog-header .bp3-icon-large,
+    .bp3-dark .bp3-dialog-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-dialog-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  line-height:18px;
+  margin:20px; }
+
+.bp3-dialog-footer{
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  margin:0 20px; }
+
+.bp3-dialog-footer-actions{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:end;
+      -ms-flex-pack:end;
+          justify-content:flex-end; }
+  .bp3-dialog-footer-actions .bp3-button{
+    margin-left:10px; }
+.bp3-multistep-dialog-panels{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+
+.bp3-multistep-dialog-left-panel{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:1;
+      -ms-flex:1;
+          flex:1;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column; }
+  .bp3-dark .bp3-multistep-dialog-left-panel{
+    background:#202b33; }
+
+.bp3-multistep-dialog-right-panel{
+  background-color:#f5f8fa;
+  border-left:1px solid rgba(16, 22, 26, 0.15);
+  border-radius:0 0 6px 0;
+  -webkit-box-flex:3;
+      -ms-flex:3;
+          flex:3;
+  min-width:0; }
+  .bp3-dark .bp3-multistep-dialog-right-panel{
+    background-color:#293742;
+    border-left:1px solid rgba(16, 22, 26, 0.4); }
+
+.bp3-multistep-dialog-footer{
+  background-color:#ffffff;
+  border-radius:0 0 6px 0;
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  padding:10px; }
+  .bp3-dark .bp3-multistep-dialog-footer{
+    background:#30404d;
+    border-top:1px solid rgba(16, 22, 26, 0.4); }
+
+.bp3-dialog-step-container{
+  background-color:#f5f8fa;
+  border-bottom:1px solid rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-dialog-step-container{
+    background:#293742;
+    border-bottom:1px solid rgba(16, 22, 26, 0.4); }
+  .bp3-dialog-step-container.bp3-dialog-step-viewed{
+    background-color:#ffffff; }
+    .bp3-dark .bp3-dialog-step-container.bp3-dialog-step-viewed{
+      background:#30404d; }
+
+.bp3-dialog-step{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:#f5f8fa;
+  border-radius:6px;
+  cursor:not-allowed;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin:4px;
+  padding:6px 14px; }
+  .bp3-dark .bp3-dialog-step{
+    background:#293742; }
+  .bp3-dialog-step-viewed .bp3-dialog-step{
+    background-color:#ffffff;
+    cursor:pointer; }
+    .bp3-dark .bp3-dialog-step-viewed .bp3-dialog-step{
+      background:#30404d; }
+  .bp3-dialog-step:hover{
+    background-color:#f5f8fa; }
+    .bp3-dark .bp3-dialog-step:hover{
+      background:#293742; }
+
+.bp3-dialog-step-icon{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:rgba(92, 112, 128, 0.6);
+  border-radius:50%;
+  color:#ffffff;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:25px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  width:25px; }
+  .bp3-dark .bp3-dialog-step-icon{
+    background-color:rgba(167, 182, 194, 0.6); }
+  .bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-icon{
+    background-color:#2b95d6; }
+  .bp3-dialog-step-viewed .bp3-dialog-step-icon{
+    background-color:#8a9ba8; }
+
+.bp3-dialog-step-title{
+  color:rgba(92, 112, 128, 0.6);
+  -webkit-box-flex:1;
+      -ms-flex:1;
+          flex:1;
+  padding-left:10px; }
+  .bp3-dark .bp3-dialog-step-title{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-title{
+    color:#2b95d6; }
+  .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
+    color:#182026; }
+    .bp3-dark .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{
+      color:#f5f8fa; }
+.bp3-drawer{
+  background:#ffffff;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0;
+  padding:0; }
+  .bp3-drawer:focus{
+    outline:0; }
+  .bp3-drawer.bp3-position-top{
+    height:50%;
+    left:0;
+    right:0;
+    top:0; }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-top.bp3-overlay-exit-active{
+      -webkit-transform:translateY(-100%);
+              transform:translateY(-100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-bottom{
+    bottom:0;
+    height:50%;
+    left:0;
+    right:0; }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-left{
+    bottom:0;
+    left:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-left.bp3-overlay-exit-active{
+      -webkit-transform:translateX(-100%);
+              transform:translateX(-100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-position-right{
+    bottom:0;
+    right:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer.bp3-position-right.bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right):not(.bp3-vertical){
+    bottom:0;
+    right:0;
+    top:0;
+    width:50%; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{
+      -webkit-transform:translateX(0);
+              transform:translateX(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{
+      -webkit-transform:translateX(0);
+              transform:translateX(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{
+      -webkit-transform:translateX(100%);
+              transform:translateX(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+  .bp3-position-right).bp3-vertical{
+    bottom:0;
+    height:50%;
+    left:0;
+    right:0; }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-appear-active{
+      -webkit-transform:translateY(0);
+              transform:translateY(0);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:200ms;
+              transition-duration:200ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit{
+      -webkit-transform:translateY(0);
+              transform:translateY(0); }
+    .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
+    .bp3-position-right).bp3-vertical.bp3-overlay-exit-active{
+      -webkit-transform:translateY(100%);
+              transform:translateY(100%);
+      -webkit-transition-delay:0;
+              transition-delay:0;
+      -webkit-transition-duration:100ms;
+              transition-duration:100ms;
+      -webkit-transition-property:-webkit-transform;
+      transition-property:-webkit-transform;
+      transition-property:transform;
+      transition-property:transform, -webkit-transform;
+      -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+              transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-drawer.bp3-dark,
+  .bp3-dark .bp3-drawer{
+    background:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+
+.bp3-drawer-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border-radius:0;
+  -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  min-height:40px;
+  padding:5px;
+  padding-left:20px;
+  position:relative; }
+  .bp3-drawer-header .bp3-icon-large,
+  .bp3-drawer-header .bp3-icon{
+    color:#5c7080;
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    margin-right:10px; }
+  .bp3-drawer-header .bp3-heading{
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:inherit;
+    margin:0; }
+    .bp3-drawer-header .bp3-heading:last-child{
+      margin-right:20px; }
+  .bp3-dark .bp3-drawer-header{
+    -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-drawer-header .bp3-icon-large,
+    .bp3-dark .bp3-drawer-header .bp3-icon{
+      color:#a7b6c2; }
+
+.bp3-drawer-body{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  line-height:18px;
+  overflow:auto; }
+
+.bp3-drawer-footer{
+  -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  padding:10px 20px;
+  position:relative; }
+  .bp3-dark .bp3-drawer-footer{
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); }
+.bp3-editable-text{
+  cursor:text;
+  display:inline-block;
+  max-width:100%;
+  position:relative;
+  vertical-align:top;
+  white-space:nowrap; }
+  .bp3-editable-text::before{
+    bottom:-3px;
+    left:-3px;
+    position:absolute;
+    right:-3px;
+    top:-3px;
+    border-radius:3px;
+    content:"";
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-editable-text.bp3-editable-text-editing::before{
+    background-color:#ffffff;
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#137cbd; }
+  .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4); }
+  .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#0f9960; }
+  .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }
+  .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#d9822b; }
+  .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }
+  .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-input,
+  .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#db3737; }
+  .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }
+  .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-editable-text:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{
+    background-color:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-disabled::before{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
+    color:#48aff0; }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);
+            box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
+    color:#3dcc91; }
+  .bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);
+            box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
+    color:#ffb366; }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
+    color:#ff7373; }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
+    -webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);
+            box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }
+  .bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+    -webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-editable-text-input,
+.bp3-editable-text-content{
+  color:inherit;
+  display:inherit;
+  font:inherit;
+  letter-spacing:inherit;
+  max-width:inherit;
+  min-width:inherit;
+  position:relative;
+  resize:none;
+  text-transform:inherit;
+  vertical-align:top; }
+
+.bp3-editable-text-input{
+  background:none;
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0;
+  white-space:pre-wrap;
+  width:100%; }
+  .bp3-editable-text-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-editable-text-input:focus{
+    outline:none; }
+  .bp3-editable-text-input::-ms-clear{
+    display:none; }
+
+.bp3-editable-text-content{
+  overflow:hidden;
+  padding-right:2px;
+  text-overflow:ellipsis;
+  white-space:pre; }
+  .bp3-editable-text-editing > .bp3-editable-text-content{
+    left:0;
+    position:absolute;
+    visibility:hidden; }
+  .bp3-editable-text-placeholder > .bp3-editable-text-content{
+    color:rgba(92, 112, 128, 0.6); }
+    .bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{
+      color:rgba(167, 182, 194, 0.6); }
+
+.bp3-editable-text.bp3-multiline{
+  display:block; }
+  .bp3-editable-text.bp3-multiline .bp3-editable-text-content{
+    overflow:auto;
+    white-space:pre-wrap;
+    word-wrap:break-word; }
+.bp3-divider{
+  border-bottom:1px solid rgba(16, 22, 26, 0.15);
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  margin:5px; }
+  .bp3-dark .bp3-divider{
+    border-color:rgba(16, 22, 26, 0.4); }
+.bp3-control-group{
+  -webkit-transform:translateZ(0);
+          transform:translateZ(0);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:stretch;
+      -ms-flex-align:stretch;
+          align-items:stretch; }
+  .bp3-control-group > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select,
+  .bp3-control-group .bp3-input,
+  .bp3-control-group .bp3-select{
+    position:relative; }
+  .bp3-control-group .bp3-input{
+    border-radius:inherit;
+    z-index:2; }
+    .bp3-control-group .bp3-input:focus{
+      border-radius:3px;
+      z-index:14; }
+    .bp3-control-group .bp3-input[class*="bp3-intent"]{
+      z-index:13; }
+      .bp3-control-group .bp3-input[class*="bp3-intent"]:focus{
+        z-index:15; }
+    .bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{
+      z-index:1; }
+  .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input{
+    z-index:13; }
+    .bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input:focus{
+      z-index:15; }
+  .bp3-control-group .bp3-button,
+  .bp3-control-group .bp3-html-select select,
+  .bp3-control-group .bp3-select select{
+    -webkit-transform:translateZ(0);
+            transform:translateZ(0);
+    border-radius:inherit;
+    z-index:4; }
+    .bp3-control-group .bp3-button:focus,
+    .bp3-control-group .bp3-html-select select:focus,
+    .bp3-control-group .bp3-select select:focus{
+      z-index:5; }
+    .bp3-control-group .bp3-button:hover,
+    .bp3-control-group .bp3-html-select select:hover,
+    .bp3-control-group .bp3-select select:hover{
+      z-index:6; }
+    .bp3-control-group .bp3-button:active,
+    .bp3-control-group .bp3-html-select select:active,
+    .bp3-control-group .bp3-select select:active{
+      z-index:7; }
+    .bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled,
+    .bp3-control-group .bp3-html-select select[readonly],
+    .bp3-control-group .bp3-html-select select:disabled,
+    .bp3-control-group .bp3-html-select select.bp3-disabled,
+    .bp3-control-group .bp3-select select[readonly],
+    .bp3-control-group .bp3-select select:disabled,
+    .bp3-control-group .bp3-select select.bp3-disabled{
+      z-index:3; }
+    .bp3-control-group .bp3-button[class*="bp3-intent"],
+    .bp3-control-group .bp3-html-select select[class*="bp3-intent"],
+    .bp3-control-group .bp3-select select[class*="bp3-intent"]{
+      z-index:9; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:focus,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:focus{
+        z-index:10; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:hover,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:hover{
+        z-index:11; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:active,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:active{
+        z-index:12; }
+      .bp3-control-group .bp3-button[class*="bp3-intent"][readonly], .bp3-control-group .bp3-button[class*="bp3-intent"]:disabled, .bp3-control-group .bp3-button[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-html-select select[class*="bp3-intent"].bp3-disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"][readonly],
+      .bp3-control-group .bp3-select select[class*="bp3-intent"]:disabled,
+      .bp3-control-group .bp3-select select[class*="bp3-intent"].bp3-disabled{
+        z-index:8; }
+  .bp3-control-group .bp3-input-group > .bp3-icon,
+  .bp3-control-group .bp3-input-group > .bp3-button,
+  .bp3-control-group .bp3-input-group > .bp3-input-left-container,
+  .bp3-control-group .bp3-input-group > .bp3-input-action{
+    z-index:16; }
+  .bp3-control-group .bp3-select::after,
+  .bp3-control-group .bp3-html-select::after,
+  .bp3-control-group .bp3-select > .bp3-icon,
+  .bp3-control-group .bp3-html-select > .bp3-icon{
+    z-index:17; }
+  .bp3-control-group .bp3-select:focus-within{
+    z-index:5; }
+  .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
+    margin-right:-1px; }
+  .bp3-control-group:not(.bp3-vertical) > .bp3-divider:not(:first-child){
+    margin-left:6px; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){
+    margin-right:0; }
+  .bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{
+    margin-left:1px; }
+  .bp3-control-group .bp3-popover-wrapper,
+  .bp3-control-group .bp3-popover-target{
+    border-radius:inherit; }
+  .bp3-control-group > :first-child{
+    border-radius:3px 0 0 3px; }
+  .bp3-control-group > :last-child{
+    border-radius:0 3px 3px 0;
+    margin-right:0; }
+  .bp3-control-group > :only-child{
+    border-radius:3px;
+    margin-right:0; }
+  .bp3-control-group .bp3-input-group .bp3-button{
+    border-radius:3px; }
+  .bp3-control-group .bp3-numeric-input:not(:first-child) .bp3-input-group{
+    border-bottom-left-radius:0;
+    border-top-left-radius:0; }
+  .bp3-control-group.bp3-fill{
+    width:100%; }
+  .bp3-control-group > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-fill > *:not(.bp3-fixed){
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto; }
+  .bp3-control-group.bp3-vertical{
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column; }
+    .bp3-control-group.bp3-vertical > *{
+      margin-top:-1px; }
+    .bp3-control-group.bp3-vertical > :first-child{
+      border-radius:3px 3px 0 0;
+      margin-top:0; }
+    .bp3-control-group.bp3-vertical > :last-child{
+      border-radius:0 0 3px 3px; }
+.bp3-control{
+  cursor:pointer;
+  display:block;
+  margin-bottom:10px;
+  position:relative;
+  text-transform:none; }
+  .bp3-control input:checked ~ .bp3-control-indicator{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+  .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+  .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    background:#0e5a8a;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
+    background-color:#0e5a8a;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-control:not(.bp3-align-right){
+    padding-left:26px; }
+    .bp3-control:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-26px; }
+  .bp3-control.bp3-align-right{
+    padding-right:26px; }
+    .bp3-control.bp3-align-right .bp3-control-indicator{
+      margin-right:-26px; }
+  .bp3-control.bp3-disabled{
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-control.bp3-inline{
+    display:inline-block;
+    margin-right:20px; }
+  .bp3-control input{
+    left:0;
+    opacity:0;
+    position:absolute;
+    top:0;
+    z-index:-1; }
+  .bp3-control .bp3-control-indicator{
+    background-clip:padding-box;
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    border:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    cursor:pointer;
+    display:inline-block;
+    font-size:16px;
+    height:1em;
+    margin-right:10px;
+    margin-top:-3px;
+    position:relative;
+    -webkit-user-select:none;
+       -moz-user-select:none;
+        -ms-user-select:none;
+            user-select:none;
+    vertical-align:middle;
+    width:1em; }
+    .bp3-control .bp3-control-indicator::before{
+      content:"";
+      display:block;
+      height:1em;
+      width:1em; }
+  .bp3-control:hover .bp3-control-indicator{
+    background-color:#ebf1f5; }
+  .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+    background:#d8e1e8;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control input:disabled ~ .bp3-control-indicator{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    cursor:not-allowed; }
+  .bp3-control input:focus ~ .bp3-control-indicator{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:2px;
+    -moz-outline-radius:6px; }
+  .bp3-control.bp3-align-right .bp3-control-indicator{
+    float:right;
+    margin-left:10px;
+    margin-top:1px; }
+  .bp3-control.bp3-large{
+    font-size:16px; }
+    .bp3-control.bp3-large:not(.bp3-align-right){
+      padding-left:30px; }
+      .bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+        margin-left:-30px; }
+    .bp3-control.bp3-large.bp3-align-right{
+      padding-right:30px; }
+      .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+        margin-right:-30px; }
+    .bp3-control.bp3-large .bp3-control-indicator{
+      font-size:20px; }
+    .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-top:0; }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    background-color:#137cbd;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+    color:#ffffff; }
+  .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }
+  .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    background:#0e5a8a;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{
+    background-color:#106ba3;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
+    background-color:#0e5a8a;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-control.bp3-checkbox .bp3-control-indicator{
+    border-radius:3px; }
+  .bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 00-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0012 5z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{
+    background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z' fill='white'/%3e%3c/svg%3e"); }
+  .bp3-control.bp3-radio .bp3-control-indicator{
+    border-radius:50%; }
+  .bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{
+    background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }
+  .bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{
+    opacity:0.5; }
+  .bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{
+    -moz-outline-radius:16px; }
+  .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(167, 182, 194, 0.5); }
+  .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(115, 134, 148, 0.5); }
+  .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(92, 112, 128, 0.5); }
+  .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(206, 217, 224, 0.5); }
+    .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(19, 124, 189, 0.5); }
+    .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(255, 255, 255, 0.8); }
+  .bp3-control.bp3-switch:not(.bp3-align-right){
+    padding-left:38px; }
+    .bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-38px; }
+  .bp3-control.bp3-switch.bp3-align-right{
+    padding-right:38px; }
+    .bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{
+      margin-right:-38px; }
+  .bp3-control.bp3-switch .bp3-control-indicator{
+    border:none;
+    border-radius:1.75em;
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important;
+    min-width:1.75em;
+    -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    width:auto; }
+    .bp3-control.bp3-switch .bp3-control-indicator::before{
+      background:#ffffff;
+      border-radius:50%;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+      height:calc(1em - 4px);
+      left:0;
+      margin:2px;
+      position:absolute;
+      -webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+      transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+      width:calc(1em - 4px); }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    left:calc(100% - 1em); }
+  .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){
+    padding-left:45px; }
+    .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
+      margin-left:-45px; }
+  .bp3-control.bp3-switch.bp3-large.bp3-align-right{
+    padding-right:45px; }
+    .bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{
+      margin-right:-45px; }
+  .bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.7); }
+  .bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
+    background:rgba(16, 22, 26, 0.9); }
+  .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
+    background:rgba(57, 75, 89, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
+    background:#137cbd; }
+  .bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
+    background:#106ba3; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
+    background:#0e5a8a; }
+  .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
+    background:rgba(14, 90, 138, 0.5); }
+    .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
+      background:rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{
+    background:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-control.bp3-switch .bp3-switch-inner-text{
+    font-size:0.7em;
+    text-align:center; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
+    line-height:0;
+    margin-left:0.5em;
+    margin-right:1.2em;
+    visibility:hidden; }
+  .bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
+    line-height:1em;
+    margin-left:1.2em;
+    margin-right:0.5em;
+    visibility:visible; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
+    line-height:1em;
+    visibility:visible; }
+  .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
+    line-height:0;
+    visibility:hidden; }
+  .bp3-dark .bp3-control{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-control.bp3-disabled{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-control .bp3-control-indicator{
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-control:hover .bp3-control-indicator{
+      background-color:#30404d; }
+    .bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
+      background:#202b33;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      cursor:not-allowed; }
+    .bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-file-input{
+  cursor:pointer;
+  display:inline-block;
+  height:30px;
+  position:relative; }
+  .bp3-file-input input{
+    margin:0;
+    min-width:200px;
+    opacity:0; }
+    .bp3-file-input input:disabled + .bp3-file-upload-input,
+    .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+      background:rgba(206, 217, 224, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      resize:none; }
+      .bp3-file-input input:disabled + .bp3-file-upload-input::after,
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+        background-color:rgba(206, 217, 224, 0.5);
+        background-image:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(92, 112, 128, 0.6);
+        cursor:not-allowed;
+        outline:none; }
+        .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active,
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{
+          background:rgba(206, 217, 224, 0.7); }
+      .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark
+      .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
+        background:rgba(57, 75, 89, 0.5);
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark
+        .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{
+          background-color:rgba(57, 75, 89, 0.5);
+          background-image:none;
+          -webkit-box-shadow:none;
+                  box-shadow:none;
+          color:rgba(167, 182, 194, 0.6); }
+          .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark
+          .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{
+            background:rgba(57, 75, 89, 0.7); }
+  .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#182026; }
+  .bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
+    color:#f5f8fa; }
+  .bp3-file-input.bp3-fill{
+    width:100%; }
+  .bp3-file-input.bp3-large,
+  .bp3-large .bp3-file-input{
+    height:40px; }
+  .bp3-file-input .bp3-file-upload-input-custom-text::after{
+    content:attr(bp3-button-text); }
+
+.bp3-file-upload-input{
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none;
+  background:#ffffff;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  height:30px;
+  line-height:30px;
+  outline:none;
+  padding:0 10px;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  vertical-align:middle;
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  color:rgba(92, 112, 128, 0.6);
+  left:0;
+  padding-right:80px;
+  position:absolute;
+  right:0;
+  top:0;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-file-upload-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-file-upload-input[type="search"], .bp3-file-upload-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-file-upload-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    resize:none; }
+  .bp3-file-upload-input::after{
+    background-color:#f5f8fa;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    color:#182026;
+    min-height:24px;
+    min-width:24px;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    border-radius:3px;
+    content:"Browse";
+    line-height:24px;
+    margin:3px;
+    position:absolute;
+    right:0;
+    text-align:center;
+    top:0;
+    width:70px; }
+    .bp3-file-upload-input::after:hover{
+      background-clip:padding-box;
+      background-color:#ebf1f5;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+    .bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{
+      background-color:#d8e1e8;
+      background-image:none;
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{
+      background-color:rgba(206, 217, 224, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(92, 112, 128, 0.6);
+      cursor:not-allowed;
+      outline:none; }
+      .bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{
+        background:rgba(206, 217, 224, 0.7); }
+  .bp3-file-upload-input:hover::after{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-file-upload-input:active::after{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-large .bp3-file-upload-input{
+    font-size:16px;
+    height:40px;
+    line-height:40px;
+    padding-right:95px; }
+    .bp3-large .bp3-file-upload-input[type="search"], .bp3-large .bp3-file-upload-input.bp3-round{
+      padding:0 15px; }
+    .bp3-large .bp3-file-upload-input::after{
+      min-height:30px;
+      min-width:30px;
+      line-height:30px;
+      margin:5px;
+      width:85px; }
+  .bp3-dark .bp3-file-upload-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-file-upload-input::after{
+      background-color:#394b59;
+      background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+      background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+      color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        color:#f5f8fa; }
+      .bp3-dark .bp3-file-upload-input::after:hover{
+        background-color:#30404d;
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{
+        background-color:#202b33;
+        background-image:none;
+        -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+                box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+      .bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{
+        background-color:rgba(57, 75, 89, 0.5);
+        background-image:none;
+        -webkit-box-shadow:none;
+                box-shadow:none;
+        color:rgba(167, 182, 194, 0.6); }
+        .bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{
+          background:rgba(57, 75, 89, 0.7); }
+      .bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{
+        background:rgba(16, 22, 26, 0.5);
+        stroke:#8a9ba8; }
+    .bp3-dark .bp3-file-upload-input:hover::after{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-file-upload-input:active::after{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+.bp3-file-upload-input::after{
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+.bp3-form-group{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin:0 0 15px; }
+  .bp3-form-group label.bp3-label{
+    margin-bottom:5px; }
+  .bp3-form-group .bp3-control{
+    margin-top:7px; }
+  .bp3-form-group .bp3-form-helper-text{
+    color:#5c7080;
+    font-size:12px;
+    margin-top:5px; }
+  .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#106ba3; }
+  .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#0d8050; }
+  .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#bf7326; }
+  .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#c23030; }
+  .bp3-form-group.bp3-inline{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start;
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row; }
+    .bp3-form-group.bp3-inline.bp3-large label.bp3-label{
+      line-height:40px;
+      margin:0 10px 0 0; }
+    .bp3-form-group.bp3-inline label.bp3-label{
+      line-height:30px;
+      margin:0 10px 0 0; }
+  .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
+    color:#48aff0; }
+  .bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
+    color:#3dcc91; }
+  .bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
+    color:#ffb366; }
+  .bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
+    color:#ff7373; }
+  .bp3-dark .bp3-form-group .bp3-form-helper-text{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-label,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted,
+  .bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{
+    color:rgba(167, 182, 194, 0.6) !important; }
+.bp3-input-group{
+  display:block;
+  position:relative; }
+  .bp3-input-group .bp3-input{
+    position:relative;
+    width:100%; }
+    .bp3-input-group .bp3-input:not(:first-child){
+      padding-left:30px; }
+    .bp3-input-group .bp3-input:not(:last-child){
+      padding-right:30px; }
+  .bp3-input-group .bp3-input-action,
+  .bp3-input-group > .bp3-input-left-container,
+  .bp3-input-group > .bp3-button,
+  .bp3-input-group > .bp3-icon{
+    position:absolute;
+    top:0; }
+    .bp3-input-group .bp3-input-action:first-child,
+    .bp3-input-group > .bp3-input-left-container:first-child,
+    .bp3-input-group > .bp3-button:first-child,
+    .bp3-input-group > .bp3-icon:first-child{
+      left:0; }
+    .bp3-input-group .bp3-input-action:last-child,
+    .bp3-input-group > .bp3-input-left-container:last-child,
+    .bp3-input-group > .bp3-button:last-child,
+    .bp3-input-group > .bp3-icon:last-child{
+      right:0; }
+  .bp3-input-group .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    margin:3px;
+    padding:0 7px; }
+    .bp3-input-group .bp3-button:empty{
+      padding:0; }
+  .bp3-input-group > .bp3-input-left-container,
+  .bp3-input-group > .bp3-icon{
+    z-index:1; }
+  .bp3-input-group > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group > .bp3-icon{
+    color:#5c7080; }
+    .bp3-input-group > .bp3-input-left-container > .bp3-icon:empty,
+    .bp3-input-group > .bp3-icon:empty{
+      font-family:"Icons16", sans-serif;
+      font-size:16px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased; }
+  .bp3-input-group > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group > .bp3-icon,
+  .bp3-input-group .bp3-input-action > .bp3-spinner{
+    margin:7px; }
+  .bp3-input-group .bp3-tag{
+    margin:5px; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus),
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+    color:#5c7080; }
+    .bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
+      color:#a7b6c2; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{
+      color:#5c7080; }
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled,
+  .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{
+    color:rgba(92, 112, 128, 0.6) !important; }
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard,
+    .bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-input-group.bp3-disabled{
+    cursor:not-allowed; }
+    .bp3-input-group.bp3-disabled .bp3-icon{
+      color:rgba(92, 112, 128, 0.6); }
+  .bp3-input-group.bp3-large .bp3-button{
+    min-height:30px;
+    min-width:30px;
+    margin:5px; }
+  .bp3-input-group.bp3-large > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group.bp3-large > .bp3-icon,
+  .bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{
+    margin:12px; }
+  .bp3-input-group.bp3-large .bp3-input{
+    font-size:16px;
+    height:40px;
+    line-height:40px; }
+    .bp3-input-group.bp3-large .bp3-input[type="search"], .bp3-input-group.bp3-large .bp3-input.bp3-round{
+      padding:0 15px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:first-child){
+      padding-left:40px; }
+    .bp3-input-group.bp3-large .bp3-input:not(:last-child){
+      padding-right:40px; }
+  .bp3-input-group.bp3-small .bp3-button{
+    min-height:20px;
+    min-width:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small .bp3-tag{
+    min-height:20px;
+    min-width:20px;
+    margin:2px; }
+  .bp3-input-group.bp3-small > .bp3-input-left-container > .bp3-icon,
+  .bp3-input-group.bp3-small > .bp3-icon,
+  .bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{
+    margin:4px; }
+  .bp3-input-group.bp3-small .bp3-input{
+    font-size:12px;
+    height:24px;
+    line-height:24px;
+    padding-left:8px;
+    padding-right:8px; }
+    .bp3-input-group.bp3-small .bp3-input[type="search"], .bp3-input-group.bp3-small .bp3-input.bp3-round{
+      padding:0 12px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:first-child){
+      padding-left:24px; }
+    .bp3-input-group.bp3-small .bp3-input:not(:last-child){
+      padding-right:24px; }
+  .bp3-input-group.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-input-group.bp3-round .bp3-button,
+  .bp3-input-group.bp3-round .bp3-input,
+  .bp3-input-group.bp3-round .bp3-tag{
+    border-radius:30px; }
+  .bp3-dark .bp3-input-group .bp3-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-input-group.bp3-intent-primary .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-primary > .bp3-icon{
+    color:#106ba3; }
+    .bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{
+      color:#48aff0; }
+  .bp3-input-group.bp3-intent-success .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-success .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-success > .bp3-icon{
+    color:#0d8050; }
+    .bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{
+      color:#3dcc91; }
+  .bp3-input-group.bp3-intent-warning .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-warning > .bp3-icon{
+    color:#bf7326; }
+    .bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{
+      color:#ffb366; }
+  .bp3-input-group.bp3-intent-danger .bp3-input{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-input-group.bp3-intent-danger > .bp3-icon{
+    color:#c23030; }
+    .bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{
+      color:#ff7373; }
+.bp3-input{
+  -webkit-appearance:none;
+     -moz-appearance:none;
+          appearance:none;
+  background:#ffffff;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+  color:#182026;
+  font-size:14px;
+  font-weight:400;
+  height:30px;
+  line-height:30px;
+  outline:none;
+  padding:0 10px;
+  -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  vertical-align:middle; }
+  .bp3-input::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input:focus, .bp3-input.bp3-active{
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-input[type="search"], .bp3-input.bp3-round{
+    border-radius:30px;
+    -webkit-box-sizing:border-box;
+            box-sizing:border-box;
+    padding-left:10px; }
+  .bp3-input[readonly]{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }
+  .bp3-input:disabled, .bp3-input.bp3-disabled{
+    background:rgba(206, 217, 224, 0.5);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    resize:none; }
+  .bp3-input.bp3-large{
+    font-size:16px;
+    height:40px;
+    line-height:40px; }
+    .bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{
+      padding:0 15px; }
+  .bp3-input.bp3-small{
+    font-size:12px;
+    height:24px;
+    line-height:24px;
+    padding-left:8px;
+    padding-right:8px; }
+    .bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{
+      padding:0 12px; }
+  .bp3-input.bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    width:100%; }
+  .bp3-dark .bp3-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-input.bp3-intent-primary{
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-primary[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+              box-shadow:inset 0 0 0 1px #137cbd; }
+    .bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary:focus{
+        -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-primary[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #137cbd;
+                box-shadow:inset 0 0 0 1px #137cbd; }
+      .bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-success{
+    -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success:focus{
+      -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-success[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+              box-shadow:inset 0 0 0 1px #0f9960; }
+    .bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success:focus{
+        -webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-success[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #0f9960;
+                box-shadow:inset 0 0 0 1px #0f9960; }
+      .bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-warning{
+    -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning:focus{
+      -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-warning[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+              box-shadow:inset 0 0 0 1px #d9822b; }
+    .bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning:focus{
+        -webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-warning[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #d9822b;
+                box-shadow:inset 0 0 0 1px #d9822b; }
+      .bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input.bp3-intent-danger{
+    -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger:focus{
+      -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-input.bp3-intent-danger[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px #db3737;
+              box-shadow:inset 0 0 0 1px #db3737; }
+    .bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-input.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger:focus{
+        -webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+                box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+      .bp3-dark .bp3-input.bp3-intent-danger[readonly]{
+        -webkit-box-shadow:inset 0 0 0 1px #db3737;
+                box-shadow:inset 0 0 0 1px #db3737; }
+      .bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+  .bp3-input::-ms-clear{
+    display:none; }
+textarea.bp3-input{
+  max-width:100%;
+  padding:10px; }
+  textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{
+    height:auto;
+    line-height:inherit; }
+  textarea.bp3-input.bp3-small{
+    padding:8px; }
+  .bp3-dark textarea.bp3-input{
+    background:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark textarea.bp3-input::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark textarea.bp3-input:focus{
+      -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input[readonly]{
+      -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{
+      background:rgba(57, 75, 89, 0.5);
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+label.bp3-label{
+  display:block;
+  margin-bottom:15px;
+  margin-top:0; }
+  label.bp3-label .bp3-html-select,
+  label.bp3-label .bp3-input,
+  label.bp3-label .bp3-select,
+  label.bp3-label .bp3-slider,
+  label.bp3-label .bp3-popover-wrapper{
+    display:block;
+    margin-top:5px;
+    text-transform:none; }
+  label.bp3-label .bp3-button-group{
+    margin-top:5px; }
+  label.bp3-label .bp3-select select,
+  label.bp3-label .bp3-html-select select{
+    font-weight:400;
+    vertical-align:top;
+    width:100%; }
+  label.bp3-label.bp3-disabled,
+  label.bp3-label.bp3-disabled .bp3-text-muted{
+    color:rgba(92, 112, 128, 0.6); }
+  label.bp3-label.bp3-inline{
+    line-height:30px; }
+    label.bp3-label.bp3-inline .bp3-html-select,
+    label.bp3-label.bp3-inline .bp3-input,
+    label.bp3-label.bp3-inline .bp3-input-group,
+    label.bp3-label.bp3-inline .bp3-select,
+    label.bp3-label.bp3-inline .bp3-popover-wrapper{
+      display:inline-block;
+      margin:0 0 0 5px;
+      vertical-align:top; }
+    label.bp3-label.bp3-inline .bp3-button-group{
+      margin:0 0 0 5px; }
+    label.bp3-label.bp3-inline .bp3-input-group .bp3-input{
+      margin-left:0; }
+    label.bp3-label.bp3-inline.bp3-large{
+      line-height:40px; }
+  label.bp3-label:not(.bp3-inline) .bp3-popover-target{
+    display:block; }
+  .bp3-dark label.bp3-label{
+    color:#f5f8fa; }
+    .bp3-dark label.bp3-label.bp3-disabled,
+    .bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{
+      color:rgba(167, 182, 194, 0.6); }
+.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 14px;
+          flex:1 1 14px;
+  min-height:0;
+  padding:0;
+  width:30px; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{
+    border-radius:0 3px 0 0; }
+  .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{
+    border-radius:0 0 3px 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{
+  border-radius:3px 0 0 0; }
+
+.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{
+  border-radius:0 0 0 3px; }
+
+.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{
+  width:40px; }
+
+form{
+  display:block; }
+.bp3-html-select select,
+.bp3-select select{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  border:none;
+  border-radius:3px;
+  cursor:pointer;
+  font-size:14px;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  padding:5px 10px;
+  text-align:left;
+  vertical-align:middle;
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  color:#182026;
+  -moz-appearance:none;
+  -webkit-appearance:none;
+  border-radius:3px;
+  height:30px;
+  padding:0 25px 0 10px;
+  width:100%; }
+  .bp3-html-select select > *, .bp3-select select > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-html-select select::before,
+  .bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{
+    margin-right:7px; }
+  .bp3-html-select select:empty::before,
+  .bp3-select select:empty::before,
+  .bp3-html-select select > :last-child,
+  .bp3-select select > :last-child{
+    margin-right:0; }
+  .bp3-html-select select:hover,
+  .bp3-select select:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-html-select select:active,
+  .bp3-select select:active, .bp3-html-select select.bp3-active,
+  .bp3-select select.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-html-select select:disabled,
+  .bp3-select select:disabled, .bp3-html-select select.bp3-disabled,
+  .bp3-select select.bp3-disabled{
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    outline:none; }
+    .bp3-html-select select:disabled.bp3-active,
+    .bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover,
+    .bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active,
+    .bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover,
+    .bp3-select select.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+
+.bp3-html-select.bp3-minimal select,
+.bp3-select.bp3-minimal select{
+  background:none;
+  -webkit-box-shadow:none;
+          box-shadow:none; }
+  .bp3-html-select.bp3-minimal select:hover,
+  .bp3-select.bp3-minimal select:hover{
+    background:rgba(167, 182, 194, 0.3);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:#182026;
+    text-decoration:none; }
+  .bp3-html-select.bp3-minimal select:active,
+  .bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active,
+  .bp3-select.bp3-minimal select.bp3-active{
+    background:rgba(115, 134, 148, 0.3);
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:#182026; }
+  .bp3-html-select.bp3-minimal select:disabled,
+  .bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover,
+  .bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled,
+  .bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover,
+  .bp3-select.bp3-minimal select.bp3-disabled:hover{
+    background:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+    .bp3-html-select.bp3-minimal select:disabled.bp3-active,
+    .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{
+      background:rgba(115, 134, 148, 0.3); }
+  .bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select,
+  .bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{
+    background:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:inherit; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{
+      background:rgba(138, 155, 168, 0.15); }
+    .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active,
+    .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{
+      background:rgba(138, 155, 168, 0.3);
+      color:#f5f8fa; }
+    .bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{
+      background:none;
+      color:rgba(167, 182, 194, 0.6);
+      cursor:not-allowed; }
+      .bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{
+        background:rgba(138, 155, 168, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-primary,
+  .bp3-select.bp3-minimal select.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:hover{
+      background:rgba(19, 124, 189, 0.15);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{
+      background:rgba(19, 124, 189, 0.3);
+      color:#106ba3; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{
+      background:none;
+      color:rgba(16, 107, 163, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{
+        background:rgba(19, 124, 189, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
+      stroke:#106ba3; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{
+      color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{
+        background:rgba(19, 124, 189, 0.2);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{
+        background:rgba(19, 124, 189, 0.3);
+        color:#48aff0; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{
+        background:none;
+        color:rgba(72, 175, 240, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{
+          background:rgba(19, 124, 189, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-success,
+  .bp3-select.bp3-minimal select.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-success:hover{
+      background:rgba(15, 153, 96, 0.15);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:active,
+    .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{
+      background:rgba(15, 153, 96, 0.3);
+      color:#0d8050; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{
+      background:none;
+      color:rgba(13, 128, 80, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{
+        background:rgba(15, 153, 96, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
+      stroke:#0d8050; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{
+      color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{
+        background:rgba(15, 153, 96, 0.2);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{
+        background:rgba(15, 153, 96, 0.3);
+        color:#3dcc91; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{
+        background:none;
+        color:rgba(61, 204, 145, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{
+          background:rgba(15, 153, 96, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-warning,
+  .bp3-select.bp3-minimal select.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:hover{
+      background:rgba(217, 130, 43, 0.15);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{
+      background:rgba(217, 130, 43, 0.3);
+      color:#bf7326; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{
+      background:none;
+      color:rgba(191, 115, 38, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{
+        background:rgba(217, 130, 43, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
+      stroke:#bf7326; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{
+      color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{
+        background:rgba(217, 130, 43, 0.2);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{
+        background:rgba(217, 130, 43, 0.3);
+        color:#ffb366; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{
+        background:none;
+        color:rgba(255, 179, 102, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{
+          background:rgba(217, 130, 43, 0.3); }
+  .bp3-html-select.bp3-minimal select.bp3-intent-danger,
+  .bp3-select.bp3-minimal select.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      background:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:hover{
+      background:rgba(219, 55, 55, 0.15);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{
+      background:rgba(219, 55, 55, 0.3);
+      color:#c23030; }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled,
+    .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{
+      background:none;
+      color:rgba(194, 48, 48, 0.5); }
+      .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active,
+      .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{
+        background:rgba(219, 55, 55, 0.3); }
+    .bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
+      stroke:#c23030; }
+    .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger,
+    .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{
+      color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{
+        background:rgba(219, 55, 55, 0.2);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{
+        background:rgba(219, 55, 55, 0.3);
+        color:#ff7373; }
+      .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled,
+      .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{
+        background:none;
+        color:rgba(255, 115, 115, 0.5); }
+        .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active,
+        .bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{
+          background:rgba(219, 55, 55, 0.3); }
+
+.bp3-html-select.bp3-large select,
+.bp3-select.bp3-large select{
+  font-size:16px;
+  height:40px;
+  padding-right:35px; }
+
+.bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{
+  background-color:#394b59;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+  color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{
+    background-color:#202b33;
+    background-image:none;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{
+    background-color:rgba(57, 75, 89, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{
+      background:rgba(57, 75, 89, 0.7); }
+  .bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{
+    background:rgba(16, 22, 26, 0.5);
+    stroke:#8a9ba8; }
+
+.bp3-html-select select:disabled,
+.bp3-select select:disabled{
+  background-color:rgba(206, 217, 224, 0.5);
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-html-select .bp3-icon,
+.bp3-select .bp3-icon, .bp3-select::after{
+  color:#5c7080;
+  pointer-events:none;
+  position:absolute;
+  right:7px;
+  top:7px; }
+  .bp3-html-select .bp3-disabled.bp3-icon,
+  .bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{
+    color:rgba(92, 112, 128, 0.6); }
+.bp3-html-select,
+.bp3-select{
+  display:inline-block;
+  letter-spacing:normal;
+  position:relative;
+  vertical-align:middle; }
+  .bp3-html-select select::-ms-expand,
+  .bp3-select select::-ms-expand{
+    display:none; }
+  .bp3-html-select .bp3-icon,
+  .bp3-select .bp3-icon{
+    color:#5c7080; }
+    .bp3-html-select .bp3-icon:hover,
+    .bp3-select .bp3-icon:hover{
+      color:#182026; }
+    .bp3-dark .bp3-html-select .bp3-icon, .bp3-dark
+    .bp3-select .bp3-icon{
+      color:#a7b6c2; }
+      .bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark
+      .bp3-select .bp3-icon:hover{
+        color:#f5f8fa; }
+  .bp3-html-select.bp3-large::after,
+  .bp3-html-select.bp3-large .bp3-icon,
+  .bp3-select.bp3-large::after,
+  .bp3-select.bp3-large .bp3-icon{
+    right:12px;
+    top:12px; }
+  .bp3-html-select.bp3-fill,
+  .bp3-html-select.bp3-fill select,
+  .bp3-select.bp3-fill,
+  .bp3-select.bp3-fill select{
+    width:100%; }
+  .bp3-dark .bp3-html-select option, .bp3-dark
+  .bp3-select option{
+    background-color:#30404d;
+    color:#f5f8fa; }
+  .bp3-dark .bp3-html-select option:disabled, .bp3-dark
+  .bp3-select option:disabled{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-html-select::after, .bp3-dark
+  .bp3-select::after{
+    color:#a7b6c2; }
+
+.bp3-select::after{
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  content:""; }
+.bp3-running-text table, table.bp3-html-table{
+  border-spacing:0;
+  font-size:14px; }
+  .bp3-running-text table th, table.bp3-html-table th,
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    padding:11px;
+    text-align:left;
+    vertical-align:top; }
+  .bp3-running-text table th, table.bp3-html-table th{
+    color:#182026;
+    font-weight:600; }
+  
+  .bp3-running-text table td,
+  table.bp3-html-table td{
+    color:#182026; }
+  .bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th,
+  .bp3-running-text table tbody tr:first-child td,
+  table.bp3-html-table tbody tr:first-child td,
+  .bp3-running-text table tfoot tr:first-child th,
+  table.bp3-html-table tfoot tr:first-child th,
+  .bp3-running-text table tfoot tr:first-child td,
+  table.bp3-html-table tfoot tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  .bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{
+    color:#f5f8fa; }
+  .bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th,
+  .bp3-dark .bp3-running-text table tbody tr:first-child td,
+  .bp3-running-text .bp3-dark table tbody tr:first-child td,
+  .bp3-dark table.bp3-html-table tbody tr:first-child td,
+  .bp3-dark .bp3-running-text table tfoot tr:first-child th,
+  .bp3-running-text .bp3-dark table tfoot tr:first-child th,
+  .bp3-dark table.bp3-html-table tfoot tr:first-child th,
+  .bp3-dark .bp3-running-text table tfoot tr:first-child td,
+  .bp3-running-text .bp3-dark table tfoot tr:first-child td,
+  .bp3-dark table.bp3-html-table tfoot tr:first-child td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+
+table.bp3-html-table.bp3-html-table-condensed th,
+table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th,
+table.bp3-html-table.bp3-small td{
+  padding-bottom:6px;
+  padding-top:6px; }
+
+table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+  background:rgba(191, 204, 214, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+  -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered tbody tr td,
+table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
+  -webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
+          box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
+  table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
+  table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
+    -webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+  -webkit-box-shadow:none;
+          box-shadow:none; }
+  table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
+
+table.bp3-html-table.bp3-interactive tbody tr:hover td{
+  background-color:rgba(191, 204, 214, 0.3);
+  cursor:pointer; }
+
+table.bp3-html-table.bp3-interactive tbody tr:active td{
+  background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-dark table.bp3-html-table{ }
+  .bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
+    background:rgba(92, 112, 128, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td,
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td{
+    -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child),
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){
+      -webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15);
+              box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); }
+  .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+    -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);
+            box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }
+    .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{
+    background-color:rgba(92, 112, 128, 0.3);
+    cursor:pointer; }
+  .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{
+    background-color:rgba(92, 112, 128, 0.4); }
+
+.bp3-key-combo{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center; }
+  .bp3-key-combo > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-key-combo > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-key-combo::before,
+  .bp3-key-combo > *{
+    margin-right:5px; }
+  .bp3-key-combo:empty::before,
+  .bp3-key-combo > :last-child{
+    margin-right:0; }
+
+.bp3-hotkey-dialog{
+  padding-bottom:0;
+  top:40px; }
+  .bp3-hotkey-dialog .bp3-dialog-body{
+    margin:0;
+    padding:0; }
+  .bp3-hotkey-dialog .bp3-hotkey-label{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1; }
+
+.bp3-hotkey-column{
+  margin:auto;
+  max-height:80vh;
+  overflow-y:auto;
+  padding:30px; }
+  .bp3-hotkey-column .bp3-heading{
+    margin-bottom:20px; }
+    .bp3-hotkey-column .bp3-heading:not(:first-child){
+      margin-top:40px; }
+
+.bp3-hotkey{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:justify;
+      -ms-flex-pack:justify;
+          justify-content:space-between;
+  margin-left:0;
+  margin-right:0; }
+  .bp3-hotkey:not(:last-child){
+    margin-bottom:10px; }
+.bp3-icon{
+  display:inline-block;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  vertical-align:text-bottom; }
+  .bp3-icon:not(:empty)::before{
+    content:"" !important;
+    content:unset !important; }
+  .bp3-icon > svg{
+    display:block; }
+    .bp3-icon > svg:not([fill]){
+      fill:currentColor; }
+
+.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{
+  color:#106ba3; }
+  .bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{
+    color:#48aff0; }
+
+.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{
+  color:#0d8050; }
+  .bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{
+    color:#3dcc91; }
+
+.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{
+  color:#bf7326; }
+  .bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{
+    color:#ffb366; }
+
+.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{
+  color:#c23030; }
+  .bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{
+    color:#ff7373; }
+
+span.bp3-icon-standard{
+  font-family:"Icons16", sans-serif;
+  font-size:16px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon-large{
+  font-family:"Icons20", sans-serif;
+  font-size:20px;
+  font-style:normal;
+  font-weight:400;
+  line-height:1;
+  -moz-osx-font-smoothing:grayscale;
+  -webkit-font-smoothing:antialiased;
+  display:inline-block; }
+
+span.bp3-icon:empty{
+  font-family:"Icons20";
+  font-size:inherit;
+  font-style:normal;
+  font-weight:400;
+  line-height:1; }
+  span.bp3-icon:empty::before{
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased; }
+
+.bp3-icon-add::before{
+  content:""; }
+
+.bp3-icon-add-column-left::before{
+  content:""; }
+
+.bp3-icon-add-column-right::before{
+  content:""; }
+
+.bp3-icon-add-row-bottom::before{
+  content:""; }
+
+.bp3-icon-add-row-top::before{
+  content:""; }
+
+.bp3-icon-add-to-artifact::before{
+  content:""; }
+
+.bp3-icon-add-to-folder::before{
+  content:""; }
+
+.bp3-icon-airplane::before{
+  content:""; }
+
+.bp3-icon-align-center::before{
+  content:""; }
+
+.bp3-icon-align-justify::before{
+  content:""; }
+
+.bp3-icon-align-left::before{
+  content:""; }
+
+.bp3-icon-align-right::before{
+  content:""; }
+
+.bp3-icon-alignment-bottom::before{
+  content:""; }
+
+.bp3-icon-alignment-horizontal-center::before{
+  content:""; }
+
+.bp3-icon-alignment-left::before{
+  content:""; }
+
+.bp3-icon-alignment-right::before{
+  content:""; }
+
+.bp3-icon-alignment-top::before{
+  content:""; }
+
+.bp3-icon-alignment-vertical-center::before{
+  content:""; }
+
+.bp3-icon-annotation::before{
+  content:""; }
+
+.bp3-icon-application::before{
+  content:""; }
+
+.bp3-icon-applications::before{
+  content:""; }
+
+.bp3-icon-archive::before{
+  content:""; }
+
+.bp3-icon-arrow-bottom-left::before{
+  content:"↙"; }
+
+.bp3-icon-arrow-bottom-right::before{
+  content:"↘"; }
+
+.bp3-icon-arrow-down::before{
+  content:"↓"; }
+
+.bp3-icon-arrow-left::before{
+  content:"←"; }
+
+.bp3-icon-arrow-right::before{
+  content:"→"; }
+
+.bp3-icon-arrow-top-left::before{
+  content:"↖"; }
+
+.bp3-icon-arrow-top-right::before{
+  content:"↗"; }
+
+.bp3-icon-arrow-up::before{
+  content:"↑"; }
+
+.bp3-icon-arrows-horizontal::before{
+  content:"↔"; }
+
+.bp3-icon-arrows-vertical::before{
+  content:"↕"; }
+
+.bp3-icon-asterisk::before{
+  content:"*"; }
+
+.bp3-icon-automatic-updates::before{
+  content:""; }
+
+.bp3-icon-badge::before{
+  content:""; }
+
+.bp3-icon-ban-circle::before{
+  content:""; }
+
+.bp3-icon-bank-account::before{
+  content:""; }
+
+.bp3-icon-barcode::before{
+  content:""; }
+
+.bp3-icon-blank::before{
+  content:""; }
+
+.bp3-icon-blocked-person::before{
+  content:""; }
+
+.bp3-icon-bold::before{
+  content:""; }
+
+.bp3-icon-book::before{
+  content:""; }
+
+.bp3-icon-bookmark::before{
+  content:""; }
+
+.bp3-icon-box::before{
+  content:""; }
+
+.bp3-icon-briefcase::before{
+  content:""; }
+
+.bp3-icon-bring-data::before{
+  content:""; }
+
+.bp3-icon-build::before{
+  content:""; }
+
+.bp3-icon-calculator::before{
+  content:""; }
+
+.bp3-icon-calendar::before{
+  content:""; }
+
+.bp3-icon-camera::before{
+  content:""; }
+
+.bp3-icon-caret-down::before{
+  content:"⌄"; }
+
+.bp3-icon-caret-left::before{
+  content:"〈"; }
+
+.bp3-icon-caret-right::before{
+  content:"〉"; }
+
+.bp3-icon-caret-up::before{
+  content:"⌃"; }
+
+.bp3-icon-cell-tower::before{
+  content:""; }
+
+.bp3-icon-changes::before{
+  content:""; }
+
+.bp3-icon-chart::before{
+  content:""; }
+
+.bp3-icon-chat::before{
+  content:""; }
+
+.bp3-icon-chevron-backward::before{
+  content:""; }
+
+.bp3-icon-chevron-down::before{
+  content:""; }
+
+.bp3-icon-chevron-forward::before{
+  content:""; }
+
+.bp3-icon-chevron-left::before{
+  content:""; }
+
+.bp3-icon-chevron-right::before{
+  content:""; }
+
+.bp3-icon-chevron-up::before{
+  content:""; }
+
+.bp3-icon-circle::before{
+  content:""; }
+
+.bp3-icon-circle-arrow-down::before{
+  content:""; }
+
+.bp3-icon-circle-arrow-left::before{
+  content:""; }
+
+.bp3-icon-circle-arrow-right::before{
+  content:""; }
+
+.bp3-icon-circle-arrow-up::before{
+  content:""; }
+
+.bp3-icon-citation::before{
+  content:""; }
+
+.bp3-icon-clean::before{
+  content:""; }
+
+.bp3-icon-clipboard::before{
+  content:""; }
+
+.bp3-icon-cloud::before{
+  content:"☁"; }
+
+.bp3-icon-cloud-download::before{
+  content:""; }
+
+.bp3-icon-cloud-upload::before{
+  content:""; }
+
+.bp3-icon-code::before{
+  content:""; }
+
+.bp3-icon-code-block::before{
+  content:""; }
+
+.bp3-icon-cog::before{
+  content:""; }
+
+.bp3-icon-collapse-all::before{
+  content:""; }
+
+.bp3-icon-column-layout::before{
+  content:""; }
+
+.bp3-icon-comment::before{
+  content:""; }
+
+.bp3-icon-comparison::before{
+  content:""; }
+
+.bp3-icon-compass::before{
+  content:""; }
+
+.bp3-icon-compressed::before{
+  content:""; }
+
+.bp3-icon-confirm::before{
+  content:""; }
+
+.bp3-icon-console::before{
+  content:""; }
+
+.bp3-icon-contrast::before{
+  content:""; }
+
+.bp3-icon-control::before{
+  content:""; }
+
+.bp3-icon-credit-card::before{
+  content:""; }
+
+.bp3-icon-cross::before{
+  content:"✗"; }
+
+.bp3-icon-crown::before{
+  content:""; }
+
+.bp3-icon-cube::before{
+  content:""; }
+
+.bp3-icon-cube-add::before{
+  content:""; }
+
+.bp3-icon-cube-remove::before{
+  content:""; }
+
+.bp3-icon-curved-range-chart::before{
+  content:""; }
+
+.bp3-icon-cut::before{
+  content:""; }
+
+.bp3-icon-dashboard::before{
+  content:""; }
+
+.bp3-icon-data-lineage::before{
+  content:""; }
+
+.bp3-icon-database::before{
+  content:""; }
+
+.bp3-icon-delete::before{
+  content:""; }
+
+.bp3-icon-delta::before{
+  content:"Δ"; }
+
+.bp3-icon-derive-column::before{
+  content:""; }
+
+.bp3-icon-desktop::before{
+  content:""; }
+
+.bp3-icon-diagnosis::before{
+  content:""; }
+
+.bp3-icon-diagram-tree::before{
+  content:""; }
+
+.bp3-icon-direction-left::before{
+  content:""; }
+
+.bp3-icon-direction-right::before{
+  content:""; }
+
+.bp3-icon-disable::before{
+  content:""; }
+
+.bp3-icon-document::before{
+  content:""; }
+
+.bp3-icon-document-open::before{
+  content:""; }
+
+.bp3-icon-document-share::before{
+  content:""; }
+
+.bp3-icon-dollar::before{
+  content:"$"; }
+
+.bp3-icon-dot::before{
+  content:"•"; }
+
+.bp3-icon-double-caret-horizontal::before{
+  content:""; }
+
+.bp3-icon-double-caret-vertical::before{
+  content:""; }
+
+.bp3-icon-double-chevron-down::before{
+  content:""; }
+
+.bp3-icon-double-chevron-left::before{
+  content:""; }
+
+.bp3-icon-double-chevron-right::before{
+  content:""; }
+
+.bp3-icon-double-chevron-up::before{
+  content:""; }
+
+.bp3-icon-doughnut-chart::before{
+  content:""; }
+
+.bp3-icon-download::before{
+  content:""; }
+
+.bp3-icon-drag-handle-horizontal::before{
+  content:""; }
+
+.bp3-icon-drag-handle-vertical::before{
+  content:""; }
+
+.bp3-icon-draw::before{
+  content:""; }
+
+.bp3-icon-drive-time::before{
+  content:""; }
+
+.bp3-icon-duplicate::before{
+  content:""; }
+
+.bp3-icon-edit::before{
+  content:"✎"; }
+
+.bp3-icon-eject::before{
+  content:"⏏"; }
+
+.bp3-icon-endorsed::before{
+  content:""; }
+
+.bp3-icon-envelope::before{
+  content:"✉"; }
+
+.bp3-icon-equals::before{
+  content:""; }
+
+.bp3-icon-eraser::before{
+  content:""; }
+
+.bp3-icon-error::before{
+  content:""; }
+
+.bp3-icon-euro::before{
+  content:"€"; }
+
+.bp3-icon-exchange::before{
+  content:""; }
+
+.bp3-icon-exclude-row::before{
+  content:""; }
+
+.bp3-icon-expand-all::before{
+  content:""; }
+
+.bp3-icon-export::before{
+  content:""; }
+
+.bp3-icon-eye-off::before{
+  content:""; }
+
+.bp3-icon-eye-on::before{
+  content:""; }
+
+.bp3-icon-eye-open::before{
+  content:""; }
+
+.bp3-icon-fast-backward::before{
+  content:""; }
+
+.bp3-icon-fast-forward::before{
+  content:""; }
+
+.bp3-icon-feed::before{
+  content:""; }
+
+.bp3-icon-feed-subscribed::before{
+  content:""; }
+
+.bp3-icon-film::before{
+  content:""; }
+
+.bp3-icon-filter::before{
+  content:""; }
+
+.bp3-icon-filter-keep::before{
+  content:""; }
+
+.bp3-icon-filter-list::before{
+  content:""; }
+
+.bp3-icon-filter-open::before{
+  content:""; }
+
+.bp3-icon-filter-remove::before{
+  content:""; }
+
+.bp3-icon-flag::before{
+  content:"⚑"; }
+
+.bp3-icon-flame::before{
+  content:""; }
+
+.bp3-icon-flash::before{
+  content:""; }
+
+.bp3-icon-floppy-disk::before{
+  content:""; }
+
+.bp3-icon-flow-branch::before{
+  content:""; }
+
+.bp3-icon-flow-end::before{
+  content:""; }
+
+.bp3-icon-flow-linear::before{
+  content:""; }
+
+.bp3-icon-flow-review::before{
+  content:""; }
+
+.bp3-icon-flow-review-branch::before{
+  content:""; }
+
+.bp3-icon-flows::before{
+  content:""; }
+
+.bp3-icon-folder-close::before{
+  content:""; }
+
+.bp3-icon-folder-new::before{
+  content:""; }
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+  content:"⎁"; }
+
+.bp3-icon-undo::before{
+  content:"⎌"; }
+
+.bp3-icon-ungroup-objects::before{
+  content:""; }
+
+.bp3-icon-unknown-vehicle::before{
+  content:""; }
+
+.bp3-icon-unlock::before{
+  content:""; }
+
+.bp3-icon-unpin::before{
+  content:""; }
+
+.bp3-icon-unresolve::before{
+  content:""; }
+
+.bp3-icon-updated::before{
+  content:""; }
+
+.bp3-icon-upload::before{
+  content:""; }
+
+.bp3-icon-user::before{
+  content:""; }
+
+.bp3-icon-variable::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-asc::before{
+  content:""; }
+
+.bp3-icon-vertical-bar-chart-desc::before{
+  content:""; }
+
+.bp3-icon-vertical-distribution::before{
+  content:""; }
+
+.bp3-icon-video::before{
+  content:""; }
+
+.bp3-icon-volume-down::before{
+  content:""; }
+
+.bp3-icon-volume-off::before{
+  content:""; }
+
+.bp3-icon-volume-up::before{
+  content:""; }
+
+.bp3-icon-walk::before{
+  content:""; }
+
+.bp3-icon-warning-sign::before{
+  content:""; }
+
+.bp3-icon-waterfall-chart::before{
+  content:""; }
+
+.bp3-icon-widget::before{
+  content:""; }
+
+.bp3-icon-widget-button::before{
+  content:""; }
+
+.bp3-icon-widget-footer::before{
+  content:""; }
+
+.bp3-icon-widget-header::before{
+  content:""; }
+
+.bp3-icon-wrench::before{
+  content:""; }
+
+.bp3-icon-zoom-in::before{
+  content:""; }
+
+.bp3-icon-zoom-out::before{
+  content:""; }
+
+.bp3-icon-zoom-to-fit::before{
+  content:""; }
+.bp3-submenu > .bp3-popover-wrapper{
+  display:block; }
+
+.bp3-submenu .bp3-popover-target{
+  display:block; }
+  .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ }
+
+.bp3-submenu.bp3-popover{
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0 5px; }
+  .bp3-submenu.bp3-popover > .bp3-popover-content{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-submenu.bp3-popover, .bp3-submenu.bp3-popover.bp3-dark{
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+    .bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+.bp3-menu{
+  background:#ffffff;
+  border-radius:3px;
+  color:#182026;
+  list-style:none;
+  margin:0;
+  min-width:180px;
+  padding:5px;
+  text-align:left; }
+
+.bp3-menu-divider{
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  display:block;
+  margin:5px; }
+  .bp3-dark .bp3-menu-divider{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-menu-item{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  border-radius:2px;
+  color:inherit;
+  line-height:20px;
+  padding:5px 7px;
+  text-decoration:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-menu-item > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-menu-item > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > *{
+    margin-right:7px; }
+  .bp3-menu-item:empty::before,
+  .bp3-menu-item > :last-child{
+    margin-right:0; }
+  .bp3-menu-item > .bp3-fill{
+    word-break:break-word; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    background-color:rgba(167, 182, 194, 0.3);
+    cursor:pointer;
+    text-decoration:none; }
+  .bp3-menu-item.bp3-disabled{
+    background-color:inherit;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-dark .bp3-menu-item{
+    color:inherit; }
+    .bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+      background-color:rgba(138, 155, 168, 0.15);
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-disabled{
+      background-color:inherit;
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-menu-item.bp3-intent-primary{
+    color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after,
+    .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+      color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{
+      background-color:#137cbd; }
+    .bp3-menu-item.bp3-intent-primary:active{
+      background-color:#106ba3; }
+    .bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after,
+    .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+    .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-success{
+    color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after,
+    .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+      color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{
+      background-color:#0f9960; }
+    .bp3-menu-item.bp3-intent-success:active{
+      background-color:#0d8050; }
+    .bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after,
+    .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after,
+    .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-warning{
+    color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after,
+    .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+      color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{
+      background-color:#d9822b; }
+    .bp3-menu-item.bp3-intent-warning:active{
+      background-color:#bf7326; }
+    .bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after,
+    .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+    .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item.bp3-intent-danger{
+    color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger .bp3-icon{
+      color:inherit; }
+    .bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after,
+    .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+      color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{
+      background-color:#db3737; }
+    .bp3-menu-item.bp3-intent-danger:active{
+      background-color:#c23030; }
+    .bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+    .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+    .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after,
+    .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+    .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-menu-item::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    margin-right:7px; }
+  .bp3-menu-item::before,
+  .bp3-menu-item > .bp3-icon{
+    color:#5c7080;
+    margin-top:2px; }
+  .bp3-menu-item .bp3-menu-item-label{
+    color:#5c7080; }
+  .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
+    color:inherit; }
+  .bp3-menu-item.bp3-active, .bp3-menu-item:active{
+    background-color:rgba(115, 134, 148, 0.3); }
+  .bp3-menu-item.bp3-disabled{
+    background-color:inherit !important;
+    color:rgba(92, 112, 128, 0.6) !important;
+    cursor:not-allowed !important;
+    outline:none !important; }
+    .bp3-menu-item.bp3-disabled::before,
+    .bp3-menu-item.bp3-disabled > .bp3-icon,
+    .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+      color:rgba(92, 112, 128, 0.6) !important; }
+  .bp3-large .bp3-menu-item{
+    font-size:16px;
+    line-height:22px;
+    padding:9px 7px; }
+    .bp3-large .bp3-menu-item .bp3-icon{
+      margin-top:3px; }
+    .bp3-large .bp3-menu-item::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1;
+      -moz-osx-font-smoothing:grayscale;
+      -webkit-font-smoothing:antialiased;
+      margin-right:10px;
+      margin-top:1px; }
+
+button.bp3-menu-item{
+  background:none;
+  border:none;
+  text-align:left;
+  width:100%; }
+.bp3-menu-header{
+  border-top:1px solid rgba(16, 22, 26, 0.15);
+  display:block;
+  margin:5px;
+  cursor:default;
+  padding-left:2px; }
+  .bp3-dark .bp3-menu-header{
+    border-top-color:rgba(255, 255, 255, 0.15); }
+  .bp3-menu-header:first-of-type{
+    border-top:none; }
+  .bp3-menu-header > h6{
+    color:#182026;
+    font-weight:600;
+    overflow:hidden;
+    text-overflow:ellipsis;
+    white-space:nowrap;
+    word-wrap:normal;
+    line-height:17px;
+    margin:0;
+    padding:10px 7px 0 1px; }
+    .bp3-dark .bp3-menu-header > h6{
+      color:#f5f8fa; }
+  .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+  .bp3-large .bp3-menu-header > h6{
+    font-size:18px;
+    padding-bottom:5px;
+    padding-top:15px; }
+  .bp3-large .bp3-menu-header:first-of-type > h6{
+    padding-top:0; }
+
+.bp3-dark .bp3-menu{
+  background:#30404d;
+  color:#f5f8fa; }
+
+.bp3-dark .bp3-menu-item{ }
+  .bp3-dark .bp3-menu-item.bp3-intent-primary{
+    color:#48aff0; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
+      color:#48aff0; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{
+      background-color:#137cbd; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:active{
+      background-color:#106ba3; }
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-success{
+    color:#3dcc91; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
+      color:#3dcc91; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{
+      background-color:#0f9960; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:active{
+      background-color:#0d8050; }
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-warning{
+    color:#ffb366; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
+      color:#ffb366; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{
+      background-color:#d9822b; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:active{
+      background-color:#bf7326; }
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item.bp3-intent-danger{
+    color:#ff7373; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{
+      color:inherit; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
+      color:#ff7373; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{
+      background-color:#db3737; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:active{
+      background-color:#c23030; }
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
+    .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label,
+    .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after,
+    .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{
+      color:#ffffff; }
+  .bp3-dark .bp3-menu-item::before,
+  .bp3-dark .bp3-menu-item > .bp3-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-menu-item .bp3-menu-item-label{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{
+    background-color:rgba(138, 155, 168, 0.3); }
+  .bp3-dark .bp3-menu-item.bp3-disabled{
+    color:rgba(167, 182, 194, 0.6) !important; }
+    .bp3-dark .bp3-menu-item.bp3-disabled::before,
+    .bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon,
+    .bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{
+      color:rgba(167, 182, 194, 0.6) !important; }
+
+.bp3-dark .bp3-menu-divider,
+.bp3-dark .bp3-menu-header{
+  border-color:rgba(255, 255, 255, 0.15); }
+
+.bp3-dark .bp3-menu-header > h6{
+  color:#f5f8fa; }
+
+.bp3-label .bp3-menu{
+  margin-top:5px; }
+.bp3-navbar{
+  background-color:#ffffff;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);
+  height:50px;
+  padding:0 15px;
+  position:relative;
+  width:100%;
+  z-index:10; }
+  .bp3-navbar.bp3-dark,
+  .bp3-dark .bp3-navbar{
+    background-color:#394b59; }
+  .bp3-navbar.bp3-dark{
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-dark .bp3-navbar{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }
+  .bp3-navbar.bp3-fixed-top{
+    left:0;
+    position:fixed;
+    right:0;
+    top:0; }
+
+.bp3-navbar-heading{
+  font-size:16px;
+  margin-right:15px; }
+
+.bp3-navbar-group{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:50px; }
+  .bp3-navbar-group.bp3-align-left{
+    float:left; }
+  .bp3-navbar-group.bp3-align-right{
+    float:right; }
+
+.bp3-navbar-divider{
+  border-left:1px solid rgba(16, 22, 26, 0.15);
+  height:20px;
+  margin:0 10px; }
+  .bp3-dark .bp3-navbar-divider{
+    border-left-color:rgba(255, 255, 255, 0.15); }
+.bp3-non-ideal-state{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  height:100%;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  text-align:center;
+  width:100%; }
+  .bp3-non-ideal-state > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-non-ideal-state > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-non-ideal-state::before,
+  .bp3-non-ideal-state > *{
+    margin-bottom:20px; }
+  .bp3-non-ideal-state:empty::before,
+  .bp3-non-ideal-state > :last-child{
+    margin-bottom:0; }
+  .bp3-non-ideal-state > *{
+    max-width:400px; }
+
+.bp3-non-ideal-state-visual{
+  color:rgba(92, 112, 128, 0.6);
+  font-size:60px; }
+  .bp3-dark .bp3-non-ideal-state-visual{
+    color:rgba(167, 182, 194, 0.6); }
+
+.bp3-overflow-list{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-wrap:nowrap;
+      flex-wrap:nowrap;
+  min-width:0; }
+
+.bp3-overflow-list-spacer{
+  -ms-flex-negative:1;
+      flex-shrink:1;
+  width:1px; }
+
+body.bp3-overlay-open{
+  overflow:hidden; }
+
+.bp3-overlay{
+  bottom:0;
+  left:0;
+  position:static;
+  right:0;
+  top:0;
+  z-index:20; }
+  .bp3-overlay:not(.bp3-overlay-open){
+    pointer-events:none; }
+  .bp3-overlay.bp3-overlay-container{
+    overflow:hidden;
+    position:fixed; }
+    .bp3-overlay.bp3-overlay-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-scroll-container{
+    overflow:auto;
+    position:fixed; }
+    .bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{
+      position:absolute; }
+  .bp3-overlay.bp3-overlay-inline{
+    display:inline;
+    overflow:visible; }
+
+.bp3-overlay-content{
+  position:fixed;
+  z-index:20; }
+  .bp3-overlay-inline .bp3-overlay-content,
+  .bp3-overlay-scroll-container .bp3-overlay-content{
+    position:absolute; }
+
+.bp3-overlay-backdrop{
+  bottom:0;
+  left:0;
+  position:fixed;
+  right:0;
+  top:0;
+  opacity:1;
+  background-color:rgba(16, 22, 26, 0.7);
+  overflow:auto;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none;
+  z-index:20; }
+  .bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{
+    opacity:0; }
+  .bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-overlay-backdrop.bp3-overlay-exit{
+    opacity:1; }
+  .bp3-overlay-backdrop.bp3-overlay-exit-active{
+    opacity:0;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-overlay-backdrop:focus{
+    outline:none; }
+  .bp3-overlay-inline .bp3-overlay-backdrop{
+    position:absolute; }
+.bp3-panel-stack{
+  overflow:hidden;
+  position:relative; }
+
+.bp3-panel-stack-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-negative:0;
+      flex-shrink:0;
+  height:30px;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack-header{
+    -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-panel-stack-header > span{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1;
+            flex:1; }
+  .bp3-panel-stack-header .bp3-heading{
+    margin:0 5px; }
+
+.bp3-button.bp3-panel-stack-header-back{
+  margin-left:5px;
+  padding-left:0;
+  white-space:nowrap; }
+  .bp3-button.bp3-panel-stack-header-back .bp3-icon{
+    margin:0 2px; }
+
+.bp3-panel-stack-view{
+  bottom:0;
+  left:0;
+  position:absolute;
+  right:0;
+  top:0;
+  background-color:#ffffff;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin-right:-1px;
+  overflow-y:auto;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack-view{
+    background-color:#30404d; }
+  .bp3-panel-stack-view:nth-last-child(n + 4){
+    display:none; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0; }
+
+.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-push .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+.bp3-panel-stack2{
+  overflow:hidden;
+  position:relative; }
+
+.bp3-panel-stack2-header{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);
+          box-shadow:0 1px rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -ms-flex-negative:0;
+      flex-shrink:0;
+  height:30px;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack2-header{
+    -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);
+            box-shadow:0 1px rgba(255, 255, 255, 0.15); }
+  .bp3-panel-stack2-header > span{
+    -webkit-box-align:stretch;
+        -ms-flex-align:stretch;
+            align-items:stretch;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-flex:1;
+        -ms-flex:1;
+            flex:1; }
+  .bp3-panel-stack2-header .bp3-heading{
+    margin:0 5px; }
+
+.bp3-button.bp3-panel-stack2-header-back{
+  margin-left:5px;
+  padding-left:0;
+  white-space:nowrap; }
+  .bp3-button.bp3-panel-stack2-header-back .bp3-icon{
+    margin:0 2px; }
+
+.bp3-panel-stack2-view{
+  bottom:0;
+  left:0;
+  position:absolute;
+  right:0;
+  top:0;
+  background-color:#ffffff;
+  border-right:1px solid rgba(16, 22, 26, 0.15);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  margin-right:-1px;
+  overflow-y:auto;
+  z-index:1; }
+  .bp3-dark .bp3-panel-stack2-view{
+    background-color:#30404d; }
+  .bp3-panel-stack2-view:nth-last-child(n + 4){
+    display:none; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-enter, .bp3-panel-stack2-push .bp3-panel-stack2-appear{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-enter-active, .bp3-panel-stack2-push .bp3-panel-stack2-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack2-push .bp3-panel-stack2-exit-active{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-enter, .bp3-panel-stack2-pop .bp3-panel-stack2-appear{
+  -webkit-transform:translateX(-50%);
+          transform:translateX(-50%);
+  opacity:0; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-enter-active, .bp3-panel-stack2-pop .bp3-panel-stack2-appear-active{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-exit{
+  -webkit-transform:translate(0%);
+          transform:translate(0%);
+  opacity:1; }
+
+.bp3-panel-stack2-pop .bp3-panel-stack2-exit-active{
+  -webkit-transform:translateX(100%);
+          transform:translateX(100%);
+  opacity:0;
+  -webkit-transition-delay:0;
+          transition-delay:0;
+  -webkit-transition-duration:400ms;
+          transition-duration:400ms;
+  -webkit-transition-property:opacity, -webkit-transform;
+  transition-property:opacity, -webkit-transform;
+  transition-property:transform, opacity;
+  transition-property:transform, opacity, -webkit-transform;
+  -webkit-transition-timing-function:ease;
+          transition-timing-function:ease; }
+.bp3-popover{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1);
+  border-radius:3px;
+  display:inline-block;
+  z-index:20; }
+  .bp3-popover .bp3-popover-arrow{
+    height:30px;
+    position:absolute;
+    width:30px; }
+    .bp3-popover .bp3-popover-arrow::before{
+      height:20px;
+      margin:5px;
+      width:20px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{
+    margin-bottom:17px;
+    margin-top:-17px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+      bottom:-11px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{
+    margin-left:17px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+      left:-11px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{
+    margin-top:17px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+      top:-11px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{
+    margin-left:-17px;
+    margin-right:17px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+      right:-11px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
+    top:-0.3934px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
+    right:-0.3934px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
+    left:-0.3934px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
+    bottom:-0.3934px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-popover .bp3-popover-content{
+    background:#ffffff;
+    color:inherit; }
+  .bp3-popover .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-popover .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-popover .bp3-popover-arrow-fill{
+    fill:#ffffff; }
+  .bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3); }
+  .bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-popover-exit > .bp3-popover{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-popover{
+    -webkit-transform:scale(0.3);
+            transform:scale(0.3);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-popover .bp3-popover-content{
+    border-radius:3px;
+    position:relative; }
+  .bp3-popover.bp3-popover-content-sizing .bp3-popover-content{
+    max-width:350px;
+    padding:20px; }
+  .bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{
+    width:350px; }
+  .bp3-popover.bp3-minimal{
+    margin:0 !important; }
+    .bp3-popover.bp3-minimal .bp3-popover-arrow{
+      display:none; }
+    .bp3-popover.bp3-minimal.bp3-popover{
+      -webkit-transform:scale(1);
+              transform:scale(1); }
+      .bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-delay:0;
+                transition-delay:0;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+      .bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1); }
+      .bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{
+        -webkit-transform:scale(1);
+                transform:scale(1);
+        -webkit-transition-delay:0;
+                transition-delay:0;
+        -webkit-transition-duration:100ms;
+                transition-duration:100ms;
+        -webkit-transition-property:-webkit-transform;
+        transition-property:-webkit-transform;
+        transition-property:transform;
+        transition-property:transform, -webkit-transform;
+        -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+                transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-popover.bp3-dark,
+  .bp3-dark .bp3-popover{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-popover .bp3-popover-content{
+      background:#30404d;
+      color:inherit; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-popover .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-popover.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-popover .bp3-popover-arrow-fill{
+      fill:#30404d; }
+
+.bp3-popover-arrow::before{
+  border-radius:2px;
+  content:"";
+  display:block;
+  position:absolute;
+  -webkit-transform:rotate(45deg);
+          transform:rotate(45deg); }
+
+.bp3-tether-pinned .bp3-popover-arrow{
+  display:none; }
+
+.bp3-popover-backdrop{
+  background:rgba(255, 255, 255, 0); }
+
+.bp3-transition-container{
+  opacity:1;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  z-index:20; }
+  .bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{
+    opacity:0; }
+  .bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-transition-container.bp3-popover-exit{
+    opacity:1; }
+  .bp3-transition-container.bp3-popover-exit-active{
+    opacity:0;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:opacity;
+    transition-property:opacity;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-transition-container:focus{
+    outline:none; }
+  .bp3-transition-container.bp3-popover-leave .bp3-popover-content{
+    pointer-events:none; }
+  .bp3-transition-container[data-x-out-of-boundaries]{
+    display:none; }
+
+span.bp3-popover-target{
+  display:inline-block; }
+
+.bp3-popover-wrapper.bp3-fill{
+  width:100%; }
+
+.bp3-portal{
+  left:0;
+  position:absolute;
+  right:0;
+  top:0; }
+@-webkit-keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+@keyframes linear-progress-bar-stripes{
+  from{
+    background-position:0 0; }
+  to{
+    background-position:30px 0; } }
+
+.bp3-progress-bar{
+  background:rgba(92, 112, 128, 0.2);
+  border-radius:40px;
+  display:block;
+  height:8px;
+  overflow:hidden;
+  position:relative;
+  width:100%; }
+  .bp3-progress-bar .bp3-progress-meter{
+    background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%);
+    background-color:rgba(92, 112, 128, 0.8);
+    background-size:30px 30px;
+    border-radius:40px;
+    height:100%;
+    position:absolute;
+    -webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    width:100%; }
+  .bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{
+    animation:linear-progress-bar-stripes 300ms linear infinite reverse; }
+  .bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{
+    background-image:none; }
+
+.bp3-dark .bp3-progress-bar{
+  background:rgba(16, 22, 26, 0.5); }
+  .bp3-dark .bp3-progress-bar .bp3-progress-meter{
+    background-color:#8a9ba8; }
+
+.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
+  background-color:#137cbd; }
+
+.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
+  background-color:#0f9960; }
+
+.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
+  background-color:#d9822b; }
+
+.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
+  background-color:#db3737; }
+@-webkit-keyframes skeleton-glow{
+  from{
+    background:rgba(206, 217, 224, 0.2);
+    border-color:rgba(206, 217, 224, 0.2); }
+  to{
+    background:rgba(92, 112, 128, 0.2);
+    border-color:rgba(92, 112, 128, 0.2); } }
+@keyframes skeleton-glow{
+  from{
+    background:rgba(206, 217, 224, 0.2);
+    border-color:rgba(206, 217, 224, 0.2); }
+  to{
+    background:rgba(92, 112, 128, 0.2);
+    border-color:rgba(92, 112, 128, 0.2); } }
+.bp3-skeleton{
+  -webkit-animation:1000ms linear infinite alternate skeleton-glow;
+          animation:1000ms linear infinite alternate skeleton-glow;
+  background:rgba(206, 217, 224, 0.2);
+  background-clip:padding-box !important;
+  border-color:rgba(206, 217, 224, 0.2) !important;
+  border-radius:2px;
+  -webkit-box-shadow:none !important;
+          box-shadow:none !important;
+  color:transparent !important;
+  cursor:default;
+  pointer-events:none;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-skeleton::before, .bp3-skeleton::after,
+  .bp3-skeleton *{
+    visibility:hidden !important; }
+.bp3-slider{
+  height:40px;
+  min-width:150px;
+  width:100%;
+  cursor:default;
+  outline:none;
+  position:relative;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-slider:hover{
+    cursor:pointer; }
+  .bp3-slider:active{
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-slider.bp3-disabled{
+    cursor:not-allowed;
+    opacity:0.5; }
+  .bp3-slider.bp3-slider-unlabeled{
+    height:16px; }
+
+.bp3-slider-track,
+.bp3-slider-progress{
+  height:6px;
+  left:0;
+  right:0;
+  top:5px;
+  position:absolute; }
+
+.bp3-slider-track{
+  border-radius:3px;
+  overflow:hidden; }
+
+.bp3-slider-progress{
+  background:rgba(92, 112, 128, 0.2); }
+  .bp3-dark .bp3-slider-progress{
+    background:rgba(16, 22, 26, 0.5); }
+  .bp3-slider-progress.bp3-intent-primary{
+    background-color:#137cbd; }
+  .bp3-slider-progress.bp3-intent-success{
+    background-color:#0f9960; }
+  .bp3-slider-progress.bp3-intent-warning{
+    background-color:#d9822b; }
+  .bp3-slider-progress.bp3-intent-danger{
+    background-color:#db3737; }
+
+.bp3-slider-handle{
+  background-color:#f5f8fa;
+  background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0)));
+  background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0));
+  -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+          box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+  color:#182026;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+  cursor:pointer;
+  height:16px;
+  left:0;
+  position:absolute;
+  top:0;
+  width:16px; }
+  .bp3-slider-handle:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }
+  .bp3-slider-handle:active, .bp3-slider-handle.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+  .bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{
+    background-color:rgba(206, 217, 224, 0.5);
+    background-image:none;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed;
+    outline:none; }
+    .bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{
+      background:rgba(206, 217, 224, 0.7); }
+  .bp3-slider-handle:focus{
+    z-index:1; }
+  .bp3-slider-handle:hover{
+    background-clip:padding-box;
+    background-color:#ebf1f5;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);
+    cursor:-webkit-grab;
+    cursor:grab;
+    z-index:2; }
+  .bp3-slider-handle.bp3-active{
+    background-color:#d8e1e8;
+    background-image:none;
+    -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+            box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);
+    cursor:-webkit-grabbing;
+    cursor:grabbing; }
+  .bp3-disabled .bp3-slider-handle{
+    background:#bfccd6;
+    -webkit-box-shadow:none;
+            box-shadow:none;
+    pointer-events:none; }
+  .bp3-dark .bp3-slider-handle{
+    background-color:#394b59;
+    background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0)));
+    background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0));
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+    color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      color:#f5f8fa; }
+    .bp3-dark .bp3-slider-handle:hover{
+      background-color:#30404d;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{
+      background-color:#202b33;
+      background-image:none;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }
+    .bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{
+      background-color:rgba(57, 75, 89, 0.5);
+      background-image:none;
+      -webkit-box-shadow:none;
+              box-shadow:none;
+      color:rgba(167, 182, 194, 0.6); }
+      .bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{
+        background:rgba(57, 75, 89, 0.7); }
+    .bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{
+      background:rgba(16, 22, 26, 0.5);
+      stroke:#8a9ba8; }
+    .bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{
+      background-color:#394b59; }
+    .bp3-dark .bp3-slider-handle.bp3-active{
+      background-color:#293742; }
+  .bp3-dark .bp3-disabled .bp3-slider-handle{
+    background:#5c7080;
+    border-color:#5c7080;
+    -webkit-box-shadow:none;
+            box-shadow:none; }
+  .bp3-slider-handle .bp3-slider-label{
+    background:#394b59;
+    border-radius:3px;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+    color:#f5f8fa;
+    margin-left:8px; }
+    .bp3-dark .bp3-slider-handle .bp3-slider-label{
+      background:#e1e8ed;
+      -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+      color:#394b59; }
+    .bp3-disabled .bp3-slider-handle .bp3-slider-label{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{
+    width:8px; }
+  .bp3-slider-handle.bp3-start{
+    border-bottom-right-radius:0;
+    border-top-right-radius:0; }
+  .bp3-slider-handle.bp3-end{
+    border-bottom-left-radius:0;
+    border-top-left-radius:0;
+    margin-left:8px; }
+    .bp3-slider-handle.bp3-end .bp3-slider-label{
+      margin-left:0; }
+
+.bp3-slider-label{
+  -webkit-transform:translate(-50%, 20px);
+          transform:translate(-50%, 20px);
+  display:inline-block;
+  font-size:12px;
+  line-height:1;
+  padding:2px 5px;
+  position:absolute;
+  vertical-align:top; }
+
+.bp3-slider.bp3-vertical{
+  height:150px;
+  min-width:40px;
+  width:40px; }
+  .bp3-slider.bp3-vertical .bp3-slider-track,
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    bottom:0;
+    height:auto;
+    left:5px;
+    top:0;
+    width:6px; }
+  .bp3-slider.bp3-vertical .bp3-slider-progress{
+    top:auto; }
+  .bp3-slider.bp3-vertical .bp3-slider-label{
+    -webkit-transform:translate(20px, 50%);
+            transform:translate(20px, 50%); }
+  .bp3-slider.bp3-vertical .bp3-slider-handle{
+    top:auto; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{
+      margin-left:0;
+      margin-top:-8px; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      height:8px;
+      margin-left:0;
+      width:16px; }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
+      border-bottom-right-radius:3px;
+      border-top-left-radius:0; }
+      .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{
+        -webkit-transform:translate(20px);
+                transform:translate(20px); }
+    .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{
+      border-bottom-left-radius:0;
+      border-bottom-right-radius:0;
+      border-top-left-radius:3px;
+      margin-bottom:8px; }
+
+@-webkit-keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+@keyframes pt-spinner-animation{
+  from{
+    -webkit-transform:rotate(0deg);
+            transform:rotate(0deg); }
+  to{
+    -webkit-transform:rotate(360deg);
+            transform:rotate(360deg); } }
+
+.bp3-spinner{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-pack:center;
+      -ms-flex-pack:center;
+          justify-content:center;
+  overflow:visible;
+  vertical-align:middle; }
+  .bp3-spinner svg{
+    display:block; }
+  .bp3-spinner path{
+    fill-opacity:0; }
+  .bp3-spinner .bp3-spinner-head{
+    stroke:rgba(92, 112, 128, 0.8);
+    stroke-linecap:round;
+    -webkit-transform-origin:center;
+            transform-origin:center;
+    -webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+    transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-spinner .bp3-spinner-track{
+    stroke:rgba(92, 112, 128, 0.2); }
+
+.bp3-spinner-animation{
+  -webkit-animation:pt-spinner-animation 500ms linear infinite;
+          animation:pt-spinner-animation 500ms linear infinite; }
+  .bp3-no-spin > .bp3-spinner-animation{
+    -webkit-animation:none;
+            animation:none; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-head{
+  stroke:#8a9ba8; }
+
+.bp3-dark .bp3-spinner .bp3-spinner-track{
+  stroke:rgba(16, 22, 26, 0.5); }
+
+.bp3-spinner.bp3-intent-primary .bp3-spinner-head{
+  stroke:#137cbd; }
+
+.bp3-spinner.bp3-intent-success .bp3-spinner-head{
+  stroke:#0f9960; }
+
+.bp3-spinner.bp3-intent-warning .bp3-spinner-head{
+  stroke:#d9822b; }
+
+.bp3-spinner.bp3-intent-danger .bp3-spinner-head{
+  stroke:#db3737; }
+.bp3-tabs.bp3-vertical{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-list{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:column;
+            flex-direction:column; }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{
+      border-radius:3px;
+      padding:0 10px;
+      width:100%; }
+      .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{
+        background-color:rgba(19, 124, 189, 0.2);
+        -webkit-box-shadow:none;
+                box-shadow:none; }
+    .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+      background-color:rgba(19, 124, 189, 0.2);
+      border-radius:3px;
+      bottom:0;
+      height:auto;
+      left:0;
+      right:0;
+      top:0; }
+  .bp3-tabs.bp3-vertical > .bp3-tab-panel{
+    margin-top:0;
+    padding-left:20px; }
+
+.bp3-tab-list{
+  -webkit-box-align:end;
+      -ms-flex-align:end;
+          align-items:flex-end;
+  border:none;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  list-style:none;
+  margin:0;
+  padding:0;
+  position:relative; }
+  .bp3-tab-list > *:not(:last-child){
+    margin-right:20px; }
+
+.bp3-tab{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  color:#182026;
+  cursor:pointer;
+  -webkit-box-flex:0;
+      -ms-flex:0 0 auto;
+          flex:0 0 auto;
+  font-size:14px;
+  line-height:30px;
+  max-width:100%;
+  position:relative;
+  vertical-align:top; }
+  .bp3-tab a{
+    color:inherit;
+    display:block;
+    text-decoration:none; }
+  .bp3-tab-indicator-wrapper ~ .bp3-tab{
+    background-color:transparent !important;
+    -webkit-box-shadow:none !important;
+            box-shadow:none !important; }
+  .bp3-tab[aria-disabled="true"]{
+    color:rgba(92, 112, 128, 0.6);
+    cursor:not-allowed; }
+  .bp3-tab[aria-selected="true"]{
+    border-radius:0;
+    -webkit-box-shadow:inset 0 -3px 0 #106ba3;
+            box-shadow:inset 0 -3px 0 #106ba3; }
+  .bp3-tab[aria-selected="true"], .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#106ba3; }
+  .bp3-tab:focus{
+    -moz-outline-radius:0; }
+  .bp3-large > .bp3-tab{
+    font-size:16px;
+    line-height:40px; }
+
+.bp3-tab-panel{
+  margin-top:20px; }
+  .bp3-tab-panel[aria-hidden="true"]{
+    display:none; }
+
+.bp3-tab-indicator-wrapper{
+  left:0;
+  pointer-events:none;
+  position:absolute;
+  top:0;
+  -webkit-transform:translateX(0), translateY(0);
+          transform:translateX(0), translateY(0);
+  -webkit-transition:height, width, -webkit-transform;
+  transition:height, width, -webkit-transform;
+  transition:height, transform, width;
+  transition:height, transform, width, -webkit-transform;
+  -webkit-transition-duration:200ms;
+          transition-duration:200ms;
+  -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+          transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tab-indicator-wrapper .bp3-tab-indicator{
+    background-color:#106ba3;
+    bottom:0;
+    height:3px;
+    left:0;
+    position:absolute;
+    right:0; }
+  .bp3-tab-indicator-wrapper.bp3-no-animation{
+    -webkit-transition:none;
+    transition:none; }
+
+.bp3-dark .bp3-tab{
+  color:#f5f8fa; }
+  .bp3-dark .bp3-tab[aria-disabled="true"]{
+    color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tab[aria-selected="true"]{
+    -webkit-box-shadow:inset 0 -3px 0 #48aff0;
+            box-shadow:inset 0 -3px 0 #48aff0; }
+  .bp3-dark .bp3-tab[aria-selected="true"], .bp3-dark .bp3-tab:not([aria-disabled="true"]):hover{
+    color:#48aff0; }
+
+.bp3-dark .bp3-tab-indicator{
+  background-color:#48aff0; }
+
+.bp3-flex-expander{
+  -webkit-box-flex:1;
+      -ms-flex:1 1;
+          flex:1 1; }
+.bp3-tag{
+  display:-webkit-inline-box;
+  display:-ms-inline-flexbox;
+  display:inline-flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  background-color:#5c7080;
+  border:none;
+  border-radius:3px;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  color:#f5f8fa;
+  font-size:12px;
+  line-height:16px;
+  max-width:100%;
+  min-height:20px;
+  min-width:20px;
+  padding:2px 6px;
+  position:relative; }
+  .bp3-tag.bp3-interactive{
+    cursor:pointer; }
+    .bp3-tag.bp3-interactive:hover{
+      background-color:rgba(92, 112, 128, 0.85); }
+    .bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{
+      background-color:rgba(92, 112, 128, 0.7); }
+  .bp3-tag > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag > .bp3-fill{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag::before,
+  .bp3-tag > *{
+    margin-right:4px; }
+  .bp3-tag:empty::before,
+  .bp3-tag > :last-child{
+    margin-right:0; }
+  .bp3-tag:focus{
+    outline:rgba(19, 124, 189, 0.6) auto 2px;
+    outline-offset:0;
+    -moz-outline-radius:6px; }
+  .bp3-tag.bp3-round{
+    border-radius:30px;
+    padding-left:8px;
+    padding-right:8px; }
+  .bp3-dark .bp3-tag{
+    background-color:#bfccd6;
+    color:#182026; }
+    .bp3-dark .bp3-tag.bp3-interactive{
+      cursor:pointer; }
+      .bp3-dark .bp3-tag.bp3-interactive:hover{
+        background-color:rgba(191, 204, 214, 0.85); }
+      .bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{
+        background-color:rgba(191, 204, 214, 0.7); }
+    .bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{
+      fill:currentColor; }
+  .bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{
+    fill:#ffffff; }
+  .bp3-tag.bp3-large,
+  .bp3-large .bp3-tag{
+    font-size:14px;
+    line-height:20px;
+    min-height:30px;
+    min-width:30px;
+    padding:5px 10px; }
+    .bp3-tag.bp3-large::before,
+    .bp3-tag.bp3-large > *,
+    .bp3-large .bp3-tag::before,
+    .bp3-large .bp3-tag > *{
+      margin-right:7px; }
+    .bp3-tag.bp3-large:empty::before,
+    .bp3-tag.bp3-large > :last-child,
+    .bp3-large .bp3-tag:empty::before,
+    .bp3-large .bp3-tag > :last-child{
+      margin-right:0; }
+    .bp3-tag.bp3-large.bp3-round,
+    .bp3-large .bp3-tag.bp3-round{
+      padding-left:12px;
+      padding-right:12px; }
+  .bp3-tag.bp3-intent-primary{
+    background:#137cbd;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.85); }
+      .bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.7); }
+  .bp3-tag.bp3-intent-success{
+    background:#0f9960;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.85); }
+      .bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.7); }
+  .bp3-tag.bp3-intent-warning{
+    background:#d9822b;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.85); }
+      .bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.7); }
+  .bp3-tag.bp3-intent-danger{
+    background:#db3737;
+    color:#ffffff; }
+    .bp3-tag.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.85); }
+      .bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.7); }
+  .bp3-tag.bp3-fill{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    width:100%; }
+  .bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{
+    fill:#5c7080; }
+  .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+    background-color:rgba(138, 155, 168, 0.2);
+    color:#182026; }
+    .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+        background-color:rgba(92, 112, 128, 0.3); }
+      .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+        background-color:rgba(92, 112, 128, 0.4); }
+    .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
+      color:#f5f8fa; }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
+          background-color:rgba(191, 204, 214, 0.3); }
+        .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
+          background-color:rgba(191, 204, 214, 0.4); }
+      .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-large{
+        fill:#a7b6c2; }
+  .bp3-tag.bp3-minimal.bp3-intent-primary{
+    background-color:rgba(19, 124, 189, 0.15);
+    color:#106ba3; }
+    .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+        background-color:rgba(19, 124, 189, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+        background-color:rgba(19, 124, 189, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{
+      fill:#137cbd; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{
+      background-color:rgba(19, 124, 189, 0.25);
+      color:#48aff0; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
+          background-color:rgba(19, 124, 189, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
+          background-color:rgba(19, 124, 189, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-success{
+    background-color:rgba(15, 153, 96, 0.15);
+    color:#0d8050; }
+    .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+        background-color:rgba(15, 153, 96, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+        background-color:rgba(15, 153, 96, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{
+      fill:#0f9960; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{
+      background-color:rgba(15, 153, 96, 0.25);
+      color:#3dcc91; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
+          background-color:rgba(15, 153, 96, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
+          background-color:rgba(15, 153, 96, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-warning{
+    background-color:rgba(217, 130, 43, 0.15);
+    color:#bf7326; }
+    .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+        background-color:rgba(217, 130, 43, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+        background-color:rgba(217, 130, 43, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{
+      fill:#d9822b; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{
+      background-color:rgba(217, 130, 43, 0.25);
+      color:#ffb366; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
+          background-color:rgba(217, 130, 43, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
+          background-color:rgba(217, 130, 43, 0.45); }
+  .bp3-tag.bp3-minimal.bp3-intent-danger{
+    background-color:rgba(219, 55, 55, 0.15);
+    color:#c23030; }
+    .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+      cursor:pointer; }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+        background-color:rgba(219, 55, 55, 0.25); }
+      .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+        background-color:rgba(219, 55, 55, 0.35); }
+    .bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{
+      fill:#db3737; }
+    .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{
+      background-color:rgba(219, 55, 55, 0.25);
+      color:#ff7373; }
+      .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
+        cursor:pointer; }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
+          background-color:rgba(219, 55, 55, 0.35); }
+        .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
+          background-color:rgba(219, 55, 55, 0.45); }
+
+.bp3-tag-remove{
+  background:none;
+  border:none;
+  color:inherit;
+  cursor:pointer;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin-bottom:-2px;
+  margin-right:-6px !important;
+  margin-top:-2px;
+  opacity:0.5;
+  padding:2px;
+  padding-left:0; }
+  .bp3-tag-remove:hover{
+    background:none;
+    opacity:0.8;
+    text-decoration:none; }
+  .bp3-tag-remove:active{
+    opacity:1; }
+  .bp3-tag-remove:empty::before{
+    font-family:"Icons16", sans-serif;
+    font-size:16px;
+    font-style:normal;
+    font-weight:400;
+    line-height:1;
+    -moz-osx-font-smoothing:grayscale;
+    -webkit-font-smoothing:antialiased;
+    content:""; }
+  .bp3-large .bp3-tag-remove{
+    margin-right:-10px !important;
+    padding:0 5px 0 0; }
+    .bp3-large .bp3-tag-remove:empty::before{
+      font-family:"Icons20", sans-serif;
+      font-size:20px;
+      font-style:normal;
+      font-weight:400;
+      line-height:1; }
+.bp3-tag-input{
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  -webkit-box-orient:horizontal;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:row;
+          flex-direction:row;
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  cursor:text;
+  height:auto;
+  line-height:inherit;
+  min-height:30px;
+  padding-left:5px;
+  padding-right:0; }
+  .bp3-tag-input > *{
+    -webkit-box-flex:0;
+        -ms-flex-positive:0;
+            flex-grow:0;
+    -ms-flex-negative:0;
+        flex-shrink:0; }
+  .bp3-tag-input > .bp3-tag-input-values{
+    -webkit-box-flex:1;
+        -ms-flex-positive:1;
+            flex-grow:1;
+    -ms-flex-negative:1;
+        flex-shrink:1; }
+  .bp3-tag-input .bp3-tag-input-icon{
+    color:#5c7080;
+    margin-left:2px;
+    margin-right:7px;
+    margin-top:7px; }
+  .bp3-tag-input .bp3-tag-input-values{
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex;
+    -webkit-box-orient:horizontal;
+    -webkit-box-direction:normal;
+        -ms-flex-direction:row;
+            flex-direction:row;
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    -ms-flex-item-align:stretch;
+        align-self:stretch;
+    -ms-flex-wrap:wrap;
+        flex-wrap:wrap;
+    margin-right:7px;
+    margin-top:5px;
+    min-width:0; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      -webkit-box-flex:0;
+          -ms-flex-positive:0;
+              flex-grow:0;
+      -ms-flex-negative:0;
+          flex-shrink:0; }
+    .bp3-tag-input .bp3-tag-input-values > .bp3-fill{
+      -webkit-box-flex:1;
+          -ms-flex-positive:1;
+              flex-grow:1;
+      -ms-flex-negative:1;
+          flex-shrink:1; }
+    .bp3-tag-input .bp3-tag-input-values::before,
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-right:5px; }
+    .bp3-tag-input .bp3-tag-input-values:empty::before,
+    .bp3-tag-input .bp3-tag-input-values > :last-child{
+      margin-right:0; }
+    .bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{
+      padding-left:5px; }
+    .bp3-tag-input .bp3-tag-input-values > *{
+      margin-bottom:5px; }
+  .bp3-tag-input .bp3-tag{
+    overflow-wrap:break-word; }
+    .bp3-tag-input .bp3-tag.bp3-active{
+      outline:rgba(19, 124, 189, 0.6) auto 2px;
+      outline-offset:0;
+      -moz-outline-radius:6px; }
+  .bp3-tag-input .bp3-input-ghost{
+    -webkit-box-flex:1;
+        -ms-flex:1 1 auto;
+            flex:1 1 auto;
+    line-height:20px;
+    width:80px; }
+    .bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{
+      cursor:not-allowed; }
+  .bp3-tag-input .bp3-button,
+  .bp3-tag-input .bp3-spinner{
+    margin:3px;
+    margin-left:0; }
+  .bp3-tag-input .bp3-button{
+    min-height:24px;
+    min-width:24px;
+    padding:0 7px; }
+  .bp3-tag-input.bp3-large{
+    height:auto;
+    min-height:40px; }
+    .bp3-tag-input.bp3-large::before,
+    .bp3-tag-input.bp3-large > *{
+      margin-right:10px; }
+    .bp3-tag-input.bp3-large:empty::before,
+    .bp3-tag-input.bp3-large > :last-child{
+      margin-right:0; }
+    .bp3-tag-input.bp3-large .bp3-tag-input-icon{
+      margin-left:5px;
+      margin-top:10px; }
+    .bp3-tag-input.bp3-large .bp3-input-ghost{
+      line-height:30px; }
+    .bp3-tag-input.bp3-large .bp3-button{
+      min-height:30px;
+      min-width:30px;
+      padding:5px 10px;
+      margin:5px;
+      margin-left:0; }
+    .bp3-tag-input.bp3-large .bp3-spinner{
+      margin:8px;
+      margin-left:0; }
+  .bp3-tag-input.bp3-active{
+    background-color:#ffffff;
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+    .bp3-tag-input.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
+  .bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{
+    color:#a7b6c2; }
+  .bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{
+    color:#f5f8fa; }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+    .bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{
+      color:rgba(167, 182, 194, 0.6); }
+  .bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{
+    background-color:rgba(16, 22, 26, 0.3);
+    -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{
+      -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
+      -webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
+      -webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+    .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
+      -webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);
+              box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
+
+.bp3-input-ghost{
+  background:none;
+  border:none;
+  -webkit-box-shadow:none;
+          box-shadow:none;
+  padding:0; }
+  .bp3-input-ghost::-webkit-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::-moz-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost:-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::-ms-input-placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost::placeholder{
+    color:rgba(92, 112, 128, 0.6);
+    opacity:1; }
+  .bp3-input-ghost:focus{
+    outline:none !important; }
+.bp3-toast{
+  -webkit-box-align:start;
+      -ms-flex-align:start;
+          align-items:flex-start;
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  margin:20px 0 0;
+  max-width:500px;
+  min-width:300px;
+  pointer-events:all;
+  position:relative !important; }
+  .bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px); }
+  .bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);
+            transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }
+  .bp3-toast.bp3-toast-exit{
+    opacity:1;
+    -webkit-filter:blur(0);
+            filter:blur(0); }
+  .bp3-toast.bp3-toast-exit-active{
+    opacity:0;
+    -webkit-filter:blur(10px);
+            filter:blur(10px);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:300ms;
+            transition-duration:300ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:opacity, filter;
+    transition-property:opacity, filter, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-toast.bp3-toast-exit ~ .bp3-toast{
+    -webkit-transform:translateY(0);
+            transform:translateY(0); }
+  .bp3-toast.bp3-toast-exit-active ~ .bp3-toast{
+    -webkit-transform:translateY(-40px);
+            transform:translateY(-40px);
+    -webkit-transition-delay:50ms;
+            transition-delay:50ms;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-toast .bp3-button-group{
+    -webkit-box-flex:0;
+        -ms-flex:0 0 auto;
+            flex:0 0 auto;
+    padding:5px;
+    padding-left:0; }
+  .bp3-toast > .bp3-icon{
+    color:#5c7080;
+    margin:12px;
+    margin-right:0; }
+  .bp3-toast.bp3-dark,
+  .bp3-dark .bp3-toast{
+    background-color:#394b59;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-toast.bp3-dark > .bp3-icon,
+    .bp3-dark .bp3-toast > .bp3-icon{
+      color:#a7b6c2; }
+  .bp3-toast[class*="bp3-intent-"] a{
+    color:rgba(255, 255, 255, 0.7); }
+    .bp3-toast[class*="bp3-intent-"] a:hover{
+      color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] > .bp3-icon{
+    color:#ffffff; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button, .bp3-toast[class*="bp3-intent-"] .bp3-button::before,
+  .bp3-toast[class*="bp3-intent-"] .bp3-button .bp3-icon, .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    color:rgba(255, 255, 255, 0.7) !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:focus{
+    outline-color:rgba(255, 255, 255, 0.5); }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:hover{
+    background-color:rgba(255, 255, 255, 0.15) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
+    background-color:rgba(255, 255, 255, 0.3) !important;
+    color:#ffffff !important; }
+  .bp3-toast[class*="bp3-intent-"] .bp3-button::after{
+    background:rgba(255, 255, 255, 0.3) !important; }
+  .bp3-toast.bp3-intent-primary{
+    background-color:#137cbd;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-success{
+    background-color:#0f9960;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-warning{
+    background-color:#d9822b;
+    color:#ffffff; }
+  .bp3-toast.bp3-intent-danger{
+    background-color:#db3737;
+    color:#ffffff; }
+
+.bp3-toast-message{
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  padding:11px;
+  word-break:break-word; }
+
+.bp3-toast-container{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box !important;
+  display:-ms-flexbox !important;
+  display:flex !important;
+  -webkit-box-orient:vertical;
+  -webkit-box-direction:normal;
+      -ms-flex-direction:column;
+          flex-direction:column;
+  left:0;
+  overflow:hidden;
+  padding:0 20px 20px;
+  pointer-events:none;
+  right:0;
+  z-index:40; }
+  .bp3-toast-container.bp3-toast-container-in-portal{
+    position:fixed; }
+  .bp3-toast-container.bp3-toast-container-inline{
+    position:absolute; }
+  .bp3-toast-container.bp3-toast-container-top{
+    top:0; }
+  .bp3-toast-container.bp3-toast-container-bottom{
+    bottom:0;
+    -webkit-box-orient:vertical;
+    -webkit-box-direction:reverse;
+        -ms-flex-direction:column-reverse;
+            flex-direction:column-reverse;
+    top:auto; }
+  .bp3-toast-container.bp3-toast-container-left{
+    -webkit-box-align:start;
+        -ms-flex-align:start;
+            align-items:flex-start; }
+  .bp3-toast-container.bp3-toast-container-right{
+    -webkit-box-align:end;
+        -ms-flex-align:end;
+            align-items:flex-end; }
+
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active),
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast,
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-exit-active ~ .bp3-toast,
+.bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{
+  -webkit-transform:translateY(60px);
+          transform:translateY(60px); }
+.bp3-tooltip{
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);
+  -webkit-transform:scale(1);
+          transform:scale(1); }
+  .bp3-tooltip .bp3-popover-arrow{
+    height:22px;
+    position:absolute;
+    width:22px; }
+    .bp3-tooltip .bp3-popover-arrow::before{
+      height:14px;
+      margin:4px;
+      width:14px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{
+    margin-bottom:11px;
+    margin-top:-11px; }
+    .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+      bottom:-8px; }
+      .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(-90deg);
+                transform:rotate(-90deg); }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{
+    margin-left:11px; }
+    .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+      left:-8px; }
+      .bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(0);
+                transform:rotate(0); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{
+    margin-top:11px; }
+    .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+      top:-8px; }
+      .bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(90deg);
+                transform:rotate(90deg); }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{
+    margin-left:-11px;
+    margin-right:11px; }
+    .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+      right:-8px; }
+      .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{
+        -webkit-transform:rotate(180deg);
+                transform:rotate(180deg); }
+  .bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{
+    top:50%;
+    -webkit-transform:translateY(-50%);
+            transform:translateY(-50%); }
+  .bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{
+    right:50%;
+    -webkit-transform:translateX(50%);
+            transform:translateX(50%); }
+  .bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
+    top:-0.22183px; }
+  .bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
+    right:-0.22183px; }
+  .bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
+    left:-0.22183px; }
+  .bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
+    bottom:-0.22183px; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:top left;
+            transform-origin:top left; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:top center;
+            transform-origin:top center; }
+  .bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:top right;
+            transform-origin:top right; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:center left;
+            transform-origin:center left; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:center center;
+            transform-origin:center center; }
+  .bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:center right;
+            transform-origin:center right; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
+    -webkit-transform-origin:bottom left;
+            transform-origin:bottom left; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
+    -webkit-transform-origin:bottom center;
+            transform-origin:bottom center; }
+  .bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
+    -webkit-transform-origin:bottom right;
+            transform-origin:bottom right; }
+  .bp3-tooltip .bp3-popover-content{
+    background:#394b59;
+    color:#f5f8fa; }
+  .bp3-tooltip .bp3-popover-arrow::before{
+    -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
+            box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
+  .bp3-tooltip .bp3-popover-arrow-border{
+    fill:#10161a;
+    fill-opacity:0.1; }
+  .bp3-tooltip .bp3-popover-arrow-fill{
+    fill:#394b59; }
+  .bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8); }
+  .bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-popover-exit > .bp3-tooltip{
+    -webkit-transform:scale(1);
+            transform:scale(1); }
+  .bp3-popover-exit-active > .bp3-tooltip{
+    -webkit-transform:scale(0.8);
+            transform:scale(0.8);
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:100ms;
+            transition-duration:100ms;
+    -webkit-transition-property:-webkit-transform;
+    transition-property:-webkit-transform;
+    transition-property:transform;
+    transition-property:transform, -webkit-transform;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tooltip .bp3-popover-content{
+    padding:10px 12px; }
+  .bp3-tooltip.bp3-dark,
+  .bp3-dark .bp3-tooltip{
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-content,
+    .bp3-dark .bp3-tooltip .bp3-popover-content{
+      background:#e1e8ed;
+      color:#394b59; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow::before,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow::before{
+      -webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
+              box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-border,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-border{
+      fill:#10161a;
+      fill-opacity:0.2; }
+    .bp3-tooltip.bp3-dark .bp3-popover-arrow-fill,
+    .bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{
+      fill:#e1e8ed; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-content{
+    background:#137cbd;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{
+    fill:#137cbd; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-content{
+    background:#0f9960;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{
+    fill:#0f9960; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-content{
+    background:#d9822b;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{
+    fill:#d9822b; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-content{
+    background:#db3737;
+    color:#ffffff; }
+  .bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{
+    fill:#db3737; }
+
+.bp3-tooltip-indicator{
+  border-bottom:dotted 1px;
+  cursor:help; }
+.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{
+  color:#5c7080; }
+  .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-tree-node-list{
+  list-style:none;
+  margin:0;
+  padding-left:0; }
+
+.bp3-tree-root{
+  background-color:transparent;
+  cursor:default;
+  padding-left:0;
+  position:relative; }
+
+.bp3-tree-node-content-0{
+  padding-left:0px; }
+
+.bp3-tree-node-content-1{
+  padding-left:23px; }
+
+.bp3-tree-node-content-2{
+  padding-left:46px; }
+
+.bp3-tree-node-content-3{
+  padding-left:69px; }
+
+.bp3-tree-node-content-4{
+  padding-left:92px; }
+
+.bp3-tree-node-content-5{
+  padding-left:115px; }
+
+.bp3-tree-node-content-6{
+  padding-left:138px; }
+
+.bp3-tree-node-content-7{
+  padding-left:161px; }
+
+.bp3-tree-node-content-8{
+  padding-left:184px; }
+
+.bp3-tree-node-content-9{
+  padding-left:207px; }
+
+.bp3-tree-node-content-10{
+  padding-left:230px; }
+
+.bp3-tree-node-content-11{
+  padding-left:253px; }
+
+.bp3-tree-node-content-12{
+  padding-left:276px; }
+
+.bp3-tree-node-content-13{
+  padding-left:299px; }
+
+.bp3-tree-node-content-14{
+  padding-left:322px; }
+
+.bp3-tree-node-content-15{
+  padding-left:345px; }
+
+.bp3-tree-node-content-16{
+  padding-left:368px; }
+
+.bp3-tree-node-content-17{
+  padding-left:391px; }
+
+.bp3-tree-node-content-18{
+  padding-left:414px; }
+
+.bp3-tree-node-content-19{
+  padding-left:437px; }
+
+.bp3-tree-node-content-20{
+  padding-left:460px; }
+
+.bp3-tree-node-content{
+  -webkit-box-align:center;
+      -ms-flex-align:center;
+          align-items:center;
+  display:-webkit-box;
+  display:-ms-flexbox;
+  display:flex;
+  height:30px;
+  padding-right:5px;
+  width:100%; }
+  .bp3-tree-node-content:hover{
+    background-color:rgba(191, 204, 214, 0.4); }
+
+.bp3-tree-node-caret,
+.bp3-tree-node-caret-none{
+  min-width:30px; }
+
+.bp3-tree-node-caret{
+  color:#5c7080;
+  cursor:pointer;
+  padding:7px;
+  -webkit-transform:rotate(0deg);
+          transform:rotate(0deg);
+  -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
+  transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-tree-node-caret:hover{
+    color:#182026; }
+  .bp3-dark .bp3-tree-node-caret{
+    color:#a7b6c2; }
+    .bp3-dark .bp3-tree-node-caret:hover{
+      color:#f5f8fa; }
+  .bp3-tree-node-caret.bp3-tree-node-caret-open{
+    -webkit-transform:rotate(90deg);
+            transform:rotate(90deg); }
+  .bp3-tree-node-caret.bp3-icon-standard::before{
+    content:""; }
+
+.bp3-tree-node-icon{
+  margin-right:7px;
+  position:relative; }
+
+.bp3-tree-node-label{
+  overflow:hidden;
+  text-overflow:ellipsis;
+  white-space:nowrap;
+  word-wrap:normal;
+  -webkit-box-flex:1;
+      -ms-flex:1 1 auto;
+          flex:1 1 auto;
+  position:relative;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-label span{
+    display:inline; }
+
+.bp3-tree-node-secondary-label{
+  padding:0 5px;
+  -webkit-user-select:none;
+     -moz-user-select:none;
+      -ms-user-select:none;
+          user-select:none; }
+  .bp3-tree-node-secondary-label .bp3-popover-wrapper,
+  .bp3-tree-node-secondary-label .bp3-popover-target{
+    -webkit-box-align:center;
+        -ms-flex-align:center;
+            align-items:center;
+    display:-webkit-box;
+    display:-ms-flexbox;
+    display:flex; }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-content{
+  background-color:inherit;
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-tree-node.bp3-disabled .bp3-tree-node-caret,
+.bp3-tree-node.bp3-disabled .bp3-tree-node-icon{
+  color:rgba(92, 112, 128, 0.6);
+  cursor:not-allowed; }
+
+.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content,
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{
+    color:#ffffff; }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{
+    color:rgba(255, 255, 255, 0.7); }
+  .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{
+    color:#ffffff; }
+
+.bp3-dark .bp3-tree-node-content:hover{
+  background-color:rgba(92, 112, 128, 0.3); }
+
+.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{
+  color:#a7b6c2; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{
+    color:#137cbd; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{
+    color:#0f9960; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{
+    color:#d9822b; }
+  .bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{
+    color:#db3737; }
+
+.bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
+  background-color:#137cbd; }
+.bp3-omnibar{
+  -webkit-filter:blur(0);
+          filter:blur(0);
+  opacity:1;
+  background-color:#ffffff;
+  border-radius:3px;
+  -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+          box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);
+  left:calc(50% - 250px);
+  top:20vh;
+  width:500px;
+  z-index:21; }
+  .bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2; }
+  .bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-omnibar.bp3-overlay-exit{
+    -webkit-filter:blur(0);
+            filter:blur(0);
+    opacity:1; }
+  .bp3-omnibar.bp3-overlay-exit-active{
+    -webkit-filter:blur(20px);
+            filter:blur(20px);
+    opacity:0.2;
+    -webkit-transition-delay:0;
+            transition-delay:0;
+    -webkit-transition-duration:200ms;
+            transition-duration:200ms;
+    -webkit-transition-property:opacity, -webkit-filter;
+    transition-property:opacity, -webkit-filter;
+    transition-property:filter, opacity;
+    transition-property:filter, opacity, -webkit-filter;
+    -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);
+            transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }
+  .bp3-omnibar .bp3-input{
+    background-color:transparent;
+    border-radius:0; }
+    .bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
+      -webkit-box-shadow:none;
+              box-shadow:none; }
+  .bp3-omnibar .bp3-menu{
+    background-color:transparent;
+    border-radius:0;
+    -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+            box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);
+    max-height:calc(60vh - 40px);
+    overflow:auto; }
+    .bp3-omnibar .bp3-menu:empty{
+      display:none; }
+  .bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{
+    background-color:#30404d;
+    -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);
+            box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }
+
+.bp3-omnibar-overlay .bp3-overlay-backdrop{
+  background-color:rgba(16, 22, 26, 0.2); }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+
+.bp3-multi-select{
+  min-width:150px; }
+
+.bp3-multi-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto; }
+
+.bp3-select-popover .bp3-popover-content{
+  padding:5px; }
+
+.bp3-select-popover .bp3-input-group{
+  margin-bottom:0; }
+
+.bp3-select-popover .bp3-menu{
+  max-height:300px;
+  max-width:400px;
+  overflow:auto;
+  padding:0; }
+  .bp3-select-popover .bp3-menu:not(:first-child){
+    padding-top:5px; }
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+/* Icons urls */
+
+:root {
+  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-bug: url(data:image/svg+xml;base64,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);
+  --jp-icon-build: url(data:image/svg+xml;base64,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);
+  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-caret-up-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iMTUuNCwxMy4zIDkuOSw3LjcgNC40LDEzLjIgMy42LDEyLjUgOS45LDYuMyAxNi4xLDEyLjYgIi8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-caret-up: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik01LjIsMTAuNUw5LDYuOGwzLjgsMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-case-sensitive: url(data:image/svg+xml;base64,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);
+  --jp-icon-check: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik05IDE2LjE3TDQuODMgMTJsLTEuNDIgMS40MUw5IDE5IDIxIDdsLTEuNDEtMS40MXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-circle-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDJDNi40NyAyIDIgNi40NyAyIDEyczQuNDcgMTAgMTAgMTAgMTAtNC40NyAxMC0xMFMxNy41MyAyIDEyIDJ6bTAgMThjLTQuNDEgMC04LTMuNTktOC04czMuNTktOCA4LTggOCAzLjU5IDggOC0zLjU5IDgtOCA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-clear: url(data:image/svg+xml;base64,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);
+  --jp-icon-close: url(data:image/svg+xml;base64,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);
+  --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==);
+  --jp-icon-console: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwMCAyMDAiPgogIDxnIGNsYXNzPSJqcC1pY29uLWJyYW5kMSBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiMwMjg4RDEiPgogICAgPHBhdGggZD0iTTIwIDE5LjhoMTYwdjE1OS45SDIweiIvPgogIDwvZz4KICA8ZyBjbGFzcz0ianAtaWNvbi1zZWxlY3RhYmxlLWludmVyc2UiIGZpbGw9IiNmZmYiPgogICAgPHBhdGggZD0iTTEwNSAxMjcuM2g0MHYxMi44aC00MHpNNTEuMSA3N0w3NCA5OS45bC0yMy4zIDIzLjMgMTAuNSAxMC41IDIzLjMtMjMuM0w5NSA5OS45IDg0LjUgODkuNCA2MS42IDY2LjV6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-copy: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTExLjksMUgzLjJDMi40LDEsMS43LDEuNywxLjcsMi41djEwLjJoMS41VjIuNWg4LjdWMXogTTE0LjEsMy45aC04Yy0wLjgsMC0xLjUsMC43LTEuNSwxLjV2MTAuMmMwLDAuOCwwLjcsMS41LDEuNSwxLjVoOCBjMC44LDAsMS41LTAuNywxLjUtMS41VjUuNEMxNS41LDQuNiwxNC45LDMuOSwxNC4xLDMuOXogTTE0LjEsMTUuNWgtOFY1LjRoOFYxNS41eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-copyright: url(data:image/svg+xml;base64,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);
+  --jp-icon-cut: url(data:image/svg+xml;base64,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);
+  --jp-icon-download: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDloLTRWM0g5djZINWw3IDcgNy03ek01IDE4djJoMTR2LTJINXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-edit: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMgMTcuMjVWMjFoMy43NUwxNy44MSA5Ljk0bC0zLjc1LTMuNzVMMyAxNy4yNXpNMjAuNzEgNy4wNGMuMzktLjM5LjM5LTEuMDIgMC0xLjQxbC0yLjM0LTIuMzRjLS4zOS0uMzktMS4wMi0uMzktMS40MSAwbC0xLjgzIDEuODMgMy43NSAzLjc1IDEuODMtMS44M3oiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-ellipses: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iNSIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxMiIgY3k9IjEyIiByPSIyIi8+CiAgICA8Y2lyY2xlIGN4PSIxOSIgY3k9IjEyIiByPSIyIi8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-extension: url(data:image/svg+xml;base64,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);
+  --jp-icon-fast-forward: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTQgMThsOC41LTZMNCA2djEyem05LTEydjEybDguNS02TDEzIDZ6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-file-upload: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTkgMTZoNnYtNmg0bC03LTctNyA3aDR6bS00IDJoMTR2Mkg1eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-file: url(data:image/svg+xml;base64,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);
+  --jp-icon-filter-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEwIDE4aDR2LTJoLTR2MnpNMyA2djJoMThWNkgzem0zIDdoMTJ2LTJINnYyeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY4YzAtMS4xLS45LTItMi0yaC04bC0yLTJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-html5: url(data:image/svg+xml;base64,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);
+  --jp-icon-image: url(data:image/svg+xml;base64,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);
+  --jp-icon-inspector: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNEg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMThjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY2YzAtMS4xLS45LTItMi0yem0tNSAxNEg0di00aDExdjR6bTAtNUg0VjloMTF2NHptNSA1aC00VjloNHY5eiIvPgo8L3N2Zz4K);
+  --jp-icon-json: url(data:image/svg+xml;base64,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);
+  --jp-icon-julia: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter-favicon: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyter: url(data:image/svg+xml;base64,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);
+  --jp-icon-jupyterlab-wordmark: url(data:image/svg+xml;base64,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+  --jp-icon-kernel: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzYxNjE2MSIgZD0iTTE1IDlIOXY2aDZWOXptLTIgNGgtMnYtMmgydjJ6bTgtMlY5aC0yVjdjMC0xLjEtLjktMi0yLTJoLTJWM2gtMnYyaC0yVjNIOXYySDdjLTEuMSAwLTIgLjktMiAydjJIM3YyaDJ2MkgzdjJoMnYyYzAgMS4xLjkgMiAyIDJoMnYyaDJ2LTJoMnYyaDJ2LTJoMmMxLjEgMCAyLS45IDItMnYtMmgydi0yaC0ydi0yaDJ6bS00IDZIN1Y3aDEwdjEweiIvPgo8L3N2Zz4K);
+  --jp-icon-keyboard: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMjAgNUg0Yy0xLjEgMC0xLjk5LjktMS45OSAyTDIgMTdjMCAxLjEuOSAyIDIgMmgxNmMxLjEgMCAyLS45IDItMlY3YzAtMS4xLS45LTItMi0yem0tOSAzaDJ2MmgtMlY4em0wIDNoMnYyaC0ydi0yek04IDhoMnYySDhWOHptMCAzaDJ2Mkg4di0yem0tMSAySDV2LTJoMnYyem0wLTNINVY4aDJ2MnptOSA3SDh2LTJoOHYyem0wLTRoLTJ2LTJoMnYyem0wLTNoLTJWOGgydjJ6bTMgM2gtMnYtMmgydjJ6bTAtM2gtMlY4aDJ2MnoiLz4KPC9zdmc+Cg==);
+  --jp-icon-launcher: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTkgMTlINVY1aDdWM0g1YTIgMiAwIDAwLTIgMnYxNGEyIDIgMCAwMDIgMmgxNGMxLjEgMCAyLS45IDItMnYtN2gtMnY3ek0xNCAzdjJoMy41OWwtOS44MyA5LjgzIDEuNDEgMS40MUwxOSA2LjQxVjEwaDJWM2gtN3oiLz4KPC9zdmc+Cg==);
+  --jp-icon-line-form: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGZpbGw9IndoaXRlIiBkPSJNNS44OCA0LjEyTDEzLjc2IDEybC03Ljg4IDcuODhMOCAyMmwxMC0xMEw4IDJ6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-link: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTMuOSAxMmMwLTEuNzEgMS4zOS0zLjEgMy4xLTMuMWg0VjdIN2MtMi43NiAwLTUgMi4yNC01IDVzMi4yNCA1IDUgNWg0di0xLjlIN2MtMS43MSAwLTMuMS0xLjM5LTMuMS0zLjF6TTggMTNoOHYtMkg4djJ6bTktNmgtNHYxLjloNGMxLjcxIDAgMy4xIDEuMzkgMy4xIDMuMXMtMS4zOSAzLjEtMy4xIDMuMWgtNFYxN2g0YzIuNzYgMCA1LTIuMjQgNS01cy0yLjI0LTUtNS01eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-list: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICAgIDxwYXRoIGNsYXNzPSJqcC1pY29uMiBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xOSA1djE0SDVWNWgxNG0xLjEtMkgzLjljLS41IDAtLjkuNC0uOS45djE2LjJjMCAuNC40LjkuOS45aDE2LjJjLjQgMCAuOS0uNS45LS45VjMuOWMwLS41LS41LS45LS45LS45ek0xMSA3aDZ2MmgtNlY3em0wIDRoNnYyaC02di0yem0wIDRoNnYyaC02ek03IDdoMnYySDd6bTAgNGgydjJIN3ptMCA0aDJ2Mkg3eiIvPgo8L3N2Zz4=);
+  --jp-icon-listings-info: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA1MC45NzggNTAuOTc4IiBzdHlsZT0iZW5hYmxlLWJhY2tncm91bmQ6bmV3IDAgMCA1MC45NzggNTAuOTc4OyIgeG1sOnNwYWNlPSJwcmVzZXJ2ZSI+Cgk8Zz4KCQk8cGF0aCBzdHlsZT0iZmlsbDojMDEwMDAyOyIgZD0iTTQzLjUyLDcuNDU4QzM4LjcxMSwyLjY0OCwzMi4zMDcsMCwyNS40ODksMEMxOC42NywwLDEyLjI2NiwyLjY0OCw3LjQ1OCw3LjQ1OAoJCQljLTkuOTQzLDkuOTQxLTkuOTQzLDI2LjExOSwwLDM2LjA2MmM0LjgwOSw0LjgwOSwxMS4yMTIsNy40NTYsMTguMDMxLDcuNDU4YzAsMCwwLjAwMSwwLDAuMDAyLDAKCQkJYzYuODE2LDAsMTMuMjIxLTIuNjQ4LDE4LjAyOS03LjQ1OGM0LjgwOS00LjgwOSw3LjQ1Ny0xMS4yMTIsNy40NTctMTguMDNDNTAuOTc3LDE4LjY3LDQ4LjMyOCwxMi4yNjYsNDMuNTIsNy40NTh6CgkJCSBNNDIuMTA2LDQyLjEwNWMtNC40MzIsNC40MzEtMTAuMzMyLDYuODcyLTE2LjYxNSw2Ljg3MmgtMC4wMDJjLTYuMjg1LTAuMDAxLTEyLjE4Ny0yLjQ0MS0xNi42MTctNi44NzIKCQkJYy05LjE2Mi05LjE2My05LjE2Mi0yNC4wNzEsMC0zMy4yMzNDMTMuMzAzLDQuNDQsMTkuMjA0LDIsMjUuNDg5LDJjNi4yODQsMCwxMi4xODYsMi40NCwxNi42MTcsNi44NzIKCQkJYzQuNDMxLDQuNDMxLDYuODcxLDEwLjMzMiw2Ljg3MSwxNi42MTdDNDguOTc3LDMxLjc3Miw0Ni41MzYsMzcuNjc1LDQyLjEwNiw0Mi4xMDV6Ii8+CgkJPHBhdGggc3R5bGU9ImZpbGw6IzAxMDAwMjsiIGQ9Ik0yMy41NzgsMzIuMjE4Yy0wLjAyMy0xLjczNCwwLjE0My0zLjA1OSwwLjQ5Ni0zLjk3MmMwLjM1My0wLjkxMywxLjExLTEuOTk3LDIuMjcyLTMuMjUzCgkJCWMwLjQ2OC0wLjUzNiwwLjkyMy0xLjA2MiwxLjM2Ny0xLjU3NWMwLjYyNi0wLjc1MywxLjEwNC0xLjQ3OCwxLjQzNi0yLjE3NWMwLjMzMS0wLjcwNywwLjQ5NS0xLjU0MSwwLjQ5NS0yLjUKCQkJYzAtMS4wOTYtMC4yNi0yLjA4OC0wLjc3OS0yLjk3OWMtMC41NjUtMC44NzktMS41MDEtMS4zMzYtMi44MDYtMS4zNjljLTEuODAyLDAuMDU3LTIuOTg1LDAuNjY3LTMuNTUsMS44MzIKCQkJYy0wLjMwMSwwLjUzNS0wLjUwMywxLjE0MS0wLjYwNywxLjgxNGMtMC4xMzksMC43MDctMC4yMDcsMS40MzItMC4yMDcsMi4xNzRoLTIuOTM3Yy0wLjA5MS0yLjIwOCwwLjQwNy00LjExNCwxLjQ5My01LjcxOQoJCQljMS4wNjItMS42NCwyLjg1NS0yLjQ4MSw1LjM3OC0yLjUyN2MyLjE2LDAuMDIzLDMuODc0LDAuNjA4LDUuMTQxLDEuNzU4YzEuMjc4LDEuMTYsMS45MjksMi43NjQsMS45NSw0LjgxMQoJCQljMCwxLjE0Mi0wLjEzNywyLjExMS0wLjQxLDIuOTExYy0wLjMwOSwwLjg0NS0wLjczMSwxLjU5My0xLjI2OCwyLjI0M2MtMC40OTIsMC42NS0xLjA2OCwxLjMxOC0xLjczLDIuMDAyCgkJCWMtMC42NSwwLjY5Ny0xLjMxMywxLjQ3OS0xLjk4NywyLjM0NmMtMC4yMzksMC4zNzctMC40MjksMC43NzctMC41NjUsMS4xOTljLTAuMTYsMC45NTktMC4yMTcsMS45NTEtMC4xNzEsMi45NzkKCQkJQzI2LjU4OSwzMi4yMTgsMjMuNTc4LDMyLjIxOCwyMy41NzgsMzIuMjE4eiBNMjMuNTc4LDM4LjIydi0zLjQ4NGgzLjA3NnYzLjQ4NEgyMy41Nzh6Ii8+Cgk8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-markdown: url(data:image/svg+xml;base64,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);
+  --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-not-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbi13YXJuMCBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K);
+  --jp-icon-offline-bolt: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyIDIuMDJjLTUuNTEgMC05Ljk4IDQuNDctOS45OCA5Ljk4czQuNDcgOS45OCA5Ljk4IDkuOTggOS45OC00LjQ3IDkuOTgtOS45OFMxNy41MSAyLjAyIDEyIDIuMDJ6TTExLjQ4IDIwdi02LjI2SDhMMTMgNHY2LjI2aDMuMzVMMTEuNDggMjB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-palette: url(data:image/svg+xml;base64,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);
+  --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-pdf: url(data:image/svg+xml;base64,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);
+  --jp-icon-python: url(data:image/svg+xml;base64,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);
+  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
+  --jp-icon-react: url(data:image/svg+xml;base64,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);
+  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
+  --jp-icon-regex: url(data:image/svg+xml;base64,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);
+  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-search: url(data:image/svg+xml;base64,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);
+  --jp-icon-settings: url(data:image/svg+xml;base64,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);
+  --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K);
+  --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4=);
+  --jp-icon-tag: url(data:image/svg+xml;base64,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);
+  --jp-icon-terminal: url(data:image/svg+xml;base64,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);
+  --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTUgMTVIM3YyaDEydi0yem0wLThIM3YyaDEyVjd6TTMgMTNoMTh2LTJIM3Yyem0wIDhoMTh2LTJIM3Yyek0zIDN2MmgxOFYzSDN6Ii8+Cjwvc3ZnPgo=);
+  --jp-icon-toc: url(data:image/svg+xml;base64,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);
+  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=);
+  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
+  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
+  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
+}
+
+/* Icon CSS class declarations */
+
+.jp-AddIcon {
+  background-image: var(--jp-icon-add);
+}
+.jp-BugIcon {
+  background-image: var(--jp-icon-bug);
+}
+.jp-BuildIcon {
+  background-image: var(--jp-icon-build);
+}
+.jp-CaretDownEmptyIcon {
+  background-image: var(--jp-icon-caret-down-empty);
+}
+.jp-CaretDownEmptyThinIcon {
+  background-image: var(--jp-icon-caret-down-empty-thin);
+}
+.jp-CaretDownIcon {
+  background-image: var(--jp-icon-caret-down);
+}
+.jp-CaretLeftIcon {
+  background-image: var(--jp-icon-caret-left);
+}
+.jp-CaretRightIcon {
+  background-image: var(--jp-icon-caret-right);
+}
+.jp-CaretUpEmptyThinIcon {
+  background-image: var(--jp-icon-caret-up-empty-thin);
+}
+.jp-CaretUpIcon {
+  background-image: var(--jp-icon-caret-up);
+}
+.jp-CaseSensitiveIcon {
+  background-image: var(--jp-icon-case-sensitive);
+}
+.jp-CheckIcon {
+  background-image: var(--jp-icon-check);
+}
+.jp-CircleEmptyIcon {
+  background-image: var(--jp-icon-circle-empty);
+}
+.jp-CircleIcon {
+  background-image: var(--jp-icon-circle);
+}
+.jp-ClearIcon {
+  background-image: var(--jp-icon-clear);
+}
+.jp-CloseIcon {
+  background-image: var(--jp-icon-close);
+}
+.jp-CodeIcon {
+  background-image: var(--jp-icon-code);
+}
+.jp-ConsoleIcon {
+  background-image: var(--jp-icon-console);
+}
+.jp-CopyIcon {
+  background-image: var(--jp-icon-copy);
+}
+.jp-CopyrightIcon {
+  background-image: var(--jp-icon-copyright);
+}
+.jp-CutIcon {
+  background-image: var(--jp-icon-cut);
+}
+.jp-DownloadIcon {
+  background-image: var(--jp-icon-download);
+}
+.jp-EditIcon {
+  background-image: var(--jp-icon-edit);
+}
+.jp-EllipsesIcon {
+  background-image: var(--jp-icon-ellipses);
+}
+.jp-ExtensionIcon {
+  background-image: var(--jp-icon-extension);
+}
+.jp-FastForwardIcon {
+  background-image: var(--jp-icon-fast-forward);
+}
+.jp-FileIcon {
+  background-image: var(--jp-icon-file);
+}
+.jp-FileUploadIcon {
+  background-image: var(--jp-icon-file-upload);
+}
+.jp-FilterListIcon {
+  background-image: var(--jp-icon-filter-list);
+}
+.jp-FolderIcon {
+  background-image: var(--jp-icon-folder);
+}
+.jp-Html5Icon {
+  background-image: var(--jp-icon-html5);
+}
+.jp-ImageIcon {
+  background-image: var(--jp-icon-image);
+}
+.jp-InspectorIcon {
+  background-image: var(--jp-icon-inspector);
+}
+.jp-JsonIcon {
+  background-image: var(--jp-icon-json);
+}
+.jp-JuliaIcon {
+  background-image: var(--jp-icon-julia);
+}
+.jp-JupyterFaviconIcon {
+  background-image: var(--jp-icon-jupyter-favicon);
+}
+.jp-JupyterIcon {
+  background-image: var(--jp-icon-jupyter);
+}
+.jp-JupyterlabWordmarkIcon {
+  background-image: var(--jp-icon-jupyterlab-wordmark);
+}
+.jp-KernelIcon {
+  background-image: var(--jp-icon-kernel);
+}
+.jp-KeyboardIcon {
+  background-image: var(--jp-icon-keyboard);
+}
+.jp-LauncherIcon {
+  background-image: var(--jp-icon-launcher);
+}
+.jp-LineFormIcon {
+  background-image: var(--jp-icon-line-form);
+}
+.jp-LinkIcon {
+  background-image: var(--jp-icon-link);
+}
+.jp-ListIcon {
+  background-image: var(--jp-icon-list);
+}
+.jp-ListingsInfoIcon {
+  background-image: var(--jp-icon-listings-info);
+}
+.jp-MarkdownIcon {
+  background-image: var(--jp-icon-markdown);
+}
+.jp-NewFolderIcon {
+  background-image: var(--jp-icon-new-folder);
+}
+.jp-NotTrustedIcon {
+  background-image: var(--jp-icon-not-trusted);
+}
+.jp-NotebookIcon {
+  background-image: var(--jp-icon-notebook);
+}
+.jp-NumberingIcon {
+  background-image: var(--jp-icon-numbering);
+}
+.jp-OfflineBoltIcon {
+  background-image: var(--jp-icon-offline-bolt);
+}
+.jp-PaletteIcon {
+  background-image: var(--jp-icon-palette);
+}
+.jp-PasteIcon {
+  background-image: var(--jp-icon-paste);
+}
+.jp-PdfIcon {
+  background-image: var(--jp-icon-pdf);
+}
+.jp-PythonIcon {
+  background-image: var(--jp-icon-python);
+}
+.jp-RKernelIcon {
+  background-image: var(--jp-icon-r-kernel);
+}
+.jp-ReactIcon {
+  background-image: var(--jp-icon-react);
+}
+.jp-RedoIcon {
+  background-image: var(--jp-icon-redo);
+}
+.jp-RefreshIcon {
+  background-image: var(--jp-icon-refresh);
+}
+.jp-RegexIcon {
+  background-image: var(--jp-icon-regex);
+}
+.jp-RunIcon {
+  background-image: var(--jp-icon-run);
+}
+.jp-RunningIcon {
+  background-image: var(--jp-icon-running);
+}
+.jp-SaveIcon {
+  background-image: var(--jp-icon-save);
+}
+.jp-SearchIcon {
+  background-image: var(--jp-icon-search);
+}
+.jp-SettingsIcon {
+  background-image: var(--jp-icon-settings);
+}
+.jp-SpreadsheetIcon {
+  background-image: var(--jp-icon-spreadsheet);
+}
+.jp-StopIcon {
+  background-image: var(--jp-icon-stop);
+}
+.jp-TabIcon {
+  background-image: var(--jp-icon-tab);
+}
+.jp-TableRowsIcon {
+  background-image: var(--jp-icon-table-rows);
+}
+.jp-TagIcon {
+  background-image: var(--jp-icon-tag);
+}
+.jp-TerminalIcon {
+  background-image: var(--jp-icon-terminal);
+}
+.jp-TextEditorIcon {
+  background-image: var(--jp-icon-text-editor);
+}
+.jp-TocIcon {
+  background-image: var(--jp-icon-toc);
+}
+.jp-TreeViewIcon {
+  background-image: var(--jp-icon-tree-view);
+}
+.jp-TrustedIcon {
+  background-image: var(--jp-icon-trusted);
+}
+.jp-UndoIcon {
+  background-image: var(--jp-icon-undo);
+}
+.jp-VegaIcon {
+  background-image: var(--jp-icon-vega);
+}
+.jp-YamlIcon {
+  background-image: var(--jp-icon-yaml);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * (DEPRECATED) Support for consuming icons as CSS background images
+ */
+
+.jp-Icon,
+.jp-MaterialIcon {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-cover {
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: cover;
+}
+
+/**
+ * (DEPRECATED) Support for specific CSS icon sizes
+ */
+
+.jp-Icon-16 {
+  background-size: 16px;
+  min-width: 16px;
+  min-height: 16px;
+}
+
+.jp-Icon-18 {
+  background-size: 18px;
+  min-width: 18px;
+  min-height: 18px;
+}
+
+.jp-Icon-20 {
+  background-size: 20px;
+  min-width: 20px;
+  min-height: 20px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for icons as inline SVG HTMLElements
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+/* recolor the accent elements of an icon */
+.jp-icon-accent0[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-accent1[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-accent2[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-accent3[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-accent4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-accent0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-accent1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-accent2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-accent3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-accent4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+/* set the color of an icon to transparent */
+.jp-icon-none[fill] {
+  fill: none;
+}
+
+.jp-icon-none[stroke] {
+  stroke: none;
+}
+/* brand icon colors. Same for light and dark */
+.jp-icon-brand0[fill] {
+  fill: var(--jp-brand-color0);
+}
+.jp-icon-brand1[fill] {
+  fill: var(--jp-brand-color1);
+}
+.jp-icon-brand2[fill] {
+  fill: var(--jp-brand-color2);
+}
+.jp-icon-brand3[fill] {
+  fill: var(--jp-brand-color3);
+}
+.jp-icon-brand4[fill] {
+  fill: var(--jp-brand-color4);
+}
+
+.jp-icon-brand0[stroke] {
+  stroke: var(--jp-brand-color0);
+}
+.jp-icon-brand1[stroke] {
+  stroke: var(--jp-brand-color1);
+}
+.jp-icon-brand2[stroke] {
+  stroke: var(--jp-brand-color2);
+}
+.jp-icon-brand3[stroke] {
+  stroke: var(--jp-brand-color3);
+}
+.jp-icon-brand4[stroke] {
+  stroke: var(--jp-brand-color4);
+}
+/* warn icon colors. Same for light and dark */
+.jp-icon-warn0[fill] {
+  fill: var(--jp-warn-color0);
+}
+.jp-icon-warn1[fill] {
+  fill: var(--jp-warn-color1);
+}
+.jp-icon-warn2[fill] {
+  fill: var(--jp-warn-color2);
+}
+.jp-icon-warn3[fill] {
+  fill: var(--jp-warn-color3);
+}
+
+.jp-icon-warn0[stroke] {
+  stroke: var(--jp-warn-color0);
+}
+.jp-icon-warn1[stroke] {
+  stroke: var(--jp-warn-color1);
+}
+.jp-icon-warn2[stroke] {
+  stroke: var(--jp-warn-color2);
+}
+.jp-icon-warn3[stroke] {
+  stroke: var(--jp-warn-color3);
+}
+/* icon colors that contrast well with each other and most backgrounds */
+.jp-icon-contrast0[fill] {
+  fill: var(--jp-icon-contrast-color0);
+}
+.jp-icon-contrast1[fill] {
+  fill: var(--jp-icon-contrast-color1);
+}
+.jp-icon-contrast2[fill] {
+  fill: var(--jp-icon-contrast-color2);
+}
+.jp-icon-contrast3[fill] {
+  fill: var(--jp-icon-contrast-color3);
+}
+
+.jp-icon-contrast0[stroke] {
+  stroke: var(--jp-icon-contrast-color0);
+}
+.jp-icon-contrast1[stroke] {
+  stroke: var(--jp-icon-contrast-color1);
+}
+.jp-icon-contrast2[stroke] {
+  stroke: var(--jp-icon-contrast-color2);
+}
+.jp-icon-contrast3[stroke] {
+  stroke: var(--jp-icon-contrast-color3);
+}
+
+/* CSS for icons in selected items in the settings editor */
+#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+#setting-editor
+  .jp-PluginList
+  .jp-mod-selected
+  .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* CSS for icons in selected filebrowser listing items */
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+/* CSS for icons in selected tabs in the sidebar tab manager */
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable[fill] {
+  fill: var(--jp-brand-color1);
+}
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-active
+  .jp-icon-hover
+  :hover
+  .jp-icon-selectable-inverse[fill] {
+  fill: #fff;
+}
+
+/**
+ * TODO: come up with non css-hack solution for showing the busy icon on top
+ *  of the close icon
+ * CSS for complex behavior of close icon of tabs in the sidebar tab manager
+ */
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+#tab-manager
+  .lm-TabBar-tab.jp-mod-dirty.jp-mod-active
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: #fff;
+}
+
+/**
+* TODO: come up with non css-hack solution for showing the busy icon on top
+*  of the close icon
+* CSS for complex behavior of close icon of tabs in the main area tabbar
+*/
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon3[fill] {
+  fill: none;
+}
+.lm-DockPanel-tabBar
+  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
+  > .lm-TabBar-tabCloseIcon
+  > :not(:hover)
+  > .jp-icon-busy[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+/* CSS for icons in status bar */
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
+  fill: #fff;
+}
+
+#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
+  fill: var(--jp-brand-color1);
+}
+/* special handling for splash icon CSS. While the theme CSS reloads during
+   splash, the splash icon can loose theming. To prevent that, we set a
+   default for its color variable */
+:root {
+  --jp-warn-color0: var(--md-orange-700);
+}
+
+/* not sure what to do with this one, used in filebrowser listing */
+.jp-DragIcon {
+  margin-right: 4px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/**
+ * Support for alt colors for icons as inline SVG HTMLElements
+ */
+
+/* alt recolor the primary elements of an icon */
+.jp-icon-alt .jp-icon0[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-alt .jp-icon1[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-alt .jp-icon2[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-alt .jp-icon3[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-alt .jp-icon4[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-alt .jp-icon0[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-alt .jp-icon1[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-alt .jp-icon2[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-alt .jp-icon3[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-alt .jp-icon4[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* alt recolor the accent elements of an icon */
+.jp-icon-alt .jp-icon-accent0[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-alt .jp-icon-accent1[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-alt .jp-icon-accent2[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-alt .jp-icon-accent3[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-alt .jp-icon-accent4[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-alt .jp-icon-accent0[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-alt .jp-icon-accent1[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-alt .jp-icon-accent2[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-alt .jp-icon-accent3[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-alt .jp-icon-accent4[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-icon-hoverShow:not(:hover) svg {
+  display: none !important;
+}
+
+/**
+ * Support for hover colors for icons as inline SVG HTMLElements
+ */
+
+/**
+ * regular colors
+ */
+
+/* recolor the primary elements of an icon */
+.jp-icon-hover :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/* recolor the accent elements of an icon */
+.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* set the color of an icon to transparent */
+.jp-icon-hover :hover .jp-icon-none-hover[fill] {
+  fill: none;
+}
+
+.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
+  stroke: none;
+}
+
+/**
+ * inverse colors
+ */
+
+/* inverse recolor the primary elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
+  fill: var(--jp-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
+  fill: var(--jp-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
+  fill: var(--jp-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
+  fill: var(--jp-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
+  fill: var(--jp-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
+  stroke: var(--jp-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
+  stroke: var(--jp-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
+  stroke: var(--jp-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
+  stroke: var(--jp-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
+  stroke: var(--jp-layout-color4);
+}
+
+/* inverse recolor the accent elements of an icon */
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
+  fill: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
+  fill: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
+  fill: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
+  fill: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
+  fill: var(--jp-inverse-layout-color4);
+}
+
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color0);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color1);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color2);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color3);
+}
+.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
+  stroke: var(--jp-inverse-layout-color4);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-switch {
+  display: flex;
+  align-items: center;
+  padding-left: 4px;
+  padding-right: 4px;
+  font-size: var(--jp-ui-font-size1);
+  background-color: transparent;
+  color: var(--jp-ui-font-color1);
+  border: none;
+  height: 20px;
+}
+
+.jp-switch:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-switch-label {
+  margin-right: 5px;
+}
+
+.jp-switch-track {
+  cursor: pointer;
+  background-color: var(--jp-border-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 34px;
+  height: 16px;
+  width: 35px;
+  position: relative;
+}
+
+.jp-switch-track::before {
+  content: '';
+  position: absolute;
+  height: 10px;
+  width: 10px;
+  margin: 3px;
+  left: 0px;
+  background-color: var(--jp-ui-inverse-font-color1);
+  -webkit-transition: 0.4s;
+  transition: 0.4s;
+  border-radius: 50%;
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track {
+  background-color: var(--jp-warn-color0);
+}
+
+.jp-switch[aria-checked='true'] .jp-switch-track::before {
+  /* track width (35) - margins (3 + 3) - thumb width (10) */
+  left: 19px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/* Sibling imports */
+
+/* Override Blueprint's _reset.scss styles */
+html {
+  box-sizing: unset;
+}
+
+*,
+*::before,
+*::after {
+  box-sizing: unset;
+}
+
+body {
+  color: unset;
+  font-family: var(--jp-ui-font-family);
+}
+
+p {
+  margin-top: unset;
+  margin-bottom: unset;
+}
+
+small {
+  font-size: unset;
+}
+
+strong {
+  font-weight: unset;
+}
+
+/* Override Blueprint's _typography.scss styles */
+a {
+  text-decoration: unset;
+  color: unset;
+}
+a:hover {
+  text-decoration: unset;
+  color: unset;
+}
+
+/* Override Blueprint's _accessibility.scss styles */
+:focus {
+  outline: unset;
+  outline-offset: unset;
+  -moz-outline-radius: unset;
+}
+
+/* Styles for ui-components */
+.jp-Button {
+  border-radius: var(--jp-border-radius);
+  padding: 0px 12px;
+  font-size: var(--jp-ui-font-size1);
+}
+
+/* Use our own theme for hover styles */
+button.jp-Button.bp3-button.bp3-minimal:hover {
+  background-color: var(--jp-layout-color2);
+}
+.jp-Button.minimal {
+  color: unset !important;
+}
+
+.jp-Button.jp-ToolbarButtonComponent {
+  text-transform: none;
+}
+
+.jp-InputGroup input {
+  box-sizing: border-box;
+  border-radius: 0;
+  background-color: transparent;
+  color: var(--jp-ui-font-color0);
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.jp-InputGroup input:focus {
+  box-shadow: inset 0 0 0 var(--jp-border-width)
+      var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-InputGroup input::placeholder,
+input::placeholder {
+  color: var(--jp-ui-font-color3);
+}
+
+.jp-BPIcon {
+  display: inline-block;
+  vertical-align: middle;
+  margin: auto;
+}
+
+/* Stop blueprint futzing with our icon fills */
+.bp3-icon.jp-BPIcon > svg:not([fill]) {
+  fill: var(--jp-inverse-layout-color3);
+}
+
+.jp-InputGroupAction {
+  padding: 6px;
+}
+
+.jp-HTMLSelect.jp-DefaultStyle select {
+  background-color: initial;
+  border: none;
+  border-radius: 0;
+  box-shadow: none;
+  color: var(--jp-ui-font-color0);
+  display: block;
+  font-size: var(--jp-ui-font-size1);
+  height: 24px;
+  line-height: 14px;
+  padding: 0 25px 0 10px;
+  text-align: left;
+  -moz-appearance: none;
+  -webkit-appearance: none;
+}
+
+/* Use our own theme for hover and option styles */
+.jp-HTMLSelect.jp-DefaultStyle select:hover,
+.jp-HTMLSelect.jp-DefaultStyle select > option {
+  background-color: var(--jp-layout-color2);
+  color: var(--jp-ui-font-color0);
+}
+select {
+  box-sizing: border-box;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapse {
+  display: flex;
+  flex-direction: column;
+  align-items: stretch;
+  border-top: 1px solid var(--jp-border-color2);
+  border-bottom: 1px solid var(--jp-border-color2);
+}
+
+.jp-Collapse-header {
+  padding: 1px 12px;
+  color: var(--jp-ui-font-color1);
+  background-color: var(--jp-layout-color1);
+  font-size: var(--jp-ui-font-size2);
+}
+
+.jp-Collapse-header:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-Collapse-contents {
+  padding: 0px 12px 0px 12px;
+  background-color: var(--jp-layout-color1);
+  color: var(--jp-ui-font-color1);
+  overflow: auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-commandpalette-search-height: 28px;
+}
+
+/*-----------------------------------------------------------------------------
+| Overall styles
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette {
+  padding-bottom: 0px;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Modal variant
+|----------------------------------------------------------------------------*/
+
+.jp-ModalCommandPalette {
+  position: absolute;
+  z-index: 10000;
+  top: 38px;
+  left: 30%;
+  margin: 0;
+  padding: 4px;
+  width: 40%;
+  box-shadow: var(--jp-elevation-z4);
+  border-radius: 4px;
+  background: var(--jp-layout-color0);
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette {
+  max-height: 40vh;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
+  display: none;
+}
+
+.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
+  margin-left: 4px;
+  margin-right: 4px;
+}
+
+.jp-ModalCommandPalette
+  .lm-CommandPalette
+  .lm-CommandPalette-item.lm-mod-disabled {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Search
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-search {
+  padding: 4px;
+  background-color: var(--jp-layout-color1);
+  z-index: 2;
+}
+
+.lm-CommandPalette-wrapper {
+  overflow: overlay;
+  padding: 0px 9px;
+  background-color: var(--jp-input-active-background);
+  height: 30px;
+  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
+}
+
+.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
+    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
+}
+
+.jp-SearchIconGroup {
+  color: white;
+  background-color: var(--jp-brand-color1);
+  position: absolute;
+  top: 4px;
+  right: 4px;
+  padding: 5px 5px 1px 5px;
+}
+
+.jp-SearchIconGroup svg {
+  height: 20px;
+  width: 20px;
+}
+
+.jp-SearchIconGroup .jp-icon3[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-input {
+  background: transparent;
+  width: calc(100% - 18px);
+  float: left;
+  border: none;
+  outline: none;
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  line-height: var(--jp-private-commandpalette-search-height);
+}
+
+.lm-CommandPalette-input::-webkit-input-placeholder,
+.lm-CommandPalette-input::-moz-placeholder,
+.lm-CommandPalette-input:-ms-input-placeholder {
+  color: var(--jp-ui-font-color2);
+  font-size: var(--jp-ui-font-size1);
+}
+
+/*-----------------------------------------------------------------------------
+| Results
+|----------------------------------------------------------------------------*/
+
+.lm-CommandPalette-header:first-child {
+  margin-top: 0px;
+}
+
+.lm-CommandPalette-header {
+  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
+  color: var(--jp-ui-font-color1);
+  cursor: pointer;
+  display: flex;
+  font-size: var(--jp-ui-font-size0);
+  font-weight: 600;
+  letter-spacing: 1px;
+  margin-top: 8px;
+  padding: 8px 0 8px 12px;
+  text-transform: uppercase;
+}
+
+.lm-CommandPalette-header.lm-mod-active {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-header > mark {
+  background-color: transparent;
+  font-weight: bold;
+  color: var(--jp-ui-font-color1);
+}
+
+.lm-CommandPalette-item {
+  padding: 4px 12px 4px 4px;
+  color: var(--jp-ui-font-color1);
+  font-size: var(--jp-ui-font-size1);
+  font-weight: 400;
+  display: flex;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item.lm-mod-active {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
+  fill: var(--jp-layout-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
+  background: var(--jp-layout-color2);
+}
+
+.lm-CommandPalette-itemContent {
+  overflow: hidden;
+}
+
+.lm-CommandPalette-itemLabel > mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled mark {
+  color: var(--jp-ui-font-color2);
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
+  margin: 0 4px 0 0;
+  position: relative;
+  width: 16px;
+  top: 2px;
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
+  opacity: 0.6;
+}
+
+.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
+  flex: 0 0 auto;
+}
+
+.lm-CommandPalette-itemCaption {
+  display: none;
+}
+
+.lm-CommandPalette-content {
+  background-color: var(--jp-layout-color1);
+}
+
+.lm-CommandPalette-content:empty:after {
+  content: 'No results';
+  margin: auto;
+  margin-top: 20px;
+  width: 100px;
+  display: block;
+  font-size: var(--jp-ui-font-size2);
+  font-family: var(--jp-ui-font-family);
+  font-weight: lighter;
+}
+
+.lm-CommandPalette-emptyMessage {
+  text-align: center;
+  margin-top: 24px;
+  line-height: 1.32;
+  padding: 0px 8px;
+  color: var(--jp-content-font-color3);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Dialog {
+  position: absolute;
+  z-index: 10000;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  top: 0px;
+  left: 0px;
+  margin: 0;
+  padding: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-dialog-background);
+}
+
+.jp-Dialog-content {
+  display: flex;
+  flex-direction: column;
+  margin-left: auto;
+  margin-right: auto;
+  background: var(--jp-layout-color1);
+  padding: 24px;
+  padding-bottom: 12px;
+  min-width: 300px;
+  min-height: 150px;
+  max-width: 1000px;
+  max-height: 500px;
+  box-sizing: border-box;
+  box-shadow: var(--jp-elevation-z20);
+  word-wrap: break-word;
+  border-radius: var(--jp-border-radius);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color1);
+  resize: both;
+}
+
+.jp-Dialog-button {
+  overflow: visible;
+}
+
+button.jp-Dialog-button:focus {
+  outline: 1px solid var(--jp-brand-color1);
+  outline-offset: 4px;
+  -moz-outline-radius: 0px;
+}
+
+button.jp-Dialog-button:focus::-moz-focus-inner {
+  border: 0;
+}
+
+button.jp-Dialog-close-button {
+  padding: 0;
+  height: 100%;
+  min-width: unset;
+  min-height: unset;
+}
+
+.jp-Dialog-header {
+  display: flex;
+  justify-content: space-between;
+  flex: 0 0 auto;
+  padding-bottom: 12px;
+  font-size: var(--jp-ui-font-size3);
+  font-weight: 400;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-body {
+  display: flex;
+  flex-direction: column;
+  flex: 1 1 auto;
+  font-size: var(--jp-ui-font-size1);
+  background: var(--jp-layout-color1);
+  overflow: auto;
+}
+
+.jp-Dialog-footer {
+  display: flex;
+  flex-direction: row;
+  justify-content: flex-end;
+  flex: 0 0 auto;
+  margin-left: -12px;
+  margin-right: -12px;
+  padding: 12px;
+}
+
+.jp-Dialog-title {
+  overflow: hidden;
+  white-space: nowrap;
+  text-overflow: ellipsis;
+}
+
+.jp-Dialog-body > .jp-select-wrapper {
+  width: 100%;
+}
+
+.jp-Dialog-body > button {
+  padding: 0px 16px;
+}
+
+.jp-Dialog-body > label {
+  line-height: 1.4;
+  color: var(--jp-ui-font-color0);
+}
+
+.jp-Dialog-button.jp-mod-styled:not(:last-child) {
+  margin-right: 12px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-HoverBox {
+  position: fixed;
+}
+
+.jp-HoverBox.jp-mod-outofview {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-IFrame {
+  width: 100%;
+  height: 100%;
+}
+
+.jp-IFrame > iframe {
+  border: none;
+}
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-IFrame {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-IFrame:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+.jp-Input-Boolean-Dialog {
+  flex-direction: row-reverse;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-Input-Boolean-Dialog > label {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MainAreaWidget > :focus {
+  outline: none;
+}
+
+/**
+ * google-material-color v1.2.6
+ * https://github.com/danlevan/google-material-color
+ */
+:root {
+  --md-red-50: #ffebee;
+  --md-red-100: #ffcdd2;
+  --md-red-200: #ef9a9a;
+  --md-red-300: #e57373;
+  --md-red-400: #ef5350;
+  --md-red-500: #f44336;
+  --md-red-600: #e53935;
+  --md-red-700: #d32f2f;
+  --md-red-800: #c62828;
+  --md-red-900: #b71c1c;
+  --md-red-A100: #ff8a80;
+  --md-red-A200: #ff5252;
+  --md-red-A400: #ff1744;
+  --md-red-A700: #d50000;
+
+  --md-pink-50: #fce4ec;
+  --md-pink-100: #f8bbd0;
+  --md-pink-200: #f48fb1;
+  --md-pink-300: #f06292;
+  --md-pink-400: #ec407a;
+  --md-pink-500: #e91e63;
+  --md-pink-600: #d81b60;
+  --md-pink-700: #c2185b;
+  --md-pink-800: #ad1457;
+  --md-pink-900: #880e4f;
+  --md-pink-A100: #ff80ab;
+  --md-pink-A200: #ff4081;
+  --md-pink-A400: #f50057;
+  --md-pink-A700: #c51162;
+
+  --md-purple-50: #f3e5f5;
+  --md-purple-100: #e1bee7;
+  --md-purple-200: #ce93d8;
+  --md-purple-300: #ba68c8;
+  --md-purple-400: #ab47bc;
+  --md-purple-500: #9c27b0;
+  --md-purple-600: #8e24aa;
+  --md-purple-700: #7b1fa2;
+  --md-purple-800: #6a1b9a;
+  --md-purple-900: #4a148c;
+  --md-purple-A100: #ea80fc;
+  --md-purple-A200: #e040fb;
+  --md-purple-A400: #d500f9;
+  --md-purple-A700: #aa00ff;
+
+  --md-deep-purple-50: #ede7f6;
+  --md-deep-purple-100: #d1c4e9;
+  --md-deep-purple-200: #b39ddb;
+  --md-deep-purple-300: #9575cd;
+  --md-deep-purple-400: #7e57c2;
+  --md-deep-purple-500: #673ab7;
+  --md-deep-purple-600: #5e35b1;
+  --md-deep-purple-700: #512da8;
+  --md-deep-purple-800: #4527a0;
+  --md-deep-purple-900: #311b92;
+  --md-deep-purple-A100: #b388ff;
+  --md-deep-purple-A200: #7c4dff;
+  --md-deep-purple-A400: #651fff;
+  --md-deep-purple-A700: #6200ea;
+
+  --md-indigo-50: #e8eaf6;
+  --md-indigo-100: #c5cae9;
+  --md-indigo-200: #9fa8da;
+  --md-indigo-300: #7986cb;
+  --md-indigo-400: #5c6bc0;
+  --md-indigo-500: #3f51b5;
+  --md-indigo-600: #3949ab;
+  --md-indigo-700: #303f9f;
+  --md-indigo-800: #283593;
+  --md-indigo-900: #1a237e;
+  --md-indigo-A100: #8c9eff;
+  --md-indigo-A200: #536dfe;
+  --md-indigo-A400: #3d5afe;
+  --md-indigo-A700: #304ffe;
+
+  --md-blue-50: #e3f2fd;
+  --md-blue-100: #bbdefb;
+  --md-blue-200: #90caf9;
+  --md-blue-300: #64b5f6;
+  --md-blue-400: #42a5f5;
+  --md-blue-500: #2196f3;
+  --md-blue-600: #1e88e5;
+  --md-blue-700: #1976d2;
+  --md-blue-800: #1565c0;
+  --md-blue-900: #0d47a1;
+  --md-blue-A100: #82b1ff;
+  --md-blue-A200: #448aff;
+  --md-blue-A400: #2979ff;
+  --md-blue-A700: #2962ff;
+
+  --md-light-blue-50: #e1f5fe;
+  --md-light-blue-100: #b3e5fc;
+  --md-light-blue-200: #81d4fa;
+  --md-light-blue-300: #4fc3f7;
+  --md-light-blue-400: #29b6f6;
+  --md-light-blue-500: #03a9f4;
+  --md-light-blue-600: #039be5;
+  --md-light-blue-700: #0288d1;
+  --md-light-blue-800: #0277bd;
+  --md-light-blue-900: #01579b;
+  --md-light-blue-A100: #80d8ff;
+  --md-light-blue-A200: #40c4ff;
+  --md-light-blue-A400: #00b0ff;
+  --md-light-blue-A700: #0091ea;
+
+  --md-cyan-50: #e0f7fa;
+  --md-cyan-100: #b2ebf2;
+  --md-cyan-200: #80deea;
+  --md-cyan-300: #4dd0e1;
+  --md-cyan-400: #26c6da;
+  --md-cyan-500: #00bcd4;
+  --md-cyan-600: #00acc1;
+  --md-cyan-700: #0097a7;
+  --md-cyan-800: #00838f;
+  --md-cyan-900: #006064;
+  --md-cyan-A100: #84ffff;
+  --md-cyan-A200: #18ffff;
+  --md-cyan-A400: #00e5ff;
+  --md-cyan-A700: #00b8d4;
+
+  --md-teal-50: #e0f2f1;
+  --md-teal-100: #b2dfdb;
+  --md-teal-200: #80cbc4;
+  --md-teal-300: #4db6ac;
+  --md-teal-400: #26a69a;
+  --md-teal-500: #009688;
+  --md-teal-600: #00897b;
+  --md-teal-700: #00796b;
+  --md-teal-800: #00695c;
+  --md-teal-900: #004d40;
+  --md-teal-A100: #a7ffeb;
+  --md-teal-A200: #64ffda;
+  --md-teal-A400: #1de9b6;
+  --md-teal-A700: #00bfa5;
+
+  --md-green-50: #e8f5e9;
+  --md-green-100: #c8e6c9;
+  --md-green-200: #a5d6a7;
+  --md-green-300: #81c784;
+  --md-green-400: #66bb6a;
+  --md-green-500: #4caf50;
+  --md-green-600: #43a047;
+  --md-green-700: #388e3c;
+  --md-green-800: #2e7d32;
+  --md-green-900: #1b5e20;
+  --md-green-A100: #b9f6ca;
+  --md-green-A200: #69f0ae;
+  --md-green-A400: #00e676;
+  --md-green-A700: #00c853;
+
+  --md-light-green-50: #f1f8e9;
+  --md-light-green-100: #dcedc8;
+  --md-light-green-200: #c5e1a5;
+  --md-light-green-300: #aed581;
+  --md-light-green-400: #9ccc65;
+  --md-light-green-500: #8bc34a;
+  --md-light-green-600: #7cb342;
+  --md-light-green-700: #689f38;
+  --md-light-green-800: #558b2f;
+  --md-light-green-900: #33691e;
+  --md-light-green-A100: #ccff90;
+  --md-light-green-A200: #b2ff59;
+  --md-light-green-A400: #76ff03;
+  --md-light-green-A700: #64dd17;
+
+  --md-lime-50: #f9fbe7;
+  --md-lime-100: #f0f4c3;
+  --md-lime-200: #e6ee9c;
+  --md-lime-300: #dce775;
+  --md-lime-400: #d4e157;
+  --md-lime-500: #cddc39;
+  --md-lime-600: #c0ca33;
+  --md-lime-700: #afb42b;
+  --md-lime-800: #9e9d24;
+  --md-lime-900: #827717;
+  --md-lime-A100: #f4ff81;
+  --md-lime-A200: #eeff41;
+  --md-lime-A400: #c6ff00;
+  --md-lime-A700: #aeea00;
+
+  --md-yellow-50: #fffde7;
+  --md-yellow-100: #fff9c4;
+  --md-yellow-200: #fff59d;
+  --md-yellow-300: #fff176;
+  --md-yellow-400: #ffee58;
+  --md-yellow-500: #ffeb3b;
+  --md-yellow-600: #fdd835;
+  --md-yellow-700: #fbc02d;
+  --md-yellow-800: #f9a825;
+  --md-yellow-900: #f57f17;
+  --md-yellow-A100: #ffff8d;
+  --md-yellow-A200: #ffff00;
+  --md-yellow-A400: #ffea00;
+  --md-yellow-A700: #ffd600;
+
+  --md-amber-50: #fff8e1;
+  --md-amber-100: #ffecb3;
+  --md-amber-200: #ffe082;
+  --md-amber-300: #ffd54f;
+  --md-amber-400: #ffca28;
+  --md-amber-500: #ffc107;
+  --md-amber-600: #ffb300;
+  --md-amber-700: #ffa000;
+  --md-amber-800: #ff8f00;
+  --md-amber-900: #ff6f00;
+  --md-amber-A100: #ffe57f;
+  --md-amber-A200: #ffd740;
+  --md-amber-A400: #ffc400;
+  --md-amber-A700: #ffab00;
+
+  --md-orange-50: #fff3e0;
+  --md-orange-100: #ffe0b2;
+  --md-orange-200: #ffcc80;
+  --md-orange-300: #ffb74d;
+  --md-orange-400: #ffa726;
+  --md-orange-500: #ff9800;
+  --md-orange-600: #fb8c00;
+  --md-orange-700: #f57c00;
+  --md-orange-800: #ef6c00;
+  --md-orange-900: #e65100;
+  --md-orange-A100: #ffd180;
+  --md-orange-A200: #ffab40;
+  --md-orange-A400: #ff9100;
+  --md-orange-A700: #ff6d00;
+
+  --md-deep-orange-50: #fbe9e7;
+  --md-deep-orange-100: #ffccbc;
+  --md-deep-orange-200: #ffab91;
+  --md-deep-orange-300: #ff8a65;
+  --md-deep-orange-400: #ff7043;
+  --md-deep-orange-500: #ff5722;
+  --md-deep-orange-600: #f4511e;
+  --md-deep-orange-700: #e64a19;
+  --md-deep-orange-800: #d84315;
+  --md-deep-orange-900: #bf360c;
+  --md-deep-orange-A100: #ff9e80;
+  --md-deep-orange-A200: #ff6e40;
+  --md-deep-orange-A400: #ff3d00;
+  --md-deep-orange-A700: #dd2c00;
+
+  --md-brown-50: #efebe9;
+  --md-brown-100: #d7ccc8;
+  --md-brown-200: #bcaaa4;
+  --md-brown-300: #a1887f;
+  --md-brown-400: #8d6e63;
+  --md-brown-500: #795548;
+  --md-brown-600: #6d4c41;
+  --md-brown-700: #5d4037;
+  --md-brown-800: #4e342e;
+  --md-brown-900: #3e2723;
+
+  --md-grey-50: #fafafa;
+  --md-grey-100: #f5f5f5;
+  --md-grey-200: #eeeeee;
+  --md-grey-300: #e0e0e0;
+  --md-grey-400: #bdbdbd;
+  --md-grey-500: #9e9e9e;
+  --md-grey-600: #757575;
+  --md-grey-700: #616161;
+  --md-grey-800: #424242;
+  --md-grey-900: #212121;
+
+  --md-blue-grey-50: #eceff1;
+  --md-blue-grey-100: #cfd8dc;
+  --md-blue-grey-200: #b0bec5;
+  --md-blue-grey-300: #90a4ae;
+  --md-blue-grey-400: #78909c;
+  --md-blue-grey-500: #607d8b;
+  --md-blue-grey-600: #546e7a;
+  --md-blue-grey-700: #455a64;
+  --md-blue-grey-800: #37474f;
+  --md-blue-grey-900: #263238;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Spinner {
+  position: absolute;
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  z-index: 10;
+  left: 0;
+  top: 0;
+  width: 100%;
+  height: 100%;
+  background: var(--jp-layout-color0);
+  outline: none;
+}
+
+.jp-SpinnerContent {
+  font-size: 10px;
+  margin: 50px auto;
+  text-indent: -9999em;
+  width: 3em;
+  height: 3em;
+  border-radius: 50%;
+  background: var(--jp-brand-color3);
+  background: linear-gradient(
+    to right,
+    #f37626 10%,
+    rgba(255, 255, 255, 0) 42%
+  );
+  position: relative;
+  animation: load3 1s infinite linear, fadeIn 1s;
+}
+
+.jp-SpinnerContent:before {
+  width: 50%;
+  height: 50%;
+  background: #f37626;
+  border-radius: 100% 0 0 0;
+  position: absolute;
+  top: 0;
+  left: 0;
+  content: '';
+}
+
+.jp-SpinnerContent:after {
+  background: var(--jp-layout-color0);
+  width: 75%;
+  height: 75%;
+  border-radius: 50%;
+  content: '';
+  margin: auto;
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+}
+
+@keyframes fadeIn {
+  0% {
+    opacity: 0;
+  }
+  100% {
+    opacity: 1;
+  }
+}
+
+@keyframes load3 {
+  0% {
+    transform: rotate(0deg);
+  }
+  100% {
+    transform: rotate(360deg);
+  }
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+button.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: none;
+  box-sizing: border-box;
+  text-align: center;
+  line-height: 32px;
+  height: 32px;
+  padding: 0px 12px;
+  letter-spacing: 0.8px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input.jp-mod-styled {
+  background: var(--jp-input-background);
+  height: 28px;
+  box-sizing: border-box;
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+  padding-left: 7px;
+  padding-right: 7px;
+  font-size: var(--jp-ui-font-size2);
+  color: var(--jp-ui-font-color0);
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+input[type='checkbox'].jp-mod-styled {
+  appearance: checkbox;
+  -webkit-appearance: checkbox;
+  -moz-appearance: checkbox;
+  height: auto;
+}
+
+input.jp-mod-styled:focus {
+  border: var(--jp-border-width) solid var(--md-blue-500);
+  box-shadow: inset 0 0 4px var(--md-blue-300);
+}
+
+.jp-FileDialog-Checkbox {
+  margin-top: 35px;
+  display: flex;
+  flex-direction: row;
+  align-items: end;
+  width: 100%;
+}
+
+.jp-FileDialog-Checkbox > label {
+  flex: 1 1 auto;
+}
+
+.jp-select-wrapper {
+  display: flex;
+  position: relative;
+  flex-direction: column;
+  padding: 1px;
+  background-color: var(--jp-layout-color1);
+  height: 28px;
+  box-sizing: border-box;
+  margin-bottom: 12px;
+}
+
+.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-input-active-background);
+}
+
+select.jp-mod-styled:hover {
+  background-color: var(--jp-layout-color1);
+  cursor: pointer;
+  color: var(--jp-ui-font-color0);
+  background-color: var(--jp-input-hover-background);
+  box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);
+}
+
+select.jp-mod-styled {
+  flex: 1 1 auto;
+  height: 32px;
+  width: 100%;
+  font-size: var(--jp-ui-font-size2);
+  background: var(--jp-input-background);
+  color: var(--jp-ui-font-color0);
+  padding: 0 25px 0 8px;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-toolbar-height: calc(
+    28px + var(--jp-border-width)
+  ); /* leave 28px for content */
+}
+
+.jp-Toolbar {
+  color: var(--jp-ui-font-color1);
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  background: var(--jp-toolbar-background);
+  min-height: var(--jp-toolbar-micro-height);
+  padding: 2px;
+  z-index: 1;
+  overflow-x: auto;
+}
+
+/* Toolbar items */
+
+.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+.jp-Toolbar-item.jp-Toolbar-kernelStatus {
+  display: inline-block;
+  width: 32px;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: 16px;
+}
+
+.jp-Toolbar > .jp-Toolbar-item {
+  flex: 0 0 auto;
+  display: flex;
+  padding-left: 1px;
+  padding-right: 1px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: var(--jp-private-toolbar-height);
+  height: 100%;
+}
+
+/* Toolbar buttons */
+
+/* This is the div we use to wrap the react component into a Widget */
+div.jp-ToolbarButton {
+  color: transparent;
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px;
+  margin: 0px;
+}
+
+button.jp-ToolbarButtonComponent {
+  background: var(--jp-layout-color1);
+  border: none;
+  box-sizing: border-box;
+  outline: none;
+  appearance: none;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  padding: 0px 6px;
+  margin: 0px;
+  height: 24px;
+  border-radius: var(--jp-border-radius);
+  display: flex;
+  align-items: center;
+  text-align: center;
+  font-size: 14px;
+  min-width: unset;
+  min-height: unset;
+}
+
+button.jp-ToolbarButtonComponent:disabled {
+  opacity: 0.4;
+}
+
+button.jp-ToolbarButtonComponent span {
+  padding: 0px;
+  flex: 0 0 auto;
+}
+
+button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
+  font-size: var(--jp-ui-font-size1);
+  line-height: 100%;
+  padding-left: 2px;
+  color: var(--jp-ui-font-color1);
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar.jp-Toolbar-micro {
+  padding: 0;
+  min-height: 0;
+}
+
+#jp-main-dock-panel[data-mode='single-document']
+  .jp-MainAreaWidget
+  > .jp-Toolbar {
+  border: none;
+  box-shadow: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2017, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Copyright (c) 2014-2017, PhosphorJS Contributors
+|
+| Distributed under the terms of the BSD 3-Clause License.
+|
+| The full license is in the file LICENSE, distributed with this software.
+|----------------------------------------------------------------------------*/
+
+
+/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
+body.lm-mod-override-cursor * {
+  cursor: inherit !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) 2014-2016, Jupyter Development Team.
+|
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-JSONEditor {
+  display: flex;
+  flex-direction: column;
+  width: 100%;
+}
+
+.jp-JSONEditor-host {
+  flex: 1 1 auto;
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  border-radius: 0px;
+  background: var(--jp-layout-color0);
+  min-height: 50px;
+  padding: 1px;
+}
+
+.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
+  border-color: red;
+  outline-color: red;
+}
+
+.jp-JSONEditor-header {
+  display: flex;
+  flex: 1 0 auto;
+  padding: 0 0 0 12px;
+}
+
+.jp-JSONEditor-header label {
+  flex: 0 0 auto;
+}
+
+.jp-JSONEditor-commitButton {
+  height: 16px;
+  width: 16px;
+  background-size: 18px;
+  background-repeat: no-repeat;
+  background-position: center;
+}
+
+.jp-JSONEditor-host.jp-mod-focused {
+  background-color: var(--jp-input-active-background);
+  border: 1px solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+.jp-Editor.jp-mod-dropTarget {
+  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+}
+
+/* BASICS */
+
+.CodeMirror {
+  /* Set height, width, borders, and global font properties here */
+  font-family: monospace;
+  height: 300px;
+  color: black;
+  direction: ltr;
+}
+
+/* PADDING */
+
+.CodeMirror-lines {
+  padding: 4px 0; /* Vertical padding around content */
+}
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  padding: 0 4px; /* Horizontal padding of content */
+}
+
+.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  background-color: white; /* The little square between H and V scrollbars */
+}
+
+/* GUTTER */
+
+.CodeMirror-gutters {
+  border-right: 1px solid #ddd;
+  background-color: #f7f7f7;
+  white-space: nowrap;
+}
+.CodeMirror-linenumbers {}
+.CodeMirror-linenumber {
+  padding: 0 3px 0 5px;
+  min-width: 20px;
+  text-align: right;
+  color: #999;
+  white-space: nowrap;
+}
+
+.CodeMirror-guttermarker { color: black; }
+.CodeMirror-guttermarker-subtle { color: #999; }
+
+/* CURSOR */
+
+.CodeMirror-cursor {
+  border-left: 1px solid black;
+  border-right: none;
+  width: 0;
+}
+/* Shown when moving in bi-directional text */
+.CodeMirror div.CodeMirror-secondarycursor {
+  border-left: 1px solid silver;
+}
+.cm-fat-cursor .CodeMirror-cursor {
+  width: auto;
+  border: 0 !important;
+  background: #7e7;
+}
+.cm-fat-cursor div.CodeMirror-cursors {
+  z-index: 1;
+}
+.cm-fat-cursor-mark {
+  background-color: rgba(20, 255, 20, 0.5);
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
+}
+.cm-animate-fat-cursor {
+  width: auto;
+  border: 0;
+  -webkit-animation: blink 1.06s steps(1) infinite;
+  -moz-animation: blink 1.06s steps(1) infinite;
+  animation: blink 1.06s steps(1) infinite;
+  background-color: #7e7;
+}
+@-moz-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+@-webkit-keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+@keyframes blink {
+  0% {}
+  50% { background-color: transparent; }
+  100% {}
+}
+
+/* Can style cursor different in overwrite (non-insert) mode */
+.CodeMirror-overwrite .CodeMirror-cursor {}
+
+.cm-tab { display: inline-block; text-decoration: inherit; }
+
+.CodeMirror-rulers {
+  position: absolute;
+  left: 0; right: 0; top: -50px; bottom: 0;
+  overflow: hidden;
+}
+.CodeMirror-ruler {
+  border-left: 1px solid #ccc;
+  top: 0; bottom: 0;
+  position: absolute;
+}
+
+/* DEFAULT THEME */
+
+.cm-s-default .cm-header {color: blue;}
+.cm-s-default .cm-quote {color: #090;}
+.cm-negative {color: #d44;}
+.cm-positive {color: #292;}
+.cm-header, .cm-strong {font-weight: bold;}
+.cm-em {font-style: italic;}
+.cm-link {text-decoration: underline;}
+.cm-strikethrough {text-decoration: line-through;}
+
+.cm-s-default .cm-keyword {color: #708;}
+.cm-s-default .cm-atom {color: #219;}
+.cm-s-default .cm-number {color: #164;}
+.cm-s-default .cm-def {color: #00f;}
+.cm-s-default .cm-variable,
+.cm-s-default .cm-punctuation,
+.cm-s-default .cm-property,
+.cm-s-default .cm-operator {}
+.cm-s-default .cm-variable-2 {color: #05a;}
+.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}
+.cm-s-default .cm-comment {color: #a50;}
+.cm-s-default .cm-string {color: #a11;}
+.cm-s-default .cm-string-2 {color: #f50;}
+.cm-s-default .cm-meta {color: #555;}
+.cm-s-default .cm-qualifier {color: #555;}
+.cm-s-default .cm-builtin {color: #30a;}
+.cm-s-default .cm-bracket {color: #997;}
+.cm-s-default .cm-tag {color: #170;}
+.cm-s-default .cm-attribute {color: #00c;}
+.cm-s-default .cm-hr {color: #999;}
+.cm-s-default .cm-link {color: #00c;}
+
+.cm-s-default .cm-error {color: #f00;}
+.cm-invalidchar {color: #f00;}
+
+.CodeMirror-composing { border-bottom: 2px solid; }
+
+/* Default styles for common addons */
+
+div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}
+div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}
+.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }
+.CodeMirror-activeline-background {background: #e8f2ff;}
+
+/* STOP */
+
+/* The rest of this file contains styles related to the mechanics of
+   the editor. You probably shouldn't touch them. */
+
+.CodeMirror {
+  position: relative;
+  overflow: hidden;
+  background: white;
+}
+
+.CodeMirror-scroll {
+  overflow: scroll !important; /* Things will break if this is overridden */
+  /* 50px is the magic margin used to hide the element's real scrollbars */
+  /* See overflow: hidden in .CodeMirror */
+  margin-bottom: -50px; margin-right: -50px;
+  padding-bottom: 50px;
+  height: 100%;
+  outline: none; /* Prevent dragging from highlighting the element */
+  position: relative;
+}
+.CodeMirror-sizer {
+  position: relative;
+  border-right: 50px solid transparent;
+}
+
+/* The fake, visible scrollbars. Used to force redraw during scrolling
+   before actual scrolling happens, thus preventing shaking and
+   flickering artifacts. */
+.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
+  position: absolute;
+  z-index: 6;
+  display: none;
+  outline: none;
+}
+.CodeMirror-vscrollbar {
+  right: 0; top: 0;
+  overflow-x: hidden;
+  overflow-y: scroll;
+}
+.CodeMirror-hscrollbar {
+  bottom: 0; left: 0;
+  overflow-y: hidden;
+  overflow-x: scroll;
+}
+.CodeMirror-scrollbar-filler {
+  right: 0; bottom: 0;
+}
+.CodeMirror-gutter-filler {
+  left: 0; bottom: 0;
+}
+
+.CodeMirror-gutters {
+  position: absolute; left: 0; top: 0;
+  min-height: 100%;
+  z-index: 3;
+}
+.CodeMirror-gutter {
+  white-space: normal;
+  height: 100%;
+  display: inline-block;
+  vertical-align: top;
+  margin-bottom: -50px;
+}
+.CodeMirror-gutter-wrapper {
+  position: absolute;
+  z-index: 4;
+  background: none !important;
+  border: none !important;
+}
+.CodeMirror-gutter-background {
+  position: absolute;
+  top: 0; bottom: 0;
+  z-index: 4;
+}
+.CodeMirror-gutter-elt {
+  position: absolute;
+  cursor: default;
+  z-index: 4;
+}
+.CodeMirror-gutter-wrapper ::selection { background-color: transparent }
+.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }
+
+.CodeMirror-lines {
+  cursor: text;
+  min-height: 1px; /* prevents collapsing before first draw */
+}
+.CodeMirror pre.CodeMirror-line,
+.CodeMirror pre.CodeMirror-line-like {
+  /* Reset some styles that the rest of the page might have set */
+  -moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;
+  border-width: 0;
+  background: transparent;
+  font-family: inherit;
+  font-size: inherit;
+  margin: 0;
+  white-space: pre;
+  word-wrap: normal;
+  line-height: inherit;
+  color: inherit;
+  z-index: 2;
+  position: relative;
+  overflow: visible;
+  -webkit-tap-highlight-color: transparent;
+  -webkit-font-variant-ligatures: contextual;
+  font-variant-ligatures: contextual;
+}
+.CodeMirror-wrap pre.CodeMirror-line,
+.CodeMirror-wrap pre.CodeMirror-line-like {
+  word-wrap: break-word;
+  white-space: pre-wrap;
+  word-break: normal;
+}
+
+.CodeMirror-linebackground {
+  position: absolute;
+  left: 0; right: 0; top: 0; bottom: 0;
+  z-index: 0;
+}
+
+.CodeMirror-linewidget {
+  position: relative;
+  z-index: 2;
+  padding: 0.1px; /* Force widget margins to stay inside of the container */
+}
+
+.CodeMirror-widget {}
+
+.CodeMirror-rtl pre { direction: rtl; }
+
+.CodeMirror-code {
+  outline: none;
+}
+
+/* Force content-box sizing for the elements where we expect it */
+.CodeMirror-scroll,
+.CodeMirror-sizer,
+.CodeMirror-gutter,
+.CodeMirror-gutters,
+.CodeMirror-linenumber {
+  -moz-box-sizing: content-box;
+  box-sizing: content-box;
+}
+
+.CodeMirror-measure {
+  position: absolute;
+  width: 100%;
+  height: 0;
+  overflow: hidden;
+  visibility: hidden;
+}
+
+.CodeMirror-cursor {
+  position: absolute;
+  pointer-events: none;
+}
+.CodeMirror-measure pre { position: static; }
+
+div.CodeMirror-cursors {
+  visibility: hidden;
+  position: relative;
+  z-index: 3;
+}
+div.CodeMirror-dragcursors {
+  visibility: visible;
+}
+
+.CodeMirror-focused div.CodeMirror-cursors {
+  visibility: visible;
+}
+
+.CodeMirror-selected { background: #d9d9d9; }
+.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; }
+.CodeMirror-crosshair { cursor: crosshair; }
+.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; }
+.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }
+
+.cm-searching {
+  background-color: #ffa;
+  background-color: rgba(255, 255, 0, .4);
+}
+
+/* Used to force a border model for a node */
+.cm-force-border { padding-right: .1px; }
+
+@media print {
+  /* Hide the cursor when printing */
+  .CodeMirror div.CodeMirror-cursors {
+    visibility: hidden;
+  }
+}
+
+/* See issue #2901 */
+.cm-tab-wrap-hack:after { content: ''; }
+
+/* Help users use markselection to safely style text background */
+span.CodeMirror-selectedtext { background: none; }
+
+.CodeMirror-dialog {
+  position: absolute;
+  left: 0; right: 0;
+  background: inherit;
+  z-index: 15;
+  padding: .1em .8em;
+  overflow: hidden;
+  color: inherit;
+}
+
+.CodeMirror-dialog-top {
+  border-bottom: 1px solid #eee;
+  top: 0;
+}
+
+.CodeMirror-dialog-bottom {
+  border-top: 1px solid #eee;
+  bottom: 0;
+}
+
+.CodeMirror-dialog input {
+  border: none;
+  outline: none;
+  background: transparent;
+  width: 20em;
+  color: inherit;
+  font-family: monospace;
+}
+
+.CodeMirror-dialog button {
+  font-size: 70%;
+}
+
+.CodeMirror-foldmarker {
+  color: blue;
+  text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px;
+  font-family: arial;
+  line-height: .3;
+  cursor: pointer;
+}
+.CodeMirror-foldgutter {
+  width: .7em;
+}
+.CodeMirror-foldgutter-open,
+.CodeMirror-foldgutter-folded {
+  cursor: pointer;
+}
+.CodeMirror-foldgutter-open:after {
+  content: "\25BE";
+}
+.CodeMirror-foldgutter-folded:after {
+  content: "\25B8";
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.CodeMirror {
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  border: 0;
+  border-radius: 0;
+  height: auto;
+  /* Changed to auto to autogrow */
+}
+
+.CodeMirror pre {
+  padding: 0 var(--jp-code-padding);
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+/* This causes https://github.com/jupyter/jupyterlab/issues/522 */
+/* May not cause it not because we changed it! */
+.CodeMirror-lines {
+  padding: var(--jp-code-padding) 0;
+}
+
+.CodeMirror-linenumber {
+  padding: 0 8px;
+}
+
+.jp-CodeMirrorEditor {
+  cursor: text;
+}
+
+.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
+}
+
+/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
+@media screen and (min-width: 2138px) and (max-width: 4319px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width1) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+/* When zoomed out less than 33% */
+@media screen and (min-width: 4320px) {
+  .jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
+    border-left: var(--jp-code-cursor-width2) solid
+      var(--jp-editor-cursor-color);
+  }
+}
+
+.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
+  display: none;
+}
+
+.CodeMirror-gutters {
+  border-right: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-layout-color0);
+}
+
+.jp-CollaboratorCursor {
+  border-left: 5px solid transparent;
+  border-right: 5px solid transparent;
+  border-top: none;
+  border-bottom: 3px solid;
+  background-clip: content-box;
+  margin-left: -5px;
+  margin-right: -5px;
+}
+
+.CodeMirror-selectedtext.cm-searching {
+  background-color: var(--jp-search-selected-match-background-color) !important;
+  color: var(--jp-search-selected-match-color) !important;
+}
+
+.cm-searching {
+  background-color: var(
+    --jp-search-unselected-match-background-color
+  ) !important;
+  color: var(--jp-search-unselected-match-color) !important;
+}
+
+.CodeMirror-focused .CodeMirror-selected {
+  background-color: var(--jp-editor-selected-focused-background);
+}
+
+.CodeMirror-selected {
+  background-color: var(--jp-editor-selected-background);
+}
+
+.jp-CollaboratorCursor-hover {
+  position: absolute;
+  z-index: 1;
+  transform: translateX(-50%);
+  color: white;
+  border-radius: 3px;
+  padding-left: 4px;
+  padding-right: 4px;
+  padding-top: 1px;
+  padding-bottom: 1px;
+  text-align: center;
+  font-size: var(--jp-ui-font-size1);
+  white-space: nowrap;
+}
+
+.jp-CodeMirror-ruler {
+  border-left: 1px dashed var(--jp-border-color2);
+}
+
+/**
+ * Here is our jupyter theme for CodeMirror syntax highlighting
+ * This is used in our marked.js syntax highlighting and CodeMirror itself
+ * The string "jupyter" is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME
+ * This came from the classic notebook, which came form highlight.js/GitHub
+ */
+
+/**
+ * CodeMirror themes are handling the background/color in this way. This works
+ * fine for CodeMirror editors outside the notebook, but the notebook styles
+ * these things differently.
+ */
+.CodeMirror.cm-s-jupyter {
+  background: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+}
+
+/* In the notebook, we want this styling to be handled by its container */
+.jp-CodeConsole .CodeMirror.cm-s-jupyter,
+.jp-Notebook .CodeMirror.cm-s-jupyter {
+  background: transparent;
+}
+
+.cm-s-jupyter .CodeMirror-cursor {
+  border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
+}
+.cm-s-jupyter span.cm-keyword {
+  color: var(--jp-mirror-editor-keyword-color);
+  font-weight: bold;
+}
+.cm-s-jupyter span.cm-atom {
+  color: var(--jp-mirror-editor-atom-color);
+}
+.cm-s-jupyter span.cm-number {
+  color: var(--jp-mirror-editor-number-color);
+}
+.cm-s-jupyter span.cm-def {
+  color: var(--jp-mirror-editor-def-color);
+}
+.cm-s-jupyter span.cm-variable {
+  color: var(--jp-mirror-editor-variable-color);
+}
+.cm-s-jupyter span.cm-variable-2 {
+  color: var(--jp-mirror-editor-variable-2-color);
+}
+.cm-s-jupyter span.cm-variable-3 {
+  color: var(--jp-mirror-editor-variable-3-color);
+}
+.cm-s-jupyter span.cm-punctuation {
+  color: var(--jp-mirror-editor-punctuation-color);
+}
+.cm-s-jupyter span.cm-property {
+  color: var(--jp-mirror-editor-property-color);
+}
+.cm-s-jupyter span.cm-operator {
+  color: var(--jp-mirror-editor-operator-color);
+  font-weight: bold;
+}
+.cm-s-jupyter span.cm-comment {
+  color: var(--jp-mirror-editor-comment-color);
+  font-style: italic;
+}
+.cm-s-jupyter span.cm-string {
+  color: var(--jp-mirror-editor-string-color);
+}
+.cm-s-jupyter span.cm-string-2 {
+  color: var(--jp-mirror-editor-string-2-color);
+}
+.cm-s-jupyter span.cm-meta {
+  color: var(--jp-mirror-editor-meta-color);
+}
+.cm-s-jupyter span.cm-qualifier {
+  color: var(--jp-mirror-editor-qualifier-color);
+}
+.cm-s-jupyter span.cm-builtin {
+  color: var(--jp-mirror-editor-builtin-color);
+}
+.cm-s-jupyter span.cm-bracket {
+  color: var(--jp-mirror-editor-bracket-color);
+}
+.cm-s-jupyter span.cm-tag {
+  color: var(--jp-mirror-editor-tag-color);
+}
+.cm-s-jupyter span.cm-attribute {
+  color: var(--jp-mirror-editor-attribute-color);
+}
+.cm-s-jupyter span.cm-header {
+  color: var(--jp-mirror-editor-header-color);
+}
+.cm-s-jupyter span.cm-quote {
+  color: var(--jp-mirror-editor-quote-color);
+}
+.cm-s-jupyter span.cm-link {
+  color: var(--jp-mirror-editor-link-color);
+}
+.cm-s-jupyter span.cm-error {
+  color: var(--jp-mirror-editor-error-color);
+}
+.cm-s-jupyter span.cm-hr {
+  color: #999;
+}
+
+.cm-s-jupyter span.cm-tab {
+  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
+  background-position: right;
+  background-repeat: no-repeat;
+}
+
+.cm-s-jupyter .CodeMirror-activeline-background,
+.cm-s-jupyter .CodeMirror-gutter {
+  background-color: var(--jp-layout-color2);
+}
+
+/* Styles for shared cursors (remote cursor locations and selected ranges) */
+.jp-CodeMirrorEditor .remote-caret {
+  position: relative;
+  border-left: 2px solid black;
+  margin-left: -1px;
+  margin-right: -1px;
+  box-sizing: border-box;
+}
+
+.jp-CodeMirrorEditor .remote-caret > div {
+  white-space: nowrap;
+  position: absolute;
+  top: -1.15em;
+  padding-bottom: 0.05em;
+  left: -2px;
+  font-size: 0.95em;
+  background-color: rgb(250, 129, 0);
+  font-family: var(--jp-ui-font-family);
+  font-weight: bold;
+  line-height: normal;
+  user-select: none;
+  color: white;
+  padding-left: 2px;
+  padding-right: 2px;
+  z-index: 3;
+  transition: opacity 0.3s ease-in-out;
+}
+
+.jp-CodeMirrorEditor .remote-caret.hide-name > div {
+  transition-delay: 0.7s;
+  opacity: 0;
+}
+
+.jp-CodeMirrorEditor .remote-caret:hover > div {
+  opacity: 1;
+  transition-delay: 0s;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| RenderedText
+|----------------------------------------------------------------------------*/
+
+:root {
+  /* This is the padding value to fill the gaps between lines containing spans with background color. */
+  --jp-private-code-span-padding: calc(
+    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
+  );
+}
+
+.jp-RenderedText {
+  text-align: left;
+  padding-left: var(--jp-code-padding);
+  line-height: var(--jp-code-line-height);
+  font-family: var(--jp-code-font-family);
+}
+
+.jp-RenderedText pre,
+.jp-RenderedJavaScript pre,
+.jp-RenderedHTMLCommon pre {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-code-font-size);
+  border: none;
+  margin: 0px;
+  padding: 0px;
+}
+
+.jp-RenderedText pre a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+.jp-RenderedText pre a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+.jp-RenderedText pre a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* console foregrounds and backgrounds */
+.jp-RenderedText pre .ansi-black-fg {
+  color: #3e424d;
+}
+.jp-RenderedText pre .ansi-red-fg {
+  color: #e75c58;
+}
+.jp-RenderedText pre .ansi-green-fg {
+  color: #00a250;
+}
+.jp-RenderedText pre .ansi-yellow-fg {
+  color: #ddb62b;
+}
+.jp-RenderedText pre .ansi-blue-fg {
+  color: #208ffb;
+}
+.jp-RenderedText pre .ansi-magenta-fg {
+  color: #d160c4;
+}
+.jp-RenderedText pre .ansi-cyan-fg {
+  color: #60c6c8;
+}
+.jp-RenderedText pre .ansi-white-fg {
+  color: #c5c1b4;
+}
+
+.jp-RenderedText pre .ansi-black-bg {
+  background-color: #3e424d;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-red-bg {
+  background-color: #e75c58;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-green-bg {
+  background-color: #00a250;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-yellow-bg {
+  background-color: #ddb62b;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-blue-bg {
+  background-color: #208ffb;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-magenta-bg {
+  background-color: #d160c4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-cyan-bg {
+  background-color: #60c6c8;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-white-bg {
+  background-color: #c5c1b4;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-black-intense-fg {
+  color: #282c36;
+}
+.jp-RenderedText pre .ansi-red-intense-fg {
+  color: #b22b31;
+}
+.jp-RenderedText pre .ansi-green-intense-fg {
+  color: #007427;
+}
+.jp-RenderedText pre .ansi-yellow-intense-fg {
+  color: #b27d12;
+}
+.jp-RenderedText pre .ansi-blue-intense-fg {
+  color: #0065ca;
+}
+.jp-RenderedText pre .ansi-magenta-intense-fg {
+  color: #a03196;
+}
+.jp-RenderedText pre .ansi-cyan-intense-fg {
+  color: #258f8f;
+}
+.jp-RenderedText pre .ansi-white-intense-fg {
+  color: #a1a6b2;
+}
+
+.jp-RenderedText pre .ansi-black-intense-bg {
+  background-color: #282c36;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-red-intense-bg {
+  background-color: #b22b31;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-green-intense-bg {
+  background-color: #007427;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-yellow-intense-bg {
+  background-color: #b27d12;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-blue-intense-bg {
+  background-color: #0065ca;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-magenta-intense-bg {
+  background-color: #a03196;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-cyan-intense-bg {
+  background-color: #258f8f;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+.jp-RenderedText pre .ansi-white-intense-bg {
+  background-color: #a1a6b2;
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-default-inverse-fg {
+  color: var(--jp-ui-inverse-font-color0);
+}
+.jp-RenderedText pre .ansi-default-inverse-bg {
+  background-color: var(--jp-inverse-layout-color0);
+  padding: var(--jp-private-code-span-padding) 0;
+}
+
+.jp-RenderedText pre .ansi-bold {
+  font-weight: bold;
+}
+.jp-RenderedText pre .ansi-underline {
+  text-decoration: underline;
+}
+
+.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
+  background: var(--jp-rendermime-error-background);
+  padding-top: var(--jp-code-padding);
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedLatex
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedLatex {
+  color: var(--jp-content-font-color1);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+}
+
+/* Left-justify outputs.*/
+.jp-OutputArea-output.jp-RenderedLatex {
+  padding: var(--jp-code-padding);
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| RenderedHTML
+|----------------------------------------------------------------------------*/
+
+.jp-RenderedHTMLCommon {
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-content-font-family);
+  font-size: var(--jp-content-font-size1);
+  line-height: var(--jp-content-line-height);
+  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
+  padding-right: 20px;
+}
+
+.jp-RenderedHTMLCommon em {
+  font-style: italic;
+}
+
+.jp-RenderedHTMLCommon strong {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon u {
+  text-decoration: underline;
+}
+
+.jp-RenderedHTMLCommon a:link {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:hover {
+  text-decoration: underline;
+  color: var(--jp-content-link-color);
+}
+
+.jp-RenderedHTMLCommon a:visited {
+  text-decoration: none;
+  color: var(--jp-content-link-color);
+}
+
+/* Headings */
+
+.jp-RenderedHTMLCommon h1,
+.jp-RenderedHTMLCommon h2,
+.jp-RenderedHTMLCommon h3,
+.jp-RenderedHTMLCommon h4,
+.jp-RenderedHTMLCommon h5,
+.jp-RenderedHTMLCommon h6 {
+  line-height: var(--jp-content-heading-line-height);
+  font-weight: var(--jp-content-heading-font-weight);
+  font-style: normal;
+  margin: var(--jp-content-heading-margin-top) 0
+    var(--jp-content-heading-margin-bottom) 0;
+}
+
+.jp-RenderedHTMLCommon h1:first-child,
+.jp-RenderedHTMLCommon h2:first-child,
+.jp-RenderedHTMLCommon h3:first-child,
+.jp-RenderedHTMLCommon h4:first-child,
+.jp-RenderedHTMLCommon h5:first-child,
+.jp-RenderedHTMLCommon h6:first-child {
+  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
+}
+
+.jp-RenderedHTMLCommon h1:last-child,
+.jp-RenderedHTMLCommon h2:last-child,
+.jp-RenderedHTMLCommon h3:last-child,
+.jp-RenderedHTMLCommon h4:last-child,
+.jp-RenderedHTMLCommon h5:last-child,
+.jp-RenderedHTMLCommon h6:last-child {
+  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
+}
+
+.jp-RenderedHTMLCommon h1 {
+  font-size: var(--jp-content-font-size5);
+}
+
+.jp-RenderedHTMLCommon h2 {
+  font-size: var(--jp-content-font-size4);
+}
+
+.jp-RenderedHTMLCommon h3 {
+  font-size: var(--jp-content-font-size3);
+}
+
+.jp-RenderedHTMLCommon h4 {
+  font-size: var(--jp-content-font-size2);
+}
+
+.jp-RenderedHTMLCommon h5 {
+  font-size: var(--jp-content-font-size1);
+}
+
+.jp-RenderedHTMLCommon h6 {
+  font-size: var(--jp-content-font-size0);
+}
+
+/* Lists */
+
+.jp-RenderedHTMLCommon ul:not(.list-inline),
+.jp-RenderedHTMLCommon ol:not(.list-inline) {
+  padding-left: 2em;
+}
+
+.jp-RenderedHTMLCommon ul {
+  list-style: disc;
+}
+
+.jp-RenderedHTMLCommon ul ul {
+  list-style: square;
+}
+
+.jp-RenderedHTMLCommon ul ul ul {
+  list-style: circle;
+}
+
+.jp-RenderedHTMLCommon ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol ol {
+  list-style: upper-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol {
+  list-style: lower-alpha;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol {
+  list-style: lower-roman;
+}
+
+.jp-RenderedHTMLCommon ol ol ol ol ol {
+  list-style: decimal;
+}
+
+.jp-RenderedHTMLCommon ol,
+.jp-RenderedHTMLCommon ul {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon ul ul,
+.jp-RenderedHTMLCommon ul ol,
+.jp-RenderedHTMLCommon ol ul,
+.jp-RenderedHTMLCommon ol ol {
+  margin-bottom: 0em;
+}
+
+.jp-RenderedHTMLCommon hr {
+  color: var(--jp-border-color2);
+  background-color: var(--jp-border-color1);
+  margin-top: 1em;
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon > pre {
+  margin: 1.5em 2em;
+}
+
+.jp-RenderedHTMLCommon pre,
+.jp-RenderedHTMLCommon code {
+  border: 0;
+  background-color: var(--jp-layout-color0);
+  color: var(--jp-content-font-color1);
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  line-height: var(--jp-code-line-height);
+  padding: 0;
+  white-space: pre-wrap;
+}
+
+.jp-RenderedHTMLCommon :not(pre) > code {
+  background-color: var(--jp-layout-color2);
+  padding: 1px 5px;
+}
+
+/* Tables */
+
+.jp-RenderedHTMLCommon table {
+  border-collapse: collapse;
+  border-spacing: 0;
+  border: none;
+  color: var(--jp-ui-font-color1);
+  font-size: 12px;
+  table-layout: fixed;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.jp-RenderedHTMLCommon thead {
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  vertical-align: bottom;
+}
+
+.jp-RenderedHTMLCommon td,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon tr {
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
+.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
+  max-width: none;
+}
+
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
+:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
+  text-align: right;
+}
+
+.jp-RenderedHTMLCommon th {
+  font-weight: bold;
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
+  background: var(--jp-layout-color0);
+}
+
+.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
+  background: var(--jp-rendermime-table-row-background);
+}
+
+.jp-RenderedHTMLCommon tbody tr:hover {
+  background: var(--jp-rendermime-table-row-hover-background);
+}
+
+.jp-RenderedHTMLCommon table {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon p {
+  text-align: left;
+  margin: 0px;
+}
+
+.jp-RenderedHTMLCommon p {
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon img {
+  -moz-force-broken-image-icon: 1;
+}
+
+/* Restrict to direct children as other images could be nested in other content. */
+.jp-RenderedHTMLCommon > img {
+  display: block;
+  margin-left: 0;
+  margin-right: 0;
+  margin-bottom: 1em;
+}
+
+/* Change color behind transparent images if they need it... */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
+  background-color: var(--jp-inverse-layout-color1);
+}
+/* ...or leave it untouched if they don't */
+[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-dark-background {
+}
+[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-light-background {
+}
+
+.jp-RenderedHTMLCommon img,
+.jp-RenderedImage img,
+.jp-RenderedHTMLCommon svg,
+.jp-RenderedSVG svg {
+  max-width: 100%;
+  height: auto;
+}
+
+.jp-RenderedHTMLCommon img.jp-mod-unconfined,
+.jp-RenderedImage img.jp-mod-unconfined,
+.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
+.jp-RenderedSVG svg.jp-mod-unconfined {
+  max-width: none;
+}
+
+.jp-RenderedHTMLCommon .alert {
+  padding: var(--jp-notebook-padding);
+  border: var(--jp-border-width) solid transparent;
+  border-radius: var(--jp-border-radius);
+  margin-bottom: 1em;
+}
+
+.jp-RenderedHTMLCommon .alert-info {
+  color: var(--jp-info-color0);
+  background-color: var(--jp-info-color3);
+  border-color: var(--jp-info-color2);
+}
+.jp-RenderedHTMLCommon .alert-info hr {
+  border-color: var(--jp-info-color3);
+}
+.jp-RenderedHTMLCommon .alert-info > p:last-child,
+.jp-RenderedHTMLCommon .alert-info > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-warning {
+  color: var(--jp-warn-color0);
+  background-color: var(--jp-warn-color3);
+  border-color: var(--jp-warn-color2);
+}
+.jp-RenderedHTMLCommon .alert-warning hr {
+  border-color: var(--jp-warn-color3);
+}
+.jp-RenderedHTMLCommon .alert-warning > p:last-child,
+.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-success {
+  color: var(--jp-success-color0);
+  background-color: var(--jp-success-color3);
+  border-color: var(--jp-success-color2);
+}
+.jp-RenderedHTMLCommon .alert-success hr {
+  border-color: var(--jp-success-color3);
+}
+.jp-RenderedHTMLCommon .alert-success > p:last-child,
+.jp-RenderedHTMLCommon .alert-success > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon .alert-danger {
+  color: var(--jp-error-color0);
+  background-color: var(--jp-error-color3);
+  border-color: var(--jp-error-color2);
+}
+.jp-RenderedHTMLCommon .alert-danger hr {
+  border-color: var(--jp-error-color3);
+}
+.jp-RenderedHTMLCommon .alert-danger > p:last-child,
+.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
+  margin-bottom: 0;
+}
+
+.jp-RenderedHTMLCommon blockquote {
+  margin: 1em 2em;
+  padding: 0 1em;
+  border-left: 5px solid var(--jp-border-color2);
+}
+
+a.jp-InternalAnchorLink {
+  visibility: hidden;
+  margin-left: 8px;
+  color: var(--md-blue-800);
+}
+
+h1:hover .jp-InternalAnchorLink,
+h2:hover .jp-InternalAnchorLink,
+h3:hover .jp-InternalAnchorLink,
+h4:hover .jp-InternalAnchorLink,
+h5:hover .jp-InternalAnchorLink,
+h6:hover .jp-InternalAnchorLink {
+  visibility: visible;
+}
+
+.jp-RenderedHTMLCommon kbd {
+  background-color: var(--jp-rendermime-table-row-background);
+  border: 1px solid var(--jp-border-color0);
+  border-bottom-color: var(--jp-border-color2);
+  border-radius: 3px;
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  display: inline-block;
+  font-size: 0.8em;
+  line-height: 1em;
+  padding: 0.2em 0.5em;
+}
+
+/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
+ * At the bottom of cells this is a bit too much as there is also spacing
+ * between cells. Going all the way to 0 gets too tight between markdown and
+ * code cells.
+ */
+.jp-RenderedHTMLCommon > *:last-child {
+  margin-bottom: 0.5em;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-MimeDocument {
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-filebrowser-button-height: 28px;
+  --jp-private-filebrowser-button-width: 48px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser {
+  display: flex;
+  flex-direction: column;
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+   * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  border-bottom: none;
+  height: auto;
+  margin: var(--jp-toolbar-header-margin);
+  box-shadow: none;
+}
+
+.jp-BreadCrumbs {
+  flex: 0 0 auto;
+  margin: 8px 12px 8px 12px;
+}
+
+.jp-BreadCrumbs-item {
+  margin: 0px 2px;
+  padding: 0px 2px;
+  border-radius: var(--jp-border-radius);
+  cursor: pointer;
+}
+
+.jp-BreadCrumbs-item:hover {
+  background-color: var(--jp-layout-color2);
+}
+
+.jp-BreadCrumbs-item:first-child {
+  margin-left: 0px;
+}
+
+.jp-BreadCrumbs-item.jp-mod-dropTarget {
+  background-color: var(--jp-brand-color2);
+  opacity: 0.7;
+}
+
+/*-----------------------------------------------------------------------------
+| Buttons
+|----------------------------------------------------------------------------*/
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  padding: 0px;
+  margin: 8px 12px 0px 12px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar {
+  justify-content: flex-start;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
+  flex: 0 0 auto;
+  padding-left: 0px;
+  padding-right: 2px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
+  width: 40px;
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent {
+  width: 72px;
+  background: var(--jp-brand-color1);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent:focus-visible {
+  background-color: var(--jp-brand-color0);
+}
+
+.jp-FileBrowser-toolbar.jp-Toolbar
+  .jp-Toolbar-item:first-child
+  .jp-ToolbarButtonComponent
+  .jp-icon3 {
+  fill: white;
+}
+
+/*-----------------------------------------------------------------------------
+| Other styles
+|----------------------------------------------------------------------------*/
+
+.jp-FileDialog.jp-mod-conflict input {
+  color: var(--jp-error-color1);
+}
+
+.jp-FileDialog .jp-new-name-title {
+  margin-top: 12px;
+}
+
+.jp-LastModified-hidden {
+  display: none;
+}
+
+.jp-FileBrowser-filterBox {
+  padding: 0px;
+  flex: 0 0 auto;
+  margin: 8px 12px 0px 12px;
+}
+
+/*-----------------------------------------------------------------------------
+| DirListing
+|----------------------------------------------------------------------------*/
+
+.jp-DirListing {
+  flex: 1 1 auto;
+  display: flex;
+  flex-direction: column;
+  outline: 0;
+}
+
+.jp-DirListing:focus-visible {
+  border: 1px solid var(--jp-brand-color1);
+}
+
+.jp-DirListing-header {
+  flex: 0 0 auto;
+  display: flex;
+  flex-direction: row;
+  overflow: hidden;
+  border-top: var(--jp-border-width) solid var(--jp-border-color2);
+  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
+  box-shadow: var(--jp-toolbar-box-shadow);
+  z-index: 2;
+}
+
+.jp-DirListing-headerItem {
+  padding: 4px 12px 2px 12px;
+  font-weight: 500;
+}
+
+.jp-DirListing-headerItem:hover {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-headerItem.jp-id-name {
+  flex: 1 0 84px;
+}
+
+.jp-DirListing-headerItem.jp-id-modified {
+  flex: 0 0 112px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+}
+
+.jp-id-narrow {
+  display: none;
+  flex: 0 0 5px;
+  padding: 4px 4px;
+  border-left: var(--jp-border-width) solid var(--jp-border-color2);
+  text-align: right;
+  color: var(--jp-border-color2);
+}
+
+.jp-DirListing-narrow .jp-id-narrow {
+  display: block;
+}
+
+.jp-DirListing-narrow .jp-id-modified,
+.jp-DirListing-narrow .jp-DirListing-itemModified {
+  display: none;
+}
+
+.jp-DirListing-headerItem.jp-mod-selected {
+  font-weight: 600;
+}
+
+/* increase specificity to override bundled default */
+.jp-DirListing-content {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-DirListing-content mark {
+  color: var(--jp-ui-font-color0);
+  background-color: transparent;
+  font-weight: bold;
+}
+
+.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
+  color: var(--jp-ui-inverse-font-color0);
+}
+
+/* Style the directory listing content when a user drops a file to upload */
+.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
+  outline: 5px dashed rgba(128, 128, 128, 0.5);
+  outline-offset: -10px;
+  cursor: copy;
+}
+
+.jp-DirListing-item {
+  display: flex;
+  flex-direction: row;
+  padding: 4px 12px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+.jp-DirListing-item[data-is-dot] {
+  opacity: 75%;
+}
+
+.jp-DirListing-item.jp-mod-selected {
+  color: var(--jp-ui-inverse-font-color1);
+  background: var(--jp-brand-color1);
+}
+
+.jp-DirListing-item.jp-mod-dropTarget {
+  background: var(--jp-brand-color3);
+}
+
+.jp-DirListing-item:hover:not(.jp-mod-selected) {
+  background: var(--jp-layout-color2);
+}
+
+.jp-DirListing-itemIcon {
+  flex: 0 0 20px;
+  margin-right: 4px;
+}
+
+.jp-DirListing-itemText {
+  flex: 1 0 64px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  user-select: none;
+}
+
+.jp-DirListing-itemModified {
+  flex: 0 0 125px;
+  text-align: right;
+}
+
+.jp-DirListing-editor {
+  flex: 1 0 64px;
+  outline: none;
+  border: none;
+}
+
+.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
+  color: var(--jp-success-color1);
+  content: '\25CF';
+  font-size: 8px;
+  position: absolute;
+  left: -8px;
+}
+
+.jp-DirListing-item.jp-mod-running.jp-mod-selected
+  .jp-DirListing-itemIcon:before {
+  color: var(--jp-ui-inverse-font-color1);
+}
+
+.jp-DirListing-item.lm-mod-drag-image,
+.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
+  font-size: var(--jp-ui-font-size1);
+  padding-left: 4px;
+  margin-left: 4px;
+  width: 160px;
+  background-color: var(--jp-ui-inverse-font-color2);
+  box-shadow: var(--jp-elevation-z2);
+  border-radius: 0px;
+  color: var(--jp-ui-font-color1);
+  transform: translateX(-40%) translateY(-58%);
+}
+
+.jp-DirListing-deadSpace {
+  flex: 1 1 auto;
+  margin: 0;
+  padding: 0;
+  list-style-type: none;
+  overflow: auto;
+  background-color: var(--jp-layout-color1);
+}
+
+.jp-Document {
+  min-width: 120px;
+  min-height: 120px;
+  outline: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+}
+
+/*-----------------------------------------------------------------------------
+| Main OutputArea
+| OutputArea has a list of Outputs
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea {
+  overflow-y: auto;
+}
+
+.jp-OutputArea-child {
+  display: flex;
+  flex-direction: row;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child {
+  flex-direction: column;
+}
+
+.jp-OutputPrompt {
+  flex: 0 0 var(--jp-cell-prompt-width);
+  color: var(--jp-cell-outprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+body[data-format='mobile'] .jp-OutputPrompt {
+  flex: 0 0 auto;
+  text-align: left;
+}
+
+.jp-OutputArea-output {
+  height: auto;
+  overflow: auto;
+  user-select: text;
+  -moz-user-select: text;
+  -webkit-user-select: text;
+  -ms-user-select: text;
+}
+
+.jp-OutputArea-child .jp-OutputArea-output {
+  flex-grow: 1;
+  flex-shrink: 1;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
+  margin-left: var(--jp-notebook-padding);
+}
+
+/**
+ * Isolated output.
+ */
+.jp-OutputArea-output.jp-mod-isolated {
+  width: 100%;
+  display: block;
+}
+
+/*
+When drag events occur, `p-mod-override-cursor` is added to the body.
+Because iframes steal all cursor events, the following two rules are necessary
+to suppress pointer events while resize drags are occurring. There may be a
+better solution to this problem.
+*/
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
+  position: relative;
+}
+
+body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: transparent;
+}
+
+/* pre */
+
+.jp-OutputArea-output pre {
+  border: none;
+  margin: 0px;
+  padding: 0px;
+  overflow-x: auto;
+  overflow-y: auto;
+  word-break: break-all;
+  word-wrap: break-word;
+  white-space: pre-wrap;
+}
+
+/* tables */
+
+.jp-OutputArea-output.jp-RenderedHTMLCommon table {
+  margin-left: 0;
+  margin-right: 0;
+}
+
+/* description lists */
+
+.jp-OutputArea-output dl,
+.jp-OutputArea-output dt,
+.jp-OutputArea-output dd {
+  display: block;
+}
+
+.jp-OutputArea-output dl {
+  width: 100%;
+  overflow: hidden;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dt {
+  font-weight: bold;
+  float: left;
+  width: 20%;
+  padding: 0;
+  margin: 0;
+}
+
+.jp-OutputArea-output dd {
+  float: left;
+  width: 80%;
+  padding: 0;
+  margin: 0;
+}
+
+/* Hide the gutter in case of
+ *  - nested output areas (e.g. in the case of output widgets)
+ *  - mirrored output areas
+ */
+.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
+  display: none;
+}
+
+/*-----------------------------------------------------------------------------
+| executeResult is added to any Output-result for the display of the object
+| returned by a cell
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  margin-left: 0px;
+  flex: 1 1 auto;
+}
+
+/* Text output with the Out[] prompt needs a top padding to match the
+ * alignment of the Out[] prompt itself.
+ */
+.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
+  padding-top: var(--jp-code-padding);
+  border-top: var(--jp-border-width) solid transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| The Stdin output
+|----------------------------------------------------------------------------*/
+
+.jp-OutputArea-stdin {
+  line-height: var(--jp-code-line-height);
+  padding-top: var(--jp-code-padding);
+  display: flex;
+}
+
+.jp-Stdin-prompt {
+  color: var(--jp-content-font-color0);
+  padding-right: var(--jp-code-padding);
+  vertical-align: baseline;
+  flex: 0 0 auto;
+}
+
+.jp-Stdin-input {
+  font-family: var(--jp-code-font-family);
+  font-size: inherit;
+  color: inherit;
+  background-color: inherit;
+  width: 42%;
+  min-width: 200px;
+  /* make sure input baseline aligns with prompt */
+  vertical-align: baseline;
+  /* padding + margin = 0.5em between prompt and cursor */
+  padding: 0em 0.25em;
+  margin: 0em 0.25em;
+  flex: 0 0 70%;
+}
+
+.jp-Stdin-input:focus {
+  box-shadow: none;
+}
+
+/*-----------------------------------------------------------------------------
+| Output Area View
+|----------------------------------------------------------------------------*/
+
+.jp-LinkedOutputView .jp-OutputArea {
+  height: 100%;
+  display: block;
+}
+
+.jp-LinkedOutputView .jp-OutputArea-output:only-child {
+  height: 100%;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+.jp-Collapser {
+  flex: 0 0 var(--jp-cell-collapser-width);
+  padding: 0px;
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+  border-radius: var(--jp-border-radius);
+  opacity: 1;
+}
+
+.jp-Collapser-child {
+  display: block;
+  width: 100%;
+  box-sizing: border-box;
+  /* height: 100% doesn't work because the height of its parent is computed from content */
+  position: absolute;
+  top: 0px;
+  bottom: 0px;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Header/Footer
+|----------------------------------------------------------------------------*/
+
+/* Hidden by zero height by default */
+.jp-CellHeader,
+.jp-CellFooter {
+  height: 0px;
+  width: 100%;
+  padding: 0px;
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Input
+|----------------------------------------------------------------------------*/
+
+/* All input areas */
+.jp-InputArea {
+  display: flex;
+  flex-direction: row;
+  overflow: hidden;
+}
+
+body[data-format='mobile'] .jp-InputArea {
+  flex-direction: column;
+}
+
+.jp-InputArea-editor {
+  flex: 1 1 auto;
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  /* This is the non-active, default styling */
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  border-radius: 0px;
+  background: var(--jp-cell-editor-background);
+}
+
+body[data-format='mobile'] .jp-InputArea-editor {
+  margin-left: var(--jp-notebook-padding);
+}
+
+.jp-InputPrompt {
+  flex: 0 0 var(--jp-cell-prompt-width);
+  color: var(--jp-cell-inprompt-font-color);
+  font-family: var(--jp-cell-prompt-font-family);
+  padding: var(--jp-code-padding);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  opacity: var(--jp-cell-prompt-opacity);
+  line-height: var(--jp-code-line-height);
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+  opacity: var(--jp-cell-prompt-opacity);
+  /* Right align prompt text, don't wrap to handle large prompt numbers */
+  text-align: right;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  /* Disable text selection */
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+body[data-format='mobile'] .jp-InputPrompt {
+  flex: 0 0 auto;
+  text-align: left;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Placeholder {
+  display: flex;
+  flex-direction: row;
+  flex: 1 1 auto;
+}
+
+.jp-Placeholder-prompt {
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content {
+  flex: 1 1 auto;
+  border: none;
+  background: transparent;
+  height: 20px;
+  box-sizing: border-box;
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon {
+  width: 32px;
+  height: 16px;
+  border: 1px solid transparent;
+  border-radius: var(--jp-border-radius);
+}
+
+.jp-Placeholder-content .jp-MoreHorizIcon:hover {
+  border: 1px solid var(--jp-border-color1);
+  box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25);
+  background-color: var(--jp-layout-color0);
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-cell-scrolling-output-offset: 5px;
+}
+
+/*-----------------------------------------------------------------------------
+| Cell
+|----------------------------------------------------------------------------*/
+
+.jp-Cell {
+  padding: var(--jp-cell-padding);
+  margin: 0px;
+  border: none;
+  outline: none;
+  background: transparent;
+}
+
+/*-----------------------------------------------------------------------------
+| Common input/output
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-inputWrapper,
+.jp-Cell-outputWrapper {
+  display: flex;
+  flex-direction: row;
+  padding: 0px;
+  margin: 0px;
+  /* Added to reveal the box-shadow on the input and output collapsers. */
+  overflow: visible;
+}
+
+/* Only input/output areas inside cells */
+.jp-Cell-inputArea,
+.jp-Cell-outputArea {
+  flex: 1 1 auto;
+}
+
+/*-----------------------------------------------------------------------------
+| Collapser
+|----------------------------------------------------------------------------*/
+
+/* Make the output collapser disappear when there is not output, but do so
+ * in a manner that leaves it in the layout and preserves its width.
+ */
+.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
+  border: none !important;
+  background: transparent !important;
+}
+
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
+  min-height: var(--jp-cell-collapser-min-height);
+}
+
+/*-----------------------------------------------------------------------------
+| Output
+|----------------------------------------------------------------------------*/
+
+/* Put a space between input and output when there IS output */
+.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
+  margin-top: 5px;
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
+  overflow-y: auto;
+  max-height: 200px;
+  box-shadow: inset 0 0 6px 2px rgba(0, 0, 0, 0.3);
+  margin-left: var(--jp-private-cell-scrolling-output-offset);
+}
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  flex: 0 0
+    calc(
+      var(--jp-cell-prompt-width) -
+        var(--jp-private-cell-scrolling-output-offset)
+    );
+}
+
+/*-----------------------------------------------------------------------------
+| CodeCell
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| MarkdownCell
+|----------------------------------------------------------------------------*/
+
+.jp-MarkdownOutput {
+  flex: 1 1 auto;
+  margin-top: 0;
+  margin-bottom: 0;
+  padding-left: var(--jp-code-padding);
+}
+
+.jp-MarkdownOutput.jp-RenderedHTMLCommon {
+  overflow: auto;
+}
+
+.jp-showHiddenCellsButton {
+  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
+  margin-top: var(--jp-code-padding);
+  border: 1px solid var(--jp-border-color2);
+  background-color: var(--jp-border-color3) !important;
+  color: var(--jp-content-font-color0) !important;
+}
+
+.jp-showHiddenCellsButton:hover {
+  background-color: var(--jp-border-color2) !important;
+}
+
+.jp-collapseHeadingButton {
+  display: none;
+}
+
+.jp-MarkdownCell:hover .jp-collapseHeadingButton {
+  display: flex;
+  min-height: var(--jp-cell-collapser-min-height);
+  position: absolute;
+  right: 0;
+  top: 0;
+  bottom: 0;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Variables
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+
+/*-----------------------------------------------------------------------------
+| Styles
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel-toolbar {
+  padding: 2px;
+}
+
+.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
+  border: none;
+  box-shadow: none;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown select {
+  height: 24px;
+  font-size: var(--jp-ui-font-size1);
+  line-height: 14px;
+  border-radius: 0;
+  display: block;
+}
+
+.jp-Notebook-toolbarCellTypeDropdown span {
+  top: 5px !important;
+}
+
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Private CSS variables
+|----------------------------------------------------------------------------*/
+
+:root {
+  --jp-private-notebook-dragImage-width: 304px;
+  --jp-private-notebook-dragImage-height: 36px;
+  --jp-private-notebook-selected-color: var(--md-blue-400);
+  --jp-private-notebook-active-color: var(--md-green-400);
+}
+
+/*-----------------------------------------------------------------------------
+| Imports
+|----------------------------------------------------------------------------*/
+
+/*-----------------------------------------------------------------------------
+| Notebook
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookPanel {
+  display: block;
+  height: 100%;
+}
+
+.jp-NotebookPanel.jp-Document {
+  min-width: 240px;
+  min-height: 120px;
+}
+
+.jp-Notebook {
+  padding: var(--jp-notebook-padding);
+  outline: none;
+  overflow: auto;
+  background: var(--jp-layout-color0);
+}
+
+.jp-Notebook.jp-mod-scrollPastEnd::after {
+  display: block;
+  content: '';
+  min-height: var(--jp-notebook-scroll-padding);
+}
+
+.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
+  contain: strict;
+}
+
+.jp-Notebook-render * {
+  contain: none !important;
+}
+
+.jp-Notebook .jp-Cell {
+  overflow: visible;
+}
+
+.jp-Notebook .jp-Cell .jp-InputPrompt {
+  cursor: move;
+  float: left;
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook state related styling
+|
+| The notebook and cells each have states, here are the possibilities:
+|
+| - Notebook
+|   - Command
+|   - Edit
+| - Cell
+|   - None
+|   - Active (only one can be active)
+|   - Selected (the cells actions are applied to)
+|   - Multiselected (when multiple selected, the cursor)
+|   - No outputs
+|----------------------------------------------------------------------------*/
+
+/* Command or edit modes */
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
+  opacity: var(--jp-cell-prompt-not-active-opacity);
+  color: var(--jp-cell-prompt-not-active-font-color);
+}
+
+/* cell is active */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
+  background: var(--jp-brand-color1);
+}
+
+/* cell is dirty */
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
+  color: var(--jp-warn-color1);
+}
+.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt:before {
+  color: var(--jp-warn-color1);
+  content: '•';
+}
+
+.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
+  background: var(--jp-warn-color1);
+}
+
+/* collapser is hovered */
+.jp-Notebook .jp-Cell .jp-Collapser:hover {
+  box-shadow: var(--jp-elevation-z2);
+  background: var(--jp-brand-color1);
+  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
+}
+
+/* cell is active and collapser is hovered */
+.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
+  background: var(--jp-brand-color0);
+  opacity: 1;
+}
+
+/* Command mode */
+
+.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
+  background: var(--jp-notebook-multiselected-color);
+}
+
+.jp-Notebook.jp-mod-commandMode
+  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
+  background: transparent;
+}
+
+/* Edit mode */
+
+.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
+  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
+  box-shadow: var(--jp-input-box-shadow);
+  background-color: var(--jp-cell-editor-active-background);
+}
+
+/*-----------------------------------------------------------------------------
+| Notebook drag and drop
+|----------------------------------------------------------------------------*/
+
+.jp-Notebook-cell.jp-mod-dropSource {
+  opacity: 0.5;
+}
+
+.jp-Notebook-cell.jp-mod-dropTarget,
+.jp-Notebook.jp-mod-commandMode
+  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
+  border-top-color: var(--jp-private-notebook-selected-color);
+  border-top-style: solid;
+  border-top-width: 2px;
+}
+
+.jp-dragImage {
+  display: block;
+  flex-direction: row;
+  width: var(--jp-private-notebook-dragImage-width);
+  height: var(--jp-private-notebook-dragImage-height);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background);
+  overflow: visible;
+}
+
+.jp-dragImage-singlePrompt {
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
+}
+
+.jp-dragImage .jp-dragImage-content {
+  flex: 1 1 auto;
+  z-index: 2;
+  font-size: var(--jp-code-font-size);
+  font-family: var(--jp-code-font-family);
+  line-height: var(--jp-code-line-height);
+  padding: var(--jp-code-padding);
+  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
+  background: var(--jp-cell-editor-background-color);
+  color: var(--jp-content-font-color3);
+  text-align: left;
+  margin: 4px 4px 4px 0px;
+}
+
+.jp-dragImage .jp-dragImage-prompt {
+  flex: 0 0 auto;
+  min-width: 36px;
+  color: var(--jp-cell-inprompt-font-color);
+  padding: var(--jp-code-padding);
+  padding-left: 12px;
+  font-family: var(--jp-cell-prompt-font-family);
+  letter-spacing: var(--jp-cell-prompt-letter-spacing);
+  line-height: 1.9;
+  font-size: var(--jp-code-font-size);
+  border: var(--jp-border-width) solid transparent;
+}
+
+.jp-dragImage-multipleBack {
+  z-index: -1;
+  position: absolute;
+  height: 32px;
+  width: 300px;
+  top: 8px;
+  left: 8px;
+  background: var(--jp-layout-color2);
+  border: var(--jp-border-width) solid var(--jp-input-border-color);
+  box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
+}
+
+/*-----------------------------------------------------------------------------
+| Cell toolbar
+|----------------------------------------------------------------------------*/
+
+.jp-NotebookTools {
+  display: block;
+  min-width: var(--jp-sidebar-min-width);
+  color: var(--jp-ui-font-color1);
+  background: var(--jp-layout-color1);
+  /* This is needed so that all font sizing of children done in ems is
+    * relative to this base size */
+  font-size: var(--jp-ui-font-size1);
+  overflow: auto;
+}
+
+.jp-NotebookTools-tool {
+  padding: 0px 12px 0 12px;
+}
+
+.jp-ActiveCellTool {
+  padding: 12px;
+  background-color: var(--jp-layout-color1);
+  border-top: none !important;
+}
+
+.jp-ActiveCellTool .jp-InputArea-prompt {
+  flex: 0 0 auto;
+  padding-left: 0px;
+}
+
+.jp-ActiveCellTool .jp-InputArea-editor {
+  flex: 1 1 auto;
+  background: var(--jp-cell-editor-background);
+  border-color: var(--jp-cell-editor-border-color);
+}
+
+.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
+  background: transparent;
+}
+
+.jp-MetadataEditorTool {
+  flex-direction: column;
+  padding: 12px 0px 12px 0px;
+}
+
+.jp-RankedPanel > :not(:first-child) {
+  margin-top: 12px;
+}
+
+.jp-KeySelector select.jp-mod-styled {
+  font-size: var(--jp-ui-font-size1);
+  color: var(--jp-ui-font-color0);
+  border: var(--jp-border-width) solid var(--jp-border-color1);
+}
+
+.jp-KeySelector label,
+.jp-MetadataEditorTool label {
+  line-height: 1.4;
+}
+
+.jp-NotebookTools .jp-select-wrapper {
+  margin-top: 4px;
+  margin-bottom: 0px;
+}
+
+.jp-NotebookTools .jp-Collapse {
+  margin-top: 16px;
+}
+
+/*-----------------------------------------------------------------------------
+| Presentation Mode (.jp-mod-presentationMode)
+|----------------------------------------------------------------------------*/
+
+.jp-mod-presentationMode .jp-Notebook {
+  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
+  --jp-code-font-size: var(--jp-code-presentation-font-size);
+}
+
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
+.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
+  flex: 0 0 110px;
+}
+
+/*-----------------------------------------------------------------------------
+| Placeholder
+|----------------------------------------------------------------------------*/
+
+.jp-Cell-Placeholder {
+  padding-left: 55px;
+}
+
+.jp-Cell-Placeholder-wrapper {
+  background: #fff;
+  border: 1px solid;
+  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
+  border-radius: 4px;
+  -webkit-border-radius: 4px;
+  margin: 10px 15px;
+}
+
+.jp-Cell-Placeholder-wrapper-inner {
+  padding: 15px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body {
+  background-repeat: repeat;
+  background-size: 50% auto;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  background: #f6f7f8;
+  background-image: -webkit-linear-gradient(
+    left,
+    #f6f7f8 0%,
+    #edeef1 20%,
+    #f6f7f8 40%,
+    #f6f7f8 100%
+  );
+  background-repeat: no-repeat;
+  background-size: 800px 104px;
+  height: 104px;
+  position: relative;
+}
+
+.jp-Cell-Placeholder-wrapper-body div {
+  position: absolute;
+  right: 15px;
+  left: 15px;
+  top: 15px;
+}
+
+div.jp-Cell-Placeholder-h1 {
+  top: 20px;
+  height: 20px;
+  left: 15px;
+  width: 150px;
+}
+
+div.jp-Cell-Placeholder-h2 {
+  left: 15px;
+  top: 50px;
+  height: 10px;
+  width: 100px;
+}
+
+div.jp-Cell-Placeholder-content-1,
+div.jp-Cell-Placeholder-content-2,
+div.jp-Cell-Placeholder-content-3 {
+  left: 15px;
+  right: 15px;
+  height: 10px;
+}
+
+div.jp-Cell-Placeholder-content-1 {
+  top: 100px;
+}
+
+div.jp-Cell-Placeholder-content-2 {
+  top: 120px;
+}
+
+div.jp-Cell-Placeholder-content-3 {
+  top: 140px;
+}
+
+</style>
+
+    <style type="text/css">
+/*-----------------------------------------------------------------------------
+| Copyright (c) Jupyter Development Team.
+| Distributed under the terms of the Modified BSD License.
+|----------------------------------------------------------------------------*/
+
+/*
+The following CSS variables define the main, public API for styling JupyterLab.
+These variables should be used by all plugins wherever possible. In other
+words, plugins should not define custom colors, sizes, etc unless absolutely
+necessary. This enables users to change the visual theme of JupyterLab
+by changing these variables.
+
+Many variables appear in an ordered sequence (0,1,2,3). These sequences
+are designed to work well together, so for example, `--jp-border-color1` should
+be used with `--jp-layout-color1`. The numbers have the following meanings:
+
+* 0: super-primary, reserved for special emphasis
+* 1: primary, most important under normal situations
+* 2: secondary, next most important under normal situations
+* 3: tertiary, next most important under normal situations
+
+Throughout JupyterLab, we are mostly following principles from Google's
+Material Design when selecting colors. We are not, however, following
+all of MD as it is not optimized for dense, information rich UIs.
+*/
+
+:root {
+  /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+  --jp-shadow-base-lightness: 0;
+  --jp-shadow-umbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.2
+  );
+  --jp-shadow-penumbra-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.14
+  );
+  --jp-shadow-ambient-color: rgba(
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    var(--jp-shadow-base-lightness),
+    0.12
+  );
+  --jp-elevation-z0: none;
+  --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+    0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 3px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+    0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 5px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+    0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 10px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+    0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+    0px 1px 18px 0px var(--jp-shadow-ambient-color);
+  --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+    0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+    0px 3px 14px 2px var(--jp-shadow-ambient-color);
+  --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+    0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+    0px 5px 22px 4px var(--jp-shadow-ambient-color);
+  --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+    0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+    0px 6px 30px 5px var(--jp-shadow-ambient-color);
+  --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+    0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+    0px 8px 38px 7px var(--jp-shadow-ambient-color);
+  --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+    0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+    0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+  /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+  --jp-border-width: 1px;
+  --jp-border-color0: var(--md-grey-400);
+  --jp-border-color1: var(--md-grey-400);
+  --jp-border-color2: var(--md-grey-300);
+  --jp-border-color3: var(--md-grey-200);
+  --jp-border-radius: 2px;
+
+  /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-ui-font-scale-factor: 1.2;
+  --jp-ui-font-size0: 0.83333em;
+  --jp-ui-font-size1: 13px; /* Base font size */
+  --jp-ui-font-size2: 1.2em;
+  --jp-ui-font-size3: 1.44em;
+
+  --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
+    Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
+
+  /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+  /* Defaults use Material Design specification */
+  --jp-ui-font-color0: rgba(0, 0, 0, 1);
+  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+  /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+  /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+  --jp-content-line-height: 1.6;
+  --jp-content-font-scale-factor: 1.2;
+  --jp-content-font-size0: 0.83333em;
+  --jp-content-font-size1: 14px; /* Base font size */
+  --jp-content-font-size2: 1.2em;
+  --jp-content-font-size3: 1.44em;
+  --jp-content-font-size4: 1.728em;
+  --jp-content-font-size5: 2.0736em;
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-content-presentation-font-size1: 17px;
+
+  --jp-content-heading-line-height: 1;
+  --jp-content-heading-margin-top: 1.2em;
+  --jp-content-heading-margin-bottom: 0.8em;
+  --jp-content-heading-font-weight: 500;
+
+  /* Defaults use Material Design specification */
+  --jp-content-font-color0: rgba(0, 0, 0, 1);
+  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+  --jp-content-link-color: var(--md-blue-700);
+
+  --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
+    Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
+    'Segoe UI Symbol';
+
+  /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+  --jp-code-font-size: 13px;
+  --jp-code-line-height: 1.3077; /* 17px for 13px base */
+  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+  --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
+  --jp-code-font-family: var(--jp-code-font-family-default);
+
+  /* This gives a magnification of about 125% in presentation mode over normal. */
+  --jp-code-presentation-font-size: 16px;
+
+  /* may need to tweak cursor width if you change font size */
+  --jp-code-cursor-width0: 1.4px;
+  --jp-code-cursor-width1: 2px;
+  --jp-code-cursor-width2: 4px;
+
+  /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+  --jp-layout-color0: white;
+  --jp-layout-color1: white;
+  --jp-layout-color2: var(--md-grey-200);
+  --jp-layout-color3: var(--md-grey-400);
+  --jp-layout-color4: var(--md-grey-600);
+
+  /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+  --jp-inverse-layout-color0: #111111;
+  --jp-inverse-layout-color1: var(--md-grey-900);
+  --jp-inverse-layout-color2: var(--md-grey-800);
+  --jp-inverse-layout-color3: var(--md-grey-700);
+  --jp-inverse-layout-color4: var(--md-grey-600);
+
+  /* Brand/accent */
+
+  --jp-brand-color0: var(--md-blue-900);
+  --jp-brand-color1: var(--md-blue-700);
+  --jp-brand-color2: var(--md-blue-300);
+  --jp-brand-color3: var(--md-blue-100);
+  --jp-brand-color4: var(--md-blue-50);
+
+  --jp-accent-color0: var(--md-green-900);
+  --jp-accent-color1: var(--md-green-700);
+  --jp-accent-color2: var(--md-green-300);
+  --jp-accent-color3: var(--md-green-100);
+
+  /* State colors (warn, error, success, info) */
+
+  --jp-warn-color0: var(--md-orange-900);
+  --jp-warn-color1: var(--md-orange-700);
+  --jp-warn-color2: var(--md-orange-300);
+  --jp-warn-color3: var(--md-orange-100);
+
+  --jp-error-color0: var(--md-red-900);
+  --jp-error-color1: var(--md-red-700);
+  --jp-error-color2: var(--md-red-300);
+  --jp-error-color3: var(--md-red-100);
+
+  --jp-success-color0: var(--md-green-900);
+  --jp-success-color1: var(--md-green-700);
+  --jp-success-color2: var(--md-green-300);
+  --jp-success-color3: var(--md-green-100);
+
+  --jp-info-color0: var(--md-cyan-900);
+  --jp-info-color1: var(--md-cyan-700);
+  --jp-info-color2: var(--md-cyan-300);
+  --jp-info-color3: var(--md-cyan-100);
+
+  /* Cell specific styles */
+
+  --jp-cell-padding: 5px;
+
+  --jp-cell-collapser-width: 8px;
+  --jp-cell-collapser-min-height: 20px;
+  --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+  --jp-cell-editor-background: var(--md-grey-100);
+  --jp-cell-editor-border-color: var(--md-grey-300);
+  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-cell-editor-active-background: var(--jp-layout-color0);
+  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+  --jp-cell-prompt-width: 64px;
+  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+  --jp-cell-prompt-letter-spacing: 0px;
+  --jp-cell-prompt-opacity: 1;
+  --jp-cell-prompt-not-active-opacity: 0.5;
+  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+  /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+  --jp-cell-inprompt-font-color: #307fc1;
+  /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+  --jp-cell-outprompt-font-color: #bf5b3d;
+
+  /* Notebook specific styles */
+
+  --jp-notebook-padding: 10px;
+  --jp-notebook-select-background: var(--jp-layout-color1);
+  --jp-notebook-multiselected-color: var(--md-blue-50);
+
+  /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+  --jp-notebook-scroll-padding: calc(
+    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+  );
+
+  /* Rendermime styles */
+
+  --jp-rendermime-error-background: #fdd;
+  --jp-rendermime-table-row-background: var(--md-grey-100);
+  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+  /* Dialog specific styles */
+
+  --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+  /* Console specific styles */
+
+  --jp-console-padding: 10px;
+
+  /* Toolbar specific styles */
+
+  --jp-toolbar-border-color: var(--jp-border-color1);
+  --jp-toolbar-micro-height: 8px;
+  --jp-toolbar-background: var(--jp-layout-color1);
+  --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+  --jp-toolbar-header-margin: 4px 4px 0px 4px;
+  --jp-toolbar-active-background: var(--md-grey-300);
+
+  /* Statusbar specific styles */
+
+  --jp-statusbar-height: 24px;
+
+  /* Input field styles */
+
+  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+  --jp-input-active-background: var(--jp-layout-color1);
+  --jp-input-hover-background: var(--jp-layout-color1);
+  --jp-input-background: var(--md-grey-100);
+  --jp-input-border-color: var(--jp-border-color1);
+  --jp-input-active-border-color: var(--jp-brand-color1);
+  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+  /* General editor styles */
+
+  --jp-editor-selected-background: #d9d9d9;
+  --jp-editor-selected-focused-background: #d7d4f0;
+  --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+  /* Code mirror specific styles */
+
+  --jp-mirror-editor-keyword-color: #008000;
+  --jp-mirror-editor-atom-color: #88f;
+  --jp-mirror-editor-number-color: #080;
+  --jp-mirror-editor-def-color: #00f;
+  --jp-mirror-editor-variable-color: var(--md-grey-900);
+  --jp-mirror-editor-variable-2-color: #05a;
+  --jp-mirror-editor-variable-3-color: #085;
+  --jp-mirror-editor-punctuation-color: #05a;
+  --jp-mirror-editor-property-color: #05a;
+  --jp-mirror-editor-operator-color: #aa22ff;
+  --jp-mirror-editor-comment-color: #408080;
+  --jp-mirror-editor-string-color: #ba2121;
+  --jp-mirror-editor-string-2-color: #708;
+  --jp-mirror-editor-meta-color: #aa22ff;
+  --jp-mirror-editor-qualifier-color: #555;
+  --jp-mirror-editor-builtin-color: #008000;
+  --jp-mirror-editor-bracket-color: #997;
+  --jp-mirror-editor-tag-color: #170;
+  --jp-mirror-editor-attribute-color: #00c;
+  --jp-mirror-editor-header-color: blue;
+  --jp-mirror-editor-quote-color: #090;
+  --jp-mirror-editor-link-color: #00c;
+  --jp-mirror-editor-error-color: #f00;
+  --jp-mirror-editor-hr-color: #999;
+
+  /* Vega extension styles */
+
+  --jp-vega-background: white;
+
+  /* Sidebar-related styles */
+
+  --jp-sidebar-min-width: 250px;
+
+  /* Search-related styles */
+
+  --jp-search-toggle-off-opacity: 0.5;
+  --jp-search-toggle-hover-opacity: 0.8;
+  --jp-search-toggle-on-opacity: 1;
+  --jp-search-selected-match-background-color: rgb(245, 200, 0);
+  --jp-search-selected-match-color: black;
+  --jp-search-unselected-match-background-color: var(
+    --jp-inverse-layout-color0
+  );
+  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+  /* Icon colors that work well with light or dark backgrounds */
+  --jp-icon-contrast-color0: var(--md-purple-600);
+  --jp-icon-contrast-color1: var(--md-green-600);
+  --jp-icon-contrast-color2: var(--md-pink-600);
+  --jp-icon-contrast-color3: var(--md-blue-600);
+}
+</style>
+
+<style type="text/css">
+/* Force rendering true colors when outputing to pdf */
+* {
+  -webkit-print-color-adjust: exact;
+}
+
+/* Misc */
+a.anchor-link {
+  display: none;
+}
+
+.highlight  {
+  margin: 0.4em;
+}
+
+/* Input area styling */
+.jp-InputArea {
+  overflow: hidden;
+}
+
+.jp-InputArea-editor {
+  overflow: hidden;
+}
+
+.CodeMirror pre {
+  margin: 0;
+  padding: 0;
+}
+
+/* Using table instead of flexbox so that we can use break-inside property */
+/* CSS rules under this comment should not be required anymore after we move to the JupyterLab 4.0 CSS */
+
+
+.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+  min-width: calc(
+    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
+  );
+}
+
+.jp-OutputArea-child {
+  display: table;
+  width: 100%;
+}
+
+.jp-OutputPrompt {
+  display: table-cell;
+  vertical-align: top;
+  min-width: var(--jp-cell-prompt-width);
+}
+
+body[data-format='mobile'] .jp-OutputPrompt {
+  display: table-row;
+}
+
+.jp-OutputArea-output {
+  display: table-cell;
+  width: 100%;
+}
+
+body[data-format='mobile'] .jp-OutputArea-child .jp-OutputArea-output {
+  display: table-row;
+}
+
+.jp-OutputArea-output.jp-OutputArea-executeResult {
+  width: 100%;
+}
+
+/* Hiding the collapser by default */
+.jp-Collapser {
+  display: none;
+}
+
+@media print {
+  .jp-Cell-inputWrapper,
+  .jp-Cell-outputWrapper {
+    display: block;
+  }
+
+  .jp-OutputArea-child {
+    break-inside: avoid-page;
+  }
+}
+</style>
+
+<!-- Load mathjax -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    init_mathjax = function() {
+        if (window.MathJax) {
+        // MathJax loaded
+            MathJax.Hub.Config({
+                TeX: {
+                    equationNumbers: {
+                    autoNumber: "AMS",
+                    useLabelIds: true
+                    }
+                },
+                tex2jax: {
+                    inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+                    displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+                    processEscapes: true,
+                    processEnvironments: true
+                },
+                displayAlign: 'center',
+                CommonHTML: {
+                    linebreaks: {
+                    automatic: true
+                    }
+                }
+            });
+
+            MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
+        }
+    }
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --></head>
+<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
+
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Artificial-Intelligence-and-Machine-Learning">Artificial Intelligence and Machine Learning<a class="anchor-link" href="#Artificial-Intelligence-and-Machine-Learning">&#182;</a></h1><h2 id="Unit-II">Unit II<a class="anchor-link" href="#Unit-II">&#182;</a></h2><h2 id="Decision-Trees-and-Random-Forest">Decision Trees and Random Forest<a class="anchor-link" href="#Decision-Trees-and-Random-Forest">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[107]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.core.display</span> <span class="kn">import</span> <span class="n">HTML</span>
+
+<span class="n">HTML</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">&lt;style&gt;</span>
+<span class="s2">.output_png {</span>
+<span class="s2">    display: table-cell;</span>
+<span class="s2">    text-align: center;</span>
+<span class="s2">    vertical-align: middle;</span>
+<span class="s2">}</span>
+<span class="s2">img[alt=regression] { width: &quot;50%&quot;; }</span>
+<span class="s2">&lt;/style&gt;</span>
+<span class="s2">&quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[107]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+
+<style>
+.output_png {
+    display: table-cell;
+    text-align: center;
+    vertical-align: middle;
+}
+img[alt=regression] { width: "50%"; }
+</style>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="My-own-latex-definitions">My own latex definitions<a class="anchor-link" href="#My-own-latex-definitions">&#182;</a></h2><p>$\require{color}$</p>
+$$\def\mbf#1{\mathbf{#1}}$$$$\def\bmf#1{\boldsymbol{#1}}$$$$\def\bx{\mbf{x}}$$$$\def\bv{\mbf{v}}$$$$\def\bz{\mbf{z}}$$$$\def\bmu{\bmf{\mu}}$$$$\def\bsigma{\bmf{\Sigma}}$$$$\def\Rd#1{\in \mathbb{R}^{#1}}$$
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[108]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="c1"># plt.style.use(&#39;seaborn-whitegrid&#39;)</span>
+
+<span class="n">font</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;family&#39;</span> <span class="p">:</span> <span class="s1">&#39;Times&#39;</span><span class="p">,</span>
+        <span class="s1">&#39;weight&#39;</span> <span class="p">:</span> <span class="s1">&#39;bold&#39;</span><span class="p">,</span>
+        <span class="s1">&#39;size&#39;</span>   <span class="p">:</span> <span class="mi">12</span><span class="p">}</span>
+
+<span class="n">matplotlib</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s1">&#39;font&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">font</span><span class="p">)</span>
+
+
+<span class="c1"># Aux functions</span>
+
+<span class="k">def</span> <span class="nf">plot_grid</span><span class="p">(</span><span class="n">Xs</span><span class="p">,</span> <span class="n">Ys</span><span class="p">,</span> <span class="n">axs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; Aux function to plot a grid&#39;&#39;&#39;</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">Xs</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="c1"># define progression of int for indexing colormap</span>
+    <span class="k">if</span> <span class="n">axs</span><span class="p">:</span>
+        <span class="n">axs</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span> <span class="c1">#plot origin</span>
+        <span class="n">axs</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">Xs</span><span class="p">,</span><span class="n">Ys</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">t</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span> <span class="c1"># scatter x vs y</span>
+        <span class="n">axs</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">)</span> <span class="c1"># axis scaled</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span> <span class="c1">#plot origin</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">Xs</span><span class="p">,</span><span class="n">Ys</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">t</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">)</span> <span class="c1"># scatter x vs y</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">)</span> <span class="c1"># axis scaled</span>
+        
+<span class="k">def</span> <span class="nf">linear_map</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">Xs</span><span class="p">,</span> <span class="n">Ys</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Map src points with A&#39;&#39;&#39;</span>
+    <span class="c1"># [NxN,NxN] -&gt; NxNx2 # add 3-rd axis, like adding another layer</span>
+    <span class="n">src</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">Xs</span><span class="p">,</span><span class="n">Ys</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="n">Xs</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span>
+    <span class="c1"># flatten first two dimension</span>
+    <span class="c1"># (NN)x2</span>
+    <span class="n">src_r</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c1">#ask reshape to keep last dimension and adjust the rest</span>
+    <span class="c1"># 2x2 @ 2x(NN)</span>
+    <span class="n">dst</span> <span class="o">=</span> <span class="n">A</span> <span class="o">@</span> <span class="n">src_r</span><span class="o">.</span><span class="n">T</span> <span class="c1"># 2xNN</span>
+    <span class="c1">#(NN)x2 and then reshape as NxNx2</span>
+    <span class="n">dst</span> <span class="o">=</span> <span class="p">(</span><span class="n">dst</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="c1"># Access X and Y</span>
+    <span class="k">return</span> <span class="n">dst</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">dst</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">plot_points</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">Xs</span><span class="p">,</span> <span class="n">Ys</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Plots points&#39;&#39;&#39;</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">,</span>  <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Xs</span><span class="p">,</span> <span class="n">Ys</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">col</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">unit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plotVectors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span> <span class="p">[</span><span class="s1">&#39;gray&#39;</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">plotVectors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">unit</span><span class="p">,</span> <span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">plotVectors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">vecs</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Plot set of vectors.&#39;&#39;&#39;</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vecs</span><span class="p">)):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">vecs</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span>
+                   <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
+                   <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]],</span>
+                   <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]],</span>
+                   <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">cols</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                   <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Recap-previous-lecture">Recap previous lecture<a class="anchor-link" href="#Recap-previous-lecture">&#182;</a></h1><ul>
+<li>Supervised Learning with $k$-NN</li>
+<li>k-NN strengths and limitations</li>
+<li>A bit of theory of learning Empirical Risk Minimization</li>
+<li>Usage of the validation set</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Today's-lecture">Today's lecture<a class="anchor-link" href="#Today's-lecture">&#182;</a></h1><h2 id="Another-%22non-parametric%22-model">Another "non-parametric" model<a class="anchor-link" href="#Another-%22non-parametric%22-model">&#182;</a></h2><h3 id="Decision-Trees">Decision Trees<a class="anchor-link" href="#Decision-Trees">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="This-lecture-material-is-taken-from">This lecture material is taken from<a class="anchor-link" href="#This-lecture-material-is-taken-from">&#182;</a></h1><ul>
+<li>Information Theory part  - (Entropy etc) is taken from <strong>Chapter 1 - Bishop</strong>.</li>
+<li>Decision Trees are very briefly covered in <strong>Bishop at page 663</strong>.</li>
+<li><a href="http://ciml.info/dl/v0_99/ciml-v0_99-ch01.pdf">Cimi Book - Chapter 01</a></li>
+<li><a href="https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/06_trees_handout.pdf">CSC411: Introduction to Machine Learning</a></li>
+<li><a href="https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial3.pdf">CSC411: Introduction to Machine Learning - Tutorial</a></li>
+<li><a href="https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote17.html">Cornell ML course</a></li>
+<li><a href="https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote18.html">Cornell ML course Bagging</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="From-$k$-NN-to-Decision-Trees">From $k$-NN to Decision Trees<a class="anchor-link" href="#From-$k$-NN-to-Decision-Trees">&#182;</a></h1><h4 id="Non-parametric-models">Non-parametric models<a class="anchor-link" href="#Non-parametric-models">&#182;</a></h4>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Recall-$k$-NN">Recall $k$-NN<a class="anchor-link" href="#Recall-$k$-NN">&#182;</a></h1><ul>
+<li>Denote the set of the $k$ nearest neighbors of $\mathbf{x}$ as $S_{\mathbf{x}}$. Formally $S_{\mathrm{x}}$ is defined as $S_{\mathrm{x}} \subseteq D$ s.t. $\left|S_{\mathrm{x}}\right|=k$ and $\forall\left(\mathbf{x}^{\prime}, y^{\prime}\right) \in D \backslash S_{\mathrm{x} \prime}$
+$$
+\operatorname{dist}\left(\mathbf{x}, \mathbf{x}^{\prime}\right) \geq \max _{\left(\mathbf{x}^{\prime \prime}, y^{\prime \prime}\right) \in S_{\mathbf{x}}} \operatorname{dist}\left(\mathbf{x}, \mathbf{x}^{\prime \prime}\right)
+$$</li>
+</ul>
+<ul>
+<li><p>(i.e. every point in $D$ but not in $S_{\mathrm{x}}$ is at least as far away from $\mathbf{x}$ as the furthest point in $S_{\mathbf{x}}$ ).</p>
+</li>
+<li><p>We can then define the classifier $h()$ as a function returning the most common label in $S_{\mathrm{x}}$ :
+$$
+h(\mathbf{x})=\operatorname{mode}\left(\left\{y^{\prime \prime}:\left(\mathbf{x}^{\prime \prime}, y^{\prime \prime}\right) \in S_{\mathbf{x}}\right\}\right)
+$$</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Ambiguous-cases-based-on-the-distance-and-neighbours">Ambiguous cases based on the distance and neighbours<a class="anchor-link" href="#Ambiguous-cases-based-on-the-distance-and-neighbours">&#182;</a></h1><p><center>
+<img width="40%" src="figs/nn.png" /></p>
+<center/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Over-or-Under-Fitting">Over or Under Fitting<a class="anchor-link" href="#Over-or-Under-Fitting">&#182;</a></h1><p><center>
+<img width="60%" src="figs/over_under_fit.png" /></p>
+<center/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="$k$-NN-has-irregular-and-non-linear-decision-boundaries">$k$-NN has irregular and non-linear decision boundaries<a class="anchor-link" href="#$k$-NN-has-irregular-and-non-linear-decision-boundaries">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="What-is-the-the-training-error-of-$k$-NN?-%F0%9F%A4%94">What is the the training error of $k$-NN? &#129300;<a class="anchor-link" href="#What-is-the-the-training-error-of-$k$-NN?-%F0%9F%A4%94">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>In $k$-NN there there is no explicit cost/loss, how can we measure the training error? </li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>What we <strong>can do is to <ins>classify the train with respect to the train</ins>, given a fixed inductive bias (configuration of $k$-NN used).</strong></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>So classify the training points as if they were validation points, given a fixed $k$-NN configuration.</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="What-happens-when-you-classify-the-training-set-with-$k=1$?">What happens when you classify the training set with $k=1$?<a class="anchor-link" href="#What-happens-when-you-classify-the-training-set-with-$k=1$?">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Ideas?">Ideas?<a class="anchor-link" href="#Ideas?">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="let's-see-with-an-example...">let's see with an example...<a class="anchor-link" href="#let's-see-with-an-example...">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[109]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span><span class="p">,</span> <span class="n">make_circles</span><span class="p">,</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+
+<span class="n">h</span> <span class="o">=</span> <span class="mf">0.02</span>  <span class="c1"># step size in the mesh</span>
+<span class="n">K</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># Neighbors</span>
+
+<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Nearest Neighbors&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">classifiers</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">K</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">linearly_separable</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">make_moons</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># iterate over datasets</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
+    <span class="c1"># preprocess dataset, split into training and test part</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="p">)</span>
+
+    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
+                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
+
+    <span class="c1"># just plot the dataset first</span>
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
+    <span class="n">cm_bright</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">([</span><span class="s2">&quot;#FF0000&quot;</span><span class="p">,</span> <span class="s2">&quot;#0000FF&quot;</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Input data&quot;</span><span class="p">)</span>
+    <span class="c1"># Plot the training points</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+               <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="c1"># iterate over classifiers</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">):</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+        <span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
+        <span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">&quot;decision_function&quot;</span><span class="p">):</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># Put the result into a color plot</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+        <span class="c1"># Plot the training points</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span>
+        <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="o">+</span><span class="sa">f</span><span class="s1">&#39; K=</span><span class="si">{</span><span class="n">K</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+            <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;0&quot;</span><span class="p">),</span>
+            <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[110]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span><span class="p">,</span> <span class="n">make_circles</span><span class="p">,</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
+
+
+<span class="n">h</span> <span class="o">=</span> <span class="mf">0.02</span>  <span class="c1"># step size in the mesh</span>
+<span class="n">K</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># Neighbors</span>
+
+<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Nearest Neighbors&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">classifiers</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">K</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">linearly_separable</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">make_moons</span><span class="p">(</span><span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># iterate over datasets</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
+    <span class="c1"># preprocess dataset, split into training and test part</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="p">)</span>
+
+    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
+                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
+
+    <span class="c1"># just plot the dataset first</span>
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
+    <span class="n">cm_bright</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">([</span><span class="s2">&quot;#FF0000&quot;</span><span class="p">,</span> <span class="s2">&quot;#0000FF&quot;</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Input data&quot;</span><span class="p">)</span>
+    <span class="c1"># Plot the training points</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
+               <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="c1"># iterate over classifiers</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">names</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">):</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">classifiers</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+
+        <span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
+        <span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">&quot;decision_function&quot;</span><span class="p">):</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># Put the result into a color plot</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+        <span class="c1"># Plot the training points</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span>
+            <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span>
+        <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">name</span><span class="o">+</span><span class="sa">f</span><span class="s1">&#39; K=</span><span class="si">{</span><span class="n">K</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+            <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;0&quot;</span><span class="p">),</span>
+            <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
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+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
+
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"
+class="
+"
+>
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
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+<h1 id="When-$k=1$-we-perfectly-classify-the-training-set!-100%25-accuracy!">When $k=1$ we perfectly classify the training set! 100% accuracy!<a class="anchor-link" href="#When-$k=1$-we-perfectly-classify-the-training-set!-100%25-accuracy!">&#182;</a></h1><p>It is easy to show that this follow by definition <strong>(each point is neighbour to itself).</strong></p>
+<p>but will this hold for $K \gt 1$?</p>
+
+</div>
+</div>
+</div>
+</div>
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
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+<h1 id="We-record-the-training-accuracy-in-function-of-increasing-$k$">We record the training accuracy in function of increasing $k$<a class="anchor-link" href="#We-record-the-training-accuracy-in-function-of-increasing-$k$">&#182;</a></h1>
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[111]:</div>
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+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">###################</span>
+<span class="n">h</span> <span class="o">=</span> <span class="mf">0.02</span>  <span class="c1"># step size in the mesh</span>
+<span class="n">plot_train_points</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">idx_plot</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># plot points of attempt idx_plot</span>
+<span class="c1">#################</span>
+
+<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Nearest Neighbors&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">Krange</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="mi">48</span><span class="p">]</span>
+
+<span class="n">accuracies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">accuracies_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># iterate over datasets</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
+    <span class="c1"># preprocess dataset, split into training and test part</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
+    <span class="c1">######### Watch out here ################</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>  <span class="c1"># in theory this is an error!</span>
+    <span class="c1"># you want to estimate the scaling of data on the training set and then apply to valid and test</span>
+    <span class="c1">##########################################</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="p">)</span>
+    <span class="c1"># validation from training (overwrite X_train)</span>
+    <span class="n">X_train_entire</span><span class="p">,</span> <span class="n">y_train_entire</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">X_train</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
+                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
+
+    <span class="c1"># just plot the dataset first</span>
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
+    <span class="n">cm_bright</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">([</span><span class="s2">&quot;#FF0000&quot;</span><span class="p">,</span> <span class="s2">&quot;#0000FF&quot;</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Input data&quot;</span><span class="p">)</span>
+    <span class="c1"># Plot the training points</span>
+    <span class="c1"># ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,</span>
+    <span class="c1">#           cmap=cm_bright, edgecolors=&quot;k&quot;)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+    <span class="c1">#i += 1</span>
+    <span class="c1"># iterate over classifiers</span>
+    <span class="n">mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span>
+    <span class="k">for</span> <span class="n">K</span> <span class="ow">in</span> <span class="n">Krange</span><span class="p">:</span>
+        <span class="n">clf</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">mid</span><span class="p">,</span> <span class="n">mid</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score_test</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
+
+        <span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
+        <span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">&quot;decision_function&quot;</span><span class="p">):</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># Put the result into a color plot</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+        <span class="c1"># Plot the training points</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">idx_plot</span> <span class="ow">and</span> <span class="n">plot_train_points</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+                <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;k&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="sa">f</span><span class="s1">&#39; K=</span><span class="si">{</span><span class="n">K</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+            <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;0&quot;</span><span class="p">),</span>
+            <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># recording the accuracies</span>
+        <span class="n">accuracies</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
+        <span class="n">accuracies_valid</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score_test</span>
+        <span class="c1"># print(accuracies,i,score)</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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WVqjc6HP/xhfOc738Ef/uEfoq+vDytXrszpeCS/UGtMqDWVrzVsf17+UG9MqDeVpTdTp07Fpz/9afzkJz/B3//93+Ozn/0sjh8/jvvuuw9vv/025s6da/33RooPdcWEulJZukIqD2qNCbWmurWm4mPGJTTfC4JbtsJvfvMbNWvWLAVAffOb37RloLz44osKgK/si56eHnXDDTeo7du3W5k4q1evtp5/4YUXFAD1j//4j7b3dXZ2qra2trTHj0Qi6nd/93cVANXY2Gi1rlAq90wcpfJ3HdyyQh577DFlGIb6u7/7O6WUUkNDQ2rWrFlqcHAw6b2XX3657bGvf/3rCoAtK8h5bYWWlhY1ceJEpZRS8+bNU3V1deq9995zPccJEyZY/7788ss9M1kee+wxdd555ykAqqWlRf3nf/6n+xfXkEwcaRtxzz33WP+/vfTSS67vGR4eVv/7v/+b9mdsbMz1+zCbr7yg1phQa6pLawTJDr377rvTniMpLNQaE2pNdWrN0NCQAqAeffTRtOdICgu1xoRaU11ac+jQIWUYBtuflxnUGxPqTeXrzejoqPrCF76gZs+erS644AL16U9/2qoMu/3229OeI8kf1BUT6krl64rz+7ACvLyg1phQa2pTa4RKixknp1RUIe973/vw+OOPo7GxEV/72tcwODhoPTdjxgwAZnaNG8ePHwdgZrJ88pOfxLe//W2sWLHCyqDxw4wZM/D666/j3XffTXruzJkzOHPmDE6fPo3p06fj5z//OR577DE0NzdjzZo12L59eyZfNSX5uA5efOpTn8L3vvc9PPLII7jrrrvwwx/+EH19fbjooovydv4AEIvF8Nu//dvWOY+Pj2Pfvn1JrwsEAkkZOm6cOnUKn/rUp/DGG2/gS1/6EgYHB3HVVVdhdHQ0o/P6yle+gjvuuAMnT57Etddei3/7t39Les2iRYtw3nnnpf355S9/mdFnk/KBWmNCrUmm0rRm/vz5AIBwOJzR+ZHiQK0xodYkU2laE41GAQALFy7M6PxIcaDWmFBrkqkUrXn00UfR0tKCCy64IKPzIsWHemNCvUmmnPVm0qRJ+Pa3v439+/fjzTffxI9//GO8+OKLCAQCViUfKR3UFRPqSjLlrCuk8qDWmFBrkqlWram0mHHVGeDj4+NWME1n4cKF+NGPfgSlFIaGhqzHZ8yYgQsvvBDf/e53ceDAAdt7XnnlFfz4xz8GAHzta1/DvHnzMH36dABw/QwvrrzySpw6dQp/9Vd/lXSuX/nKV/Dee+9h586d2LFjBwDzph4YGMCUKVPQ09MDAKirq8vocwt1HbzYu3cvfvWrX2HXrl3YsGED1q1bh5kzZ/o6V7+88847ePPNN3HLLbcAAG644QYAwE9+8hPb606cOIEjR47gxhtvTHvMe+65B0opnHPOOfjbv/1bfO9738Phw4fx+uuvp3yfUirpsXvvvRd/8id/gpGREXR2duKNN96wPf/jH/8YPT09aX8+8pGPpD1vUnqoNYljU2uqT2v+/d//HR/4wAfYkrgMoNYkjk2tqT6tefbZZ9HZ2WltMknpoNYkjk2tqR6tefTRR/GpT30q7fchxYV6kzg29aZ69AYA9uzZgx/84AdYt24dLrzwwrTfjeQP6kri2NSV6tIVUl5QaxLHptbUrtZUXMy4OIXmxWF0dFSFQiH1X//1X56vkZYBetuDn/70pwqAmjJlivrCF76g/u///b/qc5/7nFq1apXVFmD69OkqGAyqH//4x2rv3r3q61//uqqvr1eXXXaZeuWVV9SJEyfUs88+qwCo7373u7bPHB8fV4sWLVIAVEdHh9q0aZO699571SWXXKKeffZZpZTZxuKqq65SsVjMet9v//Zvq8cee8z6bsFgULW3t6uDBw+qH/3oRyW5Dkop9f73v1+9//3vtx3v4x//uPqt3/ottW7dOrVhwwZ19913q+9///vq3//9322ve9/73qcWL15se+zLX/6yAqD+4z/+w3qsrq5OdXZ2WtcjFoupP/uzP1OrVq1S4+PjSimlotGouuaaa9Q555yj/vu//9t67ze+8Q21aNEiderUKeuxSy65RJ133nlJ12H16tXq4Ycftv790ksvqfPOO0+dPHnS89oppdRFF12kAKh/+Zd/sT0+NjamLr74YgVAzZw503ZeuTBt2jQ1Y8aMvByL5A61pvDXQSlqjVKF15ojR46oq6++Wm3ZskWdPn1aKaXUyMiIuu6669SLL76Y1TFJ/qDWFP46KEWtUarwWvMv//Iv6vzzz1df//rXre+xe/duddVVV6nf/OY3WR2T5A9qTeGvg1LUGqWKu4f61a9+pQCw/XmZQb0p/HVQinqjVPFjNr/+9a/V+eefr6699lp15syZvByT+IO6UvjroBR1RanyjQUzZlwcqDWFvw5KUWuUKh+tqZaYcdUY4D/60Y+s/vhtbW3qoYce8nztZz7zGdsNqJRSP/zhD9X8+fNVfX29uvDCC9Xf/M3fWP/HKqXUU089pWbNmqWmTp2q/vRP/1QdPXpU/fEf/7FqbGxUd9xxh3ryySetz583b576h3/4B9ui99e//rW68cYb1dlnn62mTp2qPv7xj6v+/n7reZnjcNlll6l77rlHffnLX1b333+/7Ry/9rWvqYkTJ6q2tja1d+/eol+HQ4cOqW984xsKgDXP4a233rLOf/bs2ercc8+1npefv/3bv1UnTpxQ3/zmNxUAFQqF1H333adOnz6t/vVf/1XNnj1bAVB/9Ed/pI4cOaKUUurpp59W11xzjWpra1Of/OQn1e/93u+pb3/72yoajdrOd2xsTH3zm99UF198sfrzP/9z9Wd/9mfq85//vDp27JhSSqn//d//Vd/85jdVKBRSANS6devUwMCA9f7Vq1erCRMmqDVr1qjvfOc76rOf/ax69dVXPa/Zyy+/rNatW2d9tw9/+MNqy5YtllD/0z/9kzVPA4A699xz1V//9V+roaEhz2Om4he/+IV1zQOBgNq0aZP6xS9+kdWxSH6g1hT+OlBriqc1x44dUx/72MfUxIkTVTgcVn/6p3+qvv71r6uDBw9mdBySf6g1hb8O1Jriac1//Md/qLa2NhUKhSytuffee22bRlIaqDWFvw7UmuLvoZQyA2+LFi3K+v0k/1BvCn8dqDel0Ztnn31WXXjhherLX/5y0vcnhYW6UvjrQF0p31gwY8bFg1pT+OtArSk/ramWmLGhlEtNPSEZEI1Gcdttt2H9+vW2+QfvvfceDhw4gPXr1+Of//mfS3iGhJBqgFpDCCkG1BpCSDGg1hBCigX1pjB897vfxRtvvIHm5mbccsst+OAHP1jqUyKkaFBXCCHFgFpDciVY6hMglc/Xv/51/PrXv7aJEABMmDABc+fORUtLS2lOjBBSVVBrCCHFgFpDCCkG1BpCSLGg3hSGz33uc6U+BUJKBnWFEFIMqDUkV2iAk5wZHR3Fc889h7/7u7/DVVddhfPOOw/vvPMO9uzZg56eHvzlX/5lqU+REFIFUGsIIcWAWkMIKQbUGkJIsaDeEELyDXWFEFIMqDUkV9gCneTMe++9h3vuuQc/+tGPcOjQIUydOhULFizAH/3RH+GGG25AfX19qU+REFIFUGsIIcWAWkMIKQbUGkJIsaDeEELyDXWFEFIMqDUkV2iAE0IIIYQQQgghhBBCCCGEEEIIqQoCpT4BQgghhBBCCCGEEEIIIYQQQgghJB9kPQM8FovhrbfewuTJk2EYRj7PiRBSYSilcOLECXzwgx9EIJDfvBpqDSFEoNYQQooBtYYQUiwKpTfUGkKIDtc2hJBiQK0hhBSDTLQmawP8rbfewsyZM7N9OyGkCjl06BBmzJiR12NSawghTqg1hJBiQK0hhBSLfOsNtYYQ4gbXNoSQYkCtIYQUAz9ak7UBPnnyZADAg5dcjYbghGwPQwjxwdI/uwIf65mE8NSGUp+KK9Ez7+Ln//fPLF3IJ9QaQvJDeCYw87orEKyvR+j9Hyza50ZnLsTu4RiMQB2MQG5ZuqdOnsAnLl1IrSGkyITjcYazL5hT8M9qWvhbAJC1TkVnLgQAGA3Z68SJEydwwdy51BpCypR8alLTwt/KeV2UTnd2v3XScw1UqLUNtYaQzMhEV3Jdq2SKm8bsfuuk+ZiHtvz4lYMAgLNCZth37N1T+IfPdnJtQ0gRCM8szr7JC1nbRGcuzGlPlE5n3GDMhpDiUUqt8bMWylaDUu2dAHONM/buKfzTbdf60pqsDXBpNdEQnEDRIaSAXPXIZrRu2IFgyECwvjwNcKEQLWioNYTkh4s+tQwNs+cW9TOj4Rb0D8UweWrWyw1XqDWEFI85YfPPcy4svH40L5oPAAhNy65aIBpuAQAYDVPycj7UGkLKk8ZQfjTJ0pzGs7I+RjTcklJz+o+cwFmTJ6cNHudbb6g1hGRGJroy5axJWa9VsiE6ZbJNZ9LpykM7I2g65xzX57i2IaSwyN5p8sT6kp3DlLMmIXDRZQCy3xf5Xb94Qa0hpLCUUmv8xG2yjc340Z5QQ6OV4OdHa/IbkSaE5J3WDTtgwEC4qbzNb0JI+dKxdhmCRV4U7WxcCAzFEAhyqUFIpVNr5rdUOxBCyg8J9uRKMTSn/8gJ8zU5dsAhhBSWOWH/ax3RjmIhWiOk05WHdkYKfUqEkDQUY+/kRfOi+QhNm4Eoct8Xcf1CSHlSzCIFLwplfgOptSebdU7qCeGEkJLy+Jr1AEDzmxCSNR1rlwHIPsCbDTsbzTZ9NL8JIX4oJ/O7/8gJ/KT/UM7HIYQUjnwFe2h+E0KySaop1r5KOkyI1vg1vxtD3IMRUovInsqZOJMpojWEkPKlVOa330TAbGMzfszvTNc5XBURUsb09Q5idlP2LfkIIQRA0VufA4Uxv2/7zrN5PyYhxJt8VVqmQqoUsiXf5nfXroMA/SpCypJ8V39nQyaaQ/ObkMogk+rvYpvfAs1vQsqfYuyd0pFr9Xf/kRNcvxBCUuKn+jtT0iXe5LLO4cqIkDLl8TXrgd7BUp8GIaSCKVnr8wJwyz1PFuS4hJDUFCqzONeqbyD97N1MEfP7LAaPCSk78tXqLxft8Wt+s3KKkMogE7Oq2Oa3Ds1vQiqHUldl5lL9zfULIeVPKRNt0q2Fci1OSJd8k+06h6sjQsqQjrXLcOfTg6gzmHVHCMkOMb+L3frcCNTlPWNYzO9ZUyfhzbwemRDiRSE3VuXU8ly4/WevAYa5qToTzdthCSF5IJ/md6F1h5VThFQWfnSlmHO/vbSG5jch5U05VH8H2pcDyG6PlK/RLT9+5WBO7yeEpKcUiTaFNL9TJd/kY53DFRIhZcjei1bBeLoHM6dy9jchJHNk81XMKoX+oVjezW+96jvc1IDomdG8HZsQkp5CbKzK1vxGYlP1/PP/nrdjE0JyI9+V39lQKPP7oZ0RjI2ezPq8CCHZMyecmfldjH2Vm9b40RWa34SUB6Ws/s7F/BZyjeWIUUUIKQylSrQp9NxvILX+5LrO4SqJkDLj8TXr0bepm9XfhJCsCa8qXuvzQpvf4SYmAhFSLeQriFwQ87ve3BZ1d+/BjHMm4b/y9gmEkFzJV0C5nNqeS5CYIxcIKV+KaX4LmZjfD+2M0PwmhFjkMvc7F3Tjm+saQgpLqRJtCjn3O12Xm1yhKhFSRswJA329g5jddFapT4UQUqF0rF0GoHhVCvk2vx/aGUHfC4MAaH4TUk3ka+Z3PtHbnnd37wEAzJzKbhOElAv5qnLItvV5pua337WQ3sqPIxcIKT6ZaEsp5n5z5jchlYXfjhKFQKq/czW/s43nOPWI6xpCCkO1VX/71Z58rHW4WiKkjLg+tLLUp0AIqWBkQdQwu/CbLzG/A8H8LSVY9U1IeZDvzVU+ze98VH/3HzmBrl0HXc1vQkh5UarW54UyvwUaV4SUlnTaUqq53zS/CSF+aV40H0ZrZ9mY34SQwlKu1d+FML/zOVKBCkVImSDBZlZ/E0KypZitz/NpfkvVd8CgAUVIuZCvzRXNb0JINuSzmipT/fEbyMkmcKwHc7q792B87N2Mzo0Qkht+kvyK1frca31D85uQyqGUVZlGayeMYCir96cbsZAOjmAgpHiUa/V3Nh36MjG/86UxVCpCyoTrQythgHO/CSHZIQuiYrTp29m4MG/Hkqpvmt+EVB80vwkh2ZAv8zvT1ueZ6E0u5reuPzPOmYT/8n0EQkgu+NGWUprffubw0nAipPwodlWm6JQRDBWs7bAXTMQhpDSUSmfSrYcy0aBSmN8ADXBCyoKmB+4HNnWz7S8hJGvCq5YVpfW5mN/5qP6+5Z4naXwTUqXQ/CaEZEOp5n4Xy/x+sXfQemzm1AZEz4z6PgYhJHvKae53Kr1JV/1NCKlt9D1WtuO2aX4TQlJRiGTAUpnfAA1wQsqCNZu6UWew+psQUt7ky/xmy3NCypd8mE+ZGk9uZDNLygua34RUBqI/pZr7XUjzm9pDSOkph7nfXuZ3qnbENJ0IKU+K3ZbYZn7nMHc3G6hDhJSGUrU/TxfPyaT9eSnNb4AGOCEl5855KwEwEEIIyZ6OtcsKevxouAX9QzEYgbqc50T1vWBWPtH8JqR8ydZ8Kreqb4DmNyGVRr7M72yqv9OR6bxMt5bn1B5Cik+5zP2m+U1I9VHstsRifmdLNvEc6hAhpaWYOuMnGdBvzEZPuvGjPYXSGCoXISVEWp/Pbjqr1KdCCKlQJKBTqPbn+Ta/aXwTUp2Uo/l9+89eAwA01tu3PNQgQsqPfHWfALIzv1PpTj5bnhNCSoOf4HGhzG/drHIzv72g6UQIEZymVLGqv6lDhNQeftZDfs1vP/unQo95oXoRUiI61i5D+6ZuGGDrc0JI9oRXLUNwYn1Bjp0P85tV34RUP5VgfrP6kpDyJV+tz4HsdMiP7uRiflN3CCkdfqu/C21+p9IZN32h6UQIcVLs6m/qECGlpVRjFlKRToMyrfoWCqkzVDBCSkTwkuUwnn4e4SYGRAghuVGIgI2Y37nM+2bVNyHVD81vQkgulHLut58gcqYVUzS/CSk/UulLIed+p1vfeOlLvk2n7u49GB97Ny/HIoSYzAkXry1xPqq/i2V+P//8v2f0ekJIaoqtM7lUf2fbNavQSTY0wAkpAXPCQOuGHagzWP1NCMmeQmYDSuV3toj5zSQfQqqXfM7LzKv5bSSCNTS/CSl/Sjn3O5+tz/XEP4C6Q0ip8btXKlQyMZB+fePUl0KY3wAw45xJ+K+8HJEQUkz0NU4u1d9+0VsRZ6pDojeEkMrC714qlQaVq/kN0AAnpCRcH1oJgEERQkjuFKL9+c7GhQAyb5EF2Fue0/wmpHrJl/mdr0BO/5ET6Np1kOY3IRVErol82epQoc1vag4h5UOxKqd0MtEYnXya37oRNXNqA6JnRnM+JiGkNOjmdyFnf+eiQUy2IaQyyXQ/5aZBuXSZKAY0wAkpEbObzir1KRBCKpg5YXP+d74R8zub1ues+iakNsi3+Z1r9TfNb0Iqj1xbn9P8JoSkothzM4VcNSaf5jf1iJDKJh+tzwF/65lszW8m2xBSGIqxjslkPxUNtyRpUDZV30D+u92kgwY4IUVkTjhR/U0IIbkSnFif15Z92ZrfrPompDrwM8sun+Z3Ptqe0/wmpHIptvkt5Mv8luANzW9CyhM/s7/zuZfKZG3j1fo8F5xGFCGk8sm19XkxzG/qDSGFoRhdbPya3zrZGt9A8c1vgAY4IUVFzG9WfxNCciXf1d87GxfCCNRlvIC55Z4nAYCBX0JqgHwEi/NV9Q3Q/CakUsmloiEXHUoXQM7G/H6xl51vCCk3/CT0Afk3v7MlH8Fgrn8IKR7FrMwUsqn+pvlNCPHCqTHpEA2qNPMboAFOSNFoeuB+YFM3zW9CSM7IhisfQZtouAX9Q7GczG8GfgmpfpoXzS+blucAzW9CKhW/xpQTPUiTi/ntNbcOYMtzQmqFTIO+fvGzvnHO431oZyQvxjdAPSKkmJRLZaYbmc7jpflNSG2RaetzID/GN1B88xugAU5I0ejqfwt1RuYiQQghbgQn1ud8jGzNbzG+AZrfhNQC+QgU0/wmhGRbMZVr9wma34TUDul0phCtzzPFCBis+iaEeKInHmc6NsqZZJOKTEcvMOGGkOJQjC4TmayDBkbMfVIlVX3r0AAnpAg0PXA/+lj9TQjJE/lqf56L+U3jm5DqIl1VZrm0Pb/9Z68BABrrE9uY7u49DMIQUiFkWjGVL7MqH+b3Lfc8iYDBsS+ElDteOlMo8zvb9ufZBoNpQhFSOophTAnZakshWp8z4YaQ4lKoLhOZFDdYCTgjmXWVEAplfnd378H42Lu+X08DnJAisGZTNwyw+psQkj9yDdzsbFwIILMMPprfhJBMyXflN5AwvxmIIaRyyCZgnA+zyit4nOm8b1Z9E1L+pNKZQld+Z9L+PNOqSx2ufQgpPYVsf57L7O9Mqr8Bmt+E1CrZtD7PlEKY33oC4IxzJuG/fL6PBjghBeaqRzYDG3bQMCKE5IWOtblXf4v5HQj6Wwaw5Tkh1U26gHEhKi+zQdqeA6z6JqSSEI3JJGCcT/PbqUE0vwmpPvzoTCHM70wrNLtePgggu4AwTShCaoPQtBlFqf5OBzWHkOrCT/W3rj3ZVn8/tDNSkKpvIKFH0TOjvt9LA5yQAnLVI5vRumEHq78JIXmlYXb2GcfZmt80vgmpTlIFjHOd/Z1t4MaJPvP7xd5BAAzEEFJp+DW/dd2h+U0IyYR0rc8Lhd/qb1nLZBoUZstzQmoD0apMO2hlurYBUusQjW9CqpdU+yun9mRa/V3oqu9sNYkGOCEFRMxvGkeEkHzQsXYZghPrs35/Jua3BH0Bmt+EVDuFqJbKV+tzmt+EVDaZtD7Pd4viXMxvADS/CakQ5oTTJ9kUqvV5RuRgflOHCCk9hZz/ra+Bosh8D0XzmxCSinTJgG7mdyaV34U0v3PVIxrghBSIO+etBEDjiBCSX7IN3mRifkvVN4O+hFQ3fmZlZkM03JI/4xtAX69pQgHUJEIqET/V3/k0v926T2RqfstaiJpDCPHCb6eb/iMn0PXyQZrfhFQw2Yxz8YNz/ZPpPipfc7+pN4RUL+n2WeVU+a1XfAP50SQa4IQUkNlNZ5X6FAghVUIus793Ni6EEajzFfRly3NCaoNCVEsVquqbyTiEVDfNi+bnrTrTLXCcqfktQRyuhQipfArV/tzvmifTIDLAlueElCvFML8zIZvW506oN4SUF4XqNOHX/BYy3Tfl0/zOtxbRACekAEj1NyGE5JNsZn9Hwy3AUCzt4oVzLgkhQjbBYrY8J4RkSr5bnruRjfnd98IgzW9CKoRStj/3a353vXzQ9zFZhUlIbeHUp0K0PhecBhX1hpDyJJ/JNn5iO7ruZJO4l6v5XehEHBrghOSZjrXLgKdZ/U0IKT3RcAv6h2Jp256z5TkhtYWfrOJMgsU0vwkhbqQypgphfuuVU5ka34C5HgoYrPwmpFoodPV3Kpwa5Cc43N29h+seQsqQQs7+zpZMTKqHdkZsGkTjm5DypFTV30K2XbNyoRh6RAOckDzzxXcvhoHXSn0ahJAqYk4YCE6sz+g9OxsXAmnMb1Z9E1J7pJtfl2krYprfhJBsKURVptEwJeuqb66HCKks8p3Q5wc/655MNYhmFCHlTyHan+v6lEn780xbn4v5zXbnhJQ/xar+dhsbBRSv9Xkx1z40wAnJI4+vWY++3kFWfxNC8kp4VWbzv3c2LgQAmt+EEFfSVWT6JV/m9+0/MxMHX/z5oPUYdYmQyiaVMVWoqkwdmt+E1Ab5WtNkgp91j2iQs/LSCau+CSlv/IxZyBQvffI7ViFTg4rGNyHlT76rv1MVNrgl3PQfOVEU87sUekQDnJA8cdUjm3Hnhh00vwkheUUWQX6rF2h+E0K8yGelVKHMb2oSIZVPuk4TQGGqMvXqbz9wPURIdVKIEQuA/9bnfgLIrPompPwpRDtiN33KpPo7U4OKnbUIqRzylWxTyLnf6RL7UlGqtQ8NcELyiAH/M+YIIcQvftufZ2J+c74lIbVJPjZVNL8JIekoZlWmaFKmLUFpfhNSvRQqycYLv/rDSkxCKot8V38D7vrkt/o7E2h+E1IZFKLTRCbV30DmIxX8oq97gNLoEQ1wQvLAnDDQumEHDXBCSF6ZE/bf/jyd+S2BXgA0vwkhSWQ6+zsv5jfnfRNSUxSqKhOIa9KIv8pLmt+EVD5elZmFTLLxwsv8lgpMgVXfhNQ2bvrkt7ME4N+gAsB1DiEVQiFan3vhVsjgN7nGuabxQ7mse2iAE5IH3ll/P7Cpm6YSISTvBCfWpw0U72xcCCNQ57khuuWeJwHQ+CaEuOM3WJyPyu/+IyfQtesgzW9CqpR0FQyFaknsJ3ijB24YFCakckk3ZiGfOpNu7ZPOmHJWSlF3CKltMq3+zsb8frGX5jchlUSxqr8Bd71Jpy+Zzvwut243NMAJyZE5YeD6Td2oM1j9TQjJL+FVy1IuXKLhFvQPxWh+E0JyJl2wOFfzW4I3XbsOoo9BGUJIHrDpUprqbz1w42zFRwipPArRltiLTM1vt0Bxd/cernsIqSCKUZWZbrSC4NecAphgTEglUazqb69YTiYJxJma3+WkQTTACcmR60MrAZTXjU0IqX52Ni4EaH4TQnLET/V3Psxvqfru6x2kJhFCciYbXRLzm/s2QogfUrUmztT8JoRUHvlKtHEbAZNJ6/NUuCX4cZ1DSOVQSJ0B0pvffhOI01FuVd86NMAJyQOzm84q9SkQQqqMjrXus7/9VH0DNL8JIf5J12kCyN38lqpvahIhtUk+5/I6K6b8zq6j+U1IddO8aH7exyy4rX8yaXtOQ4qQyiPfVZlAdq3P/RhTgF1zqDWE1C5eayAvrcmH+V3OxrdAA5yQHOhYuwx4utRnQQipVhpm2zMB/ZjfD+2MoO8Fs+0VjSZCSCrSGVK5mN9seU4IEbwqEjIllSalWhcB/tv2EUIqk3wm2QDerYnTVX67wfUPIZWDmN/5rMr0qsj0Il1iH8csEEJ00rU+d5KNxrhRKUl+3AUSkgPtT0+CAc7+JoTkF7eM40zmfdNoIoQ4mRO2B3LSGVK5mt96y3NqEiEkX1WZmcyu49xvQmqLfOmMm/mta42+F/OqwqyUoDAhJJl8md+p8Npjpesw4TSmqDWEECA/1d9eaxonlVD1rUMDnJAseXzNeoBzLAkhBSC8apmt+lvmfQeC3r+22fKcEJIphTa/X6T5TUjNk6+qTLcKhlRBYrfKBWoRISQdmWhNugopag4htY3bGihV9bcf85uJNoQQP6TrNOGk2qq+dWiAE5Ilfb2DqDNY/U0IKSw7GxcCQFrzmyYTIcQvqQypfJrfAHWJEFKY1ueZmN+s/iakusl3oo0frUkVKKbmEEIEv7O/M03qq0QTihCSf1KNWfDqZuO2pvEzLqpSdYcGOCFZ0PTA/cCm7oq74Qkh5U/H2mUITqwHkN78lnnfNL8JIalwG6vgFozJxfwGQPObEGIjH6ZUrua3QD0ipDpJN9IlU5xak4nO6FBzCKlM9keAOdiXcxv0TGZ/0/wmhGRDqi4Tfsxvv1XfQGXrDg1wQjKkY+0ytG/q5uxvQkjeEZMqNG0GzW9CSF5JF8TJ1fyWDZVsnqhLhBAgN1Mqm2pMwF8QhxBSPeTD/NaNqVx0htXfhJBUCYB+KzIBmt+EkPToa6B0MR03nal28xugAU5IxgQvWQ7j6ec5Z5cQknfCq8zq71TmtxjfAGh+E0LS4qz+zqRFll/01ueEEALkXv3NVsSEkGKh60228751uD8jhPit/gYyH+dCjSGEeO21Uo1Y0NGT+lLR3b2n4jWHBjghGbLu+d+U+hQIIVVIx9plAIBA+3JgKJbS/KbxTQjJhFTV3/ma+Q2DVZeEEDvZVmX6Nb/9Vn1zzURIdZLPMQsDIwYwkr35XenVUYQQO8feyK4Nut/q73TrGiDZ/Ka+EEJ03Kq/nWTb+ryadIdRKkIy4M55K4HeQcxuOqvUp0IIqSLE/A5d/kn0D8VgBOqSXiPmN7tPEELyRb7MbxrfhBCdXEypaLglbzPrqqFigRCSmny0Px8YMbUl19EK1BtCqoP9keQuWn6Q9U+6lsR+1zWs+iaEuOHW1Q/Iz9zvajK+BUarCPHJnfNWAgDNb0JIQWiYPRc74+a3M/hyyz1PAgDNb0JI3iiU+c12w4QQIHNTykuTnIEbv4YUtYiQ6sYr+JsJ0XCLlXycS8tz6g0h1Uk2VeDpTKlMze9qMqEIIQmySbIB3BON3RKIBSNg+No/VbPm0AAnxAc0vwkhhaJjrTn3OxpuAYZiSRshmfdN85sQki8KXfldjZsmQkjhSGV+Z1r1DVR3AIcQkh+8zO9MjG8d6g0h1UWmVeBuSTnOlsR+k/q4jiGkNshmzAKQPtFY1xp2zaIBTkhaOtYuA56m+U0IyT9ifoemzbCqvwWp+ua8b0JIPjFaO80/MzS/ZROVyvxmBRQhJFP8tAbNtA0xwLUTIdVKthVTOvk0v7n2IYS44VzfeK1tvLppcR1DCHHiVf2tk4n5XSt6QwOckBRc9chmtG7YAQNG+hcTQkgGSPAmNG1GUvU3W54TQgpB86L5MIKhrMxvMb7Tzfyu9s0TISR/pAsOA5kbUjSjCKl+sq2YAtzN72yrvgWufQipPjKt/nbDbX2TKqmvVswoQkj2pBqz4Nf81vdLtaA3NMAJ8UA3v2lCEULyScfaZQDMud96EAag+U0IyR964KZ50XwYrZ05md/ZBoYJIbVBJsHiQpjfQi0Ecggh2eM0v7Nd31R7y1BCap1Mkm10U0qvyLQlEseh8U1IbZOPbjZAstYA9r1UKvO7lvSGUSxCXGh64H60buhGnWHUlCAQQgqPLHSc5rcRMGh+E0LyhmjNORfOtaoSjGAoo2NIwKaxPv2WgVWXhBAAmHvDct+vTdcWFMi8FTH3boRUL7kEjCVIPDBi+J6JSQgh6XBWf+tag5HUicS1aEQRQkwy7WbjpTVGw5SMK79rTXO40iPEweNr1qNvUzdnfhNCCkZwYj0A2MxvWaTQ/CaE5At9UxWaNgPRDN57+89eAwBf5rdQaxspQkiCOeGE5ri15tNJVa2QrSnFJBxCaoNMA8aiN7Lv6nr5IIDcjW9qDiHVi76mSYUYUrLusZnfgKk3NL8JIXnCqTW6+S3rG4Ca44QGOCEad85bCfQO0vwmhBQNvT2N9ldCSBY4K4P2R9xfV+04W59nSqbmN4PAhNQ2mbY+T1X5nYspVYsBHUKIN/aq7/yZ3wI1h5DqI9NOE6FpM2yJfWJ+y9pG15tam7tLCMkPekzHq/IbSL++qVXdoQFOSJzH16yn+U1IAZgTBsKrliGyradmzShBroUXMVXEkyGkitADFZKtf+yNfSU6m/JAb32erhpTEOM7m3nftbqZIqTW0cctpEM3vwW2IiaE+CFTU8rZ8lygzhBCvMhkTeNMMrbWNyMnbGsboZarLwkhuaMn23i1PSfucOVXw1z1yGY8d+O6Up9GWTAnDPT1DqLOYPklIflCN3uDE+sRXrUMYQC9W3pKe2IlJLxqGYIT6xGaNgM7GxfCCNRZz928JIy+FwZxaGSUmyJCMiCTQEUt4AwQZ2p+Z9LynBBS22Rqfuv0H0nMxQRyM6XYhYKQ2sDvWs/ZhhgAg8OEkJRkY347q7/FkNIT+1j1TQjRyTShT/RGN78FJvj5I1DqEyCl4c55K9G6YQfunLfSrHyucd5Zfz8ALkYIcTInnPjJ9H1i9jbMnovQtBlomD3Xeq6W0c1vw9Hz/MGvXscqcFIz6Nqia002GkHzO4F02sik9fntP3vNrPrOwvym8URIbZOJ/uqtz7t2HURjfRCNoWBezG/u4wipXrJZG4reFKLLBNc+hFQnmaxpnNWYgj5qQV+jcJ1CCBH8Jto0L5qP0LQZCLQvB4CkMVIAE/z8wNSAGuTOeSsBALObzsKhkVH09Q5iRYnPqRwwwOpvQnQ61iaqt6Onz2B/BpXbUvntrDxsmD0X4VXI6FjVitP81okMjyLcxA0SqV70QKYz2/7YG/swJ+xvfveccOrNg9/jVBP6tdU12Fl9KfQfOZFVy3MdBnQIIX7Qq77z0W2C5jchtUOm1d96cBjIf2UUdYeQ2qR50XwE2pcjGv+3myHlNL8JISQb9HgOx0hlD69OjTOuFJZ2LADe3F7qUyk5Ciy7JASwG9/yyzZ6oLZn6eaDjrVmRXw03AIMxTxf9+BXr8Mt9zxZvBMjpER4BTLPuXCur/nd6cxvv8epJvTuG26tz52bJgC2FsSZwgooQmqXTCoyo+EW9A/FzNEvOSbcOGFgmZDqJlOtARKtzzkXkxCSL2xtz+Gxr4pXfwtcoxBCsiFVNz+3UQskNWyBXoPsuuZdAMCB4VNY2rEAK7beXeIzKj1n330bAODQyGiJz4SQ8kDaluuIMZ7JMZyM1riRLtdUn/3txtIrFiAyTD0iJBdqzfwWvMxvN1j9TQjJBb+zv8X8dgaGc4EJOITUDn61BrC3PhfyGRzu7t7DtQ8hVUa6RBuvmd9C/5ETlubQjCKEeOEnqc+pN9FwS1LCjT5qwQ+1vnahKlcQc8LA9aGVtseyMbB7t/RgV9zI6t1C8xsw26MuXbMAfb2DpT4VQkqKl8ndMHsuRg/4b0usM3b0MKKnz1j/7q3B9udyXW0VUCm4eUkYfS8M4tDIaE0vUkhtk4/25bXW/jwdavS4fUZdfAYvIYQUEiNQV5DAMNdIhFQ3fqu/9eAw52ISQrLBK9HGaUY5jSjnuAVCSGbU2ti6dFoD2JNtdM2xjZNiso1veKUqiPCqZcDT5qxqmQ2b7fzubAwo2XxUqyit2Ho3+uatTP9CQmqU4MT6jF6vV3tnojnSwjeyraeq9KZh9lyzVZYWBE7F0isWoO8FJuWQ6sJvINNP+/L9EWAOkl+T6r2pPr+a9MaNYGSPrWLh9p+9VrqTIYRUNJmYUn4S/zKF1d+E1A5ugeLmRfNTdrspZPU3IaR20M1vN8T8lnELNKQIyY5aM8G9sMaQepjf5oM0vzOFV6uC6N3Sg8fiVeDFrAq86pHNaN2ww/r3wCNX4rkb1xXls4vN1js6sWZTN2Y3nVXqUyGkJPRu6fGsAo+ePuN7QZJtkk14VWL+eHjVMoSzPFY5Mnb0MF5tXOg7CCxV4JHhUSvpiZBqwE8bS7/ty/dHkjdLc7AvpVa5fX6ttUuXzROrvwkh2ZJOy63ATTzxL1+BYTGgWP1NSG2Sai6mTqGqv6k9pNqo9mKnXHEaUoJufjthsgwhdvTk2VrVGr8JxIKz2wTgrjckPZwBXmHsj5gm7bhSRfm8x9esR+uGHTBgYHbTWagzDJsZXm0M32rOAufsXUKKi5jfwYn11vzxhtlzEZxYj461yzJeKJQbkW09wCLT3M9kwfLgV68DABwaoSaR6iGd2SzP+90YOV/n9b5UOnLOhXMxJ5z5pqQS0dtm5QIDO4QQLyRIPDCS3yANzW9CMkPWNtW2vklXjSmw+psQb5zakItO6FpTbXozdvSw9XenIZUqtsO1CiEJTTjnwrlW8qxTI/ZH/Md+Kl1r/BSDkPxDA7wCEZP2wPApLO1YUNDPkpnYUn1YC7/AHxvbDoXiJBgQUq6MHthn+yk04VXLLONbJzRtRsat18uR/RHg1eN1WbUAXXrFAsQoSaRKkI3NsTf2pTTC/WyAMtnwyGtTbTj0DVklbqYyOuc8tc2qhXUhIfmm0gM3qc5Zr8yUIHG+KxWoO4T4w7n2qTS9cTtft+pvSbgpVCvi7u496O7eg5lTG6g/hLiga00lmjuptHFo9+u+jlGojhOEVDp6/Ecn01iOvm+qVK3xi7PbBJBY43DUQnbwilUoG9/cbv5F/iwQj41tx/WhxFzsA8OnCvp5hJDS42w5blZnx6uYC4BXy3UhNG2G1Q4dQNXNBk+HtEIv5ugLQgqJ3L9zwtm3HnerVkhX+Z1uk9S8aL4VWI2ePgNUmNZIO3gv3DZS2cIqKEKyw6lHlTp+IZWehqbNQBSAGj2OnFtNaFB3CPGP29qnktY0gn7+qWbxGg1TgJETlvmdL9h1glQzuWqCvu+o5nWNG/1HTiQl+IkpxfUKIXYkTpFppz+daja83XCb/c3259lDA5ykZH8EWLpmgVUJDpim+P4SnlOh0b8zZ4ETEm9Hk8Ecbrd2Nule2zA79WJGnh87eticEx434yspkPPRKePYfWpCVu9desUC9L0wmP6FhFQQ2d6/elB3DvYlPe52XD/mt847r//KdvxK0ho3rFm88QAxFHByLJpT9jCDwYRkht9knEqledF8y5gKRvYUJOmGukNIeqTVaDWRyvx2Ix/VUdQdQtJTbVrjB+eoBbekG+oGIXbc4inpChmIe7INyRwa4DXE42vWo693EAN3XYnnblzn+30rtt6Nb2sVmr1b7M9f9chmay740o4FWLH17rycbyn59qRX0I5JiAyPWu3fCSHpmRMG5t6w3Pp39PQZhJFcVS7sjwBhmC3X05nggBn0CAGYe0M9oqfPZGTMVzJSBU5NIsREgi3OoMuxN/YlbaT2R2AZ2W5BGqf5rbe6s9qGYl/Fb8wki7ht+mSgfZY5BzwLWNVASOa4GVKVWiXlJFNjKlsYTCYkPW6dYCpda4qlMW5QdwhJJl1CX6XvmfzgVf1NCPGPVIbPCafv5uek0tc26XCr+s62w42McallOAO8RhDzu84w0LphB656ZHNG7+/d0mP9CHPCCfPbgIHZTWfZKsUrmd4tPRi460rOAickAzrWLjOrsxE3qqfNQMPsuQhOrEfH2mWerc5FV5xzx1PNHy9FACQfqNh41u998KvX5fFMCKlM0s2K0udB6a/1G4jxO+etknCrwmybPjmnY9b6BoqQXDj2xj4ce8NMqqnUILEEnYphTDFoQ0hm6KZULu1GS4ms4aSzhF+NyVf7cyb7EZI51WxIeXW18dKcQyOjBTwbQqqPVHGeaki2SRfHchZlAO4tz5lskx28ajVEnWFg5tQGRIZH0bphBzbmcKw5YZizwTfssI5bbez7yjogtJIVl4RkgFsVt1Rtjx7Yh461y1yrwd3mju+PpJ8PnipTMB+vzyfRl54AFvw+VEzl1MKGmkRqHT+t9s65cK5rEMbrMT/HzEQ/MhkFkS/cNlW21ud5gAFhQrJD70RRScEaN6RaI5X5na/259QcQjJH1jWVan4DCZ3xi96SOF/B4WqMcRGSD/R5vs7Z35WmN5nojNd+yqk5nZ0tXL8Q4pNMf98DlZtsky7mFJo2I68jpEgCVoDXEONK4cDwKcs4SWcs+cFAdZrfgCnCW+/oLPVpEFI1BCfW+35tITZOUqHesXZZxgusfNHWnNuvXakCZ0YxIalx2xRJtaXzx+v1bu9Ph7TwAuzV6MVCPm/s6GHrsXyb39W67iOk0FRyxbcTveOPF27ac3IsmvFnUXMI8Y9zXVOpmpPNXi1fMzJpXBGSHl1rKl1vUu3XmhfNh9HqHhfOV8cJQmqddHsk0Zlq6KKVCmfxgorl3pWYaxoTGuA1woqtd+Oxse0AgAPDp7C0Y4HnTF4/7I/AahF+YPiU9ZMpd85biTvnrUTTA/dnfS6Fhm3QCfFHZFuPa9vysaOHMXpgH6Knz2SlO27HlH9nWo0pLdnFCCeEVB5+M3796kO2Vd1euBnfxUy6Gdr9OozWTkTDLTS/CSF5R/QsXUvi/iMnbIbUzUsyE0IGbAjJDmeSX6XiN4mwfyiW98/meoeQ9LglFVcSfvdnRjBk7an0bhMA2xETUmi8ChiqCRn3AiTM71xH1gGM4ehQqWuI/RFgI0wTHG9uz/l4z924Lqc26kKdYWDNpm5g3koM3HUl9n1lXdkI2vCtt2HpmvV46eevUTAIScP+CLB/Sw/mhIG5N5jV3tHTZwCY5ng2iGHesXYZRg/sQ8PsuZb57ddMnxM2K5WkPbufluyFRMXGYQRy+/VLPSK1ilvLPSGXVljSnjhVsDXd2sQriCJtSOX5Yq1x0pnfq9tnoWvXQTTW+9Mj6g4hRAhOrMfY0cMFm/3NgA0hREilNdFwC4wRA0bAyEs1JhNvCCGC20xeIH/dJgghxC/ZrnG4lzJhBTgpOTOnNmB201moMwy0btiB60MrcdUjm0t9WoSQHIiePoPo6TOIbOtB75aenDP1nCZ1pqa1W/t1McSLWZkZjOwBkH0rG7bZIiS5vaez9V4uepPrPCkvA12vCi+05ngFa5zkI6uYEFKbDO1+HYB93EK+YcCG1Ar6+BRiR7TGi/6hmM38zrYas7t7DxNvCKkx/OwX9eQbZ/U3IYQUk0zWOEzos8MKcFI2yEbj0MgoWjfsyEt1OSGk+EgleL6JbOtBeFV2742ePoOQy+PSDh3xCvVCV2bubFwIIPus4b4XBhFuYlCGEL0SXP6dr2M6ydUU15Fq8EJjBENQo8fz1gKdEEJ0RMfcEm5kfl22dHfvoQFFagZ93TEnXJ2tPbPFrduPTjTcAmjtz3MxvwEa36S6ce5xqDX+kDWNmN+s/iYkPbreUGtS47d4IRO4rkmGBjgpOQeGT6HOMKwbc+bUhqzmiRNCqptsjfX9EcCrqELaoYdXZd+m3Q8da5cheMlyGKfquGkiJAvcNlGF2ky5GdSVtnELRvbkbEIJzB4mhOh4JQvpDMRbEuv46WJDvSG1hp/7qRZxXhe3Nuhm9XddTh2yGCQmtYLcU/popkrb35QKo2EKMHKCcRxCMuScC+diDuwFC8UeC1cJhKbNyFvsRuC6xg5boJOSsvHN7VjasQDjSuHA8ClEhkcRGR4t9WklkWW3YkJImRDZ1mPNDncydvQwItt6Cr4AC4TqoWLjWb//lnueBPdcpFhIO85St+XUP78YLcT1Fuq5tlMvB9To8Zzez8AwISQdehv0aLgF/VpFphM/FZrUG1JreCX2lXoNVg4ce2Ofaxt0CRSLIZVN9TfXOKTW2B+xJ/o693u1rjf55tBI+cW2CSkG+sg6PYYjSTjEHb17X7YjF5hM7A4rwEnJWbH1bqwAkuZ+P3fj9tKckINvT3oF7ZhU6tMgxJViZ8/NCcNsGe6gGAZyLkgVuFQPOM3wQp57x9plCE6sN+d/Ny5ELBpFIJjZr1+pbGCAhhQT2ZxIlYBQ6Htd/yznBqmSqxaK0fpcJ9cqcAaGCSGp8GqDbgTY7YaQbNDXNmyNnr4KPNfqb0JqGX0sk3PPV4t64yRdW+J02tPZ2UIjitQkzkQar3uJleDpyXY/xfhNMjTAq4SrHtmM1g07bI9tvaMTw7feVqIzypznblxX6lNwpXdLDzBvZalPg5AkxFiNnj6DMOL/rRb48wBzbrYefBg7ehjhVcuKcg650LulBx1rlyF6YF/RDHtZ1Mn1WnJyL3ZmYYL3vTDI6m9SNJybFt2ELnRgxFnt7YbXHG2vqoVibqrc5lXq51ro6wYAQ7tfR/MiJLUKzQSa34S4Uw46Uw64tW3OR8INIbWI0+wGktdBzhaitYa5tnEPomc7+5sGFak1nNWX1ZRoXAhC02YgCrMSUzeixPz2oz2HRka5nyI1g76GcYuLCEy6sRMNt9iqv0n+oQFeJax7/jcwYCDcZP5iPTQyijWburF0zXqs7b675sWEkGrCaUSHAIweKOzCQT6zYXbyAkbOYezoYet1OuVUHV5sgz68ykxS0Flycm+8RWhmJjg3TqSYpNqsFLqS2W9bLKf5Iveb3iqzmJsqq2oLpZkhnq92YjS/Sa2TrgVoqiSXWmXs6GEEwi0YGMk+W4+aQ2oNt443XgFjGlMmY0cPI9C+HP1DMTzcfyQvx6RBRaodPwnGQi1qjZfuuiX2ZWJ+M8mG1CJyL8mfbmNM9NfWit7sj5hxIj867JV0Q7KHBngV8Pia9ejrHcTsprOsx2QB39c7iL7QSjwW3u46U2rutxJtx/d9ZV3VCw4hlYpsWsTgcVb2OU3WQpDuM0LTZsCY/iG898vHLaN87APz8c5Fq9AEYOFr22yvLydjvBA4q791gpE9aIub4OlahXKxQ4qJ39lvpdik6JU/qdrSNS+ab220SrGpKvZ1STdLS40e951RTPOb1DpuQWJdb7yCOLUQuPHCWZmZabu+7u491BxSczi1Ru4h+dNNa2opUOyGrjVGoA5A9tXfhNQKmZjfQi1pjXS08TLBjYYpwIh9Fq9f3aH5TWoJv3EkJzWnN7CPnvAL1zu5ESj1CVQTj69Zb/0Uizlh0+Q24B1oWNqxIElEHl+zHteHVqJ1ww60bXgebRuex5bO4p13JXJg+FSpT4HUKDJ3e+4Ny13N72LQu6UH0dNnPJ/f2bgQOxsXYuCdIBpmz0U03IKdjQvx6okJMOomAEYAexf8Phpmz0XD7LkITqxHeNWyrBdJlUKqpIFgZA/amgNQsXGomPJ8Hdufk2IwJ+w/QJKvSuNsCE2bgdC0GQi0L7d+5DH5AeyGlZxvNeuNVwVqMLIn42PRiCK1jpv5nWrtVUpNLCf6h2IZv4fmN6lF3Mxv5zpGHndSC2saJ4XqssGkP1LtZGN+C7rWVLveSLy8EFpDfSG1gB+tSVXAUEt7qUxN/kwLojo7W3BoZDSzD6kBmD6QJ+6ctxLoHUSdYWBcKfRpM6MLOYt7fwQYeORKtG7YgcjwqNUCHQAiw+Z/8N+e9Ap6tfd0rF2GO582z1V+GfPmSM3GN7eb/x8TUmTE/C6V8S24tTYXdjYuBAAEgkHEolHz30MxW3tvI5B4bsnJveZ3OXq44Odd7iQqwccB1CW1uRHzmxsnUmgk+72Um49Un++2YTIapkCNHk96PDRtBsaOHk6qGN/36BP5O9kyw3ndZA54oH2572PQiCK1jjPAK6aU8zE3zp5/PuTtkW3muJVqr2IAkiumMqn+ZmUUqWWcld/CWHx/JPrTvCj5vbKmEc2qZq1JV52ZDTS+SS3hZ2/l1nGiedF8q6uWVGcC1as3ojX5gmscUmv4Nb+99lLNi+YDLvGaatUcJ6m69mVa/S3jF7jOSUADPA9c9chmYMMOWwtyMZ8VFD46ZRzPFfDzn7txHXatXYb2pydZ84sOjYxCQWHXNe/aZt7OCQPtT0+CAcN2I8yc2oC+3kGsfuD+gpn15UgqU0+n/elJBT4TQrwptfktuM3/FoxAHWLRqO3fTsQgrwUkccHP/2/ByB4sgSQSmCY4zW9SbCohs1/uJ5nHppvfzhltgXALYruesN43dvQwwquWYb+2JqoG/Pz/5qf9OYM0hNhxM78B70rwUPzPsaOHMfeG5WbXnCof9ZJNsPjkWBSNoSANKEI0dF1xaoyb5ojOAED09BmEUd2jpTy1RiU0xQ/UHULs5pNb0rCOWxKOtO+tVr3JJ9QaUgt4FTG46YrX/gpIrG30hJxCdYApF+QaBSN7rHhW/5HEyIVsx2HS/E6GBngeaN2ww2pBrrfJHrjrSkRfegLP3biu4OfQu6UHWx+4H2s2dQMAxpXC0o4F6N1yt+11WzrXA72DtkpxIVUb9WolE2NbT3AgpJhET5+xAqulZPTAPlczvq05AEABCFi/uPuHxmEE3H/FWEZVFVeAS9V+Jiw5uRc7GxfioV2H8cve12h+k6KRS3u8fCIzkZznkapdlt7iWzd61ejx5IDy0cNVNVvKz/9vKjoGpMgm7utNjFig3hDifT+NHT2clISjByvk35buxJNuqt0EB8zgVPOi+WhrDmBgRHlWgd+8JGwL5FBzCEng1JJU2NY3gJXkV+16o68H26ZPBtpnoWvXQV8mOM1vUmu4JY3IPSQdoqIwk4ad6xkgoUO6CT60+/WamNfr3Hu2TZ+M/iMnMupyQ0gt42eElBM9IUc3watNa5zaHJo2I0l/da3JtPqbhQ3u0ADPA7uueRftT0/CgeFT2HpHJxa+tg2RbT147sbtRT2P4VtvA+attEz4FVvvTnpNX++gZeS6zbRes6kbu9Yus1WNVzMDd5nt42luk3JlfwTAth7MvaG0VeC9W3qsquaowwh3BmmCkT1A40Kr2tsIJFp7G4E6DIwArVPNmdfVWJEpZPP/V1tzAGt6B5O6dBBSaEptfmeKio5BDXRjDN73mm5SCedcONd35UK+Wv25BaDytYlL9f/b0O7X0QwArZ2uKY6SXUytISR9JbObzojGuI1jqJVRL5lUgYv5zcAMqUX0+0QSZZ1th0VLnIFQJ869l+hNte6r5NpFT59Bw+y5kH5ilgn+8sGU72clFKkldK1x2yeI+S3JsbrmuD0GJDpr6cZwtVdmBtqX++qiRUgt4mft72bs6rgl3ji1Rh/5IvETr8+uBJPcrVreqb/CQzsjWZvfXPMkQwM8D/Ru6cHG+N+Hb91um7ddbDa+6W266zOsxfx+bGw79kcSzxkw0P70JOxau6yq22gJ+76yDgiZSQM0wUm5IzPhoqfPAEDGFca5sj8C7HcY4YKzPfqSk3utv0trb8EIGObM6wuuBVQMVz2yHNGXnihq4o18B6Ec9E5mp//DV5fjlnueLO3JEJKCQgc8nHMezXnWiYCLtDaXx8eOHgaOHrYFKlwDOFqLv1SVC/lqB+8VgCpmwGho9+s4t7XT9bmuXQcRMGCNzyGkFkkVKJZ1l5v5PTCSSCtRQ8fQ1hxANF5Fpb937g3LgUefKPkao1iomHcHICBRxUDNIbWCm8bIHk7WJGNHDyMQbjETaeobgDOjKY0XXWvk/YC5n6mmSinntdP3vrZZmSr9sQ6NmCMKqT2kWknXGUpaD0dhN1rctMb5mBo9bhnnsg/TP9OLStIi/bsYrZ22a6C3JCaklvHTgU7WNl7GLuCeeCOI1gQje6y26GKCC87PL/eEnHTXTa5BPrSG6xx3aIDXEFvv6ERX/1vo6x0EYJrl+7XnxQCODI+arcFDK4F5ZpV0Mdq4A/F56nGKYYjtjwCPhbfj+tBKRIZHXVvDE1Jq9kcAPPpE2tcVC90IB0wjWW8NKkgwZsm0xOKnfygGIxBEIGj++lExhVdPBPDRS5ajA6mN6I61y2z/jmxL6IPfzZV+DDHtx8qgQstMEjDnpN9yz5Ngdy1STKT1uJBqQyObi0IFNKSaUN/EyPnIvapXTMnfz73FfZ2iG+GiUc2LEtnETpybEzHKU52v1zH045Qbt//sNcAAOjtb0N29hyY4IUgk30jgRk+ycTPBE+3p6mA0TAbgPuZFWhO7UUnBYSdObQxG9qAt3IL+oai1zhOynWFHSLXgth4ITZuB04d+Zf49bmhHwy1JwWA9INo23aE1gKU3c29YbiZKe+iNTrlrT6pgsVvVWCo6O83XsvsEqVZyGWflpi9O9P0UYO6j9LUSkJzA7NxDlavmOK+d1+gtI2D4XstQa0g1kkpnnPeNJNsMjBjASLLGuCWY6M+J1khbdOdscCeZdPkrJumumaEVKsh1kP2ljI7yWwVO3UkNDfAaYvjW27ACwLcdLc6bHrgfiM8OB2AzgSPDo2jdsAOYt9KqFvei6YH70dX/FgDg25Neydi8bnrgfrRu6LZmkV/WcTFWoPAVofsjwNYHOq356YSUI+XyS1w3vYXIth5bRbgYy1KpHoLWqi9u9ArmL/c6vHoCaL1sBcIAwki0XAfs3103refesNz6HHlPOuRcnRXrhcArYO5EN78FGlGk2OhtbJ0BDCEf5rfTMHEey/m8nIfbhkffaBnBkGe1VP+RE1CxmKU/be1ma63wqmUII5FMI7qmtyX1CiQde2NfSvO7GMa318z0dPQfOQEYiUpMMcEJqUX0+1iSb3RtcTPB+4diSQZv/5ETaJs+GQMjBlRsHG1a9QK06gWv4HC5rPNS4ZYMpH+fsaOHEQKAxoVQseRZ4Jm28SOkmtDXVvsefQJzb1huVn5PCNleJ+auXg3UtesgYACrF8+KB0jNe6tt+mSrMtPaa/kIFJdzpZRXEqGXKQUArnNeCKkR/FR+1zVMxPjoac9jyO9rr+pD3RiXtZCsi5oXJWaD67itd8plrZNOZ4xgyPkWi3RrGbYgJtVIxua3lqQm+qJiymZ2O/VG9lJOvGaDO5Fzm4PkOE0p91upzG+JYTnNb8B/8rAex6HueMNdaA3iNInWbOr2bP8tZviB4VOY+63N2O9SCX7VI5tNk3xTN+oMAzEF7L1jFZCFeW3AQLipwXU+eSHR56ezFTqpNaQqOpWBrFdOS/u50LQZGD2wzzKNEiZSckt0wDR619z7jJXk4kRBYeuXfh8fnfweOuKfI+Z2xFHJoBvLIQCjB/ahw5Hc48b+iGmwex0rX4jRjjQmuJv5TUip0OcqeQVHM9k0eFVO69XVqZ5PRfOi+Yi9N4bAhJCVYWxrhwl70Fhvj9kFhe/Nn2G2G42O4YLLVuDtBzcjOLHeViWeaoOVinKs+o4dfxuvnjC127omDlgFTmoBPyMO5N53VoID8arLoZjt9UbAsAV1AsEgBkYUWqcqRMMtkDCqzP2VY7p1miiX4LATPfAl2p1qVIUOq79JrZIqYCz3jHOfIEFjNXo8MWoh/ofMur55SRgqZi5s9EopwF+g+JwL5wKR8jPBva6XaItcKz2w7rWmccJEP1JtZJp06yfm4UxcA2BpjWC1LZ7+IYwNmEVEfoypckm88aMzUY/3ulVkumkL91OkmvBjfqfSF0mK1fVFN3xFY8T8ljWNPu7Frwku5+k0wUuxv0q350xVwCE4E2681jLUnPQw4l5mWGYygF3XvJtxFbXMtfU7z1afC54KL8NKp84wMHNqAw6NjGLNpm4MPLI5q9bpB4ZPYWnHAqzYenfG782FjW9ux+Nr1qOvd5AmOKlqnK3ExWiWx536IY+7mdrONuL7IwC29WDuDfW210vAWJJcIsOjCBiJX9SR4VEYMBAIBvHqqQCw4PfR1hywgsbvXLQKe2Gg7Vxzlrg+9y4fSHvSfC2M5Dr4McF185uzv0k5INXg+bgf/ARl9LWLdHYAUld9yyYISFQd6gFR3fx+MT76RVpgnjjzXiJ4E8/wP/eWdVADiU4wUtHgdR5y3uVqVgGJa6UGuvHq3GvwcH88SGy4Vy9w40SqmUxGE+hGbqB9OZphD+wYgbqk94gxJaxePAsDI4ZlggNAML4ecAZwyt0Idwa+UrUJjYZb0PXMQRgGq79J7eHVxUZHbx2sY6uW8qgGciKvaZ2aeH8wssdXoLgctQZIb34L+jViRSapNfya33L/jI+ehvpwS9LzqebNOo1vwDGrd/Q4jHj3Cb8j5cpl7+R13QLty5NmpOs4E/qoLaTayba7nXSzkUpvvTOUlcAX/7eeSNw2fbItsU83wSuJdEkDeqKNn/Ueq7xzhzvRMqN1ww7UGQbGlUL705OwMYP3Nj1wP67f1A08DSC0EhuxPeVrpeW3X7O3dcMO1/PZ95V15rzwOOMqeaHkFwWFrXd0mhXZJWDF1rvRN4/zwEn1Ir+InWa2GM2jB/YltQN1e72T0LQZtnbC0dNnLEOqfygGDMXw8CtHrNfr99ehkVEAwINfvQ6xqLkMMAJ1GBhJHN+omwAjYGBgRJmtRV0WQpkmDOlIRVY+kVnpHWuXwa2JllsVGQBqDykLcg1O+Kmy1F8XnFhvmd9egVtnpZRuggu2qu+4+S3JeSfH4voCI2mWnREMYYIjuKqfB+C/ItyrhXy+SXWNbVUMmiHllUXMjRSpVvT7RDTG770sc9kkIOqFBETl/jo5FkXXywexevEsm9YE2pcDmjnlJF13jFLix5SKhlswMGK4ag0htYKeyAcgaR0D2FsIOxHN0NuD3rwk+Re+HkiWSnE94Ub2Hl7JfKlahZYCtzWNXC+nBvcPxWDITFGf7c+5ziHVQqbmt9U5If64m7mrm91OUwpwN8rbppsmVTTcAviszCwXnHs1fRav22xiwL7W4/6J1AJ+tMbZAcrNsNZNcDecFeAArHWNxH4BIBA2x9hlGpspBfp4QZ3mRfNtiTZe5rfoDbUmf3BnWobElGlKHxg+5StDbk4YuD60EtjU7auF+J3z7K/1gxxTbzFsfW6cmVMTn7vrmnczrv4evvU2PBYG9t/qbdwXg13XvIv2pyfRBCdVSyZmr96uMx1ikodXmf/e2bjQrPoO1KHr5YPo6x2EM6nt0MgoYgpYesUC67G25gAGRtwz4GRm+P/7zTgaGheirTmA2K4nfH8fL2ReeaEYPbAvqSK+P35tCKlWUm2WpAJK37ikMrfd2oQCZlAUAHYOxUyTF4ZV4SwbBtGZ1YtnJb0/XYaxW5A61Sx02ewU0gRPV8nqZkwZ8DakuKEi1YoeuEk5P1bDljyizYDU5/F6IUk2gL1FMQBrNjgaF2LJyb3WZ5Vz8MYLL/PbaJiCrmdeQ2M9Qwyk9pgTTvxO1tcOcp+73Tf636VaykrMczGpvMYJiN6IzrQ1B6w1Tapq8HJKtnEGi3Xz23ktjJETKSulBLY9J9VGLm3PRWN0xHDyWtt43WeJsS9mpxu/7YnLIdnGTWvcZvEK+jWQrmLcO5FqJ9fKbyfO2d76feasAO8/ov+ONwuj2qZPhho9njKBUCintY2OV6KNl/lNvckv3J2WGY+Nbcf1oZVWRWQ6dBNaKrm93uv22mxwtk13Hmvjm9vR+2Z2xy6HBVHvlh7sWruMJjipaaTtuT4TNx1WtfcCc661zPteesUC9L0wmFT1LXHhpVcsSAoUq9g4gDrbYkDFVPxxoCEIQCnExs4gevpM0ozwdIwesC+K/I6NyIbeeBX46IF9CF3+SasinnO/CUlvAPkxrdY+cwgArKrDk2NRmxEVU8DWO6+1bbz0ALNeoam3EHVy9vzzLa1JpRduJni+N2Jem1G3OZm3S2IAITWI815JNVpBkPtHdEBMKT1Y4WzfB7hXaepI0pvMBfc7y64ccZrfOifHoq4JNzSjSLWim99OvMxvJ9ZoFsdMbzetAewdJ+zPm8Hi1qmJfVUmszPLAd38ToVzFq8Oq6ZItZJuDyAYrZ2IBkMFaR/sbGUMuHfmqgR089t6LEWCDTWFVDvpzO90Y1wA7zECABwGd2r0ThSViMSD0iXaOJEuhiR/MPpepowrhaUdC7B/q70a+qpHNtv+/erxOmBTN2Y3nYXI8CgUzPetbvsghrVK6kzMbzF9I8MJI13BFJwvvnsxgOqaka1XtQu9W3ow8MhmtG14vkRnRUjhiJ4+A2ibE2cwxtn2fGejaWi3NQes1zg3UmJ+66auGC59L5iZa7rpDZjGt1RkxqLR+PEDpkEMhdapCv1D4wgEg5b5LVVT703/EFRMYd9X1mVsXOtdLIqRdKPPXO9/exyBCRNcX8f536TaENM3VevcVIESeV4CoF4tiFe3zzLbnivg5JkoYADjMYVLOxaYhviZaFLWcTr085IOEZkkyugmuP5YsekfipnV3/Xurc8JISZ60gi0NY5uSrmRLigj1QzOBJxKNcG9kpJSBbo4t44Qd/PbK2Ds1aJ49eJZVtDYqxrcK1hcaSa4zMdMpS1u0Pgm1Yrf8VKCEbQPYZNOLU6cRkyqWb3VRvOi+TbzO50pRUitkK7y2y0R1pnAlwo3nXGL1zy0M4Kbl4TRf+QEWqf6OjTOuXBuWVSBO7tN+Em0SZXYR3KDV7XM2B8Bdq191zRo3rSb33fOWwls2OFaxXNg+BSWdizA5o+9H8/duA7DW83H54SBLZ3r0dc7mLblud42Xf4+cNeV1mPP3bgOj3csQJ3hvvhJ1Xa9XLlz3kpzZvq8lVjascD+nMe1JqSSkbnU8os4vGoZolp7bn0OL2Ca31KxJDO5vWZwO9t5S2W3BGjECH/wq9cBME1vadMHBBLHa1wIwHBtER6Nz32JHtiX08xvoHjmt62FvGEgFo0mVX/LNWLHCVItyP2lm8D6RkrfNKWq8raqf+obYNR5LFtHTuAPPjodZ02os4zwP1kSRteug7ZKcB2vDZro0Fj83/sefcL2fTKhFIa3VJpJ4oDM/tZhcJgQdySAk66KwRmwkGQ+Z6DY2dJP1xx9DTW0+/WyCNSkQu9o4VX93X/kRNI8XuoNqUXSVUHq942XwetlxEjQWDpOpEvCkXMJTZtRESb42fPPT/l8KoOqu3sPtYZUJZnO4pUEEiB5TeOG04hy6koqI7x/KOYaG9IpxzWOzOJ14mVKSTtiQqqZTBNthFQJfE7cZoKnKlZ4aGcEqxfPQv9QDEu0x9OtZYpV8JTq8wF/HQ11WKhQGGiAlyFupo7cOM7K60MjoxhXplH93I3r8NzWxHMd8Tbe6B1EnWEkbQbcDGs5jnzeczemn8etH2fjm6Wd3+0XvY377KazcGhkFC/9/DXba9yuGSHVgiwExAyXud3OOdUYikHFxh2GbR36h8Ztiw83s1ramt/UNh0AcFPbdOs4KqbQ1hw3vU8mn5/TIJbZ3/1D41h4+kzO5nehMa/psqQW8m3NAdz+zEH8sve1pPdUaWI1qXF0I1wn4xZ5Z0YBrVW5vrnq2nUQAHDfJy5C2ycusswmtJumlNeGytbO2GGG69Xp5TCeRfBqs6pvAId2v45zb1mXqP52ySLm+obUIqmCJM7qBa8AjgRtdFRsHBfPPAcAbAEdI2B4VmOKvsg56dUK5aQ5fpBWfl27Dtq6TdD8JrVMOoPZT5BY1jer4+sZsztWDCoWP4a295IqKS/Gjh627UmOvbGvrLRG1ompxm+J9na9fND2OLWG1AJ+Z/EG2pdb5ne6NU2qhJJUM3ttr4t3CUy3tyu13rgZeyo6hsCUc1O+T++2QY0htUAqrXGauX4SbIBERywnsq9yn/+dwNxP+fqokifcuCUs6UlJgl4FD1BrCg0N8Apn5tQGVyNbKpudVd/SJl0Qw/rOeStRZxho3bADWx+4H/tvvS3pmE0P3I++eLt1J2KcVwJifuvfg+JCahmpCtdbdQNmddISSHtzr+bDCaPcqzWWGN07GxciFtWPE0h6bTTcAmPE/TiZLHpKjZv5HQ23YNFXnrLpcmR4FAGDGkRqA9mMnD3/fAQn1tuCw16ZsfoMXiDZ/AZM41undaqCGj2Otulm0Eef9+1EgjmtU93nflcCcu1kthQADByrw8P9R9DXO5iUXEO9IbWAM9Ap3Rz0YITcO3LfpGw/7KI9iarugPW8BHfECJe1kRnYSSTxAMkGWbm07HND7w4ketw/FDPXbCOm+U29IbVIumop0Rm/QWKhbfpktDnWN2r0uLWvMtsZ2xNyxARXsXHo+yy9O0w5Vn7LNdR1xgvRVEnuo/lNSDK2NrspzG8Fs3GLnrinYsq3+e2kHPVFR9aA+r4zVYKAGFKs/ibEjp5Uly6hT/QjmxEDerebtuYAos32bhNyLzu1p1QJN17rGecICmdCNbWm8NAAr2B049vNfHYzqhUUtt5hBnmGNZN7accC9PWas73XbOrG0jXrsWLr3bb3rtnU7doSXIzzx0rcXsIPbuY3ISQ1YoQD5i9uWXDIbHDEq7/1jDVnBcLY0cNobf0QjGAosWBxqfx2qyRPtOIaz/WrFAVZ9KQzvwG2PCe1g219sK0nZWZxqiDxQNxsESRAo1dwq6jZwFwdf9v8c6DbNLgcRvjtP0t0YlAXT0db/HODkT2ZV6gXEb0VsRCaNsO6bgMjBj676SkEDDDBhtQ8Xoays/WlHpSQgIQeANarqJy6I2aUrkNt06dYx3G2QgfiprsWrClX41tH1xmjYQqMEbNK46GdEcv8pt6QWkRPXpE/9eCnc551/5ET1lpG1xNnENmrYsoNGTuVaBVqjqyCo9tEueEMFsv+KWm8AhKV342hII1vUtO4jZdySybWDRY3LclHAzp9rFS5jldwa0UcmjYD0ficdF1/nVWZYkhRa0it4Iw1yL/l/m5eNN/aE/g1v92KpVLN/na+Tl8rRcMtCMWfcyb2lcN+yrmeAZBU/e1V6Q5QawoFDfAKYX8E2PpAJ9Zs6kadYWBcmUKx65p3k1oBP75mPeCRNWLAwGc39ZhV4PNWWhXga7vvRl8oYQ739Q6iT5uL3Rc/nptZ41WFXk48vma99R1ofhOSGWYFeAxLTu61Zdu1NQcsw3rNxqcAmGZLTOmZemYFQvT0GeCXj9tarDsRQ925ENCN7yUn92I0T9+rEEgVvXxPuXZrXMxvQkgyurmio0aP45VDx/DwK0ds0ZouBWy5eqZNm5zm9dDu19EMwNDMLt38BmC1L1ajx5NaE5d6fpTO/oh7tZlufq/Z+BSNKFLzyL2i37tzwskB4gHpOjOSbHo7SRXkie0yK8yd8yT19udGwLAqwdXxt61AUjm3PnfTG2l5DpgVC30v0PwmtY1+D+v3jNPM1c0or/EIztc5E3JSdatZvdhsld718sGECd6+HM3aa3TNKRck0O7W+lyCxJJoLeY39YbUGn7uW30NohssziQ8L1PKS5P0mb3O95iPx9DWvhyxXU8kVWSWi964mVI6NKQISV7HOJP7pOOcJI8AyZXdzr2U130l73XqEoCkcVKJtdCUpNF1bt+h1HjpDJB8vfRCMsAcdUzNyT80wCuI4Vtvw8Ajm9G6YQcAs31575vJr5NKbjf0trsKKikw5DSy++Lzwyt5HvaccOJ7VOp3IKQYRLb1WLPAgxPrAZi/uPuHzL7jVsU3Eua3Pqs73NSAyPAoll5hJs7EolGrTV/Qw/i2DOJ7nwHwltVlYukVC5KqyJec3JuX71ko3Mxvqfqm+U2IHbcqZsFsq2uvWBAdggHbTOuTY1EzQUd7vwR/YruesOZhDxyrQ9czZuXQze3hhIkej/Pc/rPXsLp9FlqnJp9POWyiUqG3b+56xjT2ud4hJNn81jVH7hsAngHidEjVUzReZelspa4fp//ICSvAIYk7CsnGWTnqjVOrzcCTYVVj0vwmtYxTZwD3+ZmSbONaBaVVHzqNKasTVjzgq1dGy72ov04wAga6dh0BLp6OtvblZd/dhhDijXNtoBtU+ggkILlbFuCecONlfnutgdxM8MQx6gAoBMpca5wd+gAtuVG7ZrJekwQ/QmqJ/RFgDhKJK87RAUbc/NYTYrtePhgfwZK8l3JLnAH86ZLz8f4jJ9A61d6Z1Nl5olT7Ka+ROG7dDeX76Ml9AvdUhYEGeIXx3I3rsDHF8+lmUAkKKl49bv57fwTYiO1m9TiAFVvvRsfaZWh/elLSzRcZNusv9WCHAQPXh1ZiI7Zn9H2KCUWEkNTILHDRkfCqZWbbvAsWwgjU2QPEQ8ntyA+NmNpw85KwZX4Dyb/wpVVo/1AMiJvoukEsxxECwSBi0Sh2Ni40j3mgPLKIdeSaifm9s3Ehq74J8UCMHrf2fQDQ9cphGEZ886OALv1JbU908ozZTErmQblx7i3rMPBOEA/3H7GO2fXyQYzHFM6eOCFxrLEounYdROvVMxGaNgNjRw+jedH8sqlaENwCXgPvBE19jgduGKghxBtp3QckDCmZm6u3KvciqaohboIH2pcnVZPrdO06aCXwiHYBZkXSvkefsO7tcjTBndUeAyOG7fsQQhKkmmOtVzJLoq+pN3bDyhkAbps+2TLA3cY1yHtUTFn3pl5RJIaUBIilZXu56I20U3Wiaw3gX29kL8n4D6lmpCUxAEw48h9WzEWMajcjSTem3EwpZ+Ke/liqCmkjYGBgBGidmlxBXm56o+NMPNJ180WOdiE1jNyrzvEBoWkzgHg8V9DNXKcJ7px17STRHct/1fjAiIG26ZOtVuh6wk2p9cYZ13Im2rhpjXNtk6oCXI+VU5sygzvWKmNL53oYva+lfI0Y2NI6vemB+63n1t59myUS7U9PSpr5LZXjW+8w27HrFeNb7+jE8K3lZ4DvjwBL1yxIWRlPCElgLRS29WDuDcvj1d7jMALmrwyzDY33r49b7nkSALC0YwFWt8+yPadi41iCxKzvVAsbnUAwGM8ONDdV5biBkqr5nY0LsebeZ9h1gpAU2CqmsM/Wlvjm9jC6Xj5obQZOjkVN02gsav1bKrdXt8/CwIj5d1umcdyQAuIbKJfKcTkuAOv4AyMGWrXNVHjVMmBbT1nojT4nUyo91j531FyriZTS/CbEE73zhFW94AjaSFUzgKRONHqVgm5G6RUQusFlO4ZDg/qHEu1C5ZyOvbGvLLRGmBM2kyGN1k4YwZD1Pbt2HURjvf9gDSG1gFshwtjRw0ljEQTRHJnZDbhXZMraxi3BRjeHLTStOTkWRdcrh9F2dYs1C1zQ55aXilQjXfqHYtbYG2dwuLOzJWkGuDOB2vkY9YlUC7ohpSeOBB2mlNPgdib5pTPI5TVurxf9SoVelVlKvdF1xq0SU69gBRIzvwFTN/wk1DDphlQjruY3ku8jFVNYvXhWUmeIVMa3/l6/r5Vji45JJbhzfQMUP1YsncbcEvqcI7Tk+rgl9cn6RvZVurbI3/XXyHMkPTTAKxR9prUNHyZvuMmc2X3nPHPmNzZ1W0b399ffD9x6m+21Olbl+K234TEJxH7LbMs+rL2v3Pj2pFfQjkmIDI+yGpOQDAlG9gBa+3MgMfNRJ6bsnSH6egdx8yXO4LH3rO9UqJiy5ok3zJ6L8CogjEQiTznR9crhJPPbrXMGIcSOW1ACQJJJrWMFfxWA9llJ2cPO6iHnMZ2PSRW48M7rvwKQ2ACWypxymt9GMIS1zxyCYRi+AsOE1DLOzgmhaTMQhVmp1D807hnEfeilSNI6JhXOObUnx6LuQWIDMAJ16B8ax0cz/TIFRu8CtPeiVdgLA8aJuMaMnDCTk+q9NUfwGzCmRpFq4pwL51qGjwSMdVNKgsSAabZYYxHirUN1vXCbxetsmZmuC4Oz44RuSJU64ca5rtHniRoNU/DwM6+l/H7OAHBnZ4vnZ+mvE6g9pBJxjlmQyken4eI1sztV/MXLtNITA3Wc3SyAeHKfS1VmqdE7c0jnHmf7Zr3duTOxRvTGTTd0baHOkGoh1UgXHbf53XpSsW6MuyGGtttr3DTG9p6GxDxwZ8LNHBRvjeO3E7MkEUtcSvZOzvWLc30DmNqiv07/O+M+/giU+gRIdkg1s9uPH5zvcZrC0grdjcg203DaHwHeWX+/NZPcMtTLkN4tPdh6R2f6FxJCkvjw997CmnufwUM7I3hoZwS33PMk+l4YRLipwfrxiz4zPFNknjiQaDUuc7dLTXjVMoSmzbDNSRfE/F56xQK4xLIIqWmkwlAwGqaYGymFJLMb0AKhhvnTWB9EYyiIxnrTvNZJ1aa3u3uP9ZOOcy6ca23+/G5wCsHcG5Yj0L4cRjBkVoKlCQwD7hVRhNQizgCOrCec3LwkbAu06NXcgBm8eOXQMfPvQzH0HzmR1M5OT9qRx533qgSU9bmdQGk1RvR47g3LMeGyFYARQGBCYlSEVxA8FYdGRj1/CKl2dFPK2Ro4XfWkF7rO+GoJbpjrIalEl/Eupe42IVqjd5iQn9t/ltr8BmBbv6Uyv+V5+QEYJCaVjaxnhna/bhk+Y0cPW4azGEJ6q3MvzCID5Zlw47aucWqPxIjMLoF1ALyTmovJnLB9TaV343Ca30CiUMHN/Bacaxf9NfI6XW+41iGVjK41QCKpxWsP5dSRt0+O+do76Ml9+o/g9hiQMN319U2pSJUooO8VZW3zoltBqwvO9YvXa0h6aIBXIHrL8kJSZ7hn6byz3vz8x9esx5pN3Tbj/apHNhfl3DJFzpXV34RkxqtzrwFg6sGLvYPWLCT9XpKF/YNfvS7J4HUuUgDTBNeNcLfXOKvLVWw8aaGlm+ClNMLnhIHgJcuxszE+K90wEFOwBXgf/Op1VqDLayPEgDCpNSQoEZxYbwsSp2uBJYGXpI2DkXiv0TAlpfkNZB4ALQcTHNDaaKVJqHEzwWlCkVrl2Bv7bBVA1ky2QB1UTNkCK1bAtz5o+7cZ3DUDvAMjhjXKRX6AZL1xM6nk30agDkYwhHNvWQcgfZVFMRA9NoIh69qk+n6CHuh1Bo3dgsIM1pBqRFr8ugVh+4di1t/lfvJKttGDyKmSbPxizudNmODnXDi35Mk2Nq2Jm1HWd01jfhNCEog5BbgbU87W5U4jydk9q+vlg7YOFc6kPsFphsvrZVyD0dppaWGp2p+fc+Fc2+go5xzedGubSzsWWGOz9H2V/MRU4jUAjShSveg6I0iyjRNZ25zbGHJ9XsepSbquOH/k9UDi3pV7Wo8llSrJT1/7RcMtiIZbrLWfETAySiTmXin/cFVZgXT1v+VpTuscGhnFuFIZzaFds6kbu9Yuw51PD7p+Rp1hYM2mbmDeyqR26wYMtG7YgY3+v0pRmBOG6/xvfX45ANfqeZl5nuo1hFQrc7+1Ga+eMPAPX12OW+55MimBRBb9gGnwOgkY5gwl3cx2vk5aqesdb2ZObUBkeNTWBtAI1CEabvE0wUcPlG6GXXjVMrx6PAijzgyEp6roWHrFAvS9MGhVhcs1lX/rf2e7dFJLvDf9QwjENzBu82XdSNoUaIawBDbczO9M7iu9nRZQ+pmZ0kq1dSqA9lm2WeluuLXQurRjge09bJtFqp39EffEFStI6wgOA96jF9za8bkl8qVCP57RMAUYPY5zb1mHtx8sj0TisaOHYUz/EFqnhhJzh30QU/auNyfHori0YwGARIBYn00syYIA9YdUPk6dcbYkvnjmOUkJfk69kbEJwurFs5LGuGRrDBsBA/1D42hrX45mlM6Q0q/R2NHDCIRbbGaUXz0VbXFeD2f3ILfrxfELpJI59sY+W8KcPp9Xr7x2tiQGzPuhu3sPOjtbzMfiI6TS6YxXq1799SfHotY4h4ERoHVqCGjtRLN23sXm2Bv7rOvjZX7ruCUGy3gK3QTXdUZ/nok7pJpwao2OxCRknADgMqYljnPvpD+fidbo46XkeM5Z4NLlplQE2pdD1MFomAJj5IRNa+T70twuPlTnCmR12wfxy97XUr5GjNutd3RizaZuX4t8mQ2+96JVMJ7ucX39zKkNlnHsNIPl/eXG9SHv1uxb7+jE8K23oemB+/HZTT3eM8+39JR1i3dCColUIUSGR617RMxvp6EtBi+ApPspMjyKW+550nqPbn479SZgmFXgfS8MuprrbnSsXVb0meBS/W2cCtoWNl4muN5a9ZZ7ngRgXsvLOi7C6otnWNVk8jwDNKSakWDx0O7Xca6jDXAmQQTZKF16+QJ07TqIrvjjuomeicl7+zMHcd/VqefXzQkXdx643iYeiAdxRlJXyguXdiywJQfoAfXGUNDagGWaHEBINeBVialrUGMo6GnKpKrI9GrR65zLq5vgZ//ycewv8lpGZ2j361awuH8oZrUzTcfJsSiWXrHAmm+sB8FOjkVNDTLsQeOlVyywgvFc75BKx6uiOhpuSXRuQbLpomuH/ncxk/wkBAKwTK1UGIE69A+No7W1E+HTZ0qmNTIrXa+Wcmu37IbbeBz9cbcAu44kBhJSicjeYw7sxpTR2olo0Ky21Nt7uxkvgF0vul5OmN+pumYdGhm1vc9tjZMoXjAsE1wBOHv++SUxpURrnPtML6MOQFqTW3/M+Xe3kTeEVCISp3Ga4M7xBqI3zgQ2t+Q0P8a3m9boOBMGVy+ehYERA61TFULTZnjGbgqFc+2nomO2zjZAZt17dI2hluQXXs0K5KNTxnFZx0Xo6zWrtGMq2WhSUBi460o8d+Nt2Bo3d/1gwLDamntRSVXQHWuXAU+7n3OdYaCr/y2syOB4B4ZPVdT3JyRb5oSB6EtPoO3yT6J/aBxb77wWazY+ZatOXnrFgqT3icEr5q5OuKnBMsH1x9yQIKhumvcPjaPNpQocMCvBi10FPiccr5I/HkRggvsmyssI183vcaWw+uIZaGsOoH9oHIAZbH7wq9fhlnueRGR4NKlCnpBqwCtYvDpe2WyZJkKKlpjHj7+LF39uJt/EFOCS1O957+iZxo31pjHVPxTDkvjzEqCVSvBSVYHrGcV+EZPtvk9cZH8iXu2hm3BSvSnXjlpDqo19jz6BuTcsz2o2pVQdSJKf3t3GTW8Ez0CO4z1igk+4bAXmbDP3baVs32e2QDdcK6S8EPNbUDGV9Jiz2l4qqmhIkUpHDxbLfSTtLw2XhLV0RokkhwDulUJu90w6E9y8/+ow8A7QetkKdACIbOspqtbsjySbd23TJ3t27nFD1xVpKeq152JVJqlWnCMXvCqc3UzemVMbbPriTCxxSxzW1zP6Y/J66cqg0z8UQ1v7cgR3PZHp18sLYt6pgW6o9uW2ZCS3qngg2fgG4gUh8US+VN3+9PdTd0i1ot9HTvPbrcuc4LU+cSbhi9akQu86YZng8SrwUqIGumG0L3cds+CmDW5JfW4JN6mg1viDV6kCee7GdVgBYPMjm9G6YQcAuzErVdjP3bgu42NX04zsq+LXx3BGmDT6egex2mOmui64TQ/cD2zqto5FI5xUO+FVy8z24pE9aAu3AFD4/pc+jof7j6DvhcGkWd++j5uhxoSbzMXPLfc8iaUdC4CLp3ua4MUmvGqZGRyuS8yj0du5x5QZIJdEAbfN0qUdC6CUwpKTe4GTwBJAmxVThwe/ep1t09r3wiCrpEhVIoEJAFCx+JxMZZrhbdMn4/afpe58c1G42fVxP+11kwwqbdkgLUzH4i213OZfFZqOtcsw4TIzXU8PbjnbFTqRTVOS+Q2gdapC69UzrX8bDVPQHzfF+3pNHaPWkGrC2Z5Y76Igc6513IIJjaGg53iSVFrjFcjp2nUQiGucnJM6/rZp0pewOlPmBOukbUvsWBdK9yB9tmj/kROu15qQakF0RpJtAuEWW3cnIdOxCc5KS6+uNl4BY/tYKdMEN4IBTLhsBcJASbRGr/52tob3Qg8GW9oSN8P1uekykkrFlJVQyQAxqSasSvD4usaIV397tfd2++9fN6/dSLeeSbdHEK0BFIwSdJ0QPXbTGmcCkpsxZzO/Ye5J/axfxLwSuJ8ilYpXFbgbzv/ugcR6xZk84+wiker+cNMneb+YykbAsK0BwquWAUVK7nMbf2O1QHfRC7lOTk12xorTJdoI1Br/cBVYwTx34zo8Fm/B+fia9fhl72uWubTxze1FP59yan/e9MD9aN1gGtapKkwjw6PmTHPAZpRLVebWOzqxF+Zs9DrDwLhSODB8Cks7FqCvN1H5QTOcVCNWRbX8ueD3XVs9OXGr/s4FWTD19Q6ir3cQ3//Sx4HGhaZpXEIi23ow9xIzSCzf2U1vXuxNJAzoC8Pvf+nj1mt2Yob1fYLxpIP+oXEYgWDSddarzggpJzJtC+7cMFiJLY0LXV+fTfAy3SZAntc3ZjFlVhdJlWgwssdqqVUqExww5xV3PaMlAqSoiBdWtyeqpPQZXUBidpc81jZ9MhB/vbRcZVt0Uo2o6Jgtd8QtQOFmmKSqUvajNU4TS8yc/iMnEiZ4MIQJ02YgemBf0UctAGYbVcB9zIKX3kgnCfkOptE9jrbmgO11UuVpBZPj4xj0eeDUG1LpyNomevoMQgBUzFzPC35bYXoFhtMFjJ0BZn1mpr6nkK4TQHHHuswJJ8a66Mk2Usnthd7iXMWUpZt6kg0Am8YAps5IdRpNcFJNeHXSyjTJLNPfu6le79QZIKE1wYn11jkXQ2/06+OmNZlUautdKgB7wg3gPk6HkGrArbuNX9y6RcjjM6c2pC1QcItD6Il+bhXSAa3jRDHXNn6uj4zU0mPCemV4pujmtyQbcC/lTSD9S0i5Ir/Q54TNueDO5+Snq/+top3TY2PFN96dXPXIZsuwTldtGm5qwOymszC76Syr0jQybJrfA3ddia7+tyyDXAzxjW9ux9ruu/HY2Pay+L6EFILeLT2IbEtk6O69aJX1d32OtZNURnAuzJzagHBTAwIG8Nl7nwUA7IybZKMH9hW9/bnOmo1PAUjdzj3c1IC+FwZxyz1P4u2TY1DOUqlAHXZOacHOxoXY2bgwXgFuomLK2mRxU0XKFX1Nov/44dgb+xA9fSZ5ZlM8fuNWGdTdvcf6yReyEQsY5n09MGLAaJiCaLjF1jK5FO3PLYzET9oZmZrMqHiQW/4Ekmd42d5/JoqTZ6JZd/sgpFwZ2v262XFCuxf84KdKIR16sEeOpweq1ehxKxEoOLHeMomKRXjVMhjBEAZGDJvu6gGWk2PRpJ9xpbC6fRb6j5xA/5ETaJ2qksxvQQwrMa1Wt8/CzUvCVrecdC0PCSl3ZO0TnFhvJrU2B5LMEr9GrFcFppcOOTVGdEb/PDkX0cCG2XMRXrXM95otXwTi7Yj1xDy3maHyA/jbB+kao5vjNy8JW8fp7Gyh1pCq4JwL51qJa4JTb3QKmdjqvH/dziNdFWm+sc1Jj2uNjFAYj5+fc03jFuuSNs96XEbXGK9rnmokBSGVgKwNMr13U+2b/GpQqte5rQv09USxtcZtz+alC3qiULbmt8DEPv/wSpUBVz2yOeN25R1rl6H96UmJBxxzu68PrUw81ztYtArl60Mr8Vh4e0lm1gn7vrIOCK3MKmgrJvfC17YBMFukz246CweGT2HgrisBjKN1XuLajo8rYJzRYVK+dKw1fxFHtvVYv5QjPmdL7o8AkPcZAQSCQavNN2DOANcXHIUyv3Wkc0MgGEQsGsXYB+YDB/ahtwSt+2T+N+DvOwcMsy36uY0hPHjndY7qKGWa3oE6IDaOQDAIFVOIRRMLIr16hJl9pBxxbjSOvZG+gtF6bluPNZvXGEnMSrIqgjKY6Z0rUg2eaE88xQoU73v0Cft5FwkjGMKajU95zhrWAyxifv/BR6cn3h/fEKrR40mV4LoR2DZ9CtriLdPTtZ0npBJxZuhLcMJrfh2QH/NbR47zYu8g+l4YxNY7r8Urh44ldbbRExGLhYqOAai3zQ6V6gS3uZhAogLc2YIQgGuigdEwxbVNK2eBk2rBuR5SsXEAya3Qvcj1PhCNsY13gal3bdMn25JtANOsLxb7I4Cbje2siNIf13ELKKdOaOLIBVJ9iCnVvGi+NXNWjR5H2/Qpnglshe7qpM8B103iTBMO84mzIlMquVcvnoWulw9a6xuv5Bp9bIvbaBf9mPp6kpBqImlN44glAO6jXQodr5SOMPp5lYrgxHprdF4wsget4RYMjJj7Ij9tz6kfhYcV4CXGbNW9A3fOW2nOmfZJ+9OTYMCwqpd1g1t/zPlcIZnddBYMGNjSub4on1dInGaaAQNtG55H24bnYcT/BwB1dQbq6ihQpDzpWLvMrD6YWI+5Nyy3/T2TTP+9F/0ejEAdAFgzrvWKZqA45jfgqAxSCtGXniiJ+S109R/x9brI8KitDboRMGAE6qxKbwlCiZrEolHLIHcGxKVKipBywUtLZLOUS1VRYyiIF3sH8aI2dqTQBomuM61TlWkaR8cQPX0GQHHN7znhRGB6accCxFSiwsuzCixufv9/rx5B166DViBM3xSKEQ7AqsDSN7L9R04ABjwNd0IqEf3eDUb2oHWqQteug+h6+WDRzG/BrfpQujKMHT1s6U2x0OeIqti4LaDkbAcvASf5OWuCuUa0gjcOTQlG9lg/Os4gD81vUunIfXTsjX0Y2v06xo4etqrAAeChl9IvIPKpOW7HcAsQF1Nv9HWNE70iylkVpVdeuqHrjPzoHbXcgvOsAieVht5h65wL51pjmWLxlr+CiinbuqbQv19Fa/RRbXqyjfP3f6GRa+Q2tkoMbL0rRCpimu44zW8nNK9IteDs5ifJJHIvq9HjSV36ZK9Q7PV82/TJaJ1q3qdOrSl0dxv9+M5uhs4OEX662aRb6zjRNYwdJ1LDCvAywIBhttvc1I2la9Zjxda7fb2v0EZTNiS19S0Bc7+1GcaG57O6PnWGgTWburER5oz1pY7Z6pHhUes77rrmXUS29dir7QkpMyQLLYmjh83K7m09nmaOzGjbi8RiPmAkNjjhJrMau1jmt7D0igVWZXQpqqOEfV9Zh83f2oy23tcQGR5N+v6HRkZtnSge/Op1AGCdeyAYBFCH/qFxoHEhjEBdvHoKCdP7ZPLnShICIeWEV5upcy6ca1WCA97mcXjVMoSmzUAUMCuvjeTNk9ccqXwi9+3SjkSiiQSVIin0shA4N2xbrp6Jj/YOml0wPDRANkGXzz7HMqVapyq8cugYjEAdWqcqa2MYDbe4ZnBL5XdjfdCqyGTHCVINzAln1xIvn//9i+FizQCPrxPEIJPgSTHXN3rL5tiuJ9DWvhwDI8mvk8DWQzsjgDLblwv6/G8JiEkwSh+30D8UgzFywjVIzApwUqmkCrCOHT2MgNwDKcaX5HON4zR2nSaPlxlV6HmZfjTYT5tz6TZhJfjFYmhzGetijBi22eL6tafekErDqxXx0O7X0bxoPmK7nojPujas37H6f/PFWss/tDOC1YtnxUeimGuAYhrg6Vo266ZdOr0xpIWf471uRniqTkKEVBL6PdS8aL6VSKJXOEfDLVb3A2enCaB4egPA0hohNG0GmhcBZ88/v6D7KTetkT2PM76id7lxw9lpQjfB3fZM1JrM4ZUqIR1rl6E9PqvaypjrHUTfvJXY+Gb62dJuhkspiQybGy2/Bn4hSWfEHxg+Zf3dQGJW+MypDTgwfAqPxxMRNn/s/WiNB5vluLuueRe9W3rQ+2b8APMK8hUIyYl0mW6haTMQAhBeBU8TfH8EmHvJcuCEYZnczt+9xdagmAKUMu/vj+57GiWcxAsAePV4EFvvvBZdLx+0Mp7lGsVUcpt4q3VWcwD9Q1EYgbq4EW7y0M4Ibmqbbi2cZHGJeAWDLHRoRpFywU8wU57PZHa2VHwX6791PVi89IoFWL14lmXeAIkKglTBYb+BY6c++3mPBLW+/6WP4+FXjljtIlJtevTgTNczgzAMAw8pBaAJqy+eAQzFkub06uY3IdVMNNxiVQemqo7Kt/kt2flifq9un2VVSUmyjYxaKCZSSda8aD5UdAwqFsSp92C1CdWrwKVlqN7y3FkFAphmd1tzwAoC9R85YVvzCLK2oRlFKhE9AHrsjX22NZHcU4CZ3Kf/bnX+954vrdGTbICE+X1T2/R4K3b77/2xo4fxzuu/AlB481tH9jjWiJaYspnVgCNgHNdLCQTrwWIjUAejwW5IWWNcUiQdEFJpiCHlhiQSA8mtzwuBvnfq7GzBzLjWyIxtfR8VDbfAmP4hYPfrOOfCuZiDfQXVG9vsb8eMdCCzSm238S79R07YEv8keRug3pDqQNcaL83RKUSyjVuXFkkg1tufO0ctRMMtwNHDRSle0K+TmYBkN7/1EQl+EvzkPYK8102zvLqWEXeozCUkeMlyGE8/bxMHmTedbi74rmvetc8ALwMUFAbuuhLP3ZjevC8kz924Dpi3Mm2CwMY3t6PpgfuxZlO3ZYjPbjoLdYaBvt5BrH7gfrRu6AZgfjeZDV7KdsuEZIKfeW4Ns+civArYn+K/a2l/XqqEG1n4xJR5L66+eAY++t9PIXr6TNoFjR5s8XqtzEnX0TMF032GETBw85Kwa1af2yKnrTlgtkMMt+D2ZyIwDMN6b98Lg1i9eBYGRuLzAhsXAkOxpBb0hJQD2VRTpjKJxZBa3T4Lfb2DRdMct2Cxio1DRaNQA93W6/Rqdv076DqTziDXjwXAVh0P2N+7P2J/j+iGoVVdOgPqjaGgNY9XR0a3TK6fgJNnohgdB/6/V4/AaJ+FtvhlFgMrVYCekErFMzHQKE61QpL5DXfz261dZyFxXpeh3a+jGUBb+3IrQcDNBBecARn7DLtkU0oP4rhVLzDBj1Qi8jt97g3Lk57TTalCmd6Cl/m9evEsqNi42WFK6y7lbNdZaNzWjGbQ2rD2U/peSteIk2NRdL180D2x2FGN6baeIaSScf6u9uzy56AQ6xun8Q3YtQaAaztiv3rjJ36T7r1iSBmtnTCCoaRqTC9Ec7x0xpnsZ/3bI9GG+yhSacwJ29cyblojxTrO+yFf/727aYwgWuP2+1/0JhjZg7E0n5FNQYLb+1OZ387rI3sgPzFjwU13UsF9lDdcEZYhBgy0btiBx1IEUXu39ADzVuLA8KmizfhOhRjIqUz7bOlYu8wy+5d2LPBVYb71jk58dlN6o3r41tuwUfsMuZ6R4VF8dlOPVR0eGR7Fmk3d2HrHKgA0wEltcmhktOi/UKX7wtIrFgDKbAEcjOzB6OkzaZNRpIV7cGK9OdvOJQNQFi4NsxMBmbGjh+0BrEef8KyQb4KyBXP9ZPX1D8UsY/uXva9Z75P3Jiobgo5AsgkXNaRc2B8B5mCfbxNcDGQv+odiVnXz1R8vfOtt5+bKahEaT/JXA92uZtT46TOYE7YnGLkZ2qIbbsa327+9WsVHT59Bw+y51mbTFuhtN40pZ9BFr0wATKOta9dB6ztKe/SuXQfNY+yKV125JNhQc0il4gxunHPhXJx7yzoYwRAA2LopFHqsAmBvQyxBYr1iQfSmPgScSRe5yQOp2oRaCTcNZuBFjCk/8zIFI2AkaRHgPouXkEpGr/x2Cxbrs6iFQhtSgmhNW3PAdbSSX7IxpfT3yJ5MR6rAzcr0OlcTXBATXHALftuCxGkShmlMkUrD735LkmMLaX67zZpdvXiW7X7U1zeZkm7PqOPcZ6UypFLx0M6IpTMP7YzY4jryvaxqTiT0xquavBStoAnJB9HTZxCcWO+ZaGN1qQSsOES+/3tPNc9aqr/dRhFkgq6p6bpSuCVSS8w40L480c0mrntGw5SU5rXEbkRvgEQsOZ3hLVpFMoNXrAwJN5ltuOd+azP2pzCUHxvbXrL50/osbMFP23Yxms1Kce/vNieMxHd7OtGmvK93ECt8nqOC8m3Y9W7pwUYAd3okFYSbGnBoJG6CP3A/hm+9zedZEFL+BCfWo2PtMpuh7KyKfvCr11lt0IuNzM5WMYXY2GmMHvC3GZJAS6p27+FVy2zmN2APXKXLVP7olHHsPjXB1/kA5gbJCLi3/9RNcP31hFQbepW0NXv2kuXACRS9VaUY31I1LTNtW6cqxP47uW27ed71VvW783mnES7f00/Qyu14+yOArgquQRwFe3vi+qBlbOuICS5Vp136k2wRSmqAs+efD8B/MDQXnGMVnL/fVUzZKqTE7z72xj7Uhwp7bqmScpoXzTcrVh3zdPXWfRKocauUklahenKgHsiRFsfUG1INpJs1a8NIzJ0uRHvQVMHi1qnKdQZvaNqMjKrAvTrhOHELFDuD6brGmKOh7Ca4IIk3jaEgGkNBS3fE2HcLFDtbqTuhMUUqCbf7aezo4bRV4MUyvv2gt0A/9ob/9uduiTdu18PZ8lwSHb3We14thUVrdFNK5pnrGEiO0+h7MWoMqTT0+yqyrce1o40X+fzv3a3tuRPnSAJBHyOZKTKaAfDWGl1nvPZL1jlaCUCJsS2yPwJgW9fIv+V1bqRKHqbe+IM7zxISfekJKLi3MU9XBa5XRRcbMb/TmdhuSNv31g07MPDIZkRfesK1ilPMb7fqdpnPnYrhW2/DwCOb0bphR0bnt/HN7bhznntSwcypiUrwjXCY9ISUIdHTZ+AnhhqaNgM4etgywTvWJozjaKgeOFXwU01CKr+BxALHrA5ARmMI9I2hs+og3Zx0AGnbrEdfegJqwe9DAjYAbIkCYt6n4uSJd9H3wiD6XhhM+fpb7nmS7c9JWZFNC3Q9eCrMvWF5YrGvgJNnonjx54N5PNNkpCpTjO/7PnGR7Xk1etzsHAHvIK9z1qeO2/fMBnn/e9M/5FrM1DZ9slUFrtNYH7Q/Fp+dKQa/3rIvXaCYkGpgfwTAth5ccFkilXa1du/kq8uNHiT2qpY2AyDmLN5gZE+SAfWfb/r/vFQt/JyB41RmnT7fT4I5AyMG2hrs8+uAxFxer0opPciTpE00vkmVI/ez7EGi4RbApQI8V3I1pHQDLV2lpTNpJt36xq3aW0c0xjYnE8ctExywB4HddENvh+5VMZ4KBopJJZFuv6X/3pYNQyHWNdnittZJh1eXLK/XAOZaJl3Lc312t+DsPqGbUk5DysuIovlNKh3n/eSWaKNrjXSbkOS+XDTHb0Kf4Kz+dlZgAwlT22/CjXx/McL1x9xwW8sIepU8EN/7wZRnZ1ct0Q7nfkpwGxkF2DvZUG/Swx1oCZE25m6zqqUK3A0xv6UqutgoKOy65t2c2p3XGQbaNjwPhUkYcJl3PnDXlWjdsMN2DWY3nYXZTWf5rgKXWeBebeKbXCq5mx64H9jUnfRaQf5/0U3ycmhBT4gTqRocPbAvqcLZjdC0GYhqldVingcje6AaF0LFzAqmF3sHC/rLVYxvBYWtX7rabNcX7zbR/7ZC9KUnfB3Hba63E7/XKF2VQ1tzAAMj9sdkdMIt9zzpaWqLUd44eZLt9UCycS7mNxc2pNxIZQJ7IRnFenvxCUf+A23hFlz8iYvQf+REwWaA26oyOxYkGd8AEDv+NtRANyIuYxME53xuN/SKbud10s0mJ84gzzuv/wrnXpbYXPUfOWG1LLequR1V4IB9kySzM/teSCQWLL1iQcoAMduDkkpGN3vlngqvWgYjGIIaPY6BEcMKal7aYa5vcsUrSOw1HkUCwqKFftt9OvHqPKE/17HqfMuQco52ED3SKxkGRhLV20mtP2PK1nlCr5Ryg6Y3qUbc1gByb8k9FQ23oH8ohkAwv/dAPsxv/U8g9V7HueZJte7T9UT/DFtCANwDxrLvGxgxEhWaLusbwRksFj2i5pBqx2vUghGosz2WLxM8F/MbSO6q5zbyKRV+9pr63kqNHk/b7Uc3wvWKcD150Wv95jS7dRivIZWKHq8Y2v06mhfNd61wNhqmACMnACPx+1lM8FzIRGfcRix5kW+90bXGqTNGwxSbCd86VdliOIB7Vy3nmBfBaX47rzH1xj+BUp9ArbP1js6MXn/VI5tLZn4fGD5lGdKZVGC6MXNqA8JNDagzzEr3pgfutz3/3I3r8NjYdutnaccC6/OXdizw/TmPjW23zl2nzjCwxsXoXrOpO62hPbvpLNQZBuoMg+Y3KWsyuU+lrbgYx7ohvOTkXgDmBsCj20xe+f6XPo7d37oWS07uRTCyx/r56L6nEdnm/zu5tTYPr1pmC+DINZLvHw23YGfjQuxsXIjRA/sQMJD0HjekOl3Hj0aHmxqs1+l/v+WeJ60fWfRwcUPKjf0R8ydT02Zo9+tJBowzMLL0igU4NDLqqw2WX/SA8aWXp15L7Hv0CV+bJD/f3XkcPTgsPwBQ1zARzYvm45wL59p+9E2WmN+N9UGzejtuhN/3iYus6nk3GkNBvNg7aGpaUwMCBvBi76DnRpXVC6SSmRM2E23m3rAczYvmY+4Ny62KRGdbPK/gZqa4GVJeJowEWSWodOyNfZaW+A3O6Mh7rcqFcLI5p7cfbl4039IVp1kFwEoO0KuedBMcSIyL0IM10srPKxEnFUy4IZWIrjP6D6AZvQ5DqrOzJae1Tabmt6wTBFlvyVpM1mOp9jpuz7kl8rnpidMIB9zNb1s1eFxnVExZWuOFMzhM85vUGrrW6L+3czWtM6Vr18GUs2tFa2R/A/jryAcgSV/dngdMvcm0/XHb9Mlomz7ZZkrpFZlunByL2vZK+g8h1YTcT0bDFOtHv8/z8Ts30zWRV5tw3XgWdL1x4hbHca7n3H6AxNrG7TMBj043ca0RUiUP68iMdTfNIf7h6rDELHxtGxQm4cDwKRjxXjVSCeiGWf1YvNbnh0ZGMa4SjpefOd/p0GeHS1txN/QA0Iqtd2MFzAVSeNK76PX5WfsjwMAjVya1Qp85NVHJvbRjAfoyrPqg0JBKIl2Fs5i/0v68VESGR60El2zmtvghOLEe4VXLbLPAnd/bCNRBxcax96JVWPjaNus9sj8T01w2bP0ubQ0jw6MIGEDAMM3srXdeC8A0yo1AHdZsfMrzHMUEl2PoFZuElCt+K8G9AhehaTPgtG7zkUkMZL6pAszEl/05JvsJohXH3thn6g+QNDMqBFiVoHKN9L/LLDvzH3HTKd7WXDKg7/vERbj9Z68lmeB9ceMbSKxf5M9DI6Oe15hrHVKJ6KMZbCaMy2v1ip9cZvN6GVJeLdABc63RPzSOj8b/7aerhBvyPjcNdibQAO4zf/XKTKn8Brxn7MlaZvXiWdb101v5Cala9blBzSGVgn6ves3hjUKrknIhm8rMTM3vxlDQNTHOmYTobGvuTMRx6owzgUYn3VziVOiVU23TJ0ONHkf/UMw2dsEt2H5yLOpa+d3dvSfpWuVzBjshhcZt3JS5P0hOXPMiX22JU+FVvSiEps1A8yK79uidspzoaxpdZ+Q4Xp+RT9w63DiTJqklpNLx293F2VVB75iQDxM8k4Sdh3ZGsHqxe3Jcqjiy3iXLbd/llsSXCmlznnRt0nSfABJ7LOe10zvbiPawMCE/0AAvMb1berARZmX3q8fr0NX/lmXGbr2jE/tv3Z70+l3xFuherb3ziRjFQH7Mb2lL7tb2PRVSIX79pm7gaQDzVmLrHZ3uLcxhzgDvWLsMey9ahdYN3VZygY5cu5/v+HfU1bGam1QnvVt64m0/kyuidSLbzNdFtvVYBg2QyFzTZ9dlev+mQ2bxLr1igVllHo1iZ+NCq/JcCE2bgbnf2gx8ZV1WFVJyDLjMnzK/N9AAoDXcgv4hwKibgL0Lfh9tzQE0xBdSY/FZ6QCw96JV2AsjKdv6wa9eZ9soKaXQOlWhX2uTvvSKBWmN7VKMuCAkG/TAKOC9edI3FW4zpQDglUPHkqql8oWtMrPePSgMmGZz8JLlmJOiBbqQzvSX4I4cJwz/mypbW2Ikqr+d1VBduw4C7bPQOlVhy9UzASQCYV27DqYcX6E/JkEubq5IpSKBjFQjBmRdI+285fe1BG6zCRTPnNrgGSROVWFuBOpgtHYifPoMsM2eXAf4rwb3MsGbF81HoN0cORHU1jH6805apybaDwPJM/YAs02x0TDZ1jbUbXYmW4OSaibdXMhUAdB8zMvMhP6hGJZo//YzhiWVCd68aD5i740hMMFMLUq3rnE+r1dMubUP1f9uxBMI3DRGSKU57C5BKhU/SXEyZsEZjyhVW2LBqypST/IVnHrjtqbRO0m46U003IIoci+ikLWPczavPOalQYRUIvq4qFToRq8+QgrI373glrDmRrpkm2i4xdKB5kXzbZ0nvNY38logs0SaVCNd0uGWQOCWdEOtyR+8kmWCzMBeAeDba5ehd0sPhm91N5x7t/TgsTBwfch7vnU+md10Fg4Mn8JV2qzux9esd33tiq13pz3e1js6XduPe3HVI5stE1tav0eGR81jxCu4V2y925zLLcedtxJ4GjCe7kGdYXhuLA8Mn8LlV3444wpwQiqJ/REAcYMXsBvhowf2IbKtx5rJq2PfUJmG1INfvQ633PNk3gI2Yn4DiSBxIBi0TPAkTgBYfz/gSH7RybSKXeaCOjECBpRW3B0Nt+BVOSelAMPwnOl3U9t0AInvYs3kim9Ob14SZmU3qSpkI+FViejEbXMRjOwBGhei6+WDeV3spzKnbv/Za9YcbcFomAIj+raVcCPom6VsKjXlPRK8kc2kHqw595Z1UAPdlmkFAIg/3zZ9smV066iLpyceU+PoHzbw8CtH4l8GSQEwL+2mMUWqgXSmFOAIVCgzIQbIveuEW/DGq3LIXGMoDLwTxEcn1mPuDcsxtPt1WzWUBGmceuMWuEmXiOTUGj2YLNdFNEnm1anR40lGlfzb2ebUKyhMXSHVRKa/+1O1A85Gb1KtZzwxgK5XDqPtalMD9C4Qbu3JU1WDi87se/QJzL1hOWLvjWHizPMzOh3RmUzQu3V4wQopUg04Ncb5u1xPXpFEG8PRZUKSRU6ORT0TYDNB7q1sW6rL/S7drgD793DTG7e1j5f57fZ52RhSkhgp3W2c7YmlDbFArSGVit+qb0Hu4VSdJnJB1jb6/ZXrCAdZ6+hJN871DeCdaKP/2w2/6xhZB0qsSZ8DvnrxLHS9fDDJBBeYwJdfaICXIX7m9u6PABuxHXfOK44JbsCc1Y15K+MnOZj0mQeGT6Fv3sq0leLDt96GpWvWZ2w6y57n0Ii9+rSvdxB98fNyuw6HRkZdZ4DHFCzzfPUD92PNpu6UZjkhlcz+CLBfqwYX9Ipvt1/wzmxiAFY1YT6Q+805CuGz9z7r+R4FhQEtIScTxo4eRvT0Gdtj4VXL0DB7rjn/eyhmVbvHosmZhW7Xw+21bc0BAMDAiGlKLTm5FzsbFyYZ5vmupiek1DhNGABWgo3e0tuNaLgFbQAednTdzVfCjdOcaqw3s2y7dh1EF+CorK7HRyefwQV/uwVvP7jZtUVfug2j08iS6+Bsf64HhqLBEIy4+W00TMErh46hLf68Gj2OtummAaUbWUsA4GT8OEZco4xkIypfLeUJKUf0YIZoza+f7rFpTkALilrGlPYrPZf7Qw/eiM44K4ecSKJdoH05YrueQPOi+Th7/vl45/VfAbAHhUVvUlUvuAWMndVQoj9yNgEXLbKobwDOjNoqP5znD8BqTUxILeD83Z/OlEln3GaD30opINEGXa8CdwZ56xomAoCttbkk5HgZU6Kz6QLFTl1xapJXpajQNn2KrVuHm9bQ/CbVQKpqTNtapt1euCDGbSHQ76nXX3kj4zEMOnqijW5OpdIb53udeuvUFzG/3XTFjynuvJbOdSE1hlQLqXTGeY9Z987ICdva341s4zbOrnSZrHMAWPER24g57XvI6ARdS/c9+gSARMxGXpMqZqUf3ytZWPDSZd0MdzPBuaYpDNypVhAda5cheElisRN96Qk8tm07rg+tLPhnS9W1gvI026VS3CsooyMzvQWvanfArI53GuYyMz3c1JDS/JdzFqMbMCvK1z3/GwDA5o+9H89tNU35gUc2o3XDDhpSpKoRI1zHqyVvMLIHqnEhALvp2/dCYp5svpBZ2bbz8rgPI8OjWPf8b7D5kc3YF2+Hrgd8xdSXDZK+2YqePuOaZLSzcSEQr9JWsXHLsJYZnUtgtg98+JUjSck7D371OgCmyf3e9A/FHw0lqr7jx3e2dZZqekKqDecaIHz6DIIT6wEkz61zBi6yydj3g1tmsU5MmQl1S69YYD6ggC4Am696P869ZR2OfXmt55woHedcTbkW0plC11r9u6rR40lBm9t/9hpgmAkB933iopTfz1bZ6jFvlOY3qXYks18S++QeNVo7YQSTp4C7GVO5BBtS6cylHQtcq8HNtYFKtCqPG+FSqaB/D/n7vkefSLnfkvcN7X4dH7jGvr5zVkb1Hzlh64Ah+uMMHuumVabVm4RUI6kS+gqJswrcV5DYMLUm6d49etgK9DrH1OiPReNjGnTN0ZP8xAh37iedXTdkrZN0eo61nx7INrUoubWzEwaKSTXgx5SKIrXJ4kwWyeeoBbdkPx0ZyyQJu3K/x3Y9kfRaWfeo6BiaYW+PPgcJfdH3VumSbpxakk3HCZ2YMuNU1BdSK7jdV84Z10Khku3T6QwAq4BBj5E4u13pOL+Xs/MNkBzH8UpwdNMZ+XxBdFk6fgn6nssr4YZ6UxhogFcIc8JA+9OTYDz9vPWYwiRsXX8/sKm7KKat3+NfH1qJrQ90Jj3unNedDpnnDQCrAct02vjm9nhb9B0pq9/F/B6460rTJNOeW932QQBA9KVt1mPP3bjOMsEJISZtzQH0D43DCJi/LmRDle9fypkcL9zUgF/2voZX26bjo9/aDNkmSiBbDZijEKKnzyB6wF616TS/O9Yus4w5qc5WsfjiZShmVWdJO/a+3kHbJigyPIpb7nkS3//SxzF6YB8Q/7zgxHq0tS9H/9C49VmFqAAhpBKIbOtJWS2dzvTOZ9AGSJ51fWhk1Lqv9e4WMQWsA3DTxdPR+rdbLG1x4ky0SUW6jZS5CbLP1Tp5JmqZVGr0OFR0LOnzAlqVOGAACswkJjWDMzlFRrvo3RZSBYv1eyXXQLHXe3VtETNcRqEs7TATb+77xEVWNbggAeFA+3Ko6BgAYC4ShhSQbITr7fxSVafKddCvR+tU8890czRVbBxAnefzTLgh1YpufPudFZmqS0K2mqPfY37ahnbtOgh1sTmiyQjUJY1TARIdIvTOEMHIHiC+5pDEG2fysVu1lK1lajwxT/RFT/ZLtQYU86pt+mQgXiVFSC3i1BoxpHSTRUf0phDdn7xGMcj8WjHBZT/TOlXBaO209lFJa7PR4zDalyMY2ZN2PyUGlWWEx9c4ktyXaDds6opzDehc/+lmlBtXf5zds0htk0kSST7vlVQmuOibxEhMDLROVSlNcCcyfsqNVF1unAkBqa6Pbn47kXOXsRWksNAArxCkyls3oQ+NjOKzm3qsSuhyYHbTWdZ56Sgoq336rmve9dXmfc0mc+63UGcYGFemeDx34zpsBFK2gFdIvBaAZZpjww7ruJd1XIwVMM/l8TXrcSfNb1Jj6AawHwpR/Z0tn93Uja13Xmt7TMXGgQuuxUf/+ynrsXR6Iy3R9cWJXr3tbFuuB6kCBqCvaXq39KBj7TJzIyav8ZgTLuQzK5uQcuXYG/usFuClxnm/6f92tt+yOj5cPB1L0gS69Q2Us/25JAHIRioQr2pymnJdLx8ERFOMxJ8STGqdaib5OKsh9M2pzApnoJjUCm6tv3Wc95lbQlqhA5x6wo2Y4Vbizc8HEVPA7TATbj4K93angSnnQo0ex4RpMyxDKlu8KhPEmHILIFmB6aOH0da+HIBKOQ+QaxtCvNuE5pKY5vWeVJ1uAK3TjQLUxdPR1r4czfHn3BKG3NoIu2mtVzW8dKWx5ukunmUZTl7Gt1v3CfO8TK3huAVSzegJbF7o1YYyIkBwJtt0d+8pyO/imVMbXLWms7MFzzy7x6Y15v4lMeJJYiSiAfq6A9rMXiCxn3JeE90IF/T1nSQOW6a41h3Lue5xw6nbjNeQasOP1ug4k0fEtNX1Jt8JN2krwQ0k4h2W1ihXQ9ptJFSmCTdeRQx6Ql+m4yj0eI18RxYuFAauHiuAOz3mW5frzZDqvCLDo2Yb9/gsYiB1u3Rny3XnLO+Nb27H4/H26M7rIy3ZdZzJAn29g4C0V3c5BiG1QKqZbf1DMcvAfWhnpOxaQDmD2EYgiFg0ilcvMI3xj/73U1brYSDZDBfDWlqeA2Yrc/O7JyrfdZwdNy7rSLTdkc9yXjsVU4kKde3vS69YYFWAEVKt7I+4twAvd6wKBwPoeuUwcPFCtDUHbK/pH4oBANraW3BuvLrBK5MYSGQTx3Y9YbZldnlNY71dd6SiQkdaJDcvmu/ZVl2vAuf8b1Ir6MEcSTZJhwRvirG+cfsMXWsefuUIcPG1SVrjRnjVMkS2JSf5pZtRLEFiZ0t2CQjbgtFA2qostyAYIdXGnLC9Taaf+deC894oVHAz3fEOjYxaawpTa6bHk1kSphTqG9LO5QbsOpNqXmY2uFVvde06CLdFE9c2pFqQZL5MjCnniIBi4tQbMar0rlqXdizQkngTawzAPcGm0Oixo3TV34WsoieklKRLHHajdao96dUtua8Q90m6MXaCrjVyb6vR44AaB4zkETBuYxm8cK5x9M42QKLjhFtysZ+EG69W8ky8yS/pd9akLKgWY1YhceNfH1qJ60MrbeaUzsY3zbngThPb+XrnPF4nXscHzOv68x3/jtlNZ1XNNSbEL85FTzTcgp2NC62f2585iIf7j+ChnRFbq85yQWZvOwkEg5bx/OoF12LvRb9nJt7AXQ8i23owemAflpzciyUn9wJI3fpT99xjClZHib0XrUJwYj0aZpsbVn3mt4qNIxaNIhaNQsXGoWLKuqblUlFPSKEQrdEDxNFwS9p2WqUONkh7v8ZQEIZh4OFXjsBomGL7efiVI3i4/wjWPnMIA+8EYbR2onnRfFu1u1ui39Du16EGuhE7/nbKANDJsahpfitpOWxWg8rn2EytoZjW+m8yVrfPskxwQohJ2/TJSYGIcrlHGkNBa1bvwIiR9CNEwy0ITZvh2cHn2Bv7rKopSchxGthiWEvQxXlNUrX2C7QvR/9QzHZONy8JW3pVau0mpFAce2OfdU8N7X7dNeHNbW3jpjHFDmrqLYudWqOva3DGfF0wsse2HxJDTtY0ojN6xxunzvQfOYGuXQdtgfJMqqNkrSjJhl5JNgwQk2rBqzjILQnNK16h601nZ4tru/JCMHNqg/Uj/36xd9Dy551rGcD+HeTvuq6ec+FcnHPh3CSt0ZH25076j5ywGVJ6MYIXmVZvElLJuN1TmWiNG4XqOJHqB4ClNV0vH7Tfx4YZkxWNkLWNxFAySd5zru/0jhPOx3TNcb7G7d9OvMbZkOyhAV7m6HOwK4UDw6dwYPgUIsP2hZb8O/rSE3h8zXoApnH0xXcv9jyW0wQ3YKD96UlJr/Myr+X1Vz2yGYDdgJfzrKszkkx2QqqdOWGzcqhh9lzL+B4YMWAE6hAIBvHZe5/FL3tfw4u9g9ZPuKmhLAIMkeFRs6VWGsQID0yYgFdPTEDo8k8CSDb+U3WhiEW9A1aymbz5krBpuBsBayElgRrBCNRZhnjXK4exZuNT6HuhfK4pIYVgTjhxv+lGjd5i0xmw6D9yAn29g2XT+kk2HxJ0ddusyHNGoA5GMORZCeY0pMQEV9ExvHJwBF27DroGd1cvnoXV7bOsilCjYQqMYAiB9uVWcHhn40I8/MoRdO06mGyCo/TJBIQUA2cgR+5FNXrc9iOIKVPqymVncFqCJvoPYOqPBI+9EohkPi+QMKicJrgk0+icei/5MZ1A+3LrR9aLzuDOzUsyLCchpIJItV9w4lzbuGlMsUwpwbmeagwF0fXyQStYLD8DIwb6h2LW+kK0RoLE+hxwXWv2PWpWU+nBcxUbBwx9nZTQslTBX/lco2EKBkbMBMRURa7FvpaEFBNJthk7eth2f+nJIYL+e7i7e0/BWqBngmiN0ywyGqa4rmVEa9xMb/2x5kXzYbR22o4pONcnTlJVf3uNkKLOkGrCK0EfSDbB3e5T0ZpySCIWjdPXWrKecYs36bglMjoT/JKqvzXcTHA33DQnlUYxdpN/aICXMY+vWY81m7orqjJZFgUDd10JBYUDw6eSFgrtT0/CL3tfQ51h+Kp83PjmdiztWIADw6eSXi9GuhfhpgYYMNC6YQdePW6aT/v+96RleG98czs2vrkdW+/opAlOaoaOtcts5rfMu5aNggSExZzVM+vKhb4XBnHLPU+6PnfLPU8mPRcIBtE/FEs573z0wD6MHthnLfikGlw/rk5MwdWI39m40LqeglzbQNCsJA0YsLVRJ6Rakcx9wTlf0smajU9ZrfPKTXf04KsevD05FsVqrbXfe9M/lPRWPUisM7T7dbz94Oak4wuNoSC6dh1MMqwkKOwMDjfWm6+//Wev4fafvQYAVveOsruehOQRPZCjByok01+vZlSxcZv5XQ5BBj3TX6+WVDHlWZ0daF+OuTcsd21j6DSnJMCjomMZnZdUbNkqRJE6aMOqBULsWN1cygzdmLb/1NnWaZJMFF6V3ElL1xoxwYHUFWNeRnj/kRPmvtS5RjS8E5WoN6RW0I1wwRlzEE6ORcvy3hCN0U1wud+lu42gV38D7oadEQzZji2dCwH3Sm8VU57mt9PAclbSAzTBSfXjZYK7xW6cCTflgiT3Ad7G9NjRw0nmd6pqcGeBQ9fLie42zoRlN+Q80iUA6jB2k184qKtMuXPeyoqcST1zagMODJ9C64Yd2HXNuwheshzrnv8N+noHYcCwvs+hkVGkGYNgY8XWu7H6gfvR1f8WVrd9EGs2xZ/wcY3CTebMiDWbulFnGEBdypcTUhNIm24geeNU7m25xTh2dplIi1KInj7j+pQ+G7xj7TLg6GEE2pcD8axqPSlAR4wlWfxJFebNl6SugspE/wipRtTo8aSNlBi25brYFzO6S3tsXCnUac610TAF6vjbAMxA8X5NW/TZfoAZ2JHqhXMxjoe1md024jPI266eZbtmMudKXiPvkxniJ8ei6Hr5oJVQQEgtkW4mL2A3VMrlHpH5vA/tjKStqA5G9iCVnS3GuJ6EZAaLzbWNFdxVwFkTEhukRGDGvXWf1yxeoPzXkITkwv4IMAeJ3+FOUlVJyV6iXDrc+EWM7NC0GYBLa1TAXWvceGhnBKsXm+sWvR2xvX1oXfzf5oxN0Ru39VGlXUtC/JJuDvjY0cMIeHSCuXlJGA/tjFi/48tljmyqdY3RMMV1JNSxN/ZZxrebzgQjeyzd1Q1v+axM2p2nMq8IqUb2R4BFFyY/LsZw8yJ/+6lyQtYJEgeRNYcQDbd4JujJ9/azvrt5SRibnnvT137NCBiIxRReOXIiVTMbUmBYAV6G3DlvJYDKnfst593+9CS0btiB1W0fxMBdV+KyjoustuPjSkH+t/lj7/d13OFbb8OKrXejq/8t63P8XqOZUxswu+ks6095353zVqLpgfuxZlO3NceXkFqifyjmmE+d2CQcGhm1/VQKSzsWYGmHvTLb/F4KkW09KVsY6lVUUhnvNas73NSAgJGoRv/svc/is/c+i77eQVv1FiEkgXOmJGAGIG7/2WuAAVz98Zay0hxnNnNjvTk3U5YMf7x4BrZcPRNt0ydbwRu9GsGJsyoTMA2pwJRzbe3K9Z8X40mEAyOGrU3p6vZZZobzrkSrPr3KTJ/vS0itIPeVW/s+qwNFoLyzYeW+9VpLqNHjSbN53arAdZoXzTcT+2B+fwkINYaC6OsdtLREZvY6Kxnk310vJ5vfojUv9g6WbwcPQvJEqn2EnzmZ5VhJmMm+xUtvJKkPsOvtzUvCNvNar5oC3Ec+pNIbgeY3qVa8ukYJbi17neiGTCnvkZlTG6y9jI5eBQ7AMr/d5g/rI7VEZ3StcaJ/lltVplsFpl/zm3pDqg0vnRHc7kk3ymFto8dtnO3QgeQq9nTzv0VrJAlA74L10M4Izm20x3ykA4XbmkrCR/oahzHj4sKoWJlR6ea3IOcfGTYrrwFg6x2dWNt9t/Wa60MrrfbkA49sxnM3rkt5zMfXrEdf72DeKuNnN52FyPAoPrupBwYMtiMmNYmzzffOxoW4rOMiGIZ9EyCVzuXGLfc8iQe/ep3171g0ipsunp70OhUbx0f3PYPoqmWAhwnesdZs6xecWG8tctZsfAqAd7tyt02Q26LPq107IbXE2NHDCE2b4T67Vqtg7uxsQXf3npJXLMycanaQ6e7ek9RGUMwjZxs9FR2DGjDXPZFtPXCiB43n3rAcRmuna0sx5/fu6x0010BxrCQfI1HxbZ6A+cc7p99DXcBgNSapKaTLgpOk0Qsjya3n9N/dxdYdCRB7tSuVwGwsGkVbc3L+ulfwSq6HdJqQxD7ArOLuixvWAcMMGF/ascAynCQoc9PiWTb/SSrL9L9L0s2lHQuSgtyEVCvpKjR1HtoZsQVj9XUOUFzNcWqNs+uEJEO7VWO64dTcQPtyS2ucs7v1yixn4Fc37PSKKibykVrEqnpGss6kM20AeCbwlwLZTwHuldmiNXpXm+ZF8zG0+3XP724l9DVMscwtaXl8accCNIaCNo0RPUlldOuvp+6QWsJrPTO0+3VfegOUPilN1xkg0eXKCBhJnSCi4RaEYMam5Pt5fddA+3LsHIrBGDGs/aO+ppPPceqNvleS83CDWlM8eKXLkHTmrt72t9xNWzm/QyOj6Op/Cyv0fc488/nI8Kg1n9uLQrWEL/frR0ixWXJyL3DxQgSC9l8PfS8MWtpTLu10RT+cJviSk3uxs3EhYlH7rL1UrfsEvTX8mnufySo5ZubUBvS9MGhLGoiOxxCsY9MVUrvIpsJpREm1obPCR4Kz3d17ytYEb5s+2Qra6K37vCozJEg89wYzaCPBGytwEzek3DRHf+zQyCj6egex9ArTBNcrvmHAMsEBYOkVpiFV6mQCQoqNs2VfKs3R7+1SzrBz0xm3wA2QXJGhJ/aJ1oRXLcOEy1YAAAbeCVojbx7aGbHMb9GFQyOjSfPQY8pc/y29YoHNnHL7ux74od6Qaiddso1eXehV4SMmeDFxBohtqOSZuakq2vXvf86Fc3HuLeswcKwOGIoljO94cmO67ykfK3qiB4+9KMX1I6QUuJlTsofQkXtGYhDlErPx0hwFUytbpzpGLcC96tRpfg+M2MckAPbvDqQeO+fWttjLjKLWkGrFaz0DpE620X9HlzpeA8DV/HYm24jeCKFpMyyt0Su9hWi4BUbDFBgjJywD27k2ebE3Efe9+uMtrgl+TtI9T70pDDTAyxC3oIEYTyoe1dx6RycAWHOtSy022SLfa82mbmz0eM1Vj2wGNuwoSFV8ZHiUJjghPtAN5lvuebJsgptOE9wI1GFn40K0NQdsQZtouAXR5hbg6OGkubyAWf0dnFhvvfb2Zw4CeCtrfQg32QNMwbqALYBcTteQkGIS2/VEctDGKO/sV90Ev7RjAaCA1qnKNsdczO8JR/7D2iw2L5qP8Okz6NX0xml+D4wYtkpMP5oj5yMVmzpu15FBYlJr7Hv0Ccy9YbnVcaJ/KAZjJJFoUo6a45Zso5SykvnamgMwGs4xH3ck25xz4VxrLjFgGt/BifVmh4lgCAMjBoxAomXfiw7zWz7frXrj0MioFdhxCxZLEMfZwYOQWuDYG/uSWvEaDVOAeLDUeX+UIyfHooACVrfPQtv0yZZ53z8UQ5s2K1M3pGQtAwBGqxmXGjhWh8CECeZ31jTWT1WYrjM6fq4b91OkmkllTonWAInfxW6/38uBzk7TGNLXEQEtwc9rLq9uwMmaRjpMSMW3rjdu3132TILsnfy2Hy4Hc4+QUhGaNgNRJBL7nJ0SymnNLzoDJFde9x85YRUveCX3ObsU6h0mgORuPvLdJX6jXws5Fy+dSZdsQ83JP+W7Eq9Rtt7RabUMjwyPWoY3AAzcdSWiLz2B3i09GL51u/nYI5vRumFHSc41V3Zd8y7an56E2U1n4cDwKdw5byU2vrnd9po5YaB1w46CzOc+NGJe3wPDpyo6iYCQbBg9sM9W7ZwJUlFYLogJ/tDOCG5qM9ufj/38pxiD2c781QuuBeIbJXXBtVg4+C/oWLvMMqXmhIG9F60CDLNCu+uZg/hl72uoM7LXHUnukQpNwB44fvCr17EtOqk5pApcRceAuHnsVv1djog51ffCIL7/pY9bj+stQr2qF/RqTN2Qe/iVIwDgWfXt53z8BtXFlOJah1Q7EiyOnj6DgDbz22o9V4bmt+BMtll98Yykludq9LipoQ70+bsSJJbAja6zsn5z0wKvx/TWpUBy1bfb9aQpRaod3ZgaO3oYgXilkDNInE5vSjVyAUh0v1jdPst6Xh/tokaP24xvvUWoLcEoUAejLvk7+w3kZrqmEcplZA4hxUJGmgDJhtSLvYNldx8cGhl1He+id7eRLlrOqm/5rkYwZL2u/8gJ25rOzYxy4kzqe7F30HPkDCHERCqidVNYkk6cxne56U4m6FXfUZjJf4C5d8TIiUSiDdz1xplMDCSq0bPdb1by9SxnynP3X8OcffdtQGglDgyfAgA8NmYawvsjwHM3bk96/b6vrANCKyuykrl3Sw+gzTwXE/yxse1WG7/wqmXA04VtVS5JBzTCSa3Qu6XHmnftZGfjwiKfTe4cGhnF0isWWOb3kpN7IbXXRmsnjBOJTZKKmW3ORw/ss66BmN+BoDkr6pe9r+WUOS3n41YpRUitM7T7dTQDMNqXm4EbH2ZUuQY23bKHZZMoz73z+q+sWd8D7wTR9cph4PWDMGBkVPVNCPHP/giAbT244LIVVuAUqIyWc7oR9PArR9B29UzrOUm4UQNmsrReGTW0+3XXQLGY33riYiZ66gxeO6sZ2HWC1DL7IwDiHSeA5BEL5Z5sI4gZ5Rzros/kBewtiHcOxawEY2e1e7aBcbcxEOmg3pBaQ1/XiDlTTsUJbkhVpqBiypZs44WezCfV7rreCJkm2ziTgLygtpBqx63DhLP1ud5tQl/XlGN8Jh1Gw5SkjhMSvzE7ZtUltTt3ruXS6Y1boqEfqDeFpTxX5DXM/giwEQmje7+P1+9aa1ZSlzPjSmF12wcxvDXxmFNoxQS/PrQy8Z2eRkGqvwFTlKRKc+Ob29H0wP1W9T0h1U5kWw/Cq+wzr8X8ds7/diPVPKVicmhk1HYuS07uBZD4XtFgCCo2DhVLfK9ouAWheMVC1yuHgf638Mve16xj0IwiJD/sj8Bqyyuz6/QNlZ/q73ILbC69YoGZEQy7CI4dPWxlEMvGypj+IZwrxvdzh2HAgGEYVpCqFFpTrskEhBQCNdANo305VGwcXS8fKVszSsc5K7N/KGZVgVvBGr1aIR60icy9BpF3DBgBc6wCANP4/nl2xrcXjaGgFciu1OQlQvKFa2viMu4yITh15qGdEaxePMsa6yJEwy0IaX8X+l2MbyD7wLicD6syCbHj1f7cNGrsVdDl+vvWGiGFhNYAiZbEgj6PN9Wcb0GSbUQ3/H5/vUozlUnFVsSkVpA4DWCfhR11dLYRyik2kw7lCFw7W6A7x9foFd9A5ua3kGmyjf4+UhjKe2VOqgIxmYdvvc32+PWhlUnm9uymsxAZHkX705NgwChqcHj41tusinRCqh2pjgqvMtuEyyLHj/ntNqOtlEi1tczJBMwW7wCAA/uwZPZcy9w3AnVY+4y5qJHqS8B9XlQu9L0wmLYCfOkVC9D3QuatjwmpJNxm14WmzTArh5A+SFwuGyypgjx5Jpr2tVJBFZhyLm7/2WvoixveAW3Jky+9yaRaqtySCQgpFLrmqOgYul45isn1E9K+r1zuEZkbJ22JjQZzZp1zNp3QPxRDV///z969x9lZVQfcX2cyDCEkgSRczWUIATQFYSLhInjBKlEUxWqVV61CK15aFa212mLV1laxVbF4eW29tIl4qVhrUbElVMHXFAIEiZqKojFOQrgIIZjLJJlMZr9/nOwz++yz9/Ps53bO8zzn9/188jGZ65kjZ83ea+219lZp6KtbMnR8hyh7cQ/oBh1nzMRxEr2ONa61g1mQ0l3gIu0J4nVbd8oX1m1t+7w87gGl+A20s2OM3ZVpF3bKSBeB9NhxuxPcNDE80rq6RqQZb1b99/81C98RB4vSXvHkKoT7PgboNx33YQ+0127K+NrQeRFzWpU+cGMetrF/ttZBG2PEu+tra2kP29jrHLM43us1YT9g91oDuvu7rGPQlajmWHPP+2xl/BmAOpoqgl/YvLdue/y0Bb2QKOvrdO3M00WUktNlo9z68ZvlOV+5VtbumPpVt+rOza3Cd95Fb01Pl1i5djSyCH75ucOlO0wAFOXx+za2EjgTwyOy6r83TxVrYpRqg+V5yPoQ0eDo+tZpaV38Lipe2iNM47CxQj/QCWM9krgxOCQNaciu8YnSH7hxvZ6Xz28Wv3Vnpi5Iaa1xfcZ0Ca0XB22K+P5A2ZiFKbsolURZXiOXnzvcKqbZMWZieKSZHBZp68R0Fb2z/Dxp4wxQR4uH3Ydrho5bIHYJuddrlzh6v7JrfKKVG7FHoJtdpsvnz5oaed5o7/TOM+bk+TWAqnOtZeypMCLpD5wUzZUXMePNVBf7VDJHTR44ONlPOiZqiHTG1rQ/t90Nbr+vrM9p3VAAr7DFw80uapFm53QZ6bvMN43GfGAPve4jq+n8Rt/aNCoyLM3uoZDub5H2LsYy0Y9fd4JfcOWF0hhoSGPaYNuJxaIK322PpdHsAl9zywb53F++oOP9V1x9Y6HfHygTVxd4QxqlL0ZpviKzfd+3ftu6bZPyhYMdCwON4scAxyWN097FCVSVLn7rEZqXnbOoY6SdrSyjLs3Xspo8MHXvt1GU0jFn7czT2+6q03bs2COnDs/L5fGYSZvQ4lRZYjeQN3Mto+OMyFSsqSQ11Unqu5N3+YyD95vfuVke3T0uR80c6viYLLEz7ahQEeIN6sfeM+nCVOPMFTJh3P9t/u7v9dolDT1xwh6vrOmf779vWi8DDfYzQN70QRsdY4aMK5Y0/fpcuXZUbrt1Q+lfe3rKhNmIZO+TpkxrxSDzOhdTXj9v1Ncp+3NaFxTAK+g5X7lWznzvd1v/LmvxW7v7/c8WkWeLiMj/vOKtbe/r9WMvaxcr0E2jN9wscuqLRU0q7+JALwjKPLJbTSpRkwdk+bwBGbTuABfpTBCbRa28Fx3muJsrrr5Rnvas0zo+pqzPI1AmZdwQNGPKQMfbdUH8C3dtbXUsnHfBaXLbrRsKizehXeBlfB6BIukuqbsDpttovX6d6C6AFStGRJS07v7W9ISJ1ui+g1dJaLpotHr1+lwP3iQpgpflIAFQFDtZbN8fWRX6Xl591YKv+G267OzmYSJ9gNGMOVmF3surEWtQZ/bYc33IptWRub1ZsClzbkZz7VN0XkYX1/S/zbHuK9eOysyhwa40LwD9Rh+0cRW/9brGfn1WRdxVlCLSyn3rw30i+VzngvKiAF4h33rdVc17JN/73a7fj53WtEZDlr/3eyJycNz5yS9qjkMvcUc40G+GL7lQDjnygPxwZ+evBL2xEml2MpY17rQVv41uzMHR9bJ8eETWbTsgjYGpn2/L9jGZVNIqTK+5ZUMhmyvz3itTxdaQQCZmJ0PjzBWybtukd5S4VqbNh1n8Oe+ZzZhhd31rrlFh511wWsc4rbyL4PbzlWdSGqgKM9boaQyNgWnB3d+9ppPE+v7vu7dPFaXMO8D1qFBX97fI1F1y+uvlNSI0rgjOCD/0E7v4vW7r1HjwOL2OOebhucZAo208qF0I93Vn2vI6dGMWwn2INagrcx0zb9nSVlFqQqb2GOZrsuw5hS3bx1p38rqKUq41TGjMAZCOfV2Uq/htHurzdUeXiV4zzBwabD3euEK4eeBGJL+R5ygnCuAV8a6TXyRy6waZ1mhU6kVoP9ZfP7ZblnzoWtlkdYK7bNk+JgeUqkyxH6iyxmDnKDtd/C7zqduFc2bImls2yGuWz5fl8waahbWZp8u5u37c9nH6bhf98+jit7koWnPLBhl9bCz3n7eszx3QTUsuvVgaZ64QkWaH9MxD/UvQsnb16D3S8nkDIrv8he/Lzlkkq+7Y7L1z2LzDLu+xWnrzx5hA9BszmaM7pc5aeGQrkRp35UIZXicL58yQ0cfai1KaGWOaxW+rW0pJW8yxu8H118/6+PTX1Dhsg35hjyXWzLtr9fSXEGWIOSLN/Z77nswpbfFIiezaN9G2jrMP3Yjk8/NxLzj6iXnvt+s+XlPVOjKj6NjSGGi0xZrLzx2uROENqBLf2HM7r2F3f9sNPWWk1wszhwZl1/hE2/omih1ryrI+Q74ogJfYBVdeKOf812Gtf/d6XHhWOqEzcfu3Iz9OF75FmuPTzXHvAPK1eFhk8KkXt+4AN3/xl734bVp11/0iZy2QgcFBmZyYkLUzT596p+N+c7v4ffm5w62Fj+54B5Dd4uHmlAmR5kGbZhHnYe/Hl7X4bdIHbeRgd6lI835Mbfn8WbL8JafKW/7j/2KL4HnfD17m5w0o2pJLL26NPm8rSnmUsWirc9or147KZWc3RxP7fg5z+o147jkvukDNYRv0A1381sUpl5CCVNlijk4Sa7r4pKS9kd08jOM75GcWqvNY39j3guvvUbbnECiKqzBl3sdbFfq6BbsYZXdfamYsvfzcYVlzy4bc90sAOtmT7BoDjUrFGlNcEdy1ZptUzX0Y8aaeKICXlHnPd9UL37ZbP36zXHDlhSL/1fm+0cfGRImSz75jhTz2hjfLme/t/uMD6s7sYFjyoWvlhzsG5QvrtsqaWze0jdGqSvF7oCHSaDRaRW6z2K3vdjE3WJPKPw6H4jeQv8Hph7Y6Ml2FnCqMm2qNC20cvONbZCo7rETk4B2aIiJnzmnGm0/oIrjVKaWRyAXyYY8+F5mKNb7x52U+bKOTNqvu2CyrpFlwshM1el1j3xPejckTZXzOgKL5OjNDxvWW9aCIqzDV0KOyDjL3UcvnzwqaqpHXIT/zc81ieJmeQyBvvu5vuyMzdNpEL+n9k33YRqRz0o3P0551mtx264Zcr3UB+t22e+5txRrzkI1JF79nDg1WIm+hH5/dCR5Cr4H0PeAUweun/L8x+9DiYZEzK3TPd6jhuTPk14/tbo5z/y+RhnVBli5+3/3+Z8v/vOLNrWRW3Q4AAL02fMmFMjj9UBER+eHOQ+QL67bKbd+vTre3y5pbNrQVrz/3ly8QETFGhDY7pD7z588VGZgmkxPNhZBZLNcLvDrFXaBX9O/w4UsubOvIlO07W4XjsiaEo9x264bWXXY68bRrfGKqyHawGK7v0IwrggPIh77HTmQq1viSxGW/P9Yc+6vjy+XnDreNCdXu3t5oxpvtO1uTbHSypwrJcaAqHr9vY8d9mesOToLxdX+XeZ1jF6bMLi/7oLB9T3jIPed5M+8tB+rOvJNXpLmuWT6jWQSvWkfmwjkzZPXq9eKqdYeMJxbpvNalbPEUqBJzPaPZ3d9VY14Hl/X6lKIm9aG32BWXzOJhkZcPvah2xW/NLmZv2T4mk0qkOWhL5I6L9sj/HLwffPgSd5c4gOyGjlsgE8Mjsuq/N8v/3vp/lS5+249bX7cgMnWP+Wf+/LnNscXSzNkokYN3hk+0FcFrdJ0W0DPmXbwiImtnni6r/nvz1MG3Rrm7L33M+287NlWq2aWprdu6kyI40AX6moXGmSukMTgkIlN3ZNtJ4irEHXPs74oVI23FKT0SXXN1T+lksi6E6yJ4FX52oKw2jU6tbdrGEm+bbBW/XfGmKq8387CMPkBjF8ST3jlcRLdYVZ5PIKvxh+5v5WtcqnbATa9tzIPEaZiFKdf3ABDNtZ4REVFjO1pFcL2Psu/GrgLXYTn7Z7AP3rh+xqhYo78PqqVavzX7wG+v+qTIR1bXsvht+/Vju+VpF5wmIiIfPewuufXjN8utv2jefT741ItbXfAAirFu26Q0pFHp4rfPFVffKE971mmtf9t3gItME5H4kVsAkrGL30PHLZBVd90vjUajNVJKq2rc0Z0MIlMdCbr4veqOza3OqFWqWfwWMYrgjoIUgPSGL7lQDjn/hdIYHJrqXti+s21sn0i1CsD23bc6zuhOcJH2LnDXCGZX0qoKPztQRvqgjTYxPCKNGbOlsb39tVeX3+9m/Iy6QzNK1Z8DoBf0SGKzMGUWpUxVe42FFKZE2g/yRTEPI9OtCaQ3OLreedimasVvk3mQ2GROvDH3SnHXR9nxBtVDAbxEvvW6q2TNR1bLtEb/FH1f+NkPiojIrdLcWP72qk/KOR9ZLY3/+l5tu+CBXrrgygvb/m3f+10Hw3Obmys9Et0shAMoji5+63syW50L97bfwVuH5IRZoDrvgtOmCt+NqQ3Urn0TbZ3gl52zqPlx1tcBkI6OOWbx20wQV7H4rflG+XUkow6e5bv8qe7i1K7xCVlzS3Otx/2ZQHK6+D04/VAZOOfiqStdDlKTauoaFKlevLGn2thJYLsInjQhTtwBkjGnTNhjic1YI1Ld15Wvg90+cOMqXp13wWkdb6/C/cRAGSwensrVhKjatAmR6CtT9JomrvitZRmljnKp3n/JNbbm1g19ed+1HvsuItL4yM0yrdGo7EIOKLMLrmwmb/SmatVd94tIdTdOUfTPZI5Dt+l7+664+sbW2+p2GADoNteG6vJzhmXVnZtrl5gw7890ajQ7wlcd/KfuEjefB0b4AenootSME5aIHqSpi986QVyXiRNbto+1jQvVyRsRkZmHxndoPu1Z7cniOjwvQDcNTj9UGmeuEJHOezL1NAZzyk1VXlf2lQs+ZhE8NBlOdyaQju7ENGONHkksUs2CVFa+kel121sCveaaKiVSr9fazKFB+fcfbk0VS+v0PPSb/vvNiVJ518kvav29H4v/QLfp4vfamaeLyP21L/j6fr7GQEMaA1O/AvW0iS3bx2TL9jExr9RkEgUQ7vH7NraK4OYoLd2BWMfkpy9xbN+puerOqfHoIu4TxeZdU3V8roA8DU4/VMYful8Ghkfkri2PS2NgWkeCuC6vo9tu3dDW9WTGl6gOTd9IP61K9xQD3bbYOFPSds2C+McRV+31lKQIrtnFqLgksnmXZtWeH6AbFlvn1+yDNraqFmH0NVJxXZXmOkbHG3s0elVjLtArdpzR7PHnDSuJymuNw8N1QAEcPdOQhihRFL6BLhp/6H4ZOOdikW2T0uiT6xb0KHQR/51SZtFbj0y//NxhueLqG2X0sTGK4EAGeux3HTcLUSO2TGZyOCrpo99X1cQW0E3b7rlXRESOOnOFiAx2JGzqInbahLQXwc23AchGH+rTd2SahanGQENWrh2VNbdsqPReIbQIngUjigE3+wopHzt/Ucd9lX11jRmP9M+vD1WL1PM5AIp05ClLZN6ypR1vt692sfdU/fhas9cs/fgc1Am74pKY+8+fFPnI6l4/jK5KskkcfWxM1MFL7iiYA+kNHbdA1h4c/W1uHupKJ3Ruu3WDTKqporbIVHf4QENkUjUL3/b40M/95QvkiqtvpGMBCGQmb3SyWERa98/W9XXkStQAKN68ZUulMTgky+eJ3L19KmFTx0JL6Jhi/XcAxVp5+6hIox5XKCUpghNrgHyEFr+1urze7DWaPZnG/Lv9vrpOFAOKZHd/68mgPvakharR0yY037rG9zF0vddPPX571sDrPrKawm4EJUquH/+mvHzoRfLrx3aLiHBXOJCBThD3w2vIvA/8iqtvdG6aRh8bi7w7c80tG+gEBxIyx2nVufNHxxNfJ3hUksf38f0Qm4Gs5i1b2kzgGIdtTHV6HYVOm9AH/p733JHiHxTQx5bPnyVyzqLm9SY1kaQIftutzQlbHPwD0okqfquxHa2OTPP+713jE63XXlW51ma+Tktz3UNBCkjHjDV297e5f9JXu9Sl+9uMI2nyUFX9ueE20OsHACTx97/4pvz9L74pT7vgNDmglIw+Fp8IAjBl/KH7e/0QemZ47gwZnjvDu5C54uobO0aLiTS7xvVYdADxXGO1+s3q1etbf0SaGygde+p6EAAoA6XvM6mpqPih3zc8d0Zb/IkTUlgH+lW/rWlCEr52hyaAdFzF78HR9c6PNQ/r160wo/dJ5n7JRPEbSMd10Mbu/ravdtFuu3VDLV5zdnxxxRsO89UfBXBUxsdXXCVz//mT8pyvXCsv/OwH5e73P7vXDwmolNEbbpaJvftERFpjwNE0PHeGDDSad0r5CuEAom0aFXn8vo0i0txY6QTOZecsao3KrHuhxVX0NjeOZhGcpDGQD929sHz+rOYbaloD98UPO+5EfSyAMOa4UDNZrMZ2iMhUp1Rd+eIGhSggO3scMeIRc4Bk7OJ3yIG+uh8kttU9N4UpFMBRCSfMPVz+99b/k9d/5GY5873flXed/CI5873f7fXDAipl08Ga7lN++R1RohjnbVk4Z0arS1wXwgGk0zFtQomcd0G9JynEdS/YHwcgf8vnz5LLzlkkdc3fmLHDN2XC/FhiDZCcbzSxPtini+CNgYZcfu5w7eKNb2KNq/hN1xSQTNy9364rXfqtKGXiaiggOVfxe+i4BZF3f+uDxCvXjsrMIW5MRr3wX3RJPO2C02TNrRu4BzwCxTogu9EbbpYll17c/Dt3WnsNz53RujNcs67CAeCx8avfbsUZkXrek5mHhXNmdNyzSacmEG/x8FRCZ/yh+2VI3AnjLdvHapk0zetnopMT8HPdlSnSLIKvnXm6DAzWO5Vm3gduv12ErikgC1fx24w35kji5fNn1X7iBID8xB2ywRRzrbNixQi5mBqr96q9Ql742Q/KmpNfREEKQFd89s+fJ6//8E29fhilRiwGkts0GjHWr3+bF7x0Edx+G4B42+65ty1hrMZ2tJLGT3vWaXLbrRt69dBKi3gDJOPqllo+b0Du3q7a7sqs44GbLD8PSWSgU9zo86HjFsiEtK9n+lndYirQLWbx2zf63HV4uB/ZB/6IO/XECPQSufv9zxZFdhhAgYYvubDXDwFAn5gYHmklb/RIrT6e4OcVOjodgD95rEcTw801Lp14A6S3fP6s1ljipz2r3le8uJhj0qNGpbPGAdrFdX+zngGQN9/oc/OgjWvSRL9Me2GdUn8UwEvkf17xVhER+fVju3v8SADUkZ005sANgKJM7N0namK87W2XnbOoR48GQJ34RvoNjq5v3c2LKRSggOR8HVO2lbePFvxIysuMKboQTgcVkNy2e+5t/T2kK7NfilIAusucbGNe0wZUHQXwkrl+/Ju9fggAamrTaPMOcBGRc3f9WETYPAHI36aIXPDTnnUacQcAAJTS4uGpQza+jimR5ohiPd1m5lB/3yxIpzcQL278uSlq/DlFKQB5M7u/Lz93WHaNT/Tw0QD5owBeMjppTBc4gCJsGm12ZoqIPO2C/hvXB6B7GoNDdGMC6CkO3AAIZReoxh+63/lxekSxa1woANh0bPFNsNF8MQcAQiQ5aKPptYzZ/S0irSI4eynUAQXwErrjoj29fggAam7s1xtFpHkfLwsaAEWYvOPbvX4IAPoYXVIAQtkFKnMkcaQGSWIA8eKK38Ex5yDiDQCXuFjjYhe/Lz+3uShiL4W6oABeYqOPsaABkL9bP94cg37ZWQvkac+iCxxA/vR1CyJCFzgAACi9JEnjwdH1oiYPkCQGECm0I3PesqXBX5N4A8BmXuGSNw7coOoogJfQrR+/WT77jhW9fhgAamz0hptF1KSINLvAAaAIekwoAABAGaUZGToxPOJ8O0liALbQotTQcQucb18+f5YokjYAPNKsY5JYOGdGsd8AKBgFcADoQ5tGRZ4y+4C8Zvl8ESFZAyA/RW/AAAAAeq0xMK3XDwFAieXV/a3v6AUAnzTd377YsnLtaNaHA5QKBfCSesrsA6JEUZQCUJiJ25v38z7tWafRBQ4gV+YGbHB0PWPQAeRu3rKl3m4pAAi1Kcc8L11SAExxRSld/I5bz9h39AJAWuYUGzu26OL3zKHBbj4koFAUwEvqf17xVnnaBdzNC6BgSrXuruPADYC8jT90f68fAoA+s3z+LBElsmt8otcPBUAN+cafA4CWZCJWVPGb7m8AUaJijeuwsF7DNGbMdn6Ozg+byBWj6iiAA0CfuvXjN4uIEjWp6AIHkLtt99zrfPuKFSNsogAU6rJzFvX6IQCoMN9I4rjEMQBoaUYSm3Txm+5vAC6Lh5txxhVrXOuY0DXM5ecOtw4Sr1gxkvlxAr1GAbzkDigqUgCK85TZB0TEfcoPAAAAAOpu06jI4/dtbHubryuT4jeAbqH4DcAlrvNbpH0dwwE+9DMK4CX2ws9+sNcPAQAAIBPGoAPoBp3YWbd1p6y6Y3NvHwwAAEAEezzxxPAIxSkAwaI6v12H+Igv6FcUwEvsW6+7qtcPAUCNuU4MMpYYQBax991xLy+AHLjutBMxEjsNkZlDgyLC2gYAAHSPHksMAEXw5Vyiit8iImpsR1EPCSg1CuAltXhYZM2tG2Rag3E3APK3eFhk+JIL20ZqcQ84gDzYCR+9AVs+fxb38gLoml3jE9xbByA3esoEAGTlup8XAELZOZe44vfg6PqiHxJQWhTAS+iCKy+Ulw+9SBrSkIVzZvT64QCooeFLLpQZJyyRxuCQqMkDoiYV94ADyCSq+3twdD0njgEUyowzrGkApGHfA26LGx/KxAmgv8VOwzL4ClUizetcAMAWFWOiYgrQzyiAl9CPT71ERESG51L8BpC/C668sPX3wdH1snwevwoAZKM3YknG/ZEkBlCE5fNniWKkDYCENo02/zdtZyYTJwCI5Df+3JzWBwCaq/s7z+L3yrWjuX0toAyoepTM4mGR131kNaPPARRCF6lmnMCdVADyEVf8Hn/o/vY3KJLEAIrjmjbBgRsASdiJZMafA4iTpPs7i9Wr13fnGwEoFTvGzFu2NPfitzZzaDD3rwn0CgXwknn50ItERBh9DqAQw5dcKIPTD+14u5o80INHA6AufMXvbffc2/bv5fNndePhAOhT9v12u8YnevNAANRO3PhzAEjS/d1xSPggxp8D8NExJu7ObwBTKICXyLtObha/T5h7eI8fCYC6WTzcHH0+OP3QjgWSHoPOuFAAAFAX+h5wJk4AyILubwBxknZ/24eE7TgTNf6cdQ3Qf8wYU2Txe+XaUbq/UTsUwEtCBzKK3wCKoDu/OR0IIE+Lh/O75w4A8rBu22SvHwKAmqH7G0CcrHsi4gwAF/PKubyK30yaQD+hAF4SLx96kTSEe78B5O+CKy8UEUbjAMhXkk4HuqcAdFNU5xQAAEBe8rz7m6IUAJNZ/Nbyyu2yX0K/oABeAnP/+ZMiIjI8l3u/AeRLF79nnOA/jTwxPEK3FIBEXBuxOHQ1AOgF7gEHkMS2e+713s0LACY9DStL93eS8ecA+keanEse2DuhbiiA99hzvnKtvO4jq2VagwUOgGKEFL8bA9PYaAFIJMtGjE0VgKRCOqzsJLK+BxwA0pgYHok8wKcmVRcfDYAy4SooAEVxFb/1+PMi2Xun1avXy8I5NGyi2iiA99iZ7/2uNKRBMAGQO939HYXiN4Ckkoz5c23SLjtnkQj5YgApmHff+TQGpnXp0QDoZ8vnz+r1QwBQE40Zsxl/DkAWD0d3fnO1JZAcBfAe0sUpRp8DyJteMEV1f2t28XvL9rEiHhKAGgnpdtBFKnuTRsIYQFahyZ+Va0dl5tBgwY8GAAAgOxoTgP5lFr57NV1i5drRnnxfoEgUwHtk8bDIOf91mDSExQ2AYgxOPzTy/Wtnnt7xts/95QuESX4AfEJH/fmK3wAAAAAAAGiKu+876/hz+5qoKBweRt1QAO+RJR+6VkTo/gaQvwuuvFCGL7kwsvCkFz8Dg+0LmyuuvlE4dAwgD74YxHg/AABQNvqQn51kVmM7evSIAJQV938DyEtc8VvL2lzQmDE71ecxJRRVRwG8R85873dlWoMqE4B8hY4+13d/uyycw8EcAJ1C7/6OO5286o7NwgAcAEmExh8ASMOOMWaS2ZcwXrd1p6y6czOdUkCfYU0CIC8hxe95y5bmOlkvSUPCihUjuX1foFcogPfAu05+kYhQZAKQv+FLLgwefW7fL3XF1TcW9rgA1ENop0PcBo1kMYCk0nZarV69Pt8HAqCWXN3fAOBC9zeAvPQintj5YKDOKID3yAlzD+/1QwBQUyEnA+3R5xrXMgDIIi5xrE8bU5ACECqPTisOHgPwier+Hhxdzwh0ALnjsA2AkD1OHrEiyf3fNvI2qAMK4F2mu78BIG8XXBnf/e1b+ND9DSArvTmzD+F0xJ2Dh40pSAEIRacVgCKE3rsZYvXq9axtgJrLc/x5kpHGK9eOMkELqKGQ9Uce48/T3v8tQt4G1UcBvIue85VrRYTubwD50xuxuIWR6+7vlWtHRYTubwB+cckeX/Fby7LhAtC/kiSa7cM2K9eOyppbNpC0AeBkF7+TdFmt27pTVt2xuYiHBaDk8jyUl6Yzk8M2AACE4/hYF731ew9LQ7hjAUC+LrjyQhERmXGCfyM2MTzSKn677nrh+hcAcXzJnqjit5nUWbd1p7zu778jIhy4ARAuSaL57u3NBY0+3Mf6BkAUO77ETrExNYSOTKCP5Nn9beKgMNB/Qsef59H9HUrvn4C6oQO8S+b+8ydlza0bSPgCKERU8VtzFb/pjgKQh6iNmZ3UYS0EIETSRPO6bZMiIq21zm23bsj7IQGogcXDzT+hh2tcxSm7+5s7MoH+0IsrWShKAfXUjXiSdMqEebCPtQ3qguOqXfK6j6ym+xtA7kKTw+u2TcrAYGfIX3PLBrqjAHjF3Y0ZNS50YnikLWnMqFAASYUmhiaGR6SxvSGNgUZbopgDfgBMedz5vW7rThHp7P4m3gAoCtMmgPoI7f7OC/d/o9/RAd4F+u5vOp4A5GnxsMjwJRfK4PRDIz9u7czTnW/XCWIWNACixCWJ40afizSTxWtu5cANgDC+xJArGTR1zctUgCFRDMAWVfx2jRl1dU3p4rfZ20CHFFB/RYw/tw8Lu9jd38QboB5CDuJ1c/w5UGcUwAu2eFjkzPd+l+5vAIUYnH5o5KJIF7/p/gaQVFyiJ+5OKp3QWbd1Z6v7mwM3AEL5EkN23GkWv6e1vW316vXEGwAtaTu/zeKULn6vurNzog3xBqi/vMYVz1u2VBpnrmh727qtOzuuq9OYNgH0lzy7vwFQAC/cb6/6pIjQ/Q0gX7r7O+REoKv4rbF5AuAS1yUVVfy2O6ZW3bFZ1ty6gbUQgCBJuqx0vPEljQEgj7Hnmo41TJkA+kee3d+6sNUYHMo0lhhANYU0GYgU1/3dmmRjWbl2lLUNaosCeMFe95HVcsLcw3v9MADUzPAlF8qME6KTOGtnnt7REaVdcfWNRTwsADUQkiiO25CZ3d+MPgcQKu7wjWlq9PnUWmfl2lFZc8uGQh8jgOpIW/x2jT8H0L/yOECj1zED51wc9PGu8ec0MADVl+aKuTR8axkODqPfUAAv0LtOflGvHwKAGrrgygtjPyakI4puTAA+vk1Z3Dguc5O1butOed3ff0cGGkybABAu5PCNWfy21zrEHAAi4cVv39rG1Z3JfbxAf8n77m9d/A7t/qYjE6iPxcPRa5IiRp+HxBp7bSPC+gb1QgG8IHqRRPc3gCJEdX/rpLBv9Dnd3wB8ojZloeO49CZr1R2bKUQBCBaaZJ465NdZ/Kb7G4BI8uJ33NrGvJ+X+3iB/pDn9QlmYSvN6HOKUUC1he5ziu7+9nEdtmF9g7qgAF6Qlw/R/Y30xvfslJ9/7wuyb9f2Xj8UlEzIoskeB2rSxW+6vwHYQuJL1IbM7v5ecyuFKABh4pLMdkfE3ds7J9ysXDvKoRsALaFFK3ttYyeN9X2Z9v2YFKSA+sqz+K35Rp+77uR13cfL+gaotjzjSZTWYWG6vwEREWGWSgGe85VrRd77Xbq/kdiB/ftk9K4bZdMdN8jEvjFZ9JTnhX3exLhsXPM12fXoFjn08CNlz45HZdGZF8kxJ53Z8bG/ue8u2XzPTXLYEUfLvp2PycyjF8lJT3+5DEw7JO8fBwUYvuRCGZx+qPf9E8MjItsmnaPP9cKG4jcAW9Ykj7nJWrd1p6y6Y7OIkKgBEC7kPjyzMOXq/uZKO6C/JVnPuEaN+pLGq+7c7PwarHOA+okbU5zUvGVLY0efcycvUF8hjQbzli3NrftbJNmkCdfhPtY3qBMK4AV46/celmkNFi9Ibucjm2XhU1bIY5s3yLZf/yToc5SalB9+7UMiInLmpe+WgYFpsvuxB+X2le+UUy96oxy/9PzWx2798S3y09Wfk6f+4T/IzHnzRU0ekB/++4dk/X98RJb9/l9Ig/9uK8G3KDLvw/RhX4Ws9u7YJvd9/0uiJg/I5IEDMjmxT05+xitk9nEnRn7e2OMPy+3/+k5Z9pJ3ytzhU7v0aJFE3H1UoaPPRUTW3LqBwzbIJDTWKDUpW+65Wbb88CbZ89vfyPTZR8kJZ79QFpzx7B49cuRNF6l0YcrX/S1CsgbJEWvqI03x27W28SWNuYsXWYTGmsnJA7LlhzfJlvU3y57HH5bps46SRWc+T4aXP79Hj7y/5H3nd5rR53ZH5urV61nfIBixppy6ffd3CFf3twj7KYSpUqxhBHrO5v7zJ2XNrRsIFkjlyCecLIdMnymHzpwb/Dn3/+h78tjoT+Skp18qAwcLn4fPPV6ecNoz5ac3fVb2790tIs2x6j/77r/KE059usycN19EmncnnvT0S+WRjT+UBzZ8P/8fCLm64MoLI9+vi9+u08NXXH2jrLmF2IRs9u3+rdzxxb+SI+c/Uc645E9l2UveIcc+8Vy544vvkd8+tNH7eZMHJuTHN/yjTOwb6+KjRaisiR57VKju/gbSShJrfnbzv8rjW38uJ5xziZx4/u/L/r275f/+65/kwZ/+b48ePZIK6bTSRSpd/HatdTjkh6SINfWRZpJNSKeVOdXGREEKSSSJNT/6z4/J6F03yuxjF8ucBUtlbPtD8rP/+Vf5xf/3bz169P0j77HnuqjlG30ehQM3SINYUz6hEyXyvPvbd9jGdd0CV7sgjarFGgrgOXvdR1ZLQ8i+oHu2/vh7Mnjo4XLk/FPa3n7U4hGZ2Lu7Vdh++GdrZWLfHjnqxGVtH3fE8SfJIYfNktF13+naY0Z6M05wL5xa4/oYfY4C/eL7X5LJiXFZMPKc1tsWnPFsOWz2UfJ/3/knUUq5P+//+4ocdsTR3XqYSCGP0eciU3d/U4hCFqGxZvv9P5eZRy+S0194pcx/8jPlxHNfLGe94r0iIvLQz27ryWNHMnEHcMzu73XbJkWkc62zcu0oh/yQCrGmHpIWrXxTbXx3f0uDBDGyCY01v/nl3TJ4yKHytNf/o5z+witl+f/zHnnKy/5SRBqy6Y4bZP/eXT36CeqviOL30HELYkefuwpSJuINkiDWlEveEyXyEHfdAvsphKharKEAnqPnfOVaEaHIhO7Zt2u7/PaBX8rhc4/vGF9++LwniIjIIxt/KCIiD993x8G3z+/4OofPPV52PrxJ9u3aXvAjRlpRC6e1M0+XddsmZWDQfUqYOzGRh8kD++XBn/6vzDx6uDVtQjvmiefIzt/8Wh4b7by64ZGN94iIdBy+QTmEnEiOSxSbxW/u/kZWSWLNIdMPl4XL2qejzDxqYXOazuFHdO0xI5u4GNRKHjum3OhDfqxzkBSxph6Sjj2Pu9KFu7+RtySxZtumH8nvXPQGGZh2SOtjjl6yTI4+6SmiDkzI7m1bu/rY+0VRnd9a3Ohzc23jGklMvEEIYk25hMaVPMefJ+n+Xrl2lEkTSKWKsYYCeE4uuPJCOfO936X7G101tv0hEVFy6Mw5He8bmtFMxux6ZLPxsRL5sTsfYWxtmQ1OP9T7Pl/x+4qrbxQRNk3Ibve2B2RyYlyGDpvZ8b7D5+oDN/e0vX3fru2y+Yf/JSc/4xVdeYxIJuREsm9DZhe/RQ6OPm9QiEI2SWLNzKM6ixi7tz8ojYFpsvjc3yv2gSKz0O5vEfe93xrJG6RBrKm+osaei7QniokxyCJJrFl8ziUybXCo8+PmNf+7HZze+TWQTVHF76HjFkQWo6LomEP3N5Ig1pRH0riSx/hze4qNiz5s4zpow9UuCFXFWMNKPid/tucsEdlA9ze6at/ux0VEZMARTHSAGd/T3LyPH/xYV+DRn79/T/T4JfTO8CXu+7/Xzjzd+zmMPkeeJsb3iIjIfsc93kMHu5/GHnuw9TalJuWnN31Wll74WhmYxnKjrLLcR2UmdHSi+LZbGUOMbJLGGtOubVvlx9+8VkZe8g6uXagIXwxy3Zvp6/4mQYw0iDXVlmbseVKuMaHEGySVJNZMnz3P+TX279kphx1xTCuxjHzlVfzWdPE7KVdHJvsqhCLW9J55uDfvuBKCu7/RDVWMNWSkc7B4WGTNrRvkhLmH9/qhoM+oyQMiItIY6Bzm0JjWHEOhC0+TER+rR1Y0BggJZXTBlc3it12EmhgeEfGMPtf3YdKJibzohczubfd3vG9gQMeZidbbfnXbf8hxS8+XGUce250HiNzFdX+bVt2xmbu/kYuksUZEZM9vH5GN//s1eeD/fiDqwITc/dW/k9Ne8CY57klPLf4BI5Wo6xfauqckrPubBDGSItZUV9rid1SHlbm20YliV4eUCPEGyaSJNabJyQPy6Kb1csoFf9Bx7R2yCbkKKgkda1yTslzMopQdbyhIISliTW+lmSaR1/jzJN3fPqxtEKqKsYYR6Dl4+dCLev0Q0KcOOTgq4sD+8Y73TU7sFxGRocNmx3/sgYMfm2I8E4q1eLg5+nzGCe2LqInhEVm3bVIa1n0b2ppbmhMpWMQgL4fPOV5mzDle9u7YJr99aGPb+yYOnvw79OB1Ctu33Ct7d26T43/n/K4/ToQJGX8uEtb9/Zb/+D8RaY4+J+YgqySxRjvsiKPld573Bjn/tR+VE855kRzYPy4/+dYnZM9vH+na40a4kPhjx564pA2QFLGmmoosfptrGx1z6JBCVmlijen+e26WeSecLk849emFPs5+sni4uOK3jjWho8/N9Y09+px9FZIg1vROlqsU8hh/LhLd/W3GGd/hPiBUFWMNBfCMdGcm3d/ohcMP3ke3f++ujvfp0eezjl0sIlN313k/ttGQWccEVkTQNb7R5yIijYFpzoQwCxoU5YnPerWINOSn//0Z2f3YAzJ5YL888qt75JdrvioiIrOOPUH2790l9936RTnh7BfK3h3bWn/2790tIiLje3bI3h3b5MD+fT38SfpbyAYtSfe3iMht39+Q9WEBLSGxxjYwME0On/sEeeKzXi0nnvd7zc/Z+MPuPnDEios/ZuyZGB7xdn/rMaEUpJAFsaZa0iaYQ5LLOnFsJ4ptFKSQRppYIyKy69H75dFN6+XU572+ew+25vK+81uk897vpFz5G2IN0iDWdF/amJJn93fogRsdazjch6yqFmuYd5zROf91mDSEjgT0xmGzj5LD5813jp3Ys/1hERE5avHpB/93RB4b3SC7tt3fcVfd2PaH5YjjT5JDpnOQo0x8o89FRNZ5Rp+LCKPPUZhjTjlLznjx22Xj/35Nbv/Xd8r0I46W45eeL0ccf5LsemSLzDvhdNn58Kg8vvU+WfOZtzq/xo/+8xoRETnt+X8i809/VjcfPgwhxW/XtQv2vd+r7tgsehlEogZ5CYk1URaOrJBf3fYfcmB8b5ceMZIIGX1u8t39rRF7kBaxpjqKGi1qrm1cd2RqJIiRRZpYMz62Q3655np58sVvkYFph/TgUddX0cXv0GKUye7+BtIg1nRX1gM1Wbu/kxy4cRW/NfZSSKpqsYYCeAZz//mTIh9ZLcNzCRTorofvu1NmH3OCHHbkMTK8/AXy05s+Izse3iSzD3Z7i4hsv/9ncshhs+T4gyMlnvDkC+RXt39dHv3Vejn6xGWtj2t2Zz4ip1zwyq7/HPDTxW979LmIyNqZ/mTcFVffyBhiFOq4J50rxz3p3Na/D+zfJ9//1BvlyPlPlFnHDMv4np2y7Pf/ouPzHvv1T2R03Y1y8jNfKTOPXiSzjzmhi48aWtzo4ajit1ND5LZb6f5G/uJiTZTBQ5u/A30nj1E+duyJu+rFlbwB0iDWlF8RY89F3GsbfeCGjkzkLUmsObB/n/z8lutk6YWvpUkhR6FXQIXKUvzWB270RBsTsQZZEGu6o4hpEkmExBx7qo0dazhwgyyqFGsYgZ7B6z6yWqZ16bJ29Bc9GnhycqLjfY9svEfW/8eH5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+<h1 id="Let's-see-the-training-error-in-function-of-$k$">Let's see the training error in function of $k$<a class="anchor-link" href="#Let's-see-the-training-error-in-function-of-$k$">&#182;</a></h1>
+</div>
+</div>
+</div>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[112]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies</span><span class="p">,</span> <span class="s1">&#39;o--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training Error&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;K&#39;</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
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+<h1 id="Let's-see-how-it-generalizes">Let's see how it generalizes<a class="anchor-link" href="#Let's-see-how-it-generalizes">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>We are going to select the best $k^{\prime}$ from validation</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Then re-train with the best $k^{\prime}$ from validation</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>Now everything is fixed and locked and we can test</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[113]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">########## Plotting the trends ###########</span>
+<span class="c1"># Plot training and validation accuracy in function of $k$</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">accuracies</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">accuracies_valid</span><span class="p">)</span>
+<span class="c1">########### Selection on Validation ###########</span>
+<span class="c1"># Select best K on validation and then testing with best K</span>
+<span class="n">accuracies_valid</span><span class="p">,</span> <span class="n">accuracies</span>
+<span class="n">idx_best</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">accuracies_valid</span><span class="p">)</span>
+<span class="n">K_best_valid</span> <span class="o">=</span> <span class="n">Krange</span><span class="p">[</span><span class="n">idx_best</span><span class="p">]</span>
+<span class="c1">############## Testing part ####################</span>
+<span class="c1"># retrain with k_best and test</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">K_best_valid</span><span class="p">)</span>
+<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_entire</span><span class="p">,</span> <span class="n">y_train_entire</span><span class="p">)</span>  <span class="c1"># train on training with Kbest</span>
+<span class="n">score_test</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>  <span class="c1"># only now testing</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K_best_valid</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">score_test</span><span class="p">,</span> <span class="s1">&#39;ro&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;validation&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;K&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Error&#39;</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Remember-to-estimate-scaling-on-the-training-set-only!">Remember to estimate scaling on the training set only!<a class="anchor-link" href="#Remember-to-estimate-scaling-on-the-training-set-only!">&#182;</a></h1><ul>
+<li>In theory this is part below is an error.</li>
+<li>I took the code from sklearn documentation but in practice you have to estimate the scale parameters <strong>ONLY</strong> in the training set.</li>
+<li>Then applying it directly to the test set. </li>
+<li>If you work in inductive settings, you cannot do it jointly like the code above.</li>
+</ul>
+<div class="highlight"><pre><span></span><span class="c1">######### Watch out here ################</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="c1"># in theory this is an error!</span>
+    <span class="c1"># you want to estimate the scaling of data on the training set and </span>
+    <span class="c1"># then apply to valid and test</span>
+    <span class="c1">#########################################</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Why-showing-training-errors-vs-number-of-$k$?">Why showing training errors vs number of $k$?<a class="anchor-link" href="#Why-showing-training-errors-vs-number-of-$k$?">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="$k$-NN,-lessons-learned">$k$-NN, lessons learned<a class="anchor-link" href="#$k$-NN,-lessons-learned">&#182;</a></h2><ul>
+<li>We also showed how $k$-NN overfits with $k=1$ and studied smoothing regularization effect.</li>
+<li>We also found a way to <strong>measure the cost function</strong> for $k$-NN, though there is no explicit learning.</li>
+</ul>
+<h2 id="Towards-Decision-Trees">Towards Decision Trees<a class="anchor-link" href="#Towards-Decision-Trees">&#182;</a></h2><ul>
+<li>The decision that $k$-NN takes when $k \gt 1$ is related with the concept of <strong>impurity of a cluster</strong> of datapoints</li>
+<li>This notion of <strong>impurity</strong> is key for understanding <strong>Decision Trees</strong></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Impurity">Impurity<a class="anchor-link" href="#Impurity">&#182;</a></h1><ul>
+<li>We will play a game and we will try to classify each of the upcoming data points with $k$-NN!</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Game">Game<a class="anchor-link" href="#Game">&#182;</a></h1><center>
+<img src="figs/impurity_00.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Ready?">Ready?<a class="anchor-link" href="#Ready?">&#182;</a></h1><center>
+<img src="figs/impurity_01.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Go!">Go!<a class="anchor-link" href="#Go!">&#182;</a></h1><center>
+<img src="figs/impurity_02.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_03.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_04.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_05.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_06.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="3-blues-vs-3-reds-$%5Crightarrow$-Tie!-$%5Crightarrow$-high-uncertainty!">3 blues vs 3 reds $\rightarrow$ Tie! $\rightarrow$ high uncertainty!<a class="anchor-link" href="#3-blues-vs-3-reds-$%5Crightarrow$-Tie!-$%5Crightarrow$-high-uncertainty!">&#182;</a></h1><center>
+<img src="figs/impurity_07.png" width="50%"/>
+</center><h2 id="How-do-we-measure-impurity?">How do we measure impurity?<a class="anchor-link" href="#How-do-we-measure-impurity?">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Impurity-Functions">Impurity Functions<a class="anchor-link" href="#Impurity-Functions">&#182;</a></h1><p>Data: $\mathcal{D}=\left \{ \left ( \mathbf{x}_1,y_i \right ),\dots,\left ( \mathbf{x}_n,y_i \right ) \right \}, y_i\in\left \{ 1,\dots,K \right \}$, where $K$ is the number of classes</p>
+<p>We will cover 3 impurity functions:</p>
+<ul>
+<li>Misclassification</li>
+<li>Gini Index</li>
+<li>Entropy</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Misclassification">Misclassification<a class="anchor-link" href="#Misclassification">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Misclassification">Misclassification<a class="anchor-link" href="#Misclassification">&#182;</a></h1><p>Let $S_k\subseteq S$ where $S_k=\left \{ \left ( \mathbf{x},y \right )\in S:y=k \right \}$ (all inputs restricted to have labels $k$)
+<br>
+$S=S_1\cup \dots \cup S_K$
+<br><br>
+<u>Define</u>:</p>
+$$p_k=\frac{\left | S_k \right |}{\left | S \right |}\leftarrow \textrm{fraction of inputs in } S \textrm{ with label } k$$<p>$p_k$ is the probability of picking a point with label $k$, then the <strong>Misclassification</strong> is:</p>
+$$ H(S) = 1 - \max_k(p_{k}) $$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="What-is-the-misclassification-of-this-set?">What is the misclassification of this set?<a class="anchor-link" href="#What-is-the-misclassification-of-this-set?">&#182;</a></h1><p><br></p>
+<ul>
+<li>Just consider the colored points.<center>
+<img src="figs/impurity_06.png" width="50%"/>
+</center></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_06.png" width="50%"/>
+</center><ul>
+<li>We have 4 points in total.</li>
+<li>3 reds and 1 blue</li>
+<li>$p_{\text{red}}=\frac{3}{4}$ vs $p_{\text{blue}} = \frac{1}{4}$</li>
+</ul>
+<p>then Misclassification $ H(S) = 1 - \max(p_{k}) = 1-\max\{\frac{3}{4},\frac{1}{4}\} = \frac{1}{4} = 25\%$</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="What-about-this">What about this<a class="anchor-link" href="#What-about-this">&#182;</a></h1><p>We have 3 classes now</p>
+<center>
+<img src="figs/impurity_09.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<center>
+<img src="figs/impurity_09.png" width="50%"/>
+</center><ul>
+<li>We still have 4 points in total.</li>
+<li>2 reds and 1 blue and 1 green</li>
+<li>$p_{\text{red}}=\frac{2}{4}$ vs $p_{\text{blue}} = \frac{1}{4}$ vs $p_{\text{green}} = \frac{1}{4}$</li>
+</ul>
+<p>then Misclassification $ H(S) = 1 - \max(p_{k}) = 1-\max\{\frac{2}{4},\frac{1}{4},\frac{1}{4}\}=50\%$</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Which-one-is-better?">Which one is better?<a class="anchor-link" href="#Which-one-is-better?">&#182;</a></h1><center>
+<img src="figs/impurity_08.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Which-one-is-better?">Which one is better?<a class="anchor-link" href="#Which-one-is-better?">&#182;</a></h1><center>
+<img src="figs/impurity_09.png" width="50%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-lower-the-misclassification,-the-better-it-is">The lower the misclassification, the better it is<a class="anchor-link" href="#The-lower-the-misclassification,-the-better-it-is">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="This-works-for-all-impurity-metrics.">This works for all impurity metrics.<a class="anchor-link" href="#This-works-for-all-impurity-metrics.">&#182;</a></h1><h2 id="The-lower-they-are,-the-better-it-is!">The lower they are, the better it is!<a class="anchor-link" href="#The-lower-they-are,-the-better-it-is!">&#182;</a></h2><h3 id="It-is-similar-to-a-cost/loss-function">It is similar to a cost/loss function<a class="anchor-link" href="#It-is-similar-to-a-cost/loss-function">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Plot-Miclassification-function-for-binary-case">Plot Miclassification function for binary case<a class="anchor-link" href="#Plot-Miclassification-function-for-binary-case">&#182;</a></h1><div class="highlight"><pre><span></span><span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+<span class="n">missclassification_binary</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">pk</span><span class="p">)</span> <span class="c1">#runs over K</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="n">missclassification_binary</span><span class="p">)</span>
+</pre></div>
+<p>{{import numpy as np;pk = np.arange(0, 1.1, 0.1); missclassification<em>binary = 1-np.maximum(pk, 1-pk); </em> = plt.plot(pk, missclassification<em>binary);</em> = plt.xlabel('p');_ = plt.ylabel('Miclassification')}}</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Gini-impurity">Gini impurity<a class="anchor-link" href="#Gini-impurity">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Gini-impurity">Gini impurity<a class="anchor-link" href="#Gini-impurity">&#182;</a></h1><p>Let $S_k\subseteq S$ where $S_k=\left \{ \left ( \mathbf{x},y \right )\in S:y=k \right \}$ (all inputs restricted to have labels $k$)
+<br>
+$S=S_1\cup \dots \cup S_K$
+<br><br>
+<u>Define</u>:</p>
+$$p_k=\frac{\left | S_k \right |}{\left | S \right |}\leftarrow \textrm{fraction of inputs in } S \textrm{ with label } k$$<p>$p_k$ is the probability of picking a point with label $k$, then the <strong>Gini Impurity</strong> is:</p>
+$$ G(S)=\sum_{k=1}^{K}p_k(1-p_k) $$<p>In words, we aggregate over classes and for each class $k$, we multiply:</p>
+<ul>
+<li>probability of picking $k$ aka $p_k$</li>
+<li>by its inverse aka $(1-p_k)$.</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Gini-impurity">Gini impurity<a class="anchor-link" href="#Gini-impurity">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[114]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1e-9</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">)</span>
+<span class="n">missclassification_binary</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">)</span>
+<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">pk</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;p&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Miclassification&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Gini&#39;</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
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+>
+</div>
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+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Entropy">Entropy<a class="anchor-link" href="#Entropy">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Entropy:-Intuition">Entropy: Intuition<a class="anchor-link" href="#Entropy:-Intuition">&#182;</a></h1><p>In which of the two series there is more <strong>confusion</strong>?</p>
+<h4 id="Sequence-A">Sequence A<a class="anchor-link" href="#Sequence-A">&#182;</a></h4><table>
+<thead><tr>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td></td>
+</tr>
+</tbody>
+</table>
+<h4 id="Sequence-B">Sequence B<a class="anchor-link" href="#Sequence-B">&#182;</a></h4><p>|0|1|0|1|0|0|1|0|0|1|1|0|0|0|0|1|0|1|
+|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|</p>
+
+</div>
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+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h4 id="Sequence-A">Sequence A<a class="anchor-link" href="#Sequence-A">&#182;</a></h4><table>
+<thead><tr>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p>{{ import matplotlib; import matplotlib.pyplot as plt;seqA = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]; _=plt.bar(*np.unique(seqA, return_counts=True));}}</p>
+<div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">seqA</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># OR</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">seqB</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h4 id="Sequence-B">Sequence B<a class="anchor-link" href="#Sequence-B">&#182;</a></h4><table>
+<thead><tr>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>1</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>0</th>
+<th>1</th>
+<th>0</th>
+<th>1</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td></td>
+</tr>
+</tbody>
+</table>
+<p>{{ seqB = [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1]; _=plt.bar(*np.unique(seqB, return_counts=True));}}</p>
+<div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">seqA</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># OR</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">seqB</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Entropy-of-A-is-lower-than-Entropy-of-B">Entropy of A is lower than Entropy of B<a class="anchor-link" href="#Entropy-of-A-is-lower-than-Entropy-of-B">&#182;</a></h1><p><strong>A</strong></p>
+<p><div align='center'>
+<img width="30%" src="figs/ent_a.png" /></p>
+<div/>
+
+<p><strong>B</strong></p>
+<p><div align='center'>
+<img width="30%" src="figs/ent_b.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Definition-of-Entropy">Definition of Entropy<a class="anchor-link" href="#Definition-of-Entropy">&#182;</a></h1><p>If $X$ is a discrete random variable that can take values $\{x_1,\ldots,x_n\}$ with probability $\{p(x_1),\ldots,p(x_n)\}$ then:</p>
+$$
+H(X) \doteq -\sum_{x \in X} p(x)\log_2 p(x)
+$$<ul>
+<li>How surprised are we by a new value in the sequence?</li>
+<li>How much information does it convey?</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Entropy">Entropy<a class="anchor-link" href="#Entropy">&#182;</a></h1><blockquote><p>Let's say that you have a <strong>unfair dice</strong> that always return <strong><code>6</code></strong>. How would you be exicted to play a game with your friend using this dice?</p>
+</blockquote>
+<center>
+<img src="figs/dice.png" width="30%"/>
+</center>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="No-excitement!-I-always-know-the-answer-beforehand!">No excitement! I always know the answer beforehand!<a class="anchor-link" href="#No-excitement!-I-always-know-the-answer-beforehand!">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Where-the-$%5Clog()$-is-coming-from?">Where the $\log()$ is coming from?<a class="anchor-link" href="#Where-the-$%5Clog()$-is-coming-from?">&#182;</a></h2><ul>
+<li>We consider a discrete random variable $X$ and we ask how much information is received when we observe a specific value for this variable.</li>
+<li>Entropy tells the <strong>"degree of surprise"</strong>, so it must be related to probability of event $X=x$</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>$p(x) = 1$ we want $h(x) = 0 \rightarrow$    <strong>[Boring] NO surprise, a very common event happens</strong></li>
+<li>$p(x) \rightarrow 0$ then $h(x)=\infty \rightarrow$  <strong>[Excitement] Lots of surprise, a rare event happens</strong></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<ul>
+<li>We also want that if two events are <strong>independent</strong> $p(x,y) = p(x)p(y)$</li>
+<li>we want $h(x,y) = h(x) + h(y)$ <strong>we sum the surprises</strong> </li>
+</ul>
+$$ \log\big(p(x)p(y)\big) = \underbrace{\log\big(p(x)\big)}_{h(x)}+\underbrace{\log\big(p(y)\big)}_{h(y)} $$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="A-good-choice-is-$-%5Clog()$:-it-satisfies-all-the-above-properties">A good choice is $-\log()$: it satisfies all the above properties<a class="anchor-link" href="#A-good-choice-is-$-%5Clog()$:-it-satisfies-all-the-above-properties">&#182;</a></h3><ul>
+<li>You can think of $-\log(\mathbf{x})$ or else $\log(1/\mathbf{x})$</li>
+<li>We want the "inverse" of the probability and then $\log()$<div class="highlight"><pre><span></span><span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">pk</span><span class="p">))</span>
+</pre></div>
+</li>
+</ul>
+<p>{{import numpy as np;pk = np.arange(0.1, 1.1, 0.1); <em> = plt.plot(pk, -np.log(pk));</em> = plt.xlabel('px');_ = plt.ylabel('-log(px)')}}</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Back-to-Entropy">Back to Entropy<a class="anchor-link" href="#Back-to-Entropy">&#182;</a></h1><p>Now suppose that a sender wishes to transmit the value of a random variable $X$ to a receiver.</p>
+<p>The average amount of information that they transmit in the process is obtained by taking the <strong>expectation with respect to the distribution p(x) and is given by:</strong></p>
+$$
+H(X) \doteq E[h(x)] = \sum_{x \in X} p(x)~h(x)=  -\sum_{x \in X} p(x)\log_2 p(x)
+$$<blockquote><p>We can take advantage of the nonuniform distribution by using shorter codes for the more probable events, at the expense of longer codes for the less probable events, in the hope of getting a shorter average code length</p>
+</blockquote>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Measuring-the-%22divergence%22-between-two-%5Bdiscrete%5D-distributions">Measuring the "divergence" between two [discrete] distributions<a class="anchor-link" href="#Measuring-the-%22divergence%22-between-two-%5Bdiscrete%5D-distributions">&#182;</a></h1><p><strong>Objective</strong>: Estimate a sort of "distance" (or better <strong>divergence</strong>) between two distributions <strong>$p(x)$ vs $q(x)$</strong>.</p>
+<ul>
+<li>We have an <strong>unknown distribution</strong> $p(x)$, </li>
+<li>We want to model it using an <strong>approximating distribution</strong> $q(x)$. </li>
+</ul>
+<p>If we use $q(x)$ to construct a coding scheme for the purpose of transmitting values of $x$ to a receiver instead of $p(x)$, then the <strong>average additional amount of information</strong> required to specify the value of $x$ as a result of using $q(x)$ instead of the true distribution $p(x)$ is given by:</p>
+$$ 
+\underbrace{H(P,Q)}_{\text{q not p, so extra}} - \underbrace{~~H(P)~~}_{\text{best we can do}}
+$$<p><strong>Idea</strong>: if you use <strong>q</strong> instead of <strong>p</strong>, but the underlying process is governed by <strong>p</strong>, then you need to pay an extra price in transmission a bit more of information. "The bit more" is the equation above.</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Measuring-the-%22divergence%22-between-two-distributions">Measuring the "divergence" between two distributions<a class="anchor-link" href="#Measuring-the-%22divergence%22-between-two-distributions">&#182;</a></h1>$$ 
+H(P,Q) - H(P) = \underbrace{-\sum_{x \in X} p(x)\log q(x)}_{\text{cross-entropy}} - \underbrace{\big(-\sum_{x \in X} p(x)\log p(x)\big)}_{\text{entropy}}
+$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Measuring-the-%22divergence%22-between-two-distributions">Measuring the "divergence" between two distributions<a class="anchor-link" href="#Measuring-the-%22divergence%22-between-two-distributions">&#182;</a></h1>$$ 
+H(P,Q) - H(P) = \underbrace{-\sum_{x \in X} p(x)\log q(x)}_{\text{cross-entropy}} - \underbrace{\big(-\sum_{x \in X} p(x)\log p(x)\big)}_{\text{entropy}}=
+$$$$ 
+= -\sum_{x \in X} p(x)\log \Big( \frac{q(x)}{p(x)} \Big) =  \sum_{x \in X} p(x)\log \Big( \frac{p(x)}{q(x)} \Big) 
+$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Relative-entropy-or-Kullback-Leibler-(KL)-divergence">Relative entropy or Kullback-Leibler (KL) divergence<a class="anchor-link" href="#Relative-entropy-or-Kullback-Leibler-(KL)-divergence">&#182;</a></h1>$$ 
+KL(P||Q)= \sum_{x \in X} p(x)\log \Big( \frac{p(x)}{q(x)} \Big)
+$$<p><strong>Note that:</strong></p>
+<ul>
+<li>$KL(P||Q) \neq KL(Q||P)$ so it is <strong>NOT a distance metric</strong>, but thankfully the following holds:</li>
+<li>$KL(P||Q) = 0 \longleftrightarrow p = q$</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-decision-tree-is-about-reducing-impurity">Learning a decision tree is about <ins>reducing impurity</ins><a class="anchor-link" href="#Learning-a-decision-tree-is-about-reducing-impurity">&#182;</a></h1><ul>
+<li><p>So we want to reduce 1) Misclassification or 2)Gini Impurity or 3) <strong>? What else ?</strong></p>
+</li>
+<li><p><strong>Know your enemy</strong>: the uniform distributions $\mbf{x} \sim\mathcal{U}$</p>
+</li>
+<li>If we have $K$ classes then $\{q_1,\ldots, q_k\} = \{1/K,\ldots, 1/K\}$</li>
+</ul>
+<p>We want find a function $f(p)$ so that $f(p)$ is very distant from uniform distribution $Q$, so we can use KL divergence for this:</p>
+$$
+KL(P||Q)= \sum_{x=1}^X p(x)\log \Big( \frac{p(x)}{1/K} \Big) = \sum_{x=1}^X p(x)\log\big(p(x)\big)-p(x)\log\big(1/K\big)
+$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+$$
+KL(P||Q)= \sum_{x=1}^X p(x)\log \Big( \frac{p(x)}{1/K} \Big) = \sum_{x=1}^X p(x)\log\big(p(x)\big)-p(x)\log\big(1/K\big) = \sum_{x=1}^X p(x)\log\big(p(x)\big)+p(x)\log\big(K\big)
+$$$$
+\sum_{x=1}^X p(x)\log\big(p(x)\big)+ \sum_{x=1}^X p(x)\log\big(K\big) =  \sum_{x=1}^X p(x)\log\big(p(x)\big)+ \log\big(K\big)\underbrace{\sum_{x=1}^X p(x)}_{1}
+$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+$$
+KL(P||Q)=  \sum_{x=1}^X p(x)\log\big(p(x)\big)+\text{const.}
+$$<p>We want to be far away from Q so let's maximize the divergence:</p>
+$$
+\max_P \sum_{x=1}^X p(x)\log\big(p(x)\big) \rightarrow \min_P -\sum_{x=1}^X p(x)\log\big(p(x)\big)
+$$<p>So this tell us find the <strong>$P$</strong> so that we minimize the Entropy:
+$$
+\min_P \underbrace{-\sum_{x=1}^X p(x)\log\big(p(x)\big)}_{\text{entropy}} = \min_P H(P).
+$$</p>
+<p>Which is equivalent of saying: <strong>in order to find a good tree, you should minimize the entropy!</strong></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Artificial-Intelligence-and-Machine-Learning">Artificial Intelligence and Machine Learning<a class="anchor-link" href="#Artificial-Intelligence-and-Machine-Learning">&#182;</a></h1><h2 id="Unit-II">Unit II<a class="anchor-link" href="#Unit-II">&#182;</a></h2><h2 id="Decision-Trees-and-Random-Forest">Decision Trees and Random Forest<a class="anchor-link" href="#Decision-Trees-and-Random-Forest">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Today's-lecture">Today's lecture<a class="anchor-link" href="#Today's-lecture">&#182;</a></h1><h2 id="Another-%22non-parametric%22-model">Another "non-parametric" model<a class="anchor-link" href="#Another-%22non-parametric%22-model">&#182;</a></h2><h3 id="Decision-Trees-and-Random-Forest">Decision Trees and Random Forest<a class="anchor-link" href="#Decision-Trees-and-Random-Forest">&#182;</a></h3>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="This-lecture-material-is-taken-from">This lecture material is taken from<a class="anchor-link" href="#This-lecture-material-is-taken-from">&#182;</a></h1><ul>
+<li>Information Theory part  - (Entropy etc) is taken from <strong>Chapter 1 - Bishop</strong>.</li>
+<li>Decision Trees are very briefly covered in <strong>Bishop at page 663</strong>.</li>
+<li><a href="http://ciml.info/dl/v0_99/ciml-v0_99-ch01.pdf">Cimi Book - Chapter 01</a></li>
+<li><a href="https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/06_trees_handout.pdf">CSC411: Introduction to Machine Learning</a></li>
+<li><a href="https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial3.pdf">CSC411: Introduction to Machine Learning - Tutorial</a></li>
+<li><a href="https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote17.html">Cornell ML course</a></li>
+<li><a href="https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote18.html">Cornell ML course Bagging</a></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Motivation-for-Entropy">Motivation for Entropy<a class="anchor-link" href="#Motivation-for-Entropy">&#182;</a></h1><ul>
+<li>Maximizing the divergence of your tree from the uniform distribution <em>[of the classes]</em> boils down to <strong>maximize the KL divergence between a distribution P and Uniform on the classes</strong>.</li>
+<li>Maximizing KL wrt to Uniform  $\rightarrow$ <strong>minimize the entropy of data class-label.</strong></li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Impurity-functions-review">Impurity functions review<a class="anchor-link" href="#Impurity-functions-review">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[115]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1e-9</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">)</span>
+<span class="n">missclassification_binary</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">)</span>
+<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="n">missclassification_binary</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">pk</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">*</span><span class="n">pk</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">pk</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;p&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Miclassification&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;Gini&#39;</span><span class="p">,</span> <span class="s1">&#39;Entropy&#39;</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="From-Impurity-of-a-Set-to-Impurity-of-a-Tree">From Impurity of a Set to Impurity of a Tree<a class="anchor-link" href="#From-Impurity-of-a-Set-to-Impurity-of-a-Tree">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Impurity-of-a-Tree-with-a-recursive-definition:">Impurity of a Tree with a recursive definition:<a class="anchor-link" href="#Impurity-of-a-Tree-with-a-recursive-definition:">&#182;</a></h1>$$H(\texttt{Tree};S)=\frac{\left | S_L \right |}{\left | S \right |}H(S_L)+\frac{\left | S_R \right |}{\left | S \right |}H(S_R)$$<p>where:</p>
+<ul>
+<li> $\left ( S=S_L\cup S_R \right )$</li>
+<li> $S_L\cap S_R=\varnothing$</li>
+<li> $\frac{\left | S_L \right |}{\left | S \right |}\leftarrow \textrm{fraction of inputs in left subtree}$</li>
+<li> $\frac{\left | S_R \right |}{\left | S \right |}\leftarrow \textrm{fraction of inputs in right subtree}$</li>
+</ul><p>Note: you can replace $H$ with ones of the Impurity function we have seen.</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Why-Misclassification-is-a-bad-loss-function">Why Misclassification is a bad loss function<a class="anchor-link" href="#Why-Misclassification-is-a-bad-loss-function">&#182;</a></h1><ul>
+<li>Different splitting results give the same loss value if we use classification loss type. </li>
+<li>New split regions do not reduce the original loss.</li>
+<li>The misclassification loss shape is <strong>not concave</strong> (here for a binary classification problem)</li>
+</ul>
+<p><div align='center'>
+<img width="50%" src="figs/missclass_split.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><div align='center'>
+<img width="50%" src="figs/missclass_split2.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Misclassification-is-a-bad-impurity-function">Misclassification is a bad impurity function<a class="anchor-link" href="#Misclassification-is-a-bad-impurity-function">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[116]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1e-9</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">)</span>
+<span class="n">missclassification_binary</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">)</span>
+<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="n">missclassification_binary</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">pk</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pk</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">*</span><span class="n">pk</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">pk</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">pk</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;p&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Miclassification&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;Gini&#39;</span><span class="p">,</span> <span class="s1">&#39;Entropy&#39;</span><span class="p">])</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
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+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+<div class="jp-OutputArea jp-Cell-outputArea">
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+<div class="jp-OutputArea-child">
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
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+>
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+</div>
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+</div>
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+</div>
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+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-Decision-Tree">Learning a Decision Tree<a class="anchor-link" href="#Learning-a-Decision-Tree">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-Decision-Tree">Learning a Decision Tree<a class="anchor-link" href="#Learning-a-Decision-Tree">&#182;</a></h1><ul>
+<li>Learning the simplest (smallest) decision tree is an <strong>NP complete problem</strong> <em>[Hyafil &amp; Rivest 1976]</em></li>
+<li>Like in K-means we proceed with a <strong>greedy approach</strong> by minimizing the entropy of the entire tree $\min_P H(P)$</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
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+<h1 id="Learning-a-Decision-Tree:">Learning a Decision Tree:<a class="anchor-link" href="#Learning-a-Decision-Tree:">&#182;</a></h1><h2 id="Simple,-greedy,-recursive-approach,-builds-up-tree-node-by-node">Simple, greedy, recursive approach, builds up tree node-by-node<a class="anchor-link" href="#Simple,-greedy,-recursive-approach,-builds-up-tree-node-by-node">&#182;</a></h2><ul>
+<li>Resort to a <strong>greedy heuristic</strong>:<ol>
+<li>Start from an empty decision tree</li>
+<li><strong>[Greedy Step]</strong> Choose <em>A) a dimension among axes B) <strong>BEST</strong> splitting value that minimize the Impurity function on the chosen axes</em><ul>
+<li>When Impurity is Entropy we <strong>minimize the entropy</strong> also know as <strong>maximizing the Information Gain</strong></li>
+</ul>
+</li>
+<li>Once A) and B) are chosen save them as "parameters" of the model</li>
+<li>Apply <strong>recursion to the sub-problem</strong>.</li>
+</ol>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-Decision-Tree:">Learning a Decision Tree:<a class="anchor-link" href="#Learning-a-Decision-Tree:">&#182;</a></h1><h2 id="Simple,-greedy,-recursive-approach,-builds-up-tree-node-by-node">Simple, greedy, recursive approach, builds up tree node-by-node<a class="anchor-link" href="#Simple,-greedy,-recursive-approach,-builds-up-tree-node-by-node">&#182;</a></h2><ul>
+<li><strong>Termination</strong>:<ol>
+<li>if no examples – return <strong>majority</strong> from parent (Voting such as in k-NN).</li>
+<li>else if all examples in same class – return the class <strong>(pure node)</strong>.</li>
+<li>else we are not in a termination node (keep recursing)</li>
+<li><strong>[Optional]</strong> we could also terminate for some <strong>regularization</strong> parameters</li>
+</ol>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
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+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Information-Gain">Information Gain<a class="anchor-link" href="#Information-Gain">&#182;</a></h1><ul>
+<li>To minimize the entropy, we practically maximize the information gain, given <strong>we chose an attribute</strong>.</li>
+<li>We pick the maximum over information gain</li>
+</ul>
+$$IG(Y|X) = \underbrace{H(Y)}_{\text{prior entropy in the node}} - \underbrace{H(Y|X)}_{\text{new, conditioned on the split}} \qquad \forall~X~~\text{splitting attribute}$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-Decision-Tree-for-Classification:-Toy-Sample">Learning a Decision Tree for Classification: Toy Sample<a class="anchor-link" href="#Learning-a-Decision-Tree-for-Classification:-Toy-Sample">&#182;</a></h1><p><img width="40%" src="figs/data_01.png?2" /></p>
+<p><a href="https://wei2624.github.io/MachineLearning/sv_trees/">Taken from wei2624.github.io</a></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Which-axis-and-value-do-we-split?">Which axis and value do we split?<a class="anchor-link" href="#Which-axis-and-value-do-we-split?">&#182;</a></h2>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Learning-a-Decision-Tree-for-Classification:-Toy-Sample">Learning a Decision Tree for Classification: Toy Sample<a class="anchor-link" href="#Learning-a-Decision-Tree-for-Classification:-Toy-Sample">&#182;</a></h1><p><div align='center'>
+<img width="40%" src="figs/data_02.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-current-decision-tree">The current decision tree<a class="anchor-link" href="#The-current-decision-tree">&#182;</a></h1><p><div align='center'>
+<img width="40%" src="figs/data_03.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><div align='center'>
+<img width="40%" src="figs/data_02.png" />
+<img width="30%" src="figs/split_00.png?1" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Region-1-on-the-left-is-pure-so-we-are-done-on-$R_1$">Region 1 on the left is pure so we are done on $R_1$<a class="anchor-link" href="#Region-1-on-the-left-is-pure-so-we-are-done-on-$R_1$">&#182;</a></h1><p><div align='center'>
+<img width="40%" src="figs/data_02.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Let's-split-$R_2$">Let's split $R_2$<a class="anchor-link" href="#Let's-split-$R_2$">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Region-1-on-the-left-is-pure-so-we-are-done-on-$R_1$">Region 1 on the left is pure so we are done on $R_1$<a class="anchor-link" href="#Region-1-on-the-left-is-pure-so-we-are-done-on-$R_1$">&#182;</a></h1><p><div align='center'>
+<img width="35%" src="figs/data_02.png" /></p>
+<div/>
+
+<h1 id="First-try-all-values-on-horizontal-axes-for-$R_2$-(right)">First try all values on horizontal axes for $R_2$ (right)<a class="anchor-link" href="#First-try-all-values-on-horizontal-axes-for-$R_2$-(right)">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<p><div align='center'>
+<img width="40%" src="figs/data_02.png" />
+<img width="30%" src="figs/split_00.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Now-let's-try-all-values-on-vertical-axes-for-$R_2$-(right)">Now let's try all values on vertical axes for $R_2$ (right)<a class="anchor-link" href="#Now-let's-try-all-values-on-vertical-axes-for-$R_2$-(right)">&#182;</a></h1><p><div align='center'>
+<img width="35%" src="figs/data_01.png" />
+<img width="30%" src="figs/split_01.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Much-better-gain-in-split-on-vertical-axes--at-value-$%5Capprox-2.7$">Much better gain in split on vertical axes  at value $\approx 2.7$<a class="anchor-link" href="#Much-better-gain-in-split-on-vertical-axes--at-value-$%5Capprox-2.7$">&#182;</a></h1><div align='center'>
+<img width="40%" src="figs/data_04.png?2" />
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-Final-Decision-Tree">The Final Decision Tree<a class="anchor-link" href="#The-Final-Decision-Tree">&#182;</a></h1><p><img width="40%" src="figs/tree_02.png?2" /></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Test-point-to-classify?">Test point to classify?<a class="anchor-link" href="#Test-point-to-classify?">&#182;</a></h1><p><div align='center'>
+<img width="80%" src="figs/data_04.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-Final-Decision-Tree">The Final Decision Tree<a class="anchor-link" href="#The-Final-Decision-Tree">&#182;</a></h1><p><div align='center'>
+<img width="80%" src="figs/tree_02.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[117]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">datasets</span>
+<span class="c1"># import some data to play with</span>
+<span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
+<span class="c1"># we only take the first two features. We could avoid this ugly</span>
+<span class="c1"># slicing by using a two-dim dataset</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="c1"># Nx2 </span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>      <span class="c1"># Nx1</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-data">The data<a class="anchor-link" href="#The-data">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[118]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[119]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[120]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[121]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+<span class="c1"># Plot the training points</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">);</span>
+<span class="c1"># for i, color in zip(range(n_classes), plot_colors):</span>
+<span class="c1">#     idx = np.where(y == i)</span>
+<span class="c1">#     plt.scatter(</span>
+<span class="c1">#         X[idx, 0],</span>
+<span class="c1">#         X[idx, 1],</span>
+<span class="c1">#         label=iris.target_names[i],</span>
+<span class="c1">#         cmap=plt.cm.RdYlBu,</span>
+<span class="c1">#         edgecolor=&quot;black&quot;,</span>
+<span class="c1">#         s=50,</span>
+<span class="c1">#     )</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[122]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[123]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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s25AYgA4Pfff8e7d+/g6OiIdu3aISQkBEePHgUABAcHw9nZGX369MGAAQNgbGwMIyOjCsdJCCmdgJWW0hMiZWrz/PaBgYEwMTEp9Rc6UDi9sb29PXbu3FlzgUmw2lzehIgbtQwQUgcUJQGMMZHWAAD4999/cffuXcTGxmLs2LF8hEcIkXDUZ4CQWu7jx484efIkAGDXrl1ITU0VWf/gwQOsWbMGcnJy1LxOCCkR3SYg5P9Rs7F0ofIm5H+oZYCQOuzJkyfcoEd8SU9P58Y7KM3nz5+Rnp5eQxERQr5FyQAhddDHjx/h6OiIXr16cY8b1rQvX75g69at0NbWRnx8fLH1RkZG3JTEHTt25EZRjIuLw8yZM7Fv3z7MnTtXZA4DQkj1oGSAkDqoadOmmDNnDq8xJCcno3///sX6MADA06dPYWJigqdPn+Lp06f4559/ICtb2J953Lhx+OWXXzB37lysX78eEydORFJSUk2HT4hUoWSAkDrq26cKalrbtm3RsmXLEtft3LkTcnJyEAgEMDAwgIaGBgDg4cOHePHiBYYOHQqgMKnR09PD8ePHayxuQqQRJQOEVLP169fj6NGjGDduHNasWQMASElJgbW1Ndzd3WFtbc0NYRwYGIgJEyZgy5YtsLOzg4aGBmbMmIHQ0FBMmTIFbdu2xdq1awEU/sdpaWmJ1atXY/r06VBRUcHQoUNLvS2QmpoKBwcHLFy4EP369UNgYCCAwmGNV6xYgaNHj8LY2BiHDx+u1vejaErnTZs2wcDAAKNHj8anT58AADdv3kS7du24VgIA6NixI65fv16tMREi7SgZIKQavXjxAvfv38dvv/2GM2fOcPMI7Nq1CykpKbCyssL48eOxbNkyAMDPP/+M8PBwBAQEYMOGDXj48CGOHTuGS5cu4a+//sLVq1exbt06fPnyBYaGhggLC8OjR4+wf/9+vHz5Em/fvoW9vX2JscydOxfW1tbYvXs3Zs2ahV9++QWZmZm4du0a0tPT8dtvv+HSpUvcvfuvbd26tdQ5C8zNzSv0nsjJyeH27dtISkrCn3/+idu3b8PR0REA8P79e270wiJKSkp49+5dhc5BCKkYGnSIkGrUtGlT+Pn5wdnZGU5OTrCwsAAALFiwAOnp6fj8+TP+/fdfJCQkACicybBx48bo06cPFBQUoKCgADU1Nfz4448AgG7dukEoFCIlJQVt2rRBw4YN0bt3b8jLy6NNmzaws7PD6tWri8WRlJQEHx8fbjKg9PR0GBgYID4+Hi1atMCff/6JLl26YO7cuRg9enSx/W1sbDB58uQSr/HrmRQrQl5eHjNnzkTr1q0xevRobN++HfXq1Ss2tXJJUy0TQsSLkgFCqlHr1q1x4sQJzJo1C15eXjh79ix69OgBFRUV7N27F2pqavjhhx/g4eFR6jG+noioqB+AUCgscVttbe0SH+OLioqCUCjkWiC+1r59e+zYsQP29vbw9PSEt7d3sZkQlZWVi/1iF5dhw4ahUaNGyMjIgIaGBm7fvi2yPjMzk+tTQAipHnSbgJBq9O7dO4wcORJhYWHo1asXJk6cCABYtGgREhMTMXfuXMjLy4vtfN9OW1xEXV0dWVlZePToEbesoKAAz58/R2RkJObMmYPQ0FCoqKjAysqq2P7ivE3wrYKCAjRt2hQtWrSAkZERoqKiROZYiI6OFpl9kRAifpQMEFKN3rx5g5s3b0JVVRUHDx7kOsrdunWL+wX/4sUL5OTkcL/2vx0UVCgUctMZlzRgaFGHQcYYjh07BgcHh2LbamhooH///pgxYwb++ecfvH37FsuXL0eLFi1w+/ZthISEQFNTEzt27OBi/JqNjU2pUxt/r1WjpHifP3+Os2fPcus8PT2xatUqCAQC9O7dG/r6+vD39wdQ2NkwODgYM2fOLPUchJCqo9sEhFSz6dOnw8nJCbm5uXB3dwdQ2GfA0dERKSkpWLp0KYRCIWxtbTFlyhS8fPkSioqKiIuLw6NHj/DhwwecPn0aBgYGuHDhAgDAy8sLdnZ2AIDXr19j5cqViIiIwJAhQ2BmZoaMjAwcOnSI23bhwoU4dOgQpk6dCmNjY/To0QNubm5o0aIFGGMwNzfHkiVLEB8fX+KshpW5TRATE8NNQ3zy5EnuaYioqCgsXLgQu3btwqhRo6CjowMzMzNuvxMnTsDJyQnPnz9HfHw8Dh06hKZNm1b0bSeEVADNTUDI/6uNY9UbGxvD2NiYe2SRlF9tLG9CqgvdJiCkFmOMldgUTwghFUHJACG1VGBgIEJCQnDjxg0av58QUiXUZ4CQWsrY2JjG7CeEiAW1DBBSjV69elUr/sOOi4sT+zHz8vKqZcbExMRE5OXlif24hEgzSgYIqQZZWVlwcXGBjo4OXr58yVscgYGBkJeXx8GDB+Hj48Mt9/T05KYPFggEePr0KYDCPgh//PEHNmzYgA0bNmD9+vUVOt+0adO4YyopKXGjB2ZkZMDa2hqurq6wtbXlnoooj4KCArRv3547rqmpKerXrw8AePnyJWbNmgU3NzdYWVnh1atXAAqncD58+DAsLS2hpaVVoWsgRBrRbQJCqoGioiJsbW1LnSegJqmrq8Pa2lpk2e3bt7kEoF69etDT0wMAbo6D06dPAwAsLCzg4eGBWbNmlXmehIQEKCsrc8dt1KgRmjVrBgCwsrKCkZER5s6di7y8POjq6qJz587Q0dEp87gXL17E0qVLYWhoyF0PAHz58gUjR46Ej48PdHV18erVK4waNQovX75E06ZNMX36dGhqauLOnTvleZsIkWrUMkBINeF7CuHS+Pv7Iz4+HnFxcdDR0UH37t1Rr17hV4GrqyvGjBnDbWtubg5XV9dyHXfv3r2QlZXFly9fYGBggA4dOgAA4uPjcfbsWe649evXx6BBg7B///5yH1coFKJhw4YwMDDgkoHLly9DTk4Ourq6AIAuXbpAVlZWpAVEUsuAEElDyQAhZfD19YWCggIsLCyQm5uL3NxcburgovV2dnZwd3fH1KlTSxzBLy0tDVOnTuWarJ88eYKuXbti+vTp3DZubm5YunQphgwZghUrVlTb9dy/fx/BwcEYNWoU2rdvzz2J8P79e4SGhqJTp07cth07duQG/ynLixcvcOTIEfTt2xc//fQTPnz4AKAw+VBQUECrVq1EjlueaYnj4+ORmpoKOzs7dO7cGTNnzuRGarx586ZIrBU5LiFEFCUDhJRh+PDhmDFjBrKzsyEnJwc5OTm0bt0aK1euBABYW1vDxMQEVlZWEAqF8PLyKnaMxo0bY9CgQdzrnj17onfv3txrX19fxMXFYcuWLfD29sauXbtw6tSpYscxNzcvdY6ArVu3lut6nJycEBMTg4cPH0JDQwO//PIL0tLS8P79ewAQGWlQSUkJAMo1hbC3tzcSExNx7tw5xMbGckMIV2VaYnV1dTx+/BhJSUnYunUr/vrrL2zfvr3KxyWEiKI+A4SUw+zZs9G7d28kJiaiUaNGUFBQ4DqxXbp0CZ06dUJYWBji4uK46YgromgGw82bNwMABg8ejJSUlGLbeXh4ICcnp8RjVHS44N69e8PPzw96eno4e/YsNwrf11MGV3T6YFlZWYwbNw5dunRB9+7d8ebNG7FMS6ykpIQlS5ZASUkJ69atw9KlS2m6Y0LEiJIBQspBX18fBgYGOHbsGNTU1PDLL79w6xQVFeHg4AALCwu0a9eOm1SoIiIiIvDbb79hwoQJ392uefPmFT729ygoKMDc3BzJycncNMGfP3/m1mdmZgJAhacQ1tXVRffu3bnjfn3MouNWZlpiCwsLLFq0iIvp20ciabpjQiqHbhMQUk5WVlbw9PREcHAw1/s+IyMD/fv3x7Rp0/Dzzz+XeYzShg5WV1fHrVu3RJYFBQUV204ctwm+lZWVBQMDA6iqqkJXVxeRkZHcuujoaHTt2hVqamoVPm5+fj50dHTQt29fZGZm4uPHjyLHrcy0xEWxAoCRkZFIrFU5LiHSjpIBQsrJwsIC0dHRIs+tv3r1ComJifjy5QtycnIQHh7OTUf87X/8zZo1Q3x8PBISEpCUlIRXr14hIyMDAGBpaQl3d3e4u7sjLi4OXl5e3DPzX/Pw8Ch1KmEbG5syryE7Oxv79+/n/mNOTExEWloahg0bBgBYsmQJzp8/z21/8+ZNkccj/fz8YG9vX+xWxdu3b3H06FGuc5+fnx9++eUXqKiooFWrVrCwsIC3tze3/e3btzF//nzu9Z49e7B3795i8d6+fRs3btzgXh84cACbNm0CAIwbNw5ZWVkIDw8HACQlJSEzMxPm5uZlvg+EEFF0m4CQcmrUqBF+//13WFhYcMsMDAwwePBgjB07FgsXLoSlpSXWrl0LY2NjvHnzBgBw/vx56OnpYejQoRg4cCD09PRgaWmJUaNGISYmBq9fv8b06dMRFBQEOzs7rF27Fo6OjliwYEGxGKp6myAnJwceHh5Yt24dpk+fjubNm3MDEAHAjBkzEBsbi5UrV6Jp06Zo2bIlZsyYwe3/77//Yvfu3ejXrx/GjRvHLU9ISMCqVavg4uKCX3/9FW3btoWzszO33tXVFQ4ODkhJScHnz5/h5OSELl26cOuvXr2KmzdvwsLCQmS64uDgYDg7O6NPnz4YMGAAjI2NYWRkBKCwf8ClS5ewYcMG9OzZE9HR0fD29ub6chBCyo+mMCbk/5VnSlvGWK16dj0wMBDTp0/H27dvxXbMy5cvQ0NDA/r6+mI7JgBs3rwZS5Ysgays+H6jfO/6aQpjQv6HbhMQUgG1KREoIs5x/N++fYv09HSxJwJ37txBnz59xJoIAIX9FgghZaPbBITUYaqqqjA0NMSePXugoaEBMzOzKh1PU1OzWsb679evn1gTrY8fP+LSpUsICwvD0KFDxXZcQuoqSgYIqcP09PREOu5VVXW1jIj7uEVzExBCyoduExBCCCFSjpIBQgghRMpRMkAIIYRIOeozQMg3QkND+Q6B1AAqZ0L+h5IBQv6fqqoqFBUVYWlpyXcopIYoKipCVVWV7zAI4R0NOkTIV2JiYpCcnFwtx87NzYWDgwP++ecfuLi4oG/fvtVyntru3r17sLOzQ58+fbB161bIyclV27lUVVVpYiNCQMkAITUiOzsb48ePh5+fHy5evMjNBUBKdv36dZibm2Pw4ME4d+4cGjRowHdIhNRp1IGQkGqWnZ2NcePGwd/fH5cuXaJEoByGDRuGS5cuwc/PD+PGjUN2djbfIRFSp1HLACHVKDs7G2PHjkVgYCAuX76MwYMH8x1SrXLjxg2YmZnBxMQE58+fh7y8PN8hEVInUTJASDX58uULxowZg9u3b+Py5csYNGgQ3yHVSjdv3sTo0aNhZGSECxcuUEJASDWgZICQavDlyxeYm5vjzp07uHLlCgYOHMh3SLWan58fRo8ejf79++PChQtQUFDgOyRC6hRKBggRs6ysLJibm+Pu3bu4evUqTExM+A6pTvD398eoUaPw888/4+LFi5QQECJGlAwQIkZZWVkwMzPD/fv3cfXqVRgbG/MdUp0SGBiIkSNH4qeffsKlS5egqKjId0iE1AmUDBAiJllZWRg9ejT++ecfXL16FUZGRnyHVCfdunULpqam6NOnDy5fvkwJASFiQMkAIWKQmZmJ0aNH4+HDh7h27Rr69+/Pd0h12n//+1+YmprC0NAQly9fRsOGDfkOiZBajZIBQqooMzMTI0eOxOPHj3Ht2jX8/PPPfIckFW7fvo0RI0agd+/euHLlCiUEhFQBJQOEVMHnz58xcuRIPHnyBL6+vujXrx/fIUmVO3fuYMSIEejVqxeuXr1KCQEhlUTJACGV9PnzZ5iamiIoKAi+vr401wBP7t69i+HDh6Nnz564evUqGjVqxHdIhNQ6lAwQUgmfPn2Cqakpnj17huvXr+Onn37iOySpdu/ePQwfPhz6+vrw8fGBkpIS3yERUqtQMkBIBX369AkjRozAixcvcP36dfTp04fvkAiABw8eYNiwYejWrRuuXbtGCQEhFUDJACEVkJGRgREjRiA4OBh///03fvzxR75DIl/5559/MHToUOjp6eHatWtQVlbmOyRCagVKBggpp/T0dAwfPhyhoaH4+++/YWhoyHdIpAQPHz7E0KFDoaurC19fX0oICCkHSgYIKYf09HQMGzYMYWFhuHHjBn744Qe+QyLf8e+//2LIkCHQ0dGBr68vVFRU+A6JEIlGyQAhZUhLS8OwYcPw+vVr3Lx5E7169eI7JFIOjx49wpAhQ9C5c2dcv36dEgJCvqMe3wEQIsnS0tIwdOhQhIeHw8/PjxKBWuSHH37AzZs38fr1awwdOhRpaWl8h0SIxKKWAUJKkZqaiqFDhyIyMhI3b95Ejx49+A6JVMKTJ08wePBgdOjQAX///TcaN27Md0iESBxKBggpQWpqKoYMGYKoqCj4+fnBwMCA75BIFTx9+hSDBw9Gu3bt8Pfff6NJkyZ8h0SIRKFkgJBvfPz4EUOGDEF0dDT8/Pygr6/Pd0hEDIKCgjBo0CBoaWnhxo0baNq0Kd8hESIxKBkg5CsfP37E4MGDERsbCz8/P3Tv3p3vkIgYPXv2DIMGDYKGhgZu3rxJCQEh/486EBLy/1JSUjBo0CDExsbC39+fEoE6SF9fH/7+/oiNjcWgQYOQkpLCd0iESARKBggBkJycjEGDBiEuLg4BAQHo1q0b3yGRatK9e3f4+/vj3bt3GDRoEJKTk/kOiRDe0W0CIvWKEoH4+Hj4+/uja9eufIdEakBwcDAGDhyIli1bws/PD6qqqnyHRAhvqGWASLWkpCQMHDgQCQkJCAgIoERAiujp6SEgIADx8fEYOHAgkpKS+A6JEN5QMkCkVmJiIgYOHIjExEQEBARAV1eX75BIDevatSsCAgJEPguESCO6TUCkUtGXf0pKCgICAtClSxe+QyI8Cg0NhYmJCVRVVeHv7w81NTW+QyKkRlHLAJE6CQkJMDExwcePHxEYGEiJAIGOjg4CAwORkpICExMTJCQk8B0SITWKkgEiVeLj42FiYoLU1FQEBASgc+fOfIdEJESXLl0QGBiI1NRUmJiYID4+nu+QCKkxlAwQqfHhwweYmJggPT0dgYGBlAiQYjp37ozAwECkp6fDxMQEHz584DskQmoE9RkgUqEoEfj8+TMCAgLQsWNHvkMiEiw8PBwmJiZo1KgRAgIC0LJlS75DIqRaUcsAqfPev38PY2NjZGZmIjAwkBIBUqaOHTsiMDAQnz9/hrGxMd6/f893SIRUK0oGSJ0WFxcHY2NjfPnyBYGBgejQoQPfIZFaokOHDggMDERWVhaMjY0RFxfHd0iEVBtKBkid9e7dOxgbGyMnJweBgYFo37493yGRWqYoIcjOzoaJiQklBKTOoj4DpE4qSgTy8vIQGBgIbW1tvkMitVhkZCRMTEwgJyeHgIAAtGnThu+QCBErahkgdU5sbCyMjY0hFAopESBi0a5dOwQGBiIvLw/GxsaIjY3lOyRCxIqSAVKnxMTEwNjYGPn5+ZQIELHS1tZGYGAghEIhjI2NERMTw3dIhIgNJQOkzoiOjoaxsTEYYwgMDISWlhbfIZE6RktLC7du3UJBQQGMjY0RHR3Nd0iEiAUlA6ROePv2LYyNjQEAgYGB0NTU5DcgUmdpamoiMDAQAGBsbIy3b9/yGg8h4kDJAKl1kpKSMHr0aKSmpgL4XyJQr1493Lp1CxoaGjxHSOq6ooSgXr16IglBeno6zMzMaPZDUutQMkBqnRMnTuD69esQCASIioqCsbExZGVlcevWLbRt25bv8IiU0NDQQGBgIGRlZWFsbIyoqCgAwPXr13HixAmeoyOkYigZILXO6dOnMWzYMHz8+BHGxsaoX78+AgMD6XEvUuPatm0rkhCkpKRg2LBhOH36NN+hEVIhlAyQWiU2NhZ3796FsbExjI2N0aBBA0oECK/atGmDW7duoUGDBjA2NoaRkRHu3btHjx+SWoWSAVKrnD17FnJycnBxcYGCggLOnDmDy5cv47fffqM56EmNS0xMxPTp03H16lWcOXMG8vLycHFxgZycHM6ePct3eISUGyUDpFY5evQoAEAoFKJZs2bo2bMn5s+fj8TERMjIyPAcHZE2MjIy+PDhA+bMmYOePXtCTU0NQqEQwP8+q4TUBjQcMak1Xr16BR0dHQCArKwsBg0ahF9++QVjxoyBqqoqz9ERaZaUlIQLFy7g7Nmz8PPzQ35+PgDg5cuX0NXV5Tk6QspGyQCpNeLi4mBqaorp06fjt99+Q7NmzfgOiZBiUlJScPToUXh6euLKlSv0qCupFSgZIIQQQqQc9RkghBBCpJws3wHUNjExMUhOTuY7DCImqqqq1IxLOFS/6xaq3+VHyUAFxMTEQEdHB1lZWXyHQsREUVERoaGh9IVBqH7XQVS/y4+SgQpITk5GVlYWjh8/zvVqJ7VXaGgoLC0tkZycTF8WhOp3HUP1u2IoGagEHR0d9OzZk+8wCCHVgOo3kUbUgZAQQgiRcpQMEEIIIVKOkoE6LD4+nu8QCCHVhOo3ESdKBiTMnTt3MGDAAAgEAvTt2xcPHz4EALx79w6//fYb1NXV4eHhAaFQiMDAQMjLy+PgwYPw8fHhjhEeHo6JEydi8uTJIsf28vLC+vXrIRAI8Pbt2xq5nuPHj6NTp05QUVHBhAkT8PHjxxK3Y4xh3bp12L59O/7zn/9g1qxZSElJqZEYCakpda1+nzt3Dt27d4eysjKmTp2KvLy8Erfbu3cvBAKByJ+cnBwyMjJqJE5SNupAKGF+/vln+Pj4oFevXkhNTUWPHj0AFE6Tmp+fj5s3b0JPT4/bXl1dHdbW1iLHkJGRgZqaWrFZ/CZPngzGGJydnav/QgAEBATg0aNH8Pb2xsuXL2FlZYXFixeXOIHLgQMHEBcXh/379wMADh8+jBUrVuDAgQM1EishNaEu1e+nT58iPT0dQUFBeP36NYyMjHDs2DHMnDmz2LZhYWEICgqCiooKACAiIgKbN2+GsrJyjcRKykYtAxKoUaNGOHbsGMLDw7Fx40YAwLZt2/DLL7+IfFGUpl27dqWO2y8QCCocz6NHj7Br164K7/fq1Svs3LkTXbt2xa+//op58+YhICCgxG1DQkLw4cMH7rWioiI+f/5c4XMSIunqSv1u0qQJZs6ciXr16qFLly74+eefUa9e8f9S8vPz4ezsDH19fWhpaUFLSwuPHz/G2LFjK3xOUn0oGZBQhoaGsLW1xYYNG7Bz506kpqbWeOV58eIFxo4dizFjxqBly5YV3t/GxkbkdcuWLdGmTZsSt504cSKuXLmCdevWITk5GZ6enli9enWl4iZE0tWF+q2lpcX9OzExEU2bNsWUKVOKbScjI4PmzZuLLPP29oa5uXmFz0mqDyUDEmzt2rXQ0tLC6tWrsWrVqho7b1hYGCwsLDBo0CD069cP4eHh+PXXX7F161aoqqqW+FdSxf72V8rDhw8xb968Es/Zr18/HD16FOvXr0enTp3g4uKCTp06Vcv1ESIJanv9BoCCggK4ubnBwMAA//zzD6Kjo8s8f1xcHBhjaN26tbgvjVQBJQMSTEZGBh07dsTnz5+xdevWaj9fVFQUpk+fjp9++gldunRBREQElixZAgUFBQCFv/SDgoJK/PPw8PjusV+9eoX8/HxYWlqWuk1iYiJWrFiBJk2aYMSIEeX6YiGktqoL9btevXr47bffcPz4caSnp2P27NllxnH+/HlqFZBA1IFQgjk4OGDZsmXo2LEj1q9fD3Nzc+jr61fb+Z48eYLr169DR0cHpqamUFJSElmvrKxcqQ4/WVlZ2LNnD/78889St7l69Spu3LgBHx8fLFq0CKampvj9999x8+bNCp+PkNqgrtTvhg0bYuDAgdi/fz9++eWXMrc/d+4c3NzcKnweUr2oZUBCeXl5oVWrVujfvz82bNiA1q1bY9q0aaU+uiMO48ePR2RkJMaMGYORI0di/PjxCA0N5dZXthlx69atWLduHfcLRCgUFtvO19eX+yJs3LgxXFxc8ODBg2q4SkL4V1fq99c0NDRE+hGUJCkpCUlJSTT3gwSiZEACBQUF4cSJE3B0dARQmHnv3LkTz549w7p168p1DMZYpc6toKAABwcHREZGokePHujfvz9mzJiB6OjoSjUjrl27FiYmJkhISMCrV69w584deHt7AwBevnwJe3t7MMagr6+Px48fc/vl5eWhV69elboGQiRZXanfjx8/xsuXL7nXly5d4p6OAETrd5ELFy5g9OjRlYqdVC9KBiSMt7c3RowYAVlZWYSFhQEAcnJyuGeKN27ciPXr1+PTp0+lHuP+/fvw8/NDWFgY/v77b+Tk5FQ4jkaNGmHlypV48+YNWrdujZUrV0JZWRlt2rQp8e/b3sJAYSLwxx9/wNjYGDo6OtDR0YGxsTH69+8PoLAj0/nz55GdnY3ff/8dffv2haOjI/bv348zZ86UOB4BIbVZXarfgYGB6NOnDywtLbFjxw706tVLpAXh6/pd5Pz58xgzZkyF4yXVT8Aqm2JKoSdPnqBXr154/PixRMxqFhgYiOnTp1d4tDGBQICoqKgym/S+lp+fDxkZmYoFKOEkrTwJvyTt80D1u2okrTwlHbUM1HIVvceYn59fqfPUtS8KQmoDqt+kptDTBLWYqqoqDA0NsWfPHmhoaMDMzOy723t5eeHDhw8wNzeHoqJiDUVJCKkMqt+kJlEyUIvp6elxnfHKo2hiE3t7++oKiRAiJlS/SU2i2wRE4tFUrYRInorUS6rDko+SASJWubm5sLW1xfbt27FixQocPHiw1G1TUlJga2uLffv2Yc2aNVi+fLnIY0hpaWlYvnw52rdvX2zfW7du4ccff4SSkhJGjhyJtLS06rgcQsg33r9/DxsbG+6poO8pbbrlIvn5+Thy5Aj27t2LS5cuITc3Fy9fviw23bFAIMC1a9fEfSnkK5QMSDAXFxe+Q6gwZ2dn1K9fH/b29ti4cSMOHz5c6iiC8+bNw48//oi5c+dizZo1+PDhA06ePMmtT0tLQ48ePZCVlSWy37t37/Dw4UPcvXsXYWFhiIyMrJXvFZFutfUzm5OTgw4dOpSrc2PRdMsFBQXF1qWlpWHEiBFo1aoV5s2bBzMzM8jJyeHKlSvw9/dHVFQU99euXTsMHDiwOi6H/D9KBiTU8ePH4erqyncYFZKbm4uDBw+KPEdsZmZW6nWUNW2xlpYW1NTUiu0nFArh4OAAWVlZtGrVCqampiVOnUqIpKqN9buItrY2VFVVy7VtadMtFxQUwNzcHBMmTMCQIUNE1k2bNg0mJibcdMeJiYno1asXGjRoIJb4ScnoG7QGnD59Go6OjjAzM4OVlRVyc3MRGRmJ2bNnY/LkydizZw9atmwJQ0NDZGZmIj09HadPn8bHjx+xbNkyPHv2DNu2bcPQoUNx5MgRqKqqIiAgAJmZmVi+fDlcXV0xf/582Nra4suXL8jMzISLiwsMDAzg7e2Nbt26QV1dnRtF7MiRIxAIBHBwcEB2djZycnK4gUOq4tGjR0hLSxOZbbBjx474+++/S9x+8uTJcHZ2ho+PD4KCghAVFYWpU6eWeZ6vn5/OyspCWloaFi9eXKXYCaksaanf4nT8+HE8ePAAMTExGDVqFLp27Qp/f38AgLq6usi2NFBRDWGk3B4/fswAsMePH5d7n+DgYDZt2jTGGGP5+flMW1ubbd68mTHGmK2tLWvZsiV7/Pgxy8jIYI0bN2aenp6MMcY8PT2ZpqYmY4wxoVDIDhw4wBo1asQCAwPZX3/9xd69e8cmTJjAPDw8uHMNHjyY/f7774wxxq5cucIAsFu3brGCggK2detWJiMjw8LCwhhjjA0ZMoTNnTuX29fGxoYJhcJi8ZuZmbFmzZqV+LdlyxaRbc+cOcMAsJycHG7Z9evXGQCWlJRU7NgFBQVswYIFTCAQMH19fZaVlVVsm4CAAFbax9TLy4t16tSJaWpqsn/++afEbb6nMuVJ6i6q39+v31/7Ov6yrF69mhkZGYksMzU1Zebm5tzrdevWscaNG7Pk5ORi++vq6rK0tLRynetrVL8rhloGqtn+/fuRkZGBzZs3Y+vWrejduzd3D1xZWRlqamro2bMnlJSU0LVrV8TExBQ7RtFUp02aNIGRkREmT54MGRkZnDlzBsbGxtx2s2bNgqenJ7KystCwYUMAwIABAyAQCGBvbw81NTX4+fkBKHz86MiRI0hNTUV6ejqaNGlS4sAjHh4epY5XbmNjI7JtUVP918f53mAmeXl5EAqF+OOPP/DmzRuMGTOmQkOrjhs3DseOHYOKigosLCzKvR8h4iJN9VucIiIi0K9fP+71/PnzkZGRgcDAQJHtXrx4gTZt2kBFRaXaYiGFaJyBahYREQETExM4ODiUua2srGyJHW2AwiFGv74vHhsbCwAi99H09PRQUFCAuLi4YvvXq1cPmpqa3Djhw4YNg7a2Ntzd3dG8eXP8+uuvJZ63pDHJS6OhoQEA+Pz5M1d5MzMz0ahRIzRt2rTY9k5OTtDS0oKjoyPGjBmDwYMHY9u2bVi5cmW5ztegQQMYGhri5MmT0NXVRVJSUoXiJaSqpKl+i5O8vDwyMzO5140bN0bz5s2Rmpoqst25c+foFkENoZaBaqauro5bt26JLAsKCqrycdu3bw+BQIDg4GBumVAohJycHDQ1NUvcJzExEd27d+de29nZYc+ePQgKCoKBgUGJ+5ibm5c6renWrVtFtu3atSuaNWuGyMhIbll0dDSMjIxK7OD39bTF3bp1g62tbaWmLdbQ0ICKigoaN25c4X0JqQppqt/i9NNPP+HZs2ciy3JycqCrqyuyzNvbu1zTJ5Oqo5aBamZpaYlBgwZh06ZNsLS0RHBwMCIiImBgYFBsGtKvfzUoKioiLS0Nubm5+PjxIxhjIuubNm2KmTNnwt3dHcOHDwcABAQEYMGCBZCTk+O2S0xMhJqaGu7evYvGjRuLPJ4zZcoUODk5oW3btqXG7+HhUWrTvbKysshrBQUFzJs3D+fPn0ePHj0AAH5+frC1teW2OX/+PJKSkjB79mxu2uJhw4YBKHna4m/fI6BwNrS0tDT8+OOPAAqnRV23bh3q169f6nUQUh2kqX5/raR6CYjW7+9t6+DggH79+nGteUFBQejcuTP69u3LbfPmzRsoKiqiVatWpcZBxIjH/gq1TmU7pGzYsIE1adKEqampMScnJ5afn8/i4uJY//79WZMmTdiDBw/YkydPWNOmTdnAgQNZfHw8S0pKYp06dWJDhgxhgYGBzMbGhsnIyLAjR45wHfQyMzPZ7Nmz2bx585iLiwtzdHTk1hV1vFu7di2zs7Nj5ubmLDIyslhsVlZW7P3791V/c/5fbm4us7W1ZWvXrmUbN25kBw8eFFlvZ2fHxo8fzxhjLCUlhc2cOZNt2rSJ7d69mzk4OLDs7Gxu27CwMGZjY8MAsMOHD7PExETGGGMnTpxgysrKbPTo0Wz79u3szJkzlYqVOhiRr1H9Lp+goCA2duxYJi8vzy5cuMDS09O5dV/Xb8YYu3fvHuvXrx9TV1dn169fF6nfDx8+ZDNmzGAbNmxgs2bNYm/fvhU5z+bNm9mmTZsqHSfV74qhZKACatOH63u98IsIhUK2aNGimglIAtWm8iTVrzZ9Hqh+l602lackoNsEdRT7/6Y5xhgEAoHIun///Rd3795FbGwsxo4dy0d4hJAqoPpNxI06ENZBHz9+5Ib13bVrV7Eeug8ePMCaNWsgJycHIyMjPkIkhFQS1W9SHQSMldIThBTz5MkT9OrVC48fP0bPnj35DodUEZUn+Rp9HuoWKs+KoZYBKffkyZNiEwHx5d27d3yHQEidIkn1uyT5+flITk7mOwwCSgak1sePH+Ho6IhevXohMTGRlxgyMjKgrKzMTVH69fwCAQEBmDt3LjZu3IhJkyYhKiqKlxgJqY0koX7v3r0bTk5OWLlyJaytrfHlyxduHWMMZ8+eRdeuXXHlyhVe4iOiqAOhlGratCnmzJmDbdu28RbDoUOH8Ndff3HPQReNYJicnIxp06bh1atXUFRUxI0bNzBmzBgEBQUV6yxFCCmO7/r9999/48qVK9wkZU5OTnB0dORmakxJSYGRkRG1CkgQahmQYnz+x1pQUAAvLy8kJydDVVUVBgYG3JDFx44dQ/v27aGoqAgAGDx4MD58+IBr167xFi8htQ2f9Xvv3r0icw9MnjwZHh4eSElJAQCoqqqiefPmaNSoEV8hkm9QMiAB1q9fj6NHj2LcuHFYs2YNgMLM2draGu7u7rC2tuaGPA0MDMSECROwZcsW2NnZQUNDAzNmzEBoaCimTJmCtm3bYu3atQCAhw8fwtLSEqtXr8b06dOhoqKCoUOHltpsmJqaCgcHByxcuBD9+vXjJg0RCoVYsWIFjh49CmNjYxw+fLjK1/z8+XPk5OTA2toa2tracHJy4ta9efNGZEx2gUAADQ0N3Lt3r8rnJaSmSWP9/rYOF82b8PTp0yofm1QTHsc4qHWqYxCL58+fM1NTU8ZY4SAhhw4dYowx5uzszMaNG8cYY8zX15f16dOHMcZYXl4e09fXZ8OGDWNZWVnsw4cPTEZGhps29dmzZ0xWVpabDviHH35gpqam7MuXLyw2NpZ17NiRWVpaMsYYi4qKYgBYVFQUY4yxSZMmsdevXzPGGDt06BBr1qwZ+/z5M7t06RI3HWp6ejo7ffp0sevYsmVLqVOhmpmZlXr9ycnJbOnSpUwgELCTJ09y1/7t9Kj9+vVjs2fPrtibWwYalIR8jeq3+Op3//79uamdi64LADtx4oTIdpqamty0zuJG9btiqM8Az5o2bQo/Pz84OzvDycmJm4p3wYIFSE9Px+fPn/Hvv/8iISEBQOHMZ40bN0afPn2goKAABQUFqKmpceP0d+vWDUKhECkpKWjTpg0aNmyI3r17Q15eHm3atIGdnR1Wr15dLI6kpCT4+PhwEwelp6fDwMAA8fHxaNGiBf7880906dIFc+fOxejRo4vtb2Njg8mTJ5d4jV//QvhWs2bNsHnzZggEAri6umLixImwtLTEpk2bcPr0afz666+IiorCmzdvuDkMCKktpLV+z5gxA4sXL4aTkxM6duyIGzduAABat25diXeR1ARKBnjWunVrnDhxArNmzYKXlxfOnj2LHj16QEVFBXv37oWamhp++OEHeHh4lHqMrycuKbpPKBQKS9xWW1ubm+b0a1FRURAKhVi2bFmxde3bt8eOHTtgb28PT09PeHt7F5s5TVlZ+bsTm5TFwsIC3t7eAIBOnTrh2rVr+M9//oOrV6/CxMQEnz59EpmEhZDaQFrr94wZM5CZmQlra2v06NEDampqaNSoEQwNDct9DFKzqM8Az969e4eRI0ciLCwMvXr1wsSJEwEAixYtQmJiIubOnQt5eXmxne/baU6LqKurIysrC48ePeKWFRQU4Pnz54iMjMScOXMQGhoKFRUVWFlZFdt/69atpU6FWp4pSLOyskSmWR08eDB8fX1x5MgR5OXloVOnTiIdkgipDaS5fs+fPx8BAQFwcXHBixcvMGvWrO+2EhJ+UcsAz968eYPnz5/D1NQUBw8ehI6ODgDg1q1b6NOnDwDgxYsXyMnJgVAohKysbLEpQYVCITf96bfrAHAdihhjOHbsGBwcHIptq6Ghgf79+2PGjBnw8PBAixYt4ObmBjs7O/j6+iI7Oxu6urrYsWMH5syZU+wcFW1GvHLlCpo3b841fx49ehSrVq0qtl1sbCzWrVuHc+fOlXhsQiSZtNbvr127dg0PHjzAkydPiq0r6XoIPygZkADTp0+Hk5MTcnNz4e7uDqDwnqKjoyNSUlKwdOlSCIVC2NraYsqUKXj58iUUFRURFxeHR48e4cOHDzh9+jQMDAxw4cIFAICXlxfs7OwAAK9fv8bKlSsRERGBIUOGwMzMDBkZGTh06BC37cKFC3Ho0CFMnToVxsbG6NGjB9zc3NCiRQswxmBubo4lS5YgPj4eO3fuLHYNFW1GvH//Ptzc3GBqago9PT3Y2NhAV1dXZJunT5/C3t4ex48fR+/evSvxzhLCP2ms30VOnToFDw8P/Pe//4WKigq3PCMjA3///Tc+fPiA69evo2fPniW2aJCaQ3MTVEBtHOva2NgYxsbG3CNNtcHHjx9x7tw5yMrKwsLCQqzNqF+rjeVJqk9t/DxIav0ODQ3FzZs30b59e5iamvISQ20sTz5Ry0AdxxirdU1xTZs2LfG+JSFElKTWbx0dHe6WCKkdqANhHRYYGIiQkBDcuHEDd+/e5TscQogYUf0m4kQtA3WYsbExkpKS+A6DEFINqH4TcaKWAZ69evVKKit00SArJYmPj6/UMbOzs5Genl7ZkAgRO2mt36T2oWSAJ1lZWXBxcYGOjg5evnzJWxyBgYGQl5fHwYMH4ePjU2z9tWvXRAb72bt3LzflcNGfnJwcMjIyynU+Pz8/GBoaws3Nrdi68PBwTJw4sdRHmEojFArh7u6ODh06iIx9HhISgsOHD6N3796YPn16hY5JSFVIcv3Ozc3F3Llz0apVK7Rt2xYnTpwo9/Fu3bqFH3/8EUpKShg5ciTS0tK4dc+ePYO1tTW2bNmCiRMnlvgoYVnevXsHVVVVkWWnTp2Cra0t1q5di6lTp3IzHVL9Fi9KBniiqKgIW1tbvsMAUDggibW1dbFev+/evcO6deu4Z5wBICwsDEFBQYiKikJUVBRu3rwJIyOjcj129OnTJ+jp6SEnJ6fE9TIyMlBTUxM5X3kkJiZixIgRiIuLE1muq6uL6dOn89abmUgvSa7fBw8exIIFCxAXFwc7OzvMmDEDWVlZZR7n3bt3ePjwIe7evYuwsDBERkbCxcUFQGGCMW7cODg7O2Pp0qVYt24dRo0ahczMzHLHKRQK4ezszM1sCBQ+lbB27Vq4uLhg1apVGDBgAGbMmAGA6re4UTLAIz6nGC2LUCjE7t27MXPmTG5Zfn4+nJ2doa+vDy0tLWhpaeHx48cYO3ZsuY6ppKSEFi1aoEmTJiWub9euHZo1a1bhWFu1aoU2bdqUul6S32dSd0nq527UqFHQ0dGBQCDArFmzUK9e+f4bEAqFcHBwgKysLFq1agVTU1Nu36tXr6KgoABt27YFUDikeOvWrXHkyJFyx+Xi4oLff/9dZNn+/fthaGjIvZeTJk2Cj4+PSGuLpL7PtQ0lA2Lg6+sLBQUFWFhYIDc3F7m5udzUokXr7ezs4O7ujqlTp+LTp0/FjpGWloapU6dCS0sLQOEzsl27dhVp/nJzc8PSpUsxZMgQrFixolqvafv27Vi4cCFkZf/Xx1RGRgbNmzcX2c7b27tcww0TUlvVtfpdFANQOBKom5sbFBUVK7RfVlYW0tLSsHjxYgDFpywGCqctLu+04z4+PujWrVuxpP7b4yopKaFJkya4f/9+uY5Lyo+SATEYPnw4ZsyYgezsbMjJyUFOTg6tW7fGypUrAQDW1tYwMTGBlZUVhEIhvLy8ih2jcePGGDRoEPe6Z8+eIqPu+fr6Ii4uDlu2bIG3tzd27dqFU6dOFTuOubl5qWOIb926tVzXc/Xq1RIr5rfi4uLAGKOZyEidVtfqN1A4zLeNjQ2mTZuGBw8eVGisghMnTqBHjx7w8/PD69evARTOPhodHY0vX75w28nLy3P3978nJiYGwcHBGDFiRLF1zZo1w6tXr0SWlfe4pGLo0UIxmT17Nnr37o3ExEQ0atQICgoKqF+/PgDg0qVL6NSpE8LCwhAXF/fdnvSlKZrhbPPmzQAKJ/L5+t5aEQ8Pj1LvyZfnvn5RxVy6dGmZ254/f55aBYhUqCv1u0jbtm2xatUqaGtrY8WKFejbty+mTp1arn3HjRuH9u3bw8rKChYWFoiIiMCYMWNgb28PV1dXODo6IikpCUFBQdzcI6XJy8vDrl27sGXLlhLXT58+HcOGDcO9e/fQt29fBAUFITk5mX6AVANKBsREX18fBgYGOHbsGNTU1PDLL79w6xQVFeHg4AALCwu0a9euwh3kACAiIgK//fYbJkyY8N3tvm3Gr6gLFy5g9erVXBNofn4+8vPzIS8vD39/f/Tt25fb9ty5cyU+FUBIXVNX6vfXWrVqhaVLl+LJkyd48OBBuZOBBg0awNDQECdPnoSuri6SkpLQvHlz+Pv7Y82aNZgwYQKGDRtWrmnHQ0JCsGfPHuzduxfA/yYukpeXx5o1a7Bs2TIcO3YMzs7O6NSpEwwMDJCXlwdjY+MqXTspjm4TiJGVlRU8PT0RHBwMPT09AIUTcvTv3x/Tpk3Dzz//XOYxSmuuU1dXx61bt0SWBQUFFduuqs2ICxcuRHZ2Nvfn7u6OAQMGIDs7WyQRSEpKQlJSEg05SqRGXajfJdHQ0BDpD1CR/VRUVNC4cWMAQI8ePXDx4kWcOXMGmpqayM3Nxfjx4797DH19feTk5HDfN2FhYQAKxwxZtmwZgMJOg35+fnBzc0NMTAzMzMy4jopEfKhlQIwsLCxgZ2cnUrFevXqFxMREfPnyBTk5OQgPD0fLli0hFAohIyMjsn+zZs0QHx+PhIQE1KtXD69evUKrVq0AAJaWlpgzZw709fVhamqKW7duoV69ejAwMBA5hriaEcty4cIFjB49uthyPz8/+Pj4YOPGjaVObfq9+5OlrduzZw8EAgHmzZtX4WMSIg51pX57e3tj8ODBUFJSQm5uLsLCwrj+D0DpdS0sLAxpaWlc0/+FCxewbt067nZJkfT0dCxatAj79+/nJhkrKCiAra0tzMzMRPpOVERQUBAOHz5c7k6JpIIYKbfHjx8zAOzx48elbrNo0SKWmprKvc7JyWGDBw9mTZo0YatXr2b79u1j6urqzNfXl+3Zs4cBYAsWLGBJSUksOzubDR8+nKmqqrLFixezdevWMSsrKxYWFsby8/PZggULWKNGjVibNm3Y7t27xXJNAQEBTFNTs9T1np6ezMjIqNjy4cOHs/v37xdbvmnTJiYrK8vOnTtXbF12dja7evUqa9asGTMxMWEPHjwQWX/v3j3Wr18/pq6uzq5fv86ys7NFzicrK8tSUlKKHTchIYHt37+fAWCLFi1ir1+/Flm/evVqNm3atGL7lac8ifSQhvotFAqZoaEha9WqFVuxYgXbvn07+/Dhg8g+pdW1EydOMGVlZTZ69Gi2fft2dubMmWLne/PmDRsxYgQ7ffq0yPKMjAzWokULZmBg8N14o6KiWEn/Ld28eZMZGxuz4ODgYuuofosHJQMVUJ4PV0FBQQ1GVHVlJQOVcenSJRYUFCTWYzJWmGjk5eVVeD/6siDlQfX7fypa1758+cIOHTrE3NzcWFpaWqnbrVu3rkJxxMTEsH379rGTJ08yoVBY4jZUv8WDbhOIWW0cACMvL09sx3r79i3S09Ohr68vtmMCwJ07d9CnTx+RcQ/KKz8/X6yxEOklDfW7MnVNXl6eGxmwNMeOHSt3J8Uibdu2xZw5c767DdVv8aBkQMqpqqrC0NAQe/bsgYaGBszMzKp0PE1NzUp1RipLv379KvxFHBISgocPHyIhIQE9e/YUe0yESLrK1O/K1LXysLS0FOtxqX6LFyUDUk5PTw/e3t5iO151/XKqzHF1dXW58csJkUaVqd+SVIe/h+q3eNGjhYQQQoiUo2SAEEIIkXKUDBBCCCFSjvoMVEJoaCjfIRAxoHIkJaHPRd1A5VgxlAxUgKqqKhQVFWFpacl3KERMFBUVoaqqyncYRAJQ/a57qH6Xn4AxGse1ImJiYiRy+szk5GSYmZlh6tSpZT6XW1P27duH48eP4/Lly2jWrBnf4ZRIVVUVGhoafIdBJATV7/Lbt28f/vrrL1y+fBlNmzblO5wSUf0uP0oG6gg7Ozv8+eefePv2LZo0acJ3OACAjx8/QltbG7NmzcL27dv5DoeQWksS63dqaiq0tLRgZWWF//znP3yHQ6qIOhDWAfHx8XBzc8PixYsl5osCAJo2bYpFixbBzc0N8fHxfIdDSK0kqfW7SZMmWLx4Mfbt20f1uw6gZKAO2LJlCxo0aABbW1u+QynG1tYWcnJylZ5elRBpJ8n1e/HixVS/6whKBmq5Dx8+YP/+/bC1teXmFZckRb8e3Nzc8OHDB77DIaRWofpNagolA7Xc5s2bIS8vj8WLF/MdSqkWL16MBg0aYMuWLXyHQkitUpvqN7UO1G6UDNRicXFxOHDgAOzs7KCiosJ3OKVq3Lgx7OzssH//frx//57vcAipFWpj/abWgdqLkoFabPPmzVBQUMDChQv5DqVMixYtgoKCAjZv3sx3KITUCrWtfsvLy1P9rsUoGail4uLicPDgQdjb20v0r4YiKioqsLOzw8GDBxEXF8d3OIRItNpYv+3t7XHgwAGq37UUJQO11KZNm9CoUaNa8auhyKJFi6CoqEi/HggpQ22s3wsXLqT6XYtRMlALxcbGwt3dHfb29lBWVuY7nHJTVlaGvb09Dh48iHfv3vEdDiESieo34QONQFgLzZ07F6dOncLbt2+hpKTEdzgVkpGRAW1tbUyaNAl79+7lOxxCJA7Vb8IHahmoZWJiYuDh4YElS5bUui8K4H+/Hjw8PBAbG8t3OIRIlLpQv5csWUL1uxailoFaxsbGBmfPnkVUVFSt/LIAgE+fPkFbWxsTJkyAm5sb3+EQIjHqUv3+9ddfsW/fPr7DIeVELQO1SHR0NA4dOgQHB4da+0UBAEpKSliyZAn+/PNPxMTE8B0OIRKhLtVvBwcHeHh4UP2uRahloBaZPXs2zp8/j6ioKDRq1IjvcKrk8+fP0NbWxvjx47F//36+wyGEd1S/CZ+oZaCWePv2LQ4dOgRHR8da/0UBAI0aNYKDgwMOHTqE6OhovsMhhFdUvwnfqGWglrCyssLFixcRFRWFhg0b8h2OWGRmZkJLSwtjx47FwYMH+Q6HEN7U1fqtra2NsWPH4sCBA3yHQ8pALQO1QFRUFA4fPgxHR8c680UBAA0bNoSjoyM8PT0RFRXFdziE8KKu1+9Dhw7h7du3fIdDykAtA7XA77//jitXriAyMrJOfVkAhb8e2rVrh9GjR8PDw4PvcAipcdJQv83MzODu7s53OOQ7qGVAwkVGRuLIkSNYunRpnfuiAP736+HIkSOIjIzkOxxCapS01O/Dhw9T65+Eo5YBCTdz5kz4+PggMjISioqKfIdTLbKystCuXTuMHDkSf/75J9/hEFJjqH4TSUEtAxLszZs3OHr0KJYtW1ZnvygAQFFREUuXLsWRI0cQERHBdziE1Aiq30SSUMuABJs+fTquX7+OyMhIKCgo8B1Otfry5QvatWuH4cOHw9PTk+9wCKl20li/R4wYgUOHDvEdDikBtQxIqPDwcBw7dgzLli2r818UAKCgoIClS5fi2LFjePPmDd/hEFKtpLF+L1u2DEePHqX6LaGoZUBCTZs2DTdu3EBERIRUfFkAhb8e2rdvj6FDh+Lw4cN8h0NItaH6fZjvcMg3qGVAAr1+/RrHjx/H8uXLpeaLAvjfr4djx44hPDyc73AIqRZUv6l+SyJqGZBAU6dORUBAAN68eQN5eXm+w6lR2dnZaN++PQYNGoSjR4/yHQ4hYkf1m+q3JKKWAQkTFhYGLy8vLF++XOq+KABAXl4ey5cvx19//YWwsDC+wyFErKh+U/2WVNQyIGGmTJmCW7duISIiAg0aNOA7HF5kZ2ejQ4cOMDY2xvHjx/kOhxCxofr9v/ptYmKCY8eO8R0O+X/UMiBBQkNDceLECaxYsUJqvyiA//16OHHiBF69esV3OISIBdXvQvLy8lixYgW8vLyodUCCUMuABJk8eTLu3LmD8PBwqf6yAICcnBx06NABAwYMwF9//cV3OIRUGdXv/6H6LXmoZUBChISE4OTJk1L/q6FIgwYNsGLFCpw4cQKhoaF8h0NIlVD9FkX1W/JQy4CEmDRpEu7fv4/w8HDIycnxHY5EyMnJQceOHdGvXz+cOHGC73AIqTSq38VR/ZYs1DIgAV6+fInTp0/DycmJvii+0qBBAzg5OeHUqVN4+fIl3+EQUilUv0tG9VuyUMuABPj111/x8OFDvH79mr4svpGbm4uOHTuiT58+OHXqFN/hEFJhVL9LV1S/f/rpJ5w8eZLvcKQatQzw7MWLFzhz5gz9aiiFnJwcnJyccObMGQQHB/MdDiEVQvX7+4rq9+nTp6l1gGfUMsCzCRMm4NGjR3j9+jXq16/PdzgSKTc3F506dYKhoSFOnz7NdziElBvV77JR/ZYM1DLAo+fPn+Ps2bNYuXIlfVF8h5ycHFauXIkzZ87gxYsXfIdDSLlQ/S4fqt+SgVoGeDR+/HgEBQXh1atX9GVRhry8PHTu3Bk9e/bE2bNn+Q6HkDJR/S4/qt/8o5YBngQFBeH8+fP0q6Gc6tevj5UrV+LcuXN49uwZ3+EQ8l1UvyuG6jf/qGWAJ2PHjsWLFy/w6tUryMrK8h1OrZCXl4cuXbpAX18f58+f5zscQkpF9bviiuq3gYEBzp07x3c4UodaBnjw9OlTXLhwAStXrqQvigoo+vXg7e2NoKAgvsMhpERUvyunqH6fP3+e6jcPqGWAB2PGjMHLly8RGhpKXxYVJBQK0aVLF3Tr1g3e3t58h0NIMVS/K4/qN3+oZaCGPXnyBBcvXoSzszN9UVSCrKwsnJ2dceHCBTx9+pTvcAgRQfW7aqh+84daBmqYmZkZXr16hZCQEPqyqCShUAhdXV3o6Ojg4sWLfIdDCIfqd9VR/eYHtQzUoEePHuHy5ctYtWoVfVFUQdGvh0uXLuHx48d8h0MIAKrf4kL1mx/UMlCDRo0ahfDwcISEhEBGRobvcGo1oVCIrl27olOnTrh8+TLf4RBC9VuMiloHOnfuTPW7hlDLQA35999/cfXqVaxatYq+KMSg6NfDlStX8OjRI77DIVKO6rd4ycrKYtWqVVS/axAlA9WoZ8+e3Ex7a9asQZcuXTBp0iSeo6o7LCws0LlzZ6xZswYAcOrUKfTs2ZPfoIjUoPpdvb6t3ydOnKD6XY3oxlY1ioyMRGxsLP755x/4+PjAy8uLfjWIkYyMDFatWoUpU6bg4cOHiImJQVRUFN9hESlB9bt6fVu/Y2Nj8fbtW77DqrOoZaAaFXXHWLNmDXR0dPDrr7/i9u3bCA8P5zmy2i88PBy3b9/GxIkToaOjw/16oC4wpKZQ/a4+YWFh+O9//4uJEyeiS5cuVL9rACUD1YgxhujoaPj6+sLJyQn29vYYMGAALl26xHdotd6lS5cwYMAALFmyBCtWrMC1a9cQExNDXxakxlD9rj5XrlyBkZER7O3tqX7XEHqaoBopKyujZcuWKCgoQOPGjfHs2TP85z//wYIFCyAQCPgOr1ZjjMHV1RVLliyBvr4+0tLSUK9ePcTHxyM9PZ3v8IgUoPpdfb6t36mpqZCRkUFCQgLS0tL4Dq9OomSgGikqKuLLly9QUFBAy5YtcerUKfzwww98h1Wn/Pvvv5g4cSISEhKQlZUFRUVFZGZm8h0WkQJUv6vfo0ePMHHiRMTHxyMrKwsNGzbE58+f+Q6rTqLbBNUoNzcXAGBqaoonT57QF0U16N27N548eYLhw4cDAHJycniOiEgLqt/V74cffsCTJ09gamoKgOp3daKWgWpkYGCAgQMHYvv27dRsWM0YY7C3t4e/vz/NeEZqBNXvmsMYw5IlSxAYGEijElYTSgYIIYQQKUe3CQghhBApJ5ZBh2JiYpCcnCyOQ5EapKqqCg0NjQrvR+VdO1WkvKmM65bK1nWAPguSqiplWiJWRdHR0UxRUZEBoL9a9qeoqMiio6OpvKXkr7zlTWVc9/4qU9fpsyDZf5Ut09JUuWUgOTkZWVlZOH78OHR0dKp6OFJDQkNDYWlpieTk5Apll1TetVNFypvKuG6pbF0H6LMgqapSpqUR29wEOjo6NImEFKHyrvuojEkR+izUfdSBkBBCCJFylAwQQgghUo6SgVokPj6e7xCImFGZEkK+lZ2dXeNzrIitzwARlZubi6VLl6JNmzZISUmBlpYWrK2tS9w2JSUF69evR8eOHZGYmIicnBxs3LiRG9UsLS0NW7Zswe7du0XG3X/58iX09PSKHc/HxwcjRoyongsjJapIeQOFUzCvXLkSSUlJ8Pf3L3Gbd+/ewcDAQOSxrlOnTuHBgwdo0qQJwsPDsWPHDqiqqor9esj3VaS8U1NTsWzZMrRv3x4hISEYPHgwLC0tAQCMMaxatQoNGjTAx48fISsri82bN6NePdHfaUKhEMbGxli/fj2MjY2r+/LIN8RV3nv37sX8+fNFtq9fvz6Sk5OhrKwMoVAIT09P/PHHHzh+/HiNlnWtSAZcXFxgZ2fHdxgV4uzsjPr168Pe3h4A0LdvX7Rr1w6DBw8utu28efMwZswYTJo0CQAwffp0nDx5EhYWFgAKk4EePXogKytLZL8rV67A398f2tra3LJBgwZh4MCB1XVZNaKulzcAyMjIQE1NDQkJCSWuFwqFcHZ2RkpKCrcsNDQUa9euRXBwMAQCAdzd3TFjxgxcvnxZ/BdUg+p6ec+bNw+jRo3C5MmTkZOTg27duqFz587o3bs33N3dkZCQgIMHDwIApkyZgh07dnDHLbJjxw6EhIRU/4XVAGku77CwMAQFBUFFRQUAEBERgc2bN0NZWRkAkJiYiBEjRnz3h0R1kfjbBMePH4erqyvfYVRIbm4uDh48iDFjxnDLzMzMSr2OkJAQfPjwgXutqKgoMjOXlpYW1NTUiu03bdo0mJiYQEtLC1paWkhMTESvXr3QoEED8V1MDZOG8gaAdu3aoVmzZqWud3Fxwe+//y6ybP/+/TA0NORajCZNmgQfHx+8fPmyahfAo7pe3ikpKTh58iT69esHAGjQoAHMzc2xbds2AIW/FIvWAcDkyZOxfft25Ofnc8t8fHzQrVs37j+M2kyayzs/Px/Ozs7Q19fnvrMfP36MsWPHcvu3atUKbdq0qfZrKkmNJgOnT5+Go6MjzMzMYGVlhdzcXERGRmL27NmYPHky9uzZg5YtW8LQ0BCZmZlIT0/H6dOn8fHjRyxbtgzPnj3Dtm3bMHToUBw5cgSqqqoICAhAZmYmli9fDldXV8yfPx+2trb48uULMjMz4eLiAgMDA3h7e6Nbt25QV1eHh4cHAODIkSMQCARwcHBAdnY2cnJyYGlpiR07dlTpOh89eoS0tDR06tSJW9axY0f8/fffJW4/efJkODs7w8fHB0FBQYiKisLUqVPLPI+6urrI6/Pnz4t8YPlG5V1yeZel6Mv/2y+FN2/eiCR6SkpKaNKkCe7fv1+5wMWMyrt4eUdGRoIxJlJumpqauHfvHoDiZaqpqYkPHz4gKioKABAbG4vg4GBuVk5JQuVdsfKWkZFB8+bNRbb39vaGubl5leITm6qOWvT48WMGgD1+/Pi72wUHB7Np06YxxhjLz89n2trabPPmzYwxxmxtbVnLli3Z48ePWUZGBmvcuDHz9PRkjDHm6enJNDU1GWOMCYVCduDAAdaoUSMWGBjI/vrrL/bu3Ts2YcIE5uHhwZ1r8ODB7Pfff2eMMXblyhUGgN26dYsVFBSwrVu3MhkZGRYWFsYYY2zIkCFs7ty53L42NjZMKBQWi9/MzIw1a9asxL8tW7aIbHvmzBkGgOXk5HDLrl+/zgCwpKSkYscuKChgCxYsYAKBgOnr67OsrKxi2wQEBLCyiktXV5elpaV9d5si5S23yu5H5V16eRdZvXo1MzIyElkWHR3NHT8qKkqkzKdOnVps+9atW7NNmzaVeo4iFSnvynw2qLxLLu+IiAgGgAUEBHDL3N3dWYMGDRhjjLVt25atXr2aWxceHs4AsPv377Pc3Fxmb2/P8vLyGGOMaWpqihynvCpb17+3L5V35cr7a+/evWM//vhjseWMsWLH+FZVyrQ0NdZnYP/+/cjIyMDmzZsBFM5DX3QPXFlZGWpqatygFl27dkVMTEyxY8jIyKBjx45o0qQJjIyMABT2xj5z5gw2bdrEbTdr1ixMnjwZu3fvRsOGDQEAAwYMAADY29tjx44d8PPzQ6dOnWBvb4/x48dj/fr1qFevHpo0aQIZGZli5/bw8Ch1Lu1vm++KOv98fZySjlkkLy8PQqEQf/zxB7Zs2YIxY8bg0qVLFWruf/HiBdq0acPdi+IblXfp5V2avLw87Nq1C1u2bClx/fTp0zFs2DDcu3cPffv2RVBQEJKTk9G6desKn0vcqLxLLu927dphwIAB2L59O/r374/8/HzcunWLK7MZM2bA09MT8+fPh6qqKm7cuAEAaN26NbZt24ZFixZBVlbyunZReVeuvL92/vx5yWkVQA12IIyIiICJiQkcHBzK3FZWVhYFBQUlrhMIBCI9bWNjYwFA5D9OPT09FBQUIC4urtj+9erVg6amJrKzswEAw4YNg7a2Ntzd3dG8eXP8+uuvJZ732+ad7ykaHvLz58/cf86ZmZlo1KgRmjZtWmx7JycnaGlpwdHREWPGjMHgwYOxbds2rFy5stznPHfunETdIqDyLr28SxMSEoI9e/Zg7969AAp7mgOAvLw81qxZg2XLluHYsWNwdnZGp06dYGBggLy8PInoXU7lXXp5nz9/Ho6Ojhg5ciRMTEyQnp7OdfJdtWoV6tWrhwkTJuCnn35Cfn4+OnTogLZt22Lnzp1Yu3Ytd5ycnBwMHToUgwcPho+PT7njrQ5U3pUr76+dO3cObm5u5Y6jutVYnwF1dXXcunVLZFlQUFCVj9u+fXsIBAIEBwdzy4RCIeTk5KCpqVniPomJiejevTv32s7ODnv27EFQUBAMDAxK3Mfc3Byqqqol/m3dulVk265du6JZs2aIjIzklkVHR8PIyKjYI0MA4OvrC319fQBAt27dYGtriwcPHpT7PQAk7N4TqLy/V96l0dfXR05ODrKzs5GdnY2wsDAAhc8cL1u2DEBhp0E/Pz+4ubkhJiYGZmZmaNu2bbnPUV2ovEsv72bNmuHPP/+Er68vZs2ahVu3bmHu3LkACn9hrl69GgEBAdi4cSPu3LnDPXqWmJjIfRays7OhqamJv//+m/dEAKDyrmx5F0lKSkJSUpJEzfdQYy0DlpaWGDRoEDZt2gRLS0sEBwcjIiICBgYG3C+gIl9nkYqKikhLS0Nubi4+fvwIxpjI+qZNm2LmzJlwd3fnOtkEBARgwYIFkJOT47ZLTEyEmpoa7t69i8aNG4tkalOmTIGTk9N3v1Qr0qykoKCAefPm4fz58+jRowcAwM/PD7a2ttw258+fR1JSEmbPng19fX08fvwYw4YNA1DYXNyrVy+RY377Hn3tzZs3UFRURKtWrUrdpqZReZde3kW+V6ZlCQoKwuHDh7mOaHyj8i67vAFg/vz5mDVrFrff1/bv34/8/HzMmzev1DglBZV31cr7woULGD16dInnr8r3QpVUtdNBRToybNiwgTVp0oSpqakxJycnlp+fz+Li4lj//v1ZkyZN2IMHD9iTJ09Y06ZN2cCBA1l8fDxLSkpinTp1YkOGDGGBgYHMxsaGycjIsCNHjnAdOjIzM9ns2bPZvHnzmIuLC3N0dOTWFXW8W7t2LbOzs2Pm5uYsMjKyWGxWVlbs/fv3VX07OLm5uczW1patXbuWbdy4kR08eFBkvZ2dHRs/fjxjjLGUlBQ2c+ZMtmnTJrZ7927m4ODAsrOzuW3DwsKYjY0NA8AOHz7MEhMTRY61efPmcnUi+1p1dyBkjMr7a1+XN2OM3bt3j/Xr14+pq6uz69evi5R3kW87EBa5efMmMzY2ZsHBweWOr7o7EDJG5f21b8s7PT2d2djYsOXLl7OCggKRbYVCIXN1dWVjxoz5bgdgSepAyBiV99cqUt6MMTZ8+HB2//79YssTEhLY/v37GQC2aNEi9vr16xLjqY4OhDWaDPChPL3whUIhW7RoUc0EJCFqIhngQ10u75iYGLZv3z528uTJEntIf09NJAN8qA3lfenSJbZjxw725s2bYuv+/fdftmPHDnb79u1qO391JQN8qO3lLS61+mkCvrD/b3JhjHGDtRT5999/cffuXcTGxooM/EBqr7pc3m3btsWcOXP4DkOi1IbyLq05GAB++OEH/PDDDzUYTe1W28tbkkn8CIRV8fHjR5w8eRIAsGvXLqSmpoqsf/DgAdasWQM5OTnu0RZSe1F5Sxcqb+lC5V3Nqtq0IGnNSKR86uptAlKyunqbgJStLt0mIIWqo1zqdMsAIYQQQspGycD/e/LkSbFZAWtaeno6N3gGqV6SUN6k5lB5SzdJKv93797xHUKJpD4Z+PjxIxwdHdGrVy8kJibyEsOXL1+wdetWaGtrIz4+XmTdv//+i59//hmNGjXCTz/9hGfPnvESY10hCeVd5NOnT+jUqRPevn3LxdagQQMIBAKRP0kapay2kYTy3rt3LzZu3IidO3dixowZIoPXFHn69Ck2bdoELy8vREdH8xBl3SQJ5Z+RkQFlZWWuPi9evJiXOMoi9clA06ZNee+hnZycjP79+xfrEPPx40ds2bIFO3bsgK+vLz59+oQxY8aITG9KKkYSyrvI1q1bER4ezr0+f/48Tp06hcjISERFRSEqKgo9evSotb2TJQHf5e3r64vLly9jxYoVWLx4MaZNm1Ysnm3btsHd3R0ODg6YPHlyqSPtkYrju/wB4NChQ/jrr7/w9OlTPH36FAcPHuQ1ntJIfTIAoNgjKjWtbdu2aNmyZbHlvr6++PPPP9G7d2/8/PPPcHFxwdu3b7lfkqRy+C5voPALYvz48SLLRo0ahTFjxkBbWxtaWlpQUFCAnJwcb/Ob1xV8lndISAgSEhK4R+IUFRXx+fNnbv2ff/6JK1euwNXVVSInJKoL+Cz/goICeHl5ITk5GaqqqjAwMKjQfCU1SaKSgfXr1+Po0aMYN24c1qxZAwBISUmBtbU13N3dYW1tzY2HHRgYiAkTJmDLli2ws7ODhoYGZsyYgdDQUEyZMgVt27blJvl4+PAhLC0tsXr1akyfPh0qKioYOnRoqc1GqampcHBwwMKFC9GvXz8EBgYCKBwje8WKFTh69CiMjY1x+PDhan0/LCwsRGYhbNmyJWRkZKCurl6t560p0lreT548gUAgKDZu+rfl6u3tDTMzM7GcUxJIY3mPGTMGYWFhmDNnDtLT07Ft2zZu7PtPnz7B3t4erVu3xty5c9GhQwcsW7as1El9ajtpLP/nz58jJycH1tbW0NbWhpOTU5WPWW2q+jiCuB5xeP78OTM1NWWMFY4gdejQIcYYY87OzmzcuHGMMcZ8fX1Znz59GGOM5eXlMX19fTZs2DCWlZXFPnz4wGRkZLg5tZ89e8ZkZWVZVlYWY4yxH374gZmamrIvX76w2NhY1rFjR2ZpackY+9+wr1FRUYwxxiZNmsQNA3no0CHWrFkz9vnzZ3bp0iVuruz09HR2+vTpYtexZcuWUufJNjMzK/X6v42hJB4eHty83lXF96OF0lreaWlpbPny5dzr75X54MGD2cuXL8v/pn4H348WSmt5M1Y4fLSKigpTVFRkfn5+3PLTp08zeXl5btjc8PBw1qBBA+bh4VHJd7lkkvBooTSXP2OMJScns6VLlzKBQMBOnjxZpfeSsTo+AmHTpk3h5+cHZ2dnODk5wcLCAgCwYMECpKen4/Pnz/j333+RkJAAoHBazMaNG6NPnz5QUFCAgoIC1NTU8OOPPwIonP1PKBQiJSUFbdq0QcOGDdG7d2/Iy8ujTZs2sLOzw+rVq4vFkZSUBB8fH24WwfT0dBgYGCA+Ph4tWrTAn3/+iS5dumDu3Lkl3su1sbHB5MmTS7zGr6flrKicnBycPXsWXl5elT6GJJHW8t60aRNWrFhR5vuTmpqKDx8+QFdXt8xtawNpLW+gsE+QjY0N/v77b/zyyy+4efMmevbsiYiICLRr1467RdihQweYmpriwoUL+P333yv4Dks2aS5/oHAmw82bN0MgEMDV1RUTJ06s2BtYAyQmGWjdujVOnDiBWbNmwcvLC2fPnkWPHj2goqKCvXv3Qk1NDT/88AM8PDxKPcbXs1oV3ScSCoUlbqutrV3iY3xRUVEQCoXclLFfa9++PXbs2AF7e3t4enrC29u7WGcfZWXlYrNeicOGDRvg6uqKJk2aiP3YfJDG8k5PT4eLiwt27twpsrxz586YOXOmyFMDFy9exMiRI8t13NpAGssbKGwm3rhxI4KCgvDHH39g0qRJmDhxIl6/fg15eXlkZmaKbN+pUyfcuXOn3MevLaS1/L9lYWEBb2/vSu9fnSSmz8C7d+8wcuRIhIWFoVevXlzmtGjRIiQmJmLu3LmQl5cX2/m+nQO7iLq6OrKysvDo0SNuWUFBAZ4/f47IyEjMmTMHoaGhUFFRgZWVVbH9t27dWuo82ebm5pWK1dPTE2PHjkWHDh0AlF4BahNpLG8VFRXk5uaKzFEPAGFhYcUeHzx//nytnD+hNNJY3gBw48YN6OrqQiAQoEGDBnBzc8ObN2+QkpKCn376Ce/evcPHjx+57XNycupMa9DXpLX8v5WVlVWsr5CkkJhk4M2bN7h58yZUVVVx8OBBfPr0CQBw69Yt7kvzxYsXyMnJ4f4zZN/M+ywUCrnON9+uA8B1KGGM4dixY3BwcCi2rYaGBvr3748ZM2bgn3/+wdu3b7F8+XK0aNECt2/fRkhICDQ1NbFjxw4uxq/Z2NggKCioxL/vZb0lxQsAZ8+eBVA4p/arV68QFBSEAwcOlHqc2kLay/t7Pn/+jODgYK5JtC6Q1vLu3r07nj59ysWdl5cHTU1NqKqq4scff8SwYcNw4sQJLs7bt29j4cKFFXhnawdpLf8rV67gn3/+4V4fPXoUq1atKv8bV4Mk5jYBAEyfPh1OTk7Izc2Fu7s7gMJ7So6OjkhJScHSpUshFApha2uLKVOm4OXLl1BUVERcXBwePXqEDx8+4PTp0zAwMMCFCxcAAF5eXrCzswMAvH79GitXrkRERASGDBkCMzMzZGRk4NChQ9y2CxcuxKFDhzB16lQYGxujR48ecHNzQ4sWLcAYg7m5OZYsWYL4+Phizb1A5ZqRYmJicPToUQDAyZMnud6yN2/exJQpU5Cbmyuy/ZkzZyp0fEklreVdlqtXr2Lo0KES8QikOEljeQ8ZMgQLFy7EwoULoaenh7CwMFy8eJFbf/r0aSxbtgxr165FSkoK7O3toaenV8l3WLJJY/nfv38fbm5uMDU1hZ6eHmxsbCS35aeqPRBry0QWRkZGbPXq1XyHITH4fpqgulF5i+L7aYLqRuVdOkl4mqC6SVv500RFVcAYK7UpntQ9VN7ShcpbulH5V51UJAOBgYEICQnBjRs3cPfuXb7DIdWMylu6UHlLNyp/8ZCoPgPVxdjYGElJSXyHQWoIlbd0ofKWblT+4iGRLQOvXr2S6ML9dmbB8srOzkZ6erqYo6l8PJJC0su7uhQNsCINpLWMSXH0WZBMEpUMZGVlwcXFBTo6Onj58iVvcQQGBkJeXh4HDx6Ej48Ptzw8PBwTJ04sdQSq0giFQri7u6NDhw54+vSpyLqXL19i1qxZcHNzg5WVFV69elXu46alpWH58uVo3769yHJ/f3+4u7ujSZMm1T5/QlVIenkXuXbtGgYOHFhseX5+Po4cOYK9e/fi0qVLxZ76KI2fnx8MDQ0rNDUxYwx//PEHNmzYgA0bNmD9+vXcOkkub0ku44yMDFhbW8PV1RW2trZcD/XyuHXrFn788UcoKSlh5MiRSEtL49Y9e/YM1tbW2LJlCyZOnIgnT55UON53795BVVWVe/3y5ctiU1sLBAJcu3YNISEhOHz4MHr37o3p06dX+Fw1RZI/C7m5uZg7dy5atWqFtm3bco97lse5c+dga2sLV1dX2NjYlPhIYkk+f/6M2bNno2nTpmjbti12797NrfPy8sL69eshEAhqbmK6qvZAFHevxoKCAgaABQQEiOV4lREQEMA0NTWLLY+IiGDz589nRkZGFTpeXFwci42NLXZdWVlZTFNTkxt/PjQ0lLVv355lZ2eX67hRUVHs1KlTrLRiHDBgAPP09CxxnaQ8TSDJ5c0YY7Gxseynn34qVuapqalsyJAh7O+//67QuTIyMlh8fDzr3r17hXo/79u3j02YMIF7PWnSJObu7i6yjbjKW1rK+Ndff2V79+5ljDGWm5vLOnTowEJCQso8VmxsLNu6dSvLy8tjcXFxrEuXLszZ2ZkxxlhOTg5r164di4mJYYwxFhYWxlq2bMk+f/5c7ljz8vLY9OnTRer15s2bmb+/P4uKiuL+2rVrJ/JdsWrVKjZt2rRix5Okpwkk9bPg6urKQkJCWEFBAXNxcWENGjRgmZmZZR7r+fPnTEtLi+Xk5DDGGNu/fz+bMmVKueKwtbVlJ0+eZCEhIWzevHkMALt58ya3vui9KmnuEql4mkCSn61u164dmjVrVuH9WrVqVeI0tJcvX4acnBz33GmXLl0gKytb4q/TkmhpaUFNTa3U9ZL8XhaR5BiFQiF2796NmTNniiwvKCiAubk5JkyYgCFDhlTomEpKSmjRokWFh5V2dXXFmDFjuNfm5uZwdXUV2UZS30tJjCs+Ph5nz57l3tP69etj0KBB2L9/f5n7CoVCODg4QFZWFq1atYKpqSnq1Sv8Kr169SoKCgrQtm1bAIXDC7du3RpHjhwpd2wuLi7F5iaYNm0aTExMoKWlBS0tLSQmJqJXr14i4+FL4vv8LUmNcdSoUdDR0YFAIMCsWbO48iyLm5sbhgwZwg2VbG5ujlOnTpV5GyQjI4MbiVFHRwe7d+9GmzZtuBkUgZp/r6otGfD19YWCggIsLCyQm5uL3NxcbprJovV2dnZwd3fH1KlTS2xaSUtLw9SpU6GlpQWgcOrXrl27ijSFubm5YenSpRgyZEi5JoCRJDdv3kSnTp1ElnXs2BHXr1/nKaLKq4vlvX37dixcuLDYPPPHjx/HgwcPEBMTg1GjRqFr167w9/evtjjev3+P0NBQkc9Kx44d8fz58xrtL1KXytjf3x8KCgpo1aoVt6y8da8odqCw6TstLQ2LFy8GUDjS3rcT1mhqauLevXvlisvHxwfdunUr9uPh2+mtz58/L5Ic1rS69FkARMv0ypUrcHNzg6KiYpn7ffsdrq6uDnl5efj5+X13P2VlZZHbzfXq1YOamlqJPxprSrUlA8OHD8eMGTOQnZ0NOTk5yMnJoXXr1li5ciUAwNraGiYmJrCysoJQKCxxNr7GjRtj0KBB3OuePXuid+/e3GtfX1/ExcVhy5Yt8Pb2xq5du3Dq1KlixzE3Ny91POmiucX58P79+2KjWSkpKeHdu3c8RVR5da28r169WuKXMgCcOnUKI0aMwLp163DlyhVYWFhg/PjxSElJKdexK+r9+/cAIPJZUVJSAoAa/azUpTIWR907ceIEevToAT8/P7x+/RpA4ex00dHR+PLlC7edvLw8kpOTyzxeTEwMgoODMWLEiDK3vXz5Mq8TWdWlz0KR2NhY2NjYYNq0aXjw4EG5xi2oyufo61/+nz59QmJiIiZNmlTueMWtWh8tnD17Nnr37o3ExEQ0atQICgoKqF+/PgDg0qVL6NSpE8LCwhAXF1epntVFs11t3rwZADB48OASv5A9PDyQk5NT4jGqY4bB8qpXrx5kZGREln37ujapK+Vd9KW8dOnSEtdHRESINOPOnz8fq1evRmBgIMaPH1+eS6mQoibLrz8bfH1O6koZi6PujRs3Du3bt4eVlRUsLCwQERGBMWPGwN7eHq6urnB0dERSUhKCgoLKnGciLy8Pu3btwpYtW8o874sXL9CmTRuoqKhUKF5xqyufhSJt27bFqlWroK2tjRUrVqBv376YOnXqd/eRkZERy3f4f/7zH+zdu5fXMq3WZEBfXx8GBgY4duwY1NTU8Msvv3DrFBUV4eDgAAsLC7Rr146bgKIiIiIi8Ntvv2HChAnf3a558+YVPnZN0NDQQFxcnMiyzMxMaGho8BRR1dSV8r5w4QJWr17NNXnm5+cjPz8f8vLy8Pf3Lzb1bOPGjdG8eXOkpqZW6bylKfo8fP78mVtWdP6a/qzUlTLW0NAQeT+Bite9Bg0awNDQECdPnoSuri6SkpLQvHlz+Pv7Y82aNZgwYQKGDRuGT58+lfg0ytdCQkKwZ88e7N27F8D/JteRl5fHmjVrRKbcPXfuHK+3CIrUlc/C11q1aoWlS5fiyZMnePDgQZnJQNu2bav8Obp9+zbU1dVhZmZWqZjFpdo7EFpZWcHT0xPBwcHcBBwZGRno378/pk2bhp9//rnMY5TWXKOuro5bt26JLAsKCiq2naTeJjAyMkJkZKTIsujo6DK/OCRZXSjvhQsXikwz7O7ujgEDBiA7Oxt9+/bFTz/9hGfPnonsU51Tz6qqqkJXV1fksxIdHY2uXbt+twNpdakLZdy3b19kZmaKTB9c2bqnoaEBFRUVNG7cGADQo0cPXLx4EWfOnIGmpiZyc3PLbDHS19dHTk4O95kLCwsDUDg2ydeJAAB4e3tXejp0casLn4WSaGhoiPQjKM233+EZGRn49OkTjIyMynWeN2/e4OnTp5gzZw63jK8p6qt9BEILCwvY2dmJvLGvXr1CYmIivnz5gpycHISHh6Nly5YQCoXFmliaNWuG+Ph4JCQkoF69enj16hXX6cfS0hJz5syBvr4+TE1NcevWLdSrV6/YfNHivE1Q2gd3z549EAgEmDdvXrn3GzduHFasWIHw8HB07NgRSUlJyMzM5Cp6QUEBbG1tYWZmJnJvrTzx8KWulXdJHBwc0K9fP+6XYFBQEDp37oy+ffsCKBxHwMfHBxs3bizWmaxISeX2vfJesmQJzp8/j7FjxwIo7Lhkb29f5WupjLpQxq1atYKFhQW8vb25Wz63b9/mZg8FSq/TYWFhSEtL45r+L1y4gHXr1nFN5EXS09OxaNEi7N+/H/Ly8gDKV6e/582bN1BUVBTp+MinuvBZAAoTrMGDB0NJSQm5ubkICwvj+j8ApX8WFi1aBDMzM+Tl5aF+/frw8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+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[124]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+<span class="c1"># Plot the training points</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">);</span>
+<span class="c1"># for i, color in zip(range(n_classes), plot_colors):</span>
+<span class="c1">#     idx = np.where(y == i)</span>
+<span class="c1">#     plt.scatter(</span>
+<span class="c1">#         X[idx, 0],</span>
+<span class="c1">#         X[idx, 1],</span>
+<span class="c1">#         c=color,</span>
+<span class="c1">#         label=iris.target_names[i],</span>
+<span class="c1">#         cmap=plt.cm.RdYlBu,</span>
+<span class="c1">#         edgecolor=&quot;black&quot;,</span>
+<span class="c1">#         s=50,</span>
+<span class="c1">#     )</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[126]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img src="data:image/png;base64,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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[127]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+<span class="c1"># Plot the training points</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">);</span>
+<span class="c1"># for i, color in zip(range(n_classes), plot_colors):</span>
+<span class="c1">#     idx = np.where(y == i)</span>
+<span class="c1">#     plt.scatter(</span>
+<span class="c1">#         X[idx, 0],</span>
+<span class="c1">#         X[idx, 1],</span>
+<span class="c1">#         c=color,</span>
+<span class="c1">#         label=iris.target_names[i],</span>
+<span class="c1">#         cmap=plt.cm.RdYlBu,</span>
+<span class="c1">#         edgecolor=&quot;black&quot;,</span>
+<span class="c1">#         s=50,</span>
+<span class="c1">#     )</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[128]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[129]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[130]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+<span class="c1"># Plot the training points</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">);</span>
+<span class="c1"># for i, color in zip(range(n_classes), plot_colors):</span>
+<span class="c1">#     idx = np.where(y == i)</span>
+<span class="c1">#     plt.scatter(</span>
+<span class="c1">#         X[idx, 0],</span>
+<span class="c1">#         X[idx, 1],</span>
+<span class="c1">#         c=color,</span>
+<span class="c1">#         label=iris.target_names[i],</span>
+<span class="c1">#         cmap=plt.cm.RdYlBu,</span>
+<span class="c1">#         edgecolor=&quot;black&quot;,</span>
+<span class="c1">#         s=50,</span>
+<span class="c1">#     )</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;scaled&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Let's-try-our-%22problematic%22-dataset-(for-PCA-and-k-NN)">Let's try our "problematic" dataset (for PCA and k-NN)<a class="anchor-link" href="#Let's-try-our-%22problematic%22-dataset-(for-PCA-and-k-NN)">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[131]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">N_samples</span> <span class="o">=</span> <span class="mi">50</span>
+<span class="c1"># samples points for class 1</span>
+<span class="n">X_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="n">N_samples</span><span class="p">)</span>
+<span class="n">X_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">X_1</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span><span class="o">*</span><span class="n">N_samples</span><span class="p">))</span>
+<span class="c1"># samples points for class 2</span>
+<span class="n">X_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="n">N_samples</span><span class="p">)</span>
+<span class="n">X_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">X_2</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,)</span><span class="o">*</span><span class="n">N_samples</span><span class="p">))</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_1</span><span class="p">,</span> <span class="n">X_2</span><span class="p">))</span>
+<span class="c1"># data</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_1</span><span class="p">,</span> <span class="n">X_2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># labels</span>
+<span class="n">labels</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
+<span class="c1"># Plot also the training points</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+    <span class="n">x</span><span class="o">=</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">y</span><span class="o">=</span><span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">c</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
+    <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Code below wants Nx2</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[132]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[133]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[134]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+<span class="c1"># Plot the training points</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">plot_colors</span><span class="p">):</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">i</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+        <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
+        <span class="c1">#cmap=plt.cm.RdYlBu,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+        <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="c1">#plt.axis(&#39;scaled&#39;);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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MNnn1XdB77meRu7igIZx88+k/Lz/RujIYMd22m3hz6uhzK/VlGRf3362l5fQj0ua6qPUOoqKy2J6BhWVNPj6al9T8ebv8fg4EHlKi7M1RUDam7/V6zd234JpN6KdUTq2IrE6yScKtcb6DnJeX41aZu8idS+KSk6ruKCox7/lrr1M9jxd/dk3HBN94dxweybb77R0aNHNXny5Crz7Ha7JKlOnTpu0+vWrf4zDHPmzFFycrLrp/IVN09atHBcevT24dAWLeS6Qteype+2LMtSmb1QZfZCWZYV0DoVBbJ+ebmUkuL4fd8+x++e1iso8D7PU5sFBb/WV3LiSED1SAp4HU/7wNc8T2NXUaDjeOhQ4GMU6rhK/q/va3t9CfW4rKk+QqnL+f+RGsOKano8PbXv6XgL5BiUpMaNa6beiqrbL4HUW7GOSB1bkXidhFPleoM5J1WcZsI2eROpfeM8N/v7mjp0KPA+jApmJ06c0P3336+33nqrytubklxvbRYWug9oUVGRYmNjleJMIZXMnDlTeXl5rp+srCyftRw86Ei3lS7Ouc23nfxo3YEDvtuy2WyqE1tfdWLry2azBbRORYGsHxPz6wcAWrVy/O5pvQYNvM/z1GaDBr/WF5fQOKB6JAW8jqd94Guep7GrKNBxbNo08DEKdVwl/9f3tb2+hHpc1lQfodTl/P9IjWFFNT2entr3dLwFcgxK0pEjNVNvRdXtl0DqrVhHpI6tSLxOwqlyvcGckypOM2GbvInUvnGem/19TTVtGngfRgWz6dOn6/HHH1daWprH+V26dDk58O4jkpOTo27dulW5kuYUHx+vpKQktx/p1ys4lZWXOz5ZccEFUqUPhf46/90YNW3dXU0zumvFuzHVt3Vy2Tp141SnbpyatD4noHUq8nf9995z1P/++1JqquPnggsc21V5vfh4x7z33qt+TN57z/GBgtjYX+uLq5eoJq3P0XIP7VZet149KTWjh2sdf7bhwgur7gNf87yNXUWBjOOFF0pJSf6N0fIVju2MjQ19XJtmdFe9ev716Wt7fQn1uKypPkKpq07duIiOYUU1PZ6e2vd0vPl7DK54N0bx9VP0/r9qbv9XrN3bfgmk3op1ROrYisTrJJwq1xvoOcl5fjVpm7yJ1L6Jq5eo+AaNPP4tdetnhePvbsV7/L0tX5kxwWzu3LkaNWqUTj/9dNe0Xbt2uS2TlpamCy64QJs3b3abvn37dg0dOjTgPl98sepAOT9RkZXleJ6O9/nlatnxSp3S4UplZZXr2Wd9L+sUzDoV+bO+8xEVWVnSH/7g+P/hwx1vaXpa7+qrpcxMz/Mqtnn11VXrO6XDldqXVf26mZlSYaHc1vG1DZmZjv4CnVfd2FXk7zgOG+b/GO3Lcmync5nhwx1tBDuuhYX+9enP9oZjPPwd23D2EUpdkR7Dyn3X5Hh6at/T8ebPMZiVVa70M4bW+P6vWLu3/eJvvZXriNSxFYnXSThVrtffc5LzvGfiNnkTqX3T6vShyvLxNy8rS7r2WvfpCxb4136tPy5DkhYvXqxVq1Zp8ODBrml5eXnKzMzU7NmzNX78eO3fv18rV67U559/rhEjRmj79u1KSkpSZmamevfurU2bNqlRo0Z+9efpOWYtWjguPXp7jlnl+e7PMVujXRv/z69lnYJZx9P6zueYOdev7jlmLVpIa9a4P8+o4nqVn2NWuc3qn7e1Rru++r9q2636HLNqxqDS88L8nhfwc8w811DxeT59+wbWtyS3Nis/MyiYcZV+fdZT5RrD+xyz0I7LmuojlLoiPYbhqjug9lv9+pr19Br39rqv+hyzmt//brV72S+Vn2Pmbx2ROrYiOU7hULleb+ckr+c9A7fJm0jtm+3r/qZf9nzu9vf14MGqzzHz9LfL+OeYrVu3Tn379lVJSdVPSKxZs0Z9+vTRiBEjtGfPHn399deSHF/J9Prrr6tr167KzMzUbbfdpo4dO/rdpzOYndFnprJ3rtWhvV+7ns7btHV3tex4peM5Ujk7dGD7Sq/zKwpk2VDWqW59b+Lrp6jkxNEKT3fuoFL7CRXk7lHF1RqldXQ9+f/Xmn79BgFf9eXl7HA8+T97e5V2M87y/HT16sZAUlDzAn7yv4caktM6SJZOfotB4H172jfu+yHAcd20REezt7um1eiT/0M8Lmuqj1DqivQYhqvuYNqvqOKT/5ObdZBscnvyv6c6IrH/K/blbb80zah8LvKvjkgdW5Ecp3DwfU6q/rxn4jZ5E6l9c2DHGmVtfUfFhbmuafH1U9S0TS+VFORU6T/t1Mv03YdzzA9mtcEZzHpe85LqxtV3fAzaXui4udPD+86+5ge7bCjreFq/vKxcpfbjiktorDp149za9NSH43u88mSzWYqt18it74rLSwqovuraDWYMgp0XKG9jFErflZcJdVztRUdlWTbFJSTX+L0e4RzbcPYRSl2RHsPKfdfkeDrbl62uZJV6Pcb8rSMS+79iX972S7B1ROrYiuQ4hUN156RAjxHTRWo7HN9Pe0RxCY0VVy/Ra/+lJYX6fMlNBDNPKgczAACAmuRvMDPm5n8AAIBoRzADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMETdUFYuKirS4cOHFR8fr6ZNm4arJgAAgKgU8BWzQ4cOafbs2ercubMSExPVunVrpaWlqUmTJho5cqQ+/vjjmqgTAADgNy+gK2YLFy7UPffcow4dOmjAgAEaO3askpKSZFmWjh49qh9//FF//OMf1a1bN7300ktKSkqqqboBAAB+c/wOZo8++qjKysr0/fff+wxcH3zwgcaPH68FCxYQzgAAAPzk11uZy5Yt04UXXqg//elPfgWt/v37a8GCBXriiSdCLhAAACBa+HXF7NJLL/X7yldubq5SUlKUlJSkO++8M6TiAAAAoolfwcxTKLMsS4cOHVJRUZEsy3JNe+GFFzRnzhyv6wEAAMCzoB6XsWDBAt199906duyY23TLsmSz2VzBDAAAAP4LKpjNmDFDY8eO1cCBAxUbG+uaXlZWppdffjlsxQEAAESToIJZs2bN9OSTT3qc1759+1DqAQAAiFpBfSXT1KlTtWHDBo/zdu/eHVJBAAAA0SqoK2ZjxozRkCFD1Lp1a7fpdrtdn3zyibKyssJSHAAAQDQJKpgNHjxYX3zxhY4eParExETZbDZJju/OzM3NDWuBAAAA0SKoYLZx40Zt2bJF7dq1qzLvrbfeCrkoAACAaBTUPWZXXnmlTjnlFI/z+vbtG1JBAAAA0SqoYPbAAw/ogQce0N69e91+MjMz9dRTT4W7RgAAgKhgs5yP7Q/A2WefrU2bNrnuLXNyPmC2rKwsbAXWhPz8fCUnJ6vnNS+pblz92i4HAAD8xpWWFOrzJTcpLy+v2m9GCuoes3HjxunYsWM699xz3TstLdU//vGPYJoEAACIekEFsz/84Q+KiYlRw4YNq8zr2LFjyEUBAABEo6DuMUtMTNSSJUv03//+1zXtvffe03//+1+1bNkybMUBAABEk6CC2bRp0zRx4kR99dVXrmkDBgzQlClT9N1334WtOAAAgGgSVDDbtWuXdu/erSFDhrim1alTR6NGjdLEiRPDVhwAAEA0CSqYnXvuuWrTpk2V6YcPH9Z//vOfUGsCAACISkEFs4KCAu3bt89t2t69e/XII4+oU6dOYSkMAAAg2gT1qcw777xTQ4YMUbt27dSqVSv9/PPPWr58uWJiYrRo0aJw1wgAABAVgrpilpqaqo8//ljnnXeeduzYoYKCAk2ZMkXbt2/XBRdcEO4aAQAAooJfV8z27NlT5Z6y+Ph4TZo0SZMmTfK63v79+71+pyYAAADc+XXFbP/+/XrttdcCanjfvn169dVXgyoKAAAgGvkVzHr16iW73a4JEyZo79691S6bn5+vv/3tb3rooYd01113haVIAACAaOD3zf9//OMf1bx5c/Xq1UspKSk688wz1bx5c8XHx6uwsFC5ubn6/vvv9cMPP2jGjBmaP3++YmKCuoUNAAAgKgX0qcwrr7xSO3bs0Kuvvqrly5drzZo1ys3NVWJiotq3b68BAwZo6dKlfC0TAABAEAJ+XEb9+vV1yy236JZbbqmJegAAAKIW7zUCAAAYgmAGAABgCIIZAACAIQhmAAAAhgh7MHv99dfD3SQAAEBUCOpLzLds2aL7779fO3bsUElJiWu63W7XwYMHNXr06LAVCAAAEC2CCmbDhw9Xo0aNdPXVVys+Pt413W63a+XKlWErDgAAIJoEFcyOHDmi7777TnFxcVXmDR8+POSiAAAAolFQ95gNHTpUWVlZHuc1bNgwpIIAAACilV9XzN59913Z7XbX7xdddJGmTJmia665xm250tJSLVmyRKtXrw5vlQAAAFHAr2D2t7/9TWvXrq0yfdWqVVWm2Wy20KsCAACIQn69lTlo0CDt379f5eXl1f6UlJTokUceqemaAQAAfpP8Cma33nqrWrRo4fr9008/9bjcli1b1Ldv3/BUBgAAEGX8CmZ16tRx+33RokUelzv99NM1ceLE0KsCAACIQn4/LqOgoEBTp07V7t27tWPHDl166aVu8y3L0k8//STLssJepD927dqldu3a1UrfAAAA4eD34zIaNGigJ598UikpKUpMTFRGRobbz+9+9zsNGzZM77//fkgFbdiwwa/levToIZvN5vp58cUXQ+oXAACgtgX0gNkGDRrozTff1Nq1az3eS1ZSUqK6dYN6Zq3Wr1+ve+65R/v27dOePXuqXfarr75S586dddttt0lyfBK0X79+QfULAABgioBTVExMjNcb/D/77DOdOHFCAwcODKjN48ePKz09XWeccYb27dvnc/kFCxbo0UcfVWpqakD9VNb50t8rrn5iSG0AAAD4UlJ4XJ8v8b2cX8GsS5cu2rZtm18djxgxIuBglpiYqMTERDVv3tznstnZ2Vq4cKGWLFmiXr16acqUKRo0aFBA/Tld0qO16vNNBQAAoIYVHjum+X4s51cwGzx4sG644QY1bdpUlmXpvffeU+PGjXXeeee5fWJz7dq16tChQ7A1+yU3N1fTp0/XF198obVr12rNmjW666679Pjjj3tdp7i4WMXFxa7f8/PzJUmXJ61WUsOEGq0XAAAg33bCr+X8CmbXXXedW+D6/vvvPQah66+/PuCrZYHq0KGD6yG233//va6//nrNnTtXl19+eZVPijrNmTNHDz30UJXpx2YvlC0uuHviAAAA/HWspNSv5fxKJZWvgsXEeP4w57Fjx/z+VGU4dOrUSWvWrFG7du20aNEir8Fs5syZmjZtmuv3/Px8paen6/sP/6sGMXU8rgMAABAuBeVlfi3n9+My3BovKNDbb7/tNu2nn37S6NGj1bZt22CaDFrjxo119dVXKysry+sy8fHxSkpKcvsBAAAwTVDv482ZM0fDhw/XlClT1KZNG+Xm5mrnzp1KSUnRypUrw12jTykpKSor8y+JAgAAmCqoYJaYmKhVq1Zp9erVWrt2rY4dO6ZJkyZpzJgxSk5ODneNPm3atEnTp0+PeL8AAADhFNKd7/369avyYNft27erY8eOQbVXVlbm8Sudxo8fr/3792vlypWaNGmSmjdvrrvvvlv16tXT8uXL1bZtWw0YMCCoPgEAAEzhVzDbtGmTUlNT1apVK5WVlWnx4sUeA5TdbteSJUu0Zs2agIooKSnR6tWr9d577+ngwYN69dVX1bt3b2VkZEhyPCIjJydHkuNq3RNPPKHFixdr4MCB6tSpk+bP9+fJIAAAAGazWX5863hqaqr69eunRYsWSZJ69uypL7/80nODNpvx93vl5+crOTlZH7Rqx6cyAQBAjSsoL1P/fbuUl5dX7YcQ/bpi9u2336phhSfk33TTTXr33XfVpEkTt+Xsdrvmzp0bZMkAAADRza8rZpUVFRUpNjbW7an/TkeOHFHjxo3DUlxN4YoZAACIJH+vmAX1HLOpU6e67vmqzPRQBgAAYKqgPpWZnZ2tRx99VMePH9eQIUM0cOBAr98GAAAAAP8EFcwWLlyoBg0ayG63a+nSpRozZowyMjJ00003RfzJ/wAAAL8VQV3mSkhIkCTFxsZq6NChGjp0qFatWqX27dvr1ltvDWuBAAAA0SKoK2YPP/ywLrnkEi1evFj//Oc/VVJSohEjRujpp59Wz549w10jAABAVAjqU5kxMTGy2WxKTk5W165d9dZbb/1P3fTPpzIBAEAk1einMi+66CJ9++23OnLkiKZOnapbbrlFc+fOVX5+ftAFAwAARLuggtmiRYt0xhlnSJIGDx6sxx57TB999JHS09M1c+bMsBYIAAAQLYIKZnv37tXx48f12muvqW/fvmrXrp327t2rWbNmafr06eGuEQAAICoEdfP/ZZddpvLycsXGxmrEiBFat24dN/0DAACEKKhg1rJlS9199936wx/+oMTExHDXBAAAEJWCCmZr165VRkZGuGsBAACIakHdY0YoAwAACD++4BIAAMAQBDMAAABDEMwAAAAMEfZgtnHjxnA3CQAAEBX8+lTm3LlzdeLECZ/LlZeX6/333yecAQAABMGvYPbZZ59p3bp1SklJqXa5srIyHTx4MCyFAQAARBu/gtmQIUP06quv+gxmkvT888+HXBQAAEA08iuYjR49WvXq1fO53OHDh3XBBReEXBQAAEA08iuY1a9fv8q0nJwc7d69W3a73TUtOztb9913n3bs2BG+CgEAAKJEUF/J9Ne//lV33nmn63fLsiRJNptN3bt3D09lAAAAUSaoYPb5559r8+bNqlevnpYtW6aRI0fKsiytWLFCgwYNCneNAAAAUSGo55j16tVLnTt31qmnnqo6deqoadOmatOmja6//npNmTIl3DUCAABEhaCumG3dulUzZ85Uv379NHz4cF111VWaMmWKVq1apXXr1oW7RgAAgKgQVDCbMWOGrr/+erVs2VKXXHKJRowYoWHDhqmsrExjxowJd40AAABRwWY579wP0YEDB5STk6OuXbuGo7kalZ+fr+TkZH3Qqp0axNSp7XIAAMBvXEF5mfrv26W8vDwlJSV5XS6oK2aStHfvXi1ZskR79+5VixYtdNlll+m8884LtjkAAICoF1QwW7FihUaNGqWYmBi1a9dOcXFx+stf/qKePXtq0aJFSk5ODnedAAAAv3lBfSpz+vTpuuSSS7R37159++23+vrrr5WTk6P+/ftr0qRJ4a4RAAAgKgQVzHJzc/X888+rSZMmrml169bVlClTVF5eHrbiAAAAoklQwWzcuHHKz8/3OK9Zs2Zuv7/zzjvBdAEAABB1grrHrHHjxrrxxhs1aNAg2Ww21/Rdu3bp0KFDmj17tiTJbrdr6dKluvrqq8NTLQAAwG9YUMFs27Zt2rVrl1555RWP87dv3y5JKisr04EDB4IuDgAAIJoEFcxGjx6tJ554QqmpqT6Xfe6554LpAgAAIOoEdY/Z5ZdfrtTUVBUVFUlyvGX59ddfe1x2woQJwVcHAAAQRYIKZllZWTrvvPPUv39/SVJsbKwKCgp0++23y263uy1bpw5P1gcAAPBHUMFsypQpiomJUVpammta79691b59ez3wwANhKw4AACCaBBXM8vLytGHDBrVt29Ztevv27bVw4cKwFAYAABBtggpmnTp1cntMhtObb76pY8eOhVwUAABANAoqmHXo0EHLli2TZVkqKSnR5s2bNWnSJP3973/X8OHDw10jAABAVAj6HrN9+/bp//7v/5SQkKBu3brp5Zdf1uTJk/X000+Hu0YAAICoYLMsywp25dLSUu3atUtFRUU69dRT1aBBg3DWVmPy8/OVnJysD1q1U4MYPjUKAABqVkF5mfrv26W8vDwlJSV5XS6oK2ZbtmzRli1blJubqw4dOqiwsFBjxozR7bffrry8vKCLBgAAiGZBBbMLLrhA27ZtU2JiorZs2aI+ffrol19+Udu2bfXQQw+Fu0YAAICoENRXMt11110aPXq0JGnSpElq0qSJ3n//fSUmJurvf/97WAsEAACIFkFdMatb15Hnnn/+eX3++ed68sknlZiYKEnasGFD+KoDAACIIkEFs9NOO00ZGRmaPHmyZsyYoWHDhmnTpk2aOHGiXnvttXDXCAAAEBWC/lRmSUmJioqKXJ8syM7Odn2peUZGRvgqrAF8KhMAAESSv5/KDOoeM0mKi4tTXFyc6/eK35sJAACAwAX1ViYAAADCj2AGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGMC6Ybdiwwecyy5cv13XXXafZs2dr1KhR2rFjR43VU1xeroP2Eh0sLVFxeXnY2z5SVlqlXW/TA10mmP59tRuOfsOtNmqKdJ813Z+z/fyyUr/7qY1jML+sVLtLipRfVup3uyYcs5VrCOQ17u8+KS4v18HSEh20h36u8rVf/N0P3toLZJ/4GodwnS9NOk7yy0q97stQxtKfvsO5/YG2GeltCeQ4juTfxrohtxAm69ev1z333KN9+/Zpz5491S43btw47dy5U40aNdKGDRvUp08fbdu2TUlJSWGr59uiQv396CFtLjkhS5Lt5M+ZcQm6OaWpusTXD6ntN4/l6rMTx1UmqY6kCxMS1S2+vr4pLqwyfWRSiqs/b+tWXCaY/jvHJchmk7YUn/DYbjj6DbfaqCnSfdZ0f8721504LuvkNEuOf7Fd5KWfmjoGu8QlSJK2lFQ9BncWF2th/mEdKi9zvR6bxtTRmOQmGtowxWu7ztdteZB1hqpyPTGSmsTU0eHyMq81Vdwn5Sfr18n/eton3xYV6sWjh7Tl5LnKOT5d4xN0c6PAzlX+nBuc9VS3H0LZ/srrejs2w3W+NOHc5m2fV9yXlyQ0dNveQMbSn77Duf2BthmuGvxt5+38XL/OJ/60WRPjZ7Msy/K9WM06fvy4Dh8+rDlz5mjVqlXVBrM+ffqodevWeumll1zT2rdvrwkTJmj69Ol+9Zefn6/k5GR90KqdGsTUqTJ/6bFczcvN0amSbpHUVtJuSfMl/XBymTtT0nRVw0b+baCHtjtIGu+h7eaSZlSYvuDk9OkpabJkeVy34jK+avLU/2uS3pLUvtL2Otu9rH5DrS08FlK/4eZtHGuypkj3WdP9OdtPk5QteTwmd1TqJxw1eWvjeUk/ShouaUyFdrefXO80SRMrLP/Cyfr61G+oB5u2rPa1tUPSMEnbAqgzVN7qeeHkdk6U4zXn6TV+mqQJqjq+zn3lrH/psVz9JTdHkmP/VVzneUk7A9hWX+eGLpLelqrUVnk/hLL9lY8zb8ems89Qz5fhOKeGyrmtlce14nG7RY4xc27vjpPz28v9NRFo3TVxjgm0zXDV4G87sw7t19rC4z7PJ/60GejfxoLyMvXft0t5eXnVXkgyIpg5Pfjgg3rllVe8BrPc3Fw1a9ZM8+bN05QpU1zTR4wYoczMTG3cuNGvfqoLZt8WFWpyTpamSPqr3N/rLZc0VdIzcvxL5rm09IDTvD9tfyapV6XpT8uR6n2t+2w1NXnqf72ki3y0+7QcfyzfCLLfcPN3HMNZU6T7rOn+nO0Pk+OPrT/9WJZCrinQ18AzJ2u7rZrln5Y0qmEj/fPYUZ/tfirpTT/qDFUg23m+3M8r1W3rM5KulvSOpDtSmmlebk7I5wVv9VY8NwyT1NtHP09Lmp7STEMbpgS9/RWPM3+PzVDOl/6MtwnHScXj9hlJk1Uz+zyYdkJpM1w1+NvOyIaN9Maxoz7PJ9NTmun3sfHVtjlSjuMzkOPnNxnM1q1bp4svvlhLly7VVVdd5Zp+xx136Nlnn1VxcbFsNpvHdSuqLpjd98t+HTxxXN/L8w145ZI6S9onqVtCov5f6il+bp2j7ewTx/Wdj7Y7yfEirDg9RVILqdq6zpCUVk1NnvofLscVia3VtNvpZNtvBdlvuPkzjuGuKdJ91nR/zvY7yvEvu+r2v7MfSSHXFOhrIF1SA1V/3HeStEfS7yW/2n3DjzpDFeh2OrfjoKRcH+t0lGM8DsXUUXF5mVrJ9+u3hY9t9XVuGCn/zhNHYupoaat2QW1/5ePMn2MzlPOlP+Nt0nHyxsn/FkhKkn+v2UD3eTDthNJmuGrwt52fJWXI93FxJKaOTo9PqLZN5xX4QP4m+xvMjLv5vzrZ2dmSpAYNGrhNT0hIkN1u1+HDhz2uV1xcrPz8fLcfj8uVl+uzE8c1Ud4HJkbSzZKOS1p34nhANzV+duK4xvvR9lJJJyque7I/X3WNl/SZl5o89X9C0rKTfVbX7sSTy53wMr+6fsPN33EMZ02R7rOm+3O2P1bScvne/85+1oVYU6Cvgf0nf3wd9xMllUga62e7xT7qDFUwr3Xndhw/WV916yyTdK2kX8rLdFz+vX6r21Zf54Zi+X+eOFRepl9KS4La/vFynFMDOTZDOV/6M94mHSfFctS8X9INPtbxVXdNnGMCbTO/rDQsNfjb77X6dQz9OY4/rabNE3Icn6H8Ta7O/1Qws9vtkqQ6ddyvctWtW/1nGObMmaPk5GTXT3p6usflCqxylcnxPnF1fi9HGi4/uY4/Amm7TFLF6Jh/si9/1/VUk6f+808uH0xN/vYbboGOYzhqinSfNd2fs/3mCmz/18QxWF0bO+V4q8mf5S05tsefdvN91BmqYF/rzvOKt9dZxXWc/9YOdZ94q7fiuSGQ84QlKctuD2n7gzk2gz1f+jvephwngR7rgezzYNoJpc1fykrDUoO//SYpsPNJdcsG+rcz0OPnfyqYpaamSpIKCwvdphcVFSk2NlYpKVU/FSRJM2fOVF5enusnKyvL43INbDGqI8fNe9X5SY6Bizm5jj8CabuOfj3x6uT/xwSwrqeaPPWfdHL5YGryt99wC3Qcw1FTpPus6f6c7f9Xge3/mjgGq2vjVDnuE/JneZsc2+NPu0k+6gxVsK9153nF+xscv67jDBOh7hNv9VY8NwRynrBJSo+NDWn7gzk2gz1f+jvephwngR7rgezzYNoJpc3UOnXDUoO//eYrsPNJdcsG+rcz0OPnfyqYdenSRTabTQcOHHCbnpOTo27dulW5kuYUHx+vpKQktx+Py8XE6MKERL0gx7+kPCmX9KKkRDk+sh0f498QOtte4EfbQyUlVFz3ZH++6logx8d0PdXkqf8ESVed7LO6dl84uVyCl/nV9Rtu/o5jOGuKdJ813Z+z/VckDZHv/e/s56IQawr0NXDKyR9fx/0LkuIkveJnu/E+6gxVMK9153YknqyvunWukrRIUmpMHSXKv9dvddvq69wQL//PE01j6ii1blxQ279AjnNqIMdmKOdLf8bbpOMkXo6aT5H0qo91fNVdE+eYQNtMqlM3LDX42+8i/TqG/hzHF1fTZoIcx2cof5Or8z8VzNLS0nTBBRdo8+bNbtO3b9+uoUOHhqWPEQ1T9KOkO1R1wMvl+KTFdjnS98gkz1foqmv7Bz/anupher7ks64ffNTkqX9nn9W1u0O/PgsqmH7DzZ9xDHdNke6zpvtzth8j3/vf2U84agr0NeB8NICv4/Oqho38avc2P+sMVSDb6fz9Rzle577WkRz1X5/cRPnyvf9+lO9t9XVuuM2PfnZIGpPcxGt7nrZlqrwfZ/4cm6GcL/0Zb1OOE+dx+6OkmfL/NRtq34Fuf6BthqsGf9sZ0rCRX+eTMclNfLZpk+9zU7DHj1Gfyrz//vv12muvKTMz0236+PHjtX//fq1cuVKff/65RowYoe3btyspKUmZmZnq3bu3Nm3apEaNGvnVj6/nmC07dlR/yc12Pcfs93JckgzHc8ycbTufe1K5bedzapzTKz4PRZLHdQN53oun/v8h9+eYVW638rNaguk33LyNY03WFOk+a7o/Z/uVnxVV8Zis/ByzcNTkrY2KzzG7vkK7lZ9j5ly+8nOHqntt1cZzzLzV43yO14ST2+TpNe58plXl8a38HLNlx47qiVzHh6I6VFon0OeY+To3dJH7c8y87YdQtr/ycebt2HT2Ger50p9lInWcVB7XQJ5jFmzdNXGOCbTNcNXgbzsPHjqgDwuP+Tyf+NNmoH8b/6cel1FSUqLVq1fr/vvv17Zt2/Tiiy+qd+/eysjIkOR4TtmePXv09ddfS3J8JdPrr7+url27KjMzU7fddps6duzod3++gpkkbSl2PPl/U/GvT/6XpK5hePL/luJC/TO/6pOCz65XX/8pqv5J1t7WDeQpw57a6HLy6d7fennyfzj6DbfaqCnSfdZ0f872Kz5x3NeT/2vqGDwzPkGW5fnJ/7tLivVanu8ndVdut+IT1GvjmK1cT4x8P6090H2ypbhQL+Y6vqXEua02SWfFJ2hcgE/+9+fcEMiT/4PZ/srrehuHcJ0vTTi3edpWyX1fXlK/odv2BjKW/vQdzu0PtM1w1eBvO0uP5fp1PvGnzUBq/58KZpHmTzBzcn7/lWxS45i6Yb3XoLi8XAVWuRrYYtza9Tbdn3VD7d9Xu+HoN9xqo6ZI91nT/TnbryupVPKrn9o4BvPLSvVLWalS69RVUh3vn8au2IakWj9mK29TIK9xf/dJcXm5jpSXSpbUuE5o5ypf+6XYKvdrP3hrL5Bjx9c4hOt8acK5reK2FljlHvdlKGPpT9/h3P5A24z0tvh7PvGnTX/6JJhVI5BgBgAAEKrf5ANmAQAAfssIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGCI30ww27VrV22XAAAAEBIjgll+fr5uuukm/elPf9Itt9yiOXPm+FynR48estlsrp8XX3wxApUCAADUnLq1XYAkjRw5Uueff75mzZolSerbt6/q1aunO+64w+PyX331lTp37qzbbrtNkmSz2dSvX7+I1QsAAFATaj2YrV+/Xh988IEWLFjgmjZ27FhNmTJFkydPVmxsbJV1FixYoEcffVSpqamRLBUAAKBG1fpbmUuXLlXjxo3VunVr17SuXbsqNzdXa9asqbJ8dna2Fi5cqDZt2qhfv3569913I1kuAABAjan1YLZp0yalp6e7TWvVqpVrXmW5ubmaPn26evToobVr12rw4MG6++67q+2juLhY+fn5bj8AAACmqfVglp2drQYNGrhNS0hIkCQdPHiwyvIdOnTQI488oo8//lhbt25Vt27dNHfuXH300Ude+5gzZ46Sk5NdP5WDIAAAgAlqPZjZ7XbVqVPHbVrduv7d+tapUyetWbNGKSkpWrRokdflZs6cqby8PNdPVlZWSDUDAADUhFq/+T81NVWFhYVu04qKiiRJzZo187l+48aNdfXVV2vv3r1el4mPj1d8fHxohQIAANSwWr9i1rVrVx04cMBtWk5OjiTHs8r8kZKSolNOOSXstQEAAERSrQezYcOG6eDBg8rOznZN2759uxo3bqyLLrrIrzY2bdqkkSNH1lSJAAAAEVHrwezSSy/VgAED9PLLL7umvfPOO7r33ntVv359SdL48eN15ZVXSpImTZqk2bNnu97uXL58udq2basBAwZEvngAAIAwqvV7zCTp9ddf17Rp0zRjxgzVq1dPbdu21fTp013zc3NzXW9vJiYm6oknntDixYs1cOBAderUSfPnz6+t0gEAAMLGZlmWVdtFRFp+fr6Sk5P1Qat2ahBTx/cKAAAAISgoL1P/fbuUl5enpKQkr8vV+luZAAAAcCCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIaoW9sFSFJ+fr6mTp2qFi1a6MiRI2rdurVmzpzpdfnly5frzTffVPv27bVt2zbNnj1bp512WgQrBgAACD8jgtnIkSN1/vnna9asWZKkvn37ql69errjjjuqLLt+/XqNGzdOO3fuVKNGjbRhwwb16dNH27ZtU1JSUqRLBwAACJtafytz/fr1+uCDD3TjjTe6po0dO1Z//vOfZbfbqyz/4IMPatCgQWrUqJEkqVevXkpISNCLL74YqZIBAABqRK0Hs6VLl6px48Zq3bq1a1rXrl2Vm5urNWvWuC2bm5urTz/9VGeddZbb9DPPPFNvvPFGROoFAACoKbUezDZt2qT09HS3aa1atXLNq2jr1q0qLS31uPzmzZtlWVbNFgsAAFCDav0es+zsbNfbkk4JCQmSpIMHD1ZZVpIaNGhQZXm73a7Dhw+radOmVfooLi5WcXGx6/e8vDxJUkF5ecj1AwAA+OLMHL4uItV6MLPb7apTp47btLp1PZflvOfM3+Wd5syZo4ceeqjK9KsP/BRIqQAAACE5duyYkpOTvc6v9WCWmpqqwsJCt2lFRUWSpGbNmlVZVpLH5WNjY5WSkuKxj5kzZ2ratGmu38vLy3XkyBE1adJENpst5G0Il/z8fKWnpysrK4tPmHrA+PjGGFWP8fGNMaoe4+MbY+SZZVk6duyYWrZsWe1ytR7MunbtqqVLl7pNy8nJkST16NHDbXqXLl1ks9l04MCBKst369atypU0p/j4eMXHx7tNq/z2qUmSkpI4mKvB+PjGGFWP8fGNMaoe4+MbY1RVdVfKnGr95v9hw4bp4MGDrvvHJGn79u1q3LixLrroIrdl09LSdMEFF2jz5s1u07dv366hQ4dGolwAAIAaU+vB7NJLL9WAAQP08ssvu6a98847uvfee1W/fn1J0vjx43XllVdKkh599FEtX75c+fn5kqTMzEzl5ORowoQJkS8eAAAgjGr9rUxJev311zVt2jTNmDFD9erVU9u2bTV9+nTX/NzcXNfbmz179tRzzz2n8ePHq2vXrsrMzNT7779v9FuT/oqPj9esWbOqvO0KB8bHN8aoeoyPb4xR9Rgf3xij0NgsHv4FAABghFp/KxMAAAAOBDMAAABDEMwAAAAMYcTN/9Hso48+0sqVK9WuXTtdeumlOu200/T3v/9dGzZsUEZGhn788UfNmzdPzZs3r+1SI6qgoEB//vOf1ahRIxUUFOjnn3/W3Llz1aJFC0mK6jHasGGDevXq5TbN13h8//33mj17tk4//XT98MMPGj16tAYOHBjp0iPC0/gsXbpUDz74oH766SedeeaZevrpp3XWWWe55kfT+Eiex6iiqVOnqlGjRnrwwQdd0xijXx04cEDPPvuskpKS1LlzZ11xxRWSomuMPI3Pm2++qS+//FJNmjTR1q1bdf3117vGRoqu8QmJhVpRUFBgXXvttda4ceOs48ePu6YvXrzYat++vVVSUmJZlmUtXLjQ6tatm1VaWlpbpdaKG264wfrrX//q+v2VV16x+vfvb1lW9I7RZ599ZvXq1cvKyMhwm+5rPHJzc63mzZtbn3zyiWVZlnX06FGrefPm1saNGyNaf03zNj7Lli2z+vfvby1ZssSaN2+e1ahRI6tJkyZWTk6OZVnRMz6W5X2MKtqwYYNVt25da9asWa5pjNGvFi5caJ199tnW5s2b3aZHyxh5G5+PPvrI6tatm1VeXm5Z1q/b//PPP1uWFT3jEw68lVkLysrKNHLkSB0/flwLFixw+1L2Bx54QNdee61iY2MlSSNGjNCOHTu0bNmyWqq2dixdulRt2rRx/d69e3d9/vnnkqJzjI4fP6709HSdccYZVeb5Go9nnnlGCQkJuvjiiyU5njw9cOBAzZ49O2L117Tqxsd5VXrUqFG64447tGDBAh0+fFjLly+XFB3jI1U/Rk7FxcV69tlnde6557pNZ4wcFi9erMmTJ+utt97SmWee6TYvGsaouvFZtmyZ0tPTXV9zmJycrPT0dH3zzTeSomN8woVgVgueeeYZffjhh3rmmWfcvqtz69at2rVrl9tbLHFxcerYsaPeeOON2ii11qSlpWn+/PkqLy+XJH399de68MILo3aMEhMTlZGRUeXtWn/GY+nSpW7zJcdXoa1evVpHjx6t8dojwdv4lJSUaNSoUW5f19avXz9JjucjStExPpL3Maro4Ycf1owZM1S3rvtdLoyRtH//fo0fP15/+tOf3P7R6BQNY1Td+KSlpWn16tXavXu3JMf3ZWZmZuqcc86RFB3jEy4EswgrLy/XnDlz1K5dOz300EPq1KmT2rdvr1deeUWbNm2SJKWnp7ut06pVK9e8aHHfffdp1apV+sMf/qBPPvlEr732mubPn88YVeJrPMrLy/Xtt996nF9SUqJt27ZFrNbaEBcXp549e7pNc4b9rl27Rv34VPSf//xHsbGx6tKli9t0xsjhySefVEFBgfbt26fLLrtMzZs310033aRjx44xRpJuvvlmJSUl6dJLL9Wnn36qcePG6amnnlLr1q0ZnwARzCLs3//+t7Kzs9WnTx+9+OKL+v777zV69GjdeOONOnjwoCS5vbUpSQkJCa550WLMmDF64okn9M9//lOXX365nnzySbVq1cr1naqMkYOv8Th8+LDKyso8zpcUlWP20Ucf6cwzz1SfPn0Yn5PsdrvmzZunGTNmVJnHGDmsXLlSv//973X//fdr7dq1Wr16td58801NmDCBMZKUmpqqDz/8UKWlperdu7dOPfVUXXPNNZI4hgJFMIuwzMxMSdINN9zgmnbfffcpJSVF9913nyS5ve0iqcrbCtGgrKxMWVlZmj59uuLi4nTxxRdr06ZNstvtkhgjJ1/jwXi5Ky8v17x58/TSSy/JZrMxPic9/vjjuuOOOxQXF1dlHmPkkJmZqYEDB6pJkyaSpC5duuiGG27Q66+/zhidlJmZqf79++vcc8/VI4884vpUL+MTGIJZhDnfRql4AoyNjVX37t1d0woLC93WKSoqUrNmzSJXpAGmT5+uhg0bau7cudq4caMaNWqkwYMHq2HDhpIYI6fU1FRJ3sejcePGiomJ8ThfUtSN2cMPP6zJkyerW7duksT4SPruu+9UXFzsuheoMsbIoby8vEpwdT4u4qeffor6Mfr66681ffp0vfDCC1q3bp1uuOEGPfTQQ3rvvfc4hgJEMIuw9u3bS3LcSFpRo0aNXKHtwIEDbvNycnLUo0ePyBRogOLiYj333HMaNmyYJKlDhw5asWKF9u3b57pJNNrHyKlr166SvI9HvXr11KFDB4/z69at6woo0eBf//qXYmNjNXr0aNc0xkd6++239dhjj6levXqun3Xr1un//b//p3r16ik7Ozvqx0hynLs9nbclqWXLllE/Rk8//bQuv/xyxcXFKS4uTi+//LJ69Oihf/zjH7zOAkQwi7CuXbuqbdu2+uSTT9ymHzx4UNOmTVNGRoY2b97sNu+HH37Q0KFDI1dkLSsuLlZpaakrqErSGWecodTUVHXr1o0xquCcc87xOR7Dhg2rMn/79u3q06ePEhMTI1Rp7dq2bZs++eQT3XPPPa5pdrtdmZmZUT8+t956q7799ltt3rzZ9XPOOedo4sSJ2rx5s1q2bBn1YyRJw4cP16effirLslzTDh48qNatW6tNmzZRP0YFBQVu52ybzaaLL77YNV7RPj4Bqe0HqUWj1atXW23atLHy8/Mty7KszMxMq3nz5tbhw4etN954wzr99NMtu91uWZZlrV+/3uratetv/uGplfXv39+65557XL/v2bPHNWbRPEb33Xef1bp1a7dpvsbjyJEjVsuWLa0vvvjCsizLstvtVufOnV2//5Z4Gp8DBw5YF110kfXGG29Yb775pvXmm29ar7/+unXDDTdYeXl5UTU+luV5jCq7+OKL3R4wyxhZ1vHjx62OHTtaS5cudU3r37+/9eqrr1qWFV1j5Gl8Xn/9datNmzbWiRMnXNMuu+wy13hF0/iEijvvakHfvn01f/58TZgwQa1atdK+ffv0/vvvq3Hjxho5cqTy8vJ000036fTTT1dmZqZWrlxZ5abJ37rFixdr5syZuuOOO9SiRQvt2bNHq1atUsOGDaNyjEpKSrR69Wq99957OnjwoF599VX17t1bGRkZPscjJSVFH374oe6//3516dJFR44c0cMPP6zzzjuvlrcqfLyNT1pamq644gpt3rxZ69atc1vn2muvVVJSkiT95sdHqv4Y8iUajiGp+jFq0KCBPvnkE82cOVPr16/X0aNHdfnll2vMmDGSomOMqhufa665RoWFhbrxxhvVuXNnHTlyRGPGjNFVV10lKTrGJ1xsllXhuiwAAABqDfeYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgB+J9VUFCgv//97xo0aJAee+wxj8tkZWXpscce0/nnn6/PPvsswhU65Ofna86cOerZs6e++eaboNpYuXKlDh8+HObKpL179+qjjz4Ke7sAgkMwA/A/Kz4+XldffbXWrVunEydOeFwmLS1NPXr00JdffqmysrKI1nf8+HFJUsOGDdW7d2998cUXbl/07K8///nPio2NVZMmTcJdolq3bq0jR47olVdeCXvbAAJHMAPwP6tu3bpq3LixUlJSvC4TFxen3/3udxGsymH//v2aNWuWJMlms6lFixZBtfPCCy/Isiz169cvnOW5GT58uL744gt9/PHHNdYHAP/wJeYAEGaFhYUaMWKE2rdvH1I7+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+class="
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+>
+</div>
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+</div>
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+</div>
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+</div>
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+</div>
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Pro:-Decision-trees-do-not-need-feature-preprocessing!">Pro: Decision trees do not need feature preprocessing!<a class="anchor-link" href="#Pro:-Decision-trees-do-not-need-feature-preprocessing!">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Con:-Decision-Trees-are-very-prone-to-overfit">Con: Decision Trees are very prone to overfit<a class="anchor-link" href="#Con:-Decision-Trees-are-very-prone-to-overfit">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[135]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">datasets</span>
+<span class="c1"># import some data to play with</span>
+<span class="n">iris</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_iris</span><span class="p">()</span>
+<span class="c1"># we only take the first two features. We could avoid this ugly</span>
+<span class="c1"># slicing by using a two-dim dataset</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="c1"># Nx2 </span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span>      <span class="c1"># Nx1</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="The-data">The data<a class="anchor-link" href="#The-data">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[136]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;jet&#39;</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
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+<div class="jp-OutputArea jp-Cell-outputArea">
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+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[137]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">)</span>
+<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[138]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">clf</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[139]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Parameters #########</span>
+<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">plot_colors</span> <span class="o">=</span> <span class="s2">&quot;ryb&quot;</span>
+<span class="n">plot_step</span> <span class="o">=</span> <span class="mf">0.02</span>
+<span class="c1">######################</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
+<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">plot_step</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">(</span><span class="n">h_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">w_pad</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">2.5</span><span class="p">)</span>
+
+<span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="n">cs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdYlBu</span><span class="p">)</span>
+
+
+<span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="c1"># Plot the training points</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">),</span> <span class="n">plot_colors</span><span class="p">):</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">i</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+        <span class="n">c</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+        <span class="n">label</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
+        <span class="c1">#cmap=plt.cm.RdYlBu,</span>
+        <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+        <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+<span class="c1">#plt.axis(&#39;scaled&#39;);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Decision-Tree-Setup">Decision Tree Setup<a class="anchor-link" href="#Decision-Tree-Setup">&#182;</a></h1><div class="highlight"><pre><span></span><span class="c1">#########################################</span>
+        <span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span>
+            <span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> 
+            <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span>
+            <span class="n">max_depth</span><span class="o">=</span><span class="n">depth</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">min_impurity_decrease</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
+       <span class="c1">################################</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="min_impurity_decrease-:-float,-default=0.0"><strong>min_impurity_decrease : float, default=0.0</strong><a class="anchor-link" href="#min_impurity_decrease-:-float,-default=0.0">&#182;</a></h3><p>A node will be split if this split induces a decrease of the impurity
+greater than or equal to this value.</p>
+<p>The weighted impurity decrease equation is the following:</p>
+
+<pre><code>    N_t / N * (impurity - N_t_R / N_t * right_impurity
+                                - N_t_L / N_t * left_impurity)</code></pre>
+<p>where:</p>
+<ul>
+<li><code>N</code> is the total number of samples</li>
+<li><code>N_t</code> is the number of samples at the current node, </li>
+<li><code>N_t_L</code> is the number of samples in the
+left child, and </li>
+<li><code>N_t_R</code> is the number of samples in the right child.</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[140]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">matplotlib.cbook</span>
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span><span class="n">category</span><span class="o">=</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">cbook</span><span class="o">.</span><span class="n">mplDeprecation</span><span class="p">)</span>
+<span class="c1">###################</span>
+<span class="n">h</span> <span class="o">=</span> <span class="mf">0.02</span>  <span class="c1"># step size in the mesh</span>
+<span class="n">plot_train_points</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">idx_plot</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># plot points of attempt idx_plot</span>
+<span class="c1">#################</span>
+
+<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;DT&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">Krange</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">27</span><span class="p">))</span>
+<span class="n">accuracies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">accuracies_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># iterate over datasets</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
+    <span class="c1"># preprocess dataset, split into training and test part</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="p">)</span>
+    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
+                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
+
+    <span class="c1"># just plot the dataset first</span>
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
+    <span class="n">cm_bright</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">([</span><span class="s2">&quot;#FF0000&quot;</span><span class="p">,</span> <span class="s2">&quot;#0000FF&quot;</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Input data&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+    <span class="c1">#i += 1</span>
+    <span class="c1"># iterate over classifiers</span>
+    <span class="n">mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span>
+    <span class="k">for</span> <span class="n">depth</span> <span class="ow">in</span> <span class="n">Krange</span><span class="p">:</span>
+        <span class="c1">#########################################</span>
+        <span class="n">clf</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span>
+            <span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span> 
+            <span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span>
+            <span class="n">max_depth</span><span class="o">=</span><span class="n">depth</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">min_impurity_decrease</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
+       <span class="c1">################################</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">mid</span><span class="p">,</span> <span class="n">mid</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score_test</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+
+        <span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
+        <span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">&quot;decision_function&quot;</span><span class="p">):</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># Put the result into a color plot</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+        <span class="c1"># Plot the training points</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">idx_plot</span> <span class="ow">and</span> <span class="n">plot_train_points</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+                <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">[:],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span>  <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span>
+            <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="sa">f</span><span class="s1">&#39; Depth=</span><span class="si">{</span><span class="n">depth</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+            <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;0&quot;</span><span class="p">),</span>
+            <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># recording the accuracies</span>
+        <span class="n">accuracies</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
+        <span class="n">accuracies_valid</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score_test</span>
+        <span class="c1"># print(accuracies,i,score)</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
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+<img 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+class="
+"
+>
+</div>
+
+</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[141]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies</span><span class="p">,</span> <span class="s1">&#39;o--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies_valid</span><span class="p">,</span> <span class="s1">&#39;o--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Decision Trees Training/Valid Error&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span><span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Depth&#39;</span><span class="p">)</span>
+</pre></div>
+
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Wrap-up-for-classification">Wrap-up for classification<a class="anchor-link" href="#Wrap-up-for-classification">&#182;</a></h1><p>Given training vectors $\{\mbf{x}_i, y_i\}$ a decision tree recursively partitions the feature space such that the samples with the same labels are grouped
+together <strong>(decrease impurity)</strong></p>
+<p>Let the data at node $m$ be represented by $Q_m$ with $N_m$
+samples.</p>
+<p>For each candidate split $\boldsymbol{\theta} = (j, t_m)$ consisting of a
+<strong>feature $j$</strong> and <strong>threshold $t_m$</strong>, partition the data into
+$Q_m^{L}(\theta)$ and $Q_m^{R}(\theta)$ subsets</p>
+$$Q_m^{L}(\theta) = \{(x, y) | x_j \le t_m\} \\ Q_m^{R}(\theta) = Q_m \setminus Q_m^{L}(\theta)
+$$<p>The set of all splits $\{\boldsymbol{\theta}_m = (j, t_m)\}_{m=1}^M$ can be interpreted as the "parameters" of the model.</p>
+<p><a href="https://scikit-learn.org/stable/modules/tree.html#mathematical-formulation">From sklean</a></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Decision-Tree-at-a-glance">Decision Tree at a glance<a class="anchor-link" href="#Decision-Tree-at-a-glance">&#182;</a></h1><p>The <strong>quality of a candidate</strong> split of node $m$ is then computed using an
+<strong>impurity function or loss function $H()$</strong>.</p>
+$$G(Q, \theta) = \frac{N^{L}}{N} H(Q^{L}(\theta))   + \frac{N^{R}}{N} H(Q^{R}(\theta))
+$$<p>Select the parameters that minimises the impurity</p>
+$$
+\boldsymbol{\theta}^* = \operatorname{argmin}_\boldsymbol{\theta}  G(Q_m, \theta)
+$$
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Decision-tree-for-regression">Decision tree for regression<a class="anchor-link" href="#Decision-tree-for-regression">&#182;</a></h1><p>The idea is the same but changes the loss function from <strong>impurity</strong> to $\ell_2^2$(MSE - Mean Square Error) or $\ell_1$ (Mean Absolute Error)</p>
+$$\bar{y}_m = \frac{1}{N_m} \sum_{y \in Q_m} y$$$$H(Q_m) = \frac{1}{N_m} \sum_{y \in Q_m} (y - \bar{y}_m)^2$$
+</div>
+</div>
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+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[142]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="c1"># Create a random dataset</span>
+<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="mi">5</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">80</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
+<span class="n">y</span><span class="p">[::</span><span class="mi">5</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.5</span> <span class="o">-</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
+
+<span class="c1"># Fit regression model</span>
+<span class="n">regr_1</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">regr_2</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">regr_1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="n">regr_2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="c1"># Predict</span>
+<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="n">y_1</span> <span class="o">=</span> <span class="n">regr_1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+<span class="n">y_2</span> <span class="o">=</span> <span class="n">regr_2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+
+<span class="c1"># Plot the results</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;darkorange&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;cornflowerblue&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;max_depth=1&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;yellowgreen&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;max_depth=2&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Data $x$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Target $y$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Decision Tree Regression&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
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+<h1 id="Decision-Regression-Trees">Decision Regression Trees<a class="anchor-link" href="#Decision-Regression-Trees">&#182;</a></h1><p>Approximate the underlying function as a <strong>piecewise constant function with discontinuities (non-smooth).</strong></p>
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+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[143]:</div>
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LBp0yZ07NgR48ePx65du8rcJxFhxYoVOHjwIDw9PWWWicVi6OnpldrHqFGjkJmZqXDZnTt3kJycjDt37sDe3l5hna5duyI9PR3vvfdemeNmrDg+MsAYUxsrVqxAUlISPvnkE9StW1dmmbu7O+zs7LB7925cunSpzH0ePnwYhoaG6NevHwwNDWVeZUkEfv31V+jq6qJPnz5K+z9w4IDSRODp06c4d+4cBg8eXOaYGXsTJwOMMbVARAgMDAQA9OzZU2Gd7t27AwACAgLK3O/y5cuRmpqKiRMnIigoCI8fPy5z28jISAQEBGDjxo1K68ycObPEX/y7du1CYWEhBg0aVOb1MvYmTgbYW4uKikK/fv3QtWtX2NvbCxczKXqYT0pKCmbPng13d3d069YN3bp1w5AhQ3D58mWMHTsWW7duBQBcvHgR7du3F/oqfr69b9++MDQ0FJadOXNGbj179+6Fp6cn+vTpAwcHB7Rs2RIrV66Uu3CPiHD27Fl8+eWXePjwIWJjY+Hu7o46deogODhYqPfy5UvMnDkTgwcPhr29PaytrTFjxgy8evVK4Ta5f/8+hg8fDldXV3Tu3BleXl64ePFi+TduKS5cuAAfHx94e3vD3Nwcbm5uMnFL5eTkIDg4GMOGDQMAHDx4EM2aNYO1tTViYmIAAImJiVi0aBH8/f2Rn5+PSZMmwcTEBAMHDpTp6/Tp0/j444/RpUsXuLm5wdraGkOHDsWNGzfk1lvW7fsuREVF4enTpwCg9Fe2ra0tAODIkSNl6vPixYsICwtDXFwcfv31V4wYMQLW1tYYOHAgEhISSmz7+vVrDB8+HFu3bn2rR0vv3LkTrq6ufIqAvR1i7A3h4eEEgMLDw0utGxMTQwYGBhQQECCUHThwgLS0tOjGjRsydW/fvk2NGjWiCRMmUG5urlC+fv16AkAAKDAwUCiXSCRkY2MjV05EdOfOHaHN6dOnZZbNmDGDANChQ4eEsh9++IEA0PLly4Wy5cuXk7W1tdDPmTNnyNramkQiEQGgvn37EhHR/fv3ycnJiSIjI4W2CxYsIADUsmVLSk1NlVn/2bNnycDAgBYsWEASiYSIiB48eEBt2rQR1hUfH1/api2Vv78/eXt7U1ZWFhERpaSkkIuLCwGgOXPmEBFReno6TZgwgYyMjAgAWVpa0vHjx0lfX1+IZf78+dSzZ0/hffv6+tLw4cNJW1tbqPP8+XMiIpo7dy7p6OjIbNvo6GiysbEhsVhM69atK/f2fVe2bdsm937etGfPHqHOkydPSu3z8uXLNHfuXBo+fDg1adJEaAuATExM6OzZs0rbfvHFF/Tbb78J/+/j46Pws16SBw8ekEgkomXLlpW5TXnGN1MfnAwwOeXZWcyaNYsA0OvXr2XKx48fL5MMpKenU9OmTcne3l74gpSSSCTCl9WbO0IPDw+F5YWFhQqTgZSUFNLQ0CAAMl/eUVFRBIDs7Oxk+gkJCRH66devHyUmJtKZM2fI3d2ddu/eTfn5+eTi4kJ+fn4y7QoKCqh+/foEgCZPniyUP378mOrWrUtdunSR21bnz5+vtGTgypUrpKurK/eFtX//fmEdxf9+c+bMEb6gxo0bR9nZ2bR+/Xrq0KEDxcbGUk5ODjVr1owAkK2tLe3YsYNevnxJQ4YMoeHDhxMR0ZYtW4Rk4U0XL14kACQSiejw4cNCeWnbV5n4+HiZL9ayvkqyYsUKoV7xZLS448ePC3WuX79eYn+KXL58mXr37i30YWpqSk+fPpWrFxAQQIMGDZIpq0gyIH1P//77b5nbcDLAFOFkgMkpz85i6tSpBIDWr18vU37p0iWKi4sT/n/evHkEgNauXauwH+kvyLImA0SkMBkoKCig7t27k5OTE7148UIol3656OnpyfSRlZUl9HPw4EG5dWzfvl3pl3ffvn0JAFlYWAhl48aNIwC0a9cuufoZGRmVlgz06NGDPDw85MqfP38urOP7778Xyo8cOUIASCwWyx3JkBo8eDABoE8//VRuWUFBAVlaWhIAuSM+Um5ubgSAnJ2dhbLStq8yjx49Iltb23K/SrJw4UIhlsLCQoV1QkNDhToXLlwoc7xv2rhxo5CUTps2TWZZVFQUOTo6Unp6ukx5RZKBNm3akKura7li42SAKcK3FrK38vHHH2PNmjWYNGkSwsPDsWjRIlhaWqJ9+/Yy9f78808AgKura5XGIxaLceLECZmyK1euCBeOZWdnyywrfrW3jY2NXH9///03AOCrr76SW/by5UtYW1tDJBIhLy8PYrEYf/31FwCgTZs2cvUNDAzK9V6UefXqFUJDQ2FmZiZ3KxtQ9D6ISGYCHen7rFOnDurUqaOwX2kdRdvh+vXrwoVxLVq0UNh+6NChuHr1KiIjI5GUlAQLC4tSt68ylpaWiI2NLXP9sii+/QsLCyEWi+XqFL/P/827Dcpj3LhxiIuLw6pVq3DgwAGsWrUKAJCZmSnMOWFsbFzh/oGiiZPCw8OxbNmyt+qHMYDnGWBvycPDA7/88gumTZuGwMBA/PXXX5gyZQrmzZsn3MP96tUrxMfHAwDq16//TuIqKCjAjh07cPr0afTu3RtTp04VJm4pD+kX0rFjx0qdJyAmJka4oLBRo0blD7qM4uLiIJFI4OnpiZ07d1bZeoqT/v0AQEdHR2Gd4hflJSYmwsLCosrjKo/in73MzEyFX8bFk0Vzc/O3Wt/06dOxatUqPHr0SCibMGECRowYoTBZLC/pBZh8FwGrDHw3AXtrkyZNwvXr19GxY0fk5ORg+fLlcHFxQWJiIoCiq6alpFdzV6Xo6Gg4OTnh5s2b2LRpEz777LMKT/gj/aWobHa54orPXPfy5csKra+yY6osWlpawr9fvHihsE7jxo0V1q8unJychH8/f/5cYZ3k5GQARUc/yjKFcEksLS1hamoqk3Rs3boVU6dOVTiV8JYtWwAAI0aMEMpKwncRsMrERwbYW5EeDnZ2dkZYWBj27NmDKVOmIC4uDkOGDEFYWJjMYenIyEh4eHhUWTxPnz5Ft27d0KhRI6xcufKt+2vSpAni4uJw8uRJpYfHJRIJ7t+/L/PlcffuXVhaWr71+pXFBADh4eFITU1V+qWVkJAAc3PzMk18U5riv/qvX7+ODz/8UK5Oeno6AEBTU1Pptiqrx48fw8vLq9ztSjq14OjoCHNzczx79gxxcXFo3ry5XJ379+8DQIXWrYhIJJI5ZSa9dVGRpKQkvHr1Cg0bNoSJiUmJ/cbExCA6OppPEbBKw0cG2FtZtmyZzC//gQMH4tKlSzAwMMCFCxeQkpICAwMD4cvht99+Q2FhoVw/BQUFCvuX/jp686lveXl5wr+p2NwBmzZtwosXL+TOTxevXx7SSWhWrFiBtLQ0hXW+++47vHz5EnZ2dsKhaOmvvKrQuHFj2NraIjc3FwsXLlRYJyUlBVOmTFF6SL+8WrZsCQcHBwBQOlWv9Iu0T58+MDIyeqv15efn4+7du+V+lUQsFsPHxwcAcOrUKYV1rl+/DgAYOXLkW8UPAPfu3UNKSgq+/vproSw2Nlbpa8CAAQCKxpS0TBnptSl8ioBVFk4G2FvJy8vDtm3bZMqsrKxgb28PXV1d4boB6U747t27GDt2rHBxW2ZmJtatW4cnT54o7N/MzAxA0Ze89BDu/fv3MXDgQOECr1u3bgn1nz17BgAIDQ3FzZs3ARTNR798+XKhTlJSkvDFXjwxUZQwjBo1CiYmJkhISICHhweuXLkis66xY8ciOTkZHTp0gIaGBqZMmQKg6HxudHS0TF/FH1tcPIGqiOnTpwMA1qxZg2+++UbYNgAQFhaGrl27YuLEidDQ0JB5nyUlRaXVWblyJTQ0NBAcHKzwi2rbtm3Q1dXF0qVL5fosbd1vkl4EWd5Xab7//nuYm5tj+/btck8ofP78Oc6dO4f+/fvjgw8+kGvr5+cHe3t7bN++HUBRAnvw4EGlSciSJUswZswYpdMMv43g4GA+RcAqlwruYGDVXHluPRo3bhwZGxvLTK4SERFBOjo6NHfuXKEsJyeH2rdvL9y2paurSzY2NqSrq0sLFiygevXqKbytKjg4WGgjFoupcePGpKenR0ePHiVbW1sCQEZGRrR48WIiItq3b59MfWtra7K0tKTz58+Tnp4eAaC6deuSp6cnERGFhYUJ9aV9vGnXrl3CbWIAqH79+mRpaUkaGhrUs2dPmXvW8/LyyMvLiwCQmZkZbdiwgSIiIujw4cPUq1cv0tLSIgDk5uZWroli3iSRSKh///4y79XGxoZMTExILBbTpk2bZOovXrxYqBsWFibXX3Z2Njk6OhIAcnJyEiYyelNAQABpamqSvb093bt3TyjfunUr6erq0t69e2Xql2X7vmuXL18mIyMj+vTTTykjI4OIiDIzM6l///7k6Oio9NZLQ0NDAkCOjo5ERPT3338Lcyv4+PjQ48ePiajoMzBnzhyaMWMGFRQUlDmust5aGBERQQDop59+KnPfxfGthUwRTgaYnPImA9KdvZ2dHXXv3p06d+5MO3bskKubkZFB06ZNo/r165ORkRF16dJFmKBGWTJARBQUFER2dnZkaGhIXl5edO3aNSIiGj58OAUFBVFmZqZM/ZUrV1KjRo2oXr16NHLkSHr06BERES1dupSMjIyoV69e9PTpU2rXrh2JxWKZSWuaN29OR44ckYvh7Nmz1LNnTzI2NiZdXV1q3bo1rVmzRuHOPicnhxYvXkzvv/8+aWtrU8OGDWnkyJH0+PFjcnZ2ppkzZ1JYWFi5vigUyc/PJz8/P2rZsiXp6OiQqakp9evXT+b++Li4OGEWR+lLQ0ODWrduTUlJSURE5OfnRyYmJjJ1TE1Nafz48QrXGxkZSUOGDKEmTZpQu3btqFu3bjR8+HCKiYmRqVee7fuuxcfHk4+PD9na2pKHhwd17tyZFixYIDd5VnHTp08nQ0NDWrVqFREVfZ5Hjx5NjRs3Ji0tLapbty4NGzaMfH196ebNm+WOqazJwHfffVfuiYaK42SAKSIiKsOxNaZWIiIi0KZNG4SHh1f5vABSZmZmePnyJQIDAxXe088YqxyqGN+s+uNrBhhjjDE1x7cWMqZCn3/+ebnnXti5cycaNmxYRRExxtQRJwNM5YgImZmZqg5DJd7VDIKMMVYSPk3AVC4kJAQ5OTkAZG8TZIwx9m5wMsBUJj4+Hk5OTjL3Ya9evRru7u7IyspSYWSMMaZe+DQBU5mmTZvykQDGGKsG+MgAY4wxpuY4GWCMMcbUHJ8mYKyWefXqFU6cOIH9+/cjOzsbu3fvVnVIjLFqjpMBxmqR69ev4++//8aWLVvw5MmTKn1ctKrl5uZi/fr1OHDgAICiBw0BwGeffYbvvvtO4aObc3NzsWHDBuzYsQPa2trIzMxEixYtMHfuXLRu3fqdxs9YdcLJAGO1SNu2bdG2bVs0bNhQeIJibZSeno7u3bvDyMgIe/bsER4dff78eXz00UcICQnBmTNnoK2tLbTJyMhAv379cOfOHYSEhMDZ2RkSiQTTp09Hhw4dsGfPHvTt21dVb4kxleJrBhirhYyNjVUdQpWaP38+rl+/Dn9/fyERAIDOnTvju+++w6VLl7Bp0yaZNjNnzsTZs2exfPlyODs7AwDEYjFWrlwJGxsbDB06FI8fP36Xb4OxaoOTAcZYjRMSEgIAqFu3rtyyVq1aAZCdwOru3bvw9/eHgYEBPv/8c5n6mpqa8PHxQWpqKhYuXFiFUTNWfXEywFgVyMjIQF5enqrDqLWkSUBwcLDcsn///RcAYG9vL5T98ccfICJ07txZ4bUE3bt3BwBs27YN2dnZVREyY9UaJwNMbUVFRaFfv37o2rUr7O3tIRKJIBKJEBkZKVNPIpFg06ZN6NSpE/r06QNbW1u4urpiy5YtMvUKCwsRGhqKESNGoGHDhoiPj0dMTAyGDBmCBg0awNjYGB999JFwKPrmzZv44osv0LBhQxgaGqJPnz54+PChXJwXLlzAV199hcOHDyM1NRXTp09H48aNYWhoCG9vb9y5c6fc7/3ChQvw8fGBt7c3zM3N4ebmpvCLFQD279+PLl26wNPTE5aWlhCJRKhTp06511mZPvroIwDAd999h7Nnzwrlr169wqpVq9CyZUuMGTNGKD9x4gQA2QShOFtbWwBAVlYWTp8+XVVhM1Z9EWNvCA8PJwAUHh6u6lCqTExMDBkYGFBAQIBQduDAAdLS0qIbN27I1B04cCABoKioKCIiKiwspOHDhxMACg4OFurt37+fxowZQwAIAE2ePJl8fHzo9OnTdOnSJfLy8iIA1LlzZ1q3bh199tlndPr0abp48SJ5e3sTAOrUqZPQX0hICLVr107o7+eff6YWLVqQhYUFmZiYCOUmJiYUHR0tE3NgYCABIA8PD7n37u/vT97e3pSVlUVERCkpKeTi4kIAaM6cOTJ1t27dSrq6unTr1i0iIpJIJPT999+TiYlJubd5ZcrJyaHWrVsTANLR0SE/Pz+KjY0lFxcX6tevHz1//lyoK5FISEdHhwDQihUrlPZpaGhIAGjp0qXv4i2ojDqMb1Z+nAwwOeqws5g1axYBoNevX8uUjx8/XiYZiIyMFL5009LShPKDBw8SAOrdu7dc32ZmZgSAFi1aJFP+4MEDoa83v3STk5NJQ0ODANDTp0+F8szMTNLX1ycA1KxZM7p48SIRERUUFNC6detILBYLCUZxypKBK1eukK6uLj158kSmfP/+/UJsxf/ubm5u1KZNG5m6hYWF5O7uLve+FfHw8BD6LesrMDCwTH0nJyeTu7u70E4kEtHPP/8sV+/58+dCHX9/f6X9WVhYEACaOHFimdZfU6nD+Gblx7cWMrUkPZ+/ZcsWfPPNN0K5j48PDAwMhP+3sbGBq6sr9PX1ZcoNDQ0BAA8ePJDr28DAAMnJyejUqZNMuZWVFUxMTJCeno4ePXrILKtXrx7q16+PZ8+eITExEebm5gAAfX19mJmZITExEd9++y06dOgAoOgq+IkTJ+Lp06dYsmQJzp8/j4SEBNjY2JT4vufOnYt27drBwsJCplzaLwDs2bMHrq6uwnaKiYlBRESEUCYSiTBu3LgS11P8PUsPwZeViYlJmerVq1cPe/bsgaOjI1JSUkBEmDdvHszNzTF06FChnvSJmACgpaWltD/pstzc3HLFy1htwMkAU0sff/wx1qxZg0mTJiE8PByLFi2CpaUl2rdvL1PPxMQE4eHhwv8TEUJDQ+Hv7w8A5X66opGREdLT0xUu09XVBQC5C9hEIhGAosTgTRMmTMCSJUsAFF09X1Iy8OrVK4SGhsLMzAyenp5yy21sbEBEyMjIEMoGDBiAyMhIdOnSBXPmzMHUqVOhr6+P4cOHl/g+pbZu3VqmehVx9epVDBw4EMuXL4e5uTnGjBmDZ8+eYdiwYUhMTMScOXMAQCaJKywsVNpffn4+AMV3KDBW2/EFhEwteXh44JdffoGGhgYCAwPRvHlzzJ49W+mXe3Z2NtatW4cxY8YgNzcXY8eOrdB6xWJxqXVK+sJ6U6NGjYQvr9evX5dYNy4uDhKJBJ6enjhz5ozcKz4+HgkJCfjll1+ENrNnz8aXX36JzMxM+Pr64r333hOuzFel6OhoeHl5wdvbG6NHj8aHH36IW7duCUdcfH19sXfvXgBFCZ30V39mZqbSPqVJmPSoDGPqhJMBprYmTZqE69evo2PHjsjJycHy5cvh4uKCxMREmXrnzp2Dra0tCgoKsGnTJvTt2xeamtXnoJr0Vrl69eqVWE/6y1fRHQvKaGtrY9u2bdi9ezesra3x9OlTjBo1Cp988olKD6d///33yMjIkDld0aBBAxw9ehSffPKJUAcoSsAcHBwA/P+UxW8qKCgQjti0a9euKkNnrFriZICppaSkJACAs7MzwsLCsHv3bjRq1AhxcXEYMmSIUC86Ohq9e/eGq6srpk2bpqpwlSIipKamQiQSoU2bNiXWbdKkCQAgPDwcqampSuslJCQIv5Kl22ngwIGIjY3FkiVLoKOjg3/++QeLFi0qNb7hw4fDzs6uXK99+/aV2u+ZM2cAAHZ2djLlYrEYGzZsAADcu3dP+IKXziMQFxensL///vsPRAQjIyO4ubmVun7GahtOBphaWrZsmcxh9YEDB+LSpUswMDDAhQsXkJKSAgBYs2YNsrOz5c7FV5cJhe7cuYOsrCz06dNHZlpeRRo3bgxbW1vk5uYqnWkvJSUFU6ZMgY6ODgAI592Bomsa5syZg+3btwOA8ICgkiQmJuLu3bvleim7pqI46XUUT58+lVtmbm4OMzMziEQiaGgU7eK++uoraGho4OzZsygoKJBrc/36dQBFyUt1OurD2LvCyQBTS3l5edi2bZtMmZWVFezt7aGrqytcrPfs2TMAwN69e4XD60lJSVi7di2Aoi/P3NxcpKWlCf1Iv2wUnfuXXpNQ/Ar3N9spOx+v6HoGf39/aGpqYunSpTLlyvqYPn06gKIk55tvvkFycrKwLCwsDF27dsXEiROFL9ErV67g9u3bMn307t0bQNkutDtz5gyo6BbmMr+++uqrUvv19vYGAPzzzz9yy168eIGXL1+iW7duMDIyAgA4ODhg9OjReP78ucI2u3fvhoGBgXBqgTG1827vZGQ1gTrchzxu3DgyNjams2fPCmURERGko6NDc+fOFcpWr14t3KOura1NVlZW1KJFCzp9+rRQ3qBBAxo2bBgREf3333/CfAG//PKLzDqjoqKENt9++63MsoSEBNLU1CQA9NNPP8kss7a2JgBkY2ND9+/fF8oDAwNJLBbT77//Lvf+5s+fTwDIzs5OplwikVD//v2FOMRiMdnY2JCJiQmJxWLatGmTTH1bW1tycnKiBw8eCGUbNmwgsVhMx44dK3EbV6UnT56QnZ0dGRsb05EjR4Ty1NRU+vDDD6lx48Yy24qIKCsrizw8PMjc3JwiIyOF8j///JO0tbXp6NGj7yx+VVKH8c3Kj5MBJkcddhbjxo0TvhDt7Oyoe/fu1LlzZ9qxY4dMvYKCApo1axbVr1+fLCwsaPr06ZSamkpERBMmTCAjIyP6/PPPKSMjg2bMmCHMYgeANDQ0yMXFhbKysujrr78mbW1tmcl17OzsKCkpiaZPn066uroyyxwcHCglJYWI/j8Z+OGHH2jQoEHUs2dPcnZ2pq5du9KpU6dk4o2JiaGZM2eSkZGR0JePjw8dOnRIqJOfn09+fn7UsmVL0tHRIVNTU+rXrx9duHBBbjvZ2toSANLU1KS2bdtSt27dyNvbm65cuVLJf5Hyy8zMpKVLl5KzszPZ2dmRu7s7tWnThmbOnEnJyckK2+Tn59OKFSvIycmJ3NzcyMvLiwYNGiTMLqkO1GF8s/ITEan4HiFW7URERKBNmzYIDw8XJpphqmNjY4MHDx4gMDCwTIfQGSsJj2+mCF8zwBhjjKk5TgYYY4wxNcfJAGPVnEQikfkvY4xVNk4GGKvGHj9+LNxLf/XqVZVPA8wYq504GWCsmpo6dSocHByE+QcCAgJgaWkJPz8/FUfGGKtteKotxqqpNWvWYM2aNaoOgzGmBvjIAGOMMabmOBlgjDHG1BwnA4xVslu3bmHp0qVwc3NDWFiYqsOp0fLz87Fp0ybcuXNH1aEwVqtxMsBYJVq5ciUWLFgAX19fXLt2TeET8mqjffv2oVu3bmjfvj0++OADdOnSReEDgcqqoKAAf/zxB2xtbTFmzBg8f/68xPrPnz/H9OnT0axZM2hra6Nhw4b48ssvcffu3Uptw1itpeLpkFk1xHOXvz1TU1MCQKdPn1Z1KFVu+vTpJBKJaNu2bUJZcHAwaWho0Lx588rd36lTp8jX15eGDBkiPF+hpO0YGRlJ9evXJwBkYmIi84wHQ0NDhc9cqEib2oLHN1OEjwwwVgWMjY1VHcI7ERwcjFWrVmHYsGH48ssvhfLBgwdj1KhRWLhwIQ4fPlyuPrt27YrFixcjMDCw1Lp5eXkYNGgQBg0ahKSkJKSlpSEtLQ1Lly6FtrY2MjIyMHToUOTn579VG8ZqO04GGGMVIpFIMHPmTADA6NGj5ZaPGjUKADBjxoxyTZYkEokAANra2qXW3bVrF7y8vPDrr7+iYcOGAAATExN8//33WL16NQAgPj4ely5deqs2jNV2nAwwxiokJCQEDx8+hL6+Pjp27Ci33M3NDcbGxoiNjcW5c+eqJIbjx49j6dKlCpeNGDECWlpaAIpmcnybNozVdpwMMLVx6tQpODk5QSQSQSQSwdPTU+ZL6unTpxg5ciREIhE++OAD3L59W1iWlpYGX19fdOrUCd26dYONjQ169OiBy5cvl3n9gwYNgp6enrBuqY0bN8La2lqIS9FjirOysrBgwQJ8/vnncHFxgYWFBcaOHStMVawKJ06cAADY2tpCQ0N+VyISidCiRQsAKPepgrJavXo1TE1NFS7T09ND/fr1AQDm5uZv1Yax2o6TAaY2vLy8cPLkSeF8/qBBg9ClSxdhecOGDbFy5UqIxWIEBwfDwcEBAJCamgo3Nzf89ddfOHr0KEJDQ3Hr1i0kJSWhe/fuiI+PL9P6d+/eLRxWL278+PF48OAB2rVrp7Ddy5cv4eHhgY4dO2Lnzp24ceMGfvrpJ/z+++9o06YNHjx4UN5NUSmioqIAABYWFkrrSJfdvHmzSmIwMzNTuqywsBCpqakwNjZGhw4d3qoNY7UdJwNMrTRo0ABff/01gKJzx286ceIEPvzwQzRr1kwo27p1K+7duwcTExMYGRkBKLpAsHfv3sjMzFTYjzLF+32Tra2twvIRI0agXbt26N69u1Dm4+MDFxcXPHnyBJMnTy51vfPnzxeOPJT1pegIRXFPnjwBUHS+XRnp9pLWfZcuXryI7OxsTJkyBXp6elXWhrHagJ9NwNTO+PHj4efnh3PnziEmJgb29vbCsqCgIEyZMkWmfocOHWBjY4OuXbvKlBsaGgJApf0yl144V9zFixdx8OBBnD59Wm5Zhw4dcOPGDRw5cgSZmZkwMDBQ2reZmZnSZEOZkn7xA0BOTg4ACOfYFZEuy83NLde6K8OqVavg6uoKX1/fKm3DWG3AyQBTOzY2NvD29sbBgwfh7+8vPAzo4cOHiIuLQ48ePWTqu7m5yZwKyM7Oxt69e3HkyBEARefzq8rff/8NAJg9ezZ0dXVllqWnp8Pa2hoA8OzZsxKPOkycOBETJ06s1NikyUdhYaHSOtLb8+rWrVup6y7NoUOHcO3aNVy+fBk6OjpV1oax2oKTAaaWvvnmGxw8eBBbt27FsmXLoKenh82bN2PkyJEKL4YDgKSkJPzyyy/Izc3FsGHD0LdvX1y7dq1K44yNjQVQdMTCzs6uStdVXtIL7TIzM5XWyc7OBvBuL8aLj4/H9OnTcfz4cVhaWlZZG8ZqE75mgKmlnj17onnz5khNTcWuXbtQWFiIP//8EyNHjlRYf/369XB1dUXv3r2xatUquLi4vJM4pb+sHz58+E7WVx5OTk4AUOJUwcnJyQCg9OLIypaamoohQ4Zg27ZtaNmyZZW1Yay24SMDTC2JRCJ8/fXX+Pbbb+Hv74/69evD2dlZmISmuM2bN2PSpElYvXo1PDw83mmcTZo0AQCcPHlS7vRFcXfu3Cnxi2z9+vVYv359udY9YMAALFu2TOny7t27Y82aNYiLi1Na5/79+wCK7uSoatnZ2fjiiy/g5+cHNze3KmvDWG3ERwaY2hoxYgT09PRw6dIlzJ49G2PHjlVYT/qFaGNjI1Oel5dX7nVKLxJMTU2VWybtr/hsfdI7CAICApReqLh+/XrcunWrxPUmJyfj7t275XolJSWV2GevXr1gaWmJFy9eCLcZFvfkyRMkJSWhVatWVf5Fm5eXh2HDhmH27Nno1KmTwjqnTp166zaM1VacDDC1ZWpqiiFDhgAAMjIy0LNnT4X1nj17BqBociDpFfTh4eHYvXs3AAhfmmlpaUIbZdPvSu9xj46OxqFDhwAUXZW/aNEiXL16FQAQExMjPO1wwIABaNq0KdLS0tClSxeEhIQIfaenp2Pu3LnYt28fBg8eXOJ7nT9/PoioXK+goKAS+9TU1ISfnx8AwN/fX265dPssWLBA7k6JV69eoU+fPujYsaPSUyDFnw1Q0nTGBQUFGDVqFL755huZyZykUlJS4OvrK1y/UNE2jNVq7/zRSKzaU6enmkVERBAAWrJkidI6/fv3F55oZ2xsTI0aNaJevXpRQECAUN6oUSPauHEjERFlZ2eTrq4uAaDg4GCZvl6/fk0NGjQQ2pmbm5Oenh75+PjQsmXLhHJvb296/PgxERGFhYWRnp6esMzU1JSsrKxILBaTs7MzpaamVtn2KYtZs2aRhoYGbd++XSiLjIwkMzMzmjt3rsI2e/bsEd7Pzz//rLBOaGioUGflypUK6+Tn59OgQYNIJBKRWCyWe2loaBAAsrKyIolEUuE2tYk6jW9WdpwMMDnqtrPw8vKipKQkpcufPXtGn3zyCRkZGZGtrS2tX7+eJBIJvX79mtzd3cnU1JQWLVpEREQBAQHk4eEhfIk1a9aMli1bRs+fPxf6u3PnDvXp04cMDQ3p/fffp3Xr1pFEIqHNmzfT7Nmz6f79+3Ix3Lp1iwYMGEB169YlbW1tsrOzo/nz51NmZmblb5AK+Pvvv6lTp07UunVr6tGjB3l5edHBgweV1n/+/Dk5OjqSlZUVxcbGyiwLDAwkFxcX0tLSErajSCQiW1tbmjJlikzd4cOHyzx+WNlL+vepaJvaRN3GNysbEVE5HifG1EJERATatGmD8PBwuLq6qjqcKkdECif8Yaw2UrfxzcqGrxlgao8TAcaYuuNkgDHGGFNznAwwxhhjao6TAcYYY0zNcTLAGGOMqTlOBhhjjDE1x8kAY4wxpuY4GWCMMcbUHCcDjDHGmJrjZIAxxhhTc5wMMMYYY2qOkwHGGGNMzXEywBhjjKk5TVUHwKqvmJgYVYfAGKtkPK6ZIpwMMDlmZmbQ19fH0KFDVR0KY6wK6Ovrw8zMTNVhsGpERESk6iBY9ZOYmIjk5GRVh1FjLV++HAcOHMDOnTvRpEkTVYdTKzx8+BCfffYZ+vfvj++++07V4dRoZmZmsLKyUnUYrBrhZICxShYaGgovLy+sW7cOEydOVHU4tcq6deswefJkhIaGomvXrqoOh7Fag5MBxirR69ev0apVKzRt2hSnTp2ChgZfo1uZCgsL0a1bNzx48AC3bt2CkZGRqkNirFbgPRVjlWjmzJlITk7GH3/8wYlAFdDQ0MAff/yBFy9eYNasWaoOh7Fag/dWjFWSkydPwt/fHytWrEDTpk1VHU6t1axZM6xYsQIbN27EyZMnVR0OY7UCnyZgrBK8evUKjo6OeP/99xESEsJHBapYYWEhevTogX///RdRUVEwNjZWdUiM1Wi8x2KsEnz77bdITU3F5s2bORF4BzQ0NLB582akpKRgxowZqg6HsRqP91qMvaVjx45h06ZNWLVqFaytrVUdjtqwsbHBypUr8fvvv+P48eOqDoexGo1PEzD2FtLS0uDo6AgHBwccO3YMIpFI1SGpFSJCr169EBMTg+joaJiYmKg6JMZqJD4ywNhbmD59Ol6/fo1NmzZxIqACIpEImzZtQnp6OqZPn67qcBirsTgZYKyCDh8+jMDAQKxevZpnGVQhKysrrF69Gn/88QeOHDmi6nAYq5H4NAFjFZCamgoHBwc4Ozvj8OHDfFRAxYgIffv2xa1btxAdHQ1TU1NVh8RYjcJHBhirgClTpiArKwu///47JwLVgEgkwu+//47MzExMnTpV1eEwVuNwMsBYOR04cAB//vkn1q5dC0tLS1WHw/6ncePGWLNmDbZu3YqDBw+qOhzGahQ+TcBYObx8+RKOjo5o27YtDhw4wEcFqhkiwocffojw8HDcvn0bdevWVXVIjNUIfGSAsXKYPHkycnNz4e/vz4lANSQSiRAQEICcnBxMnjxZ1eEwVmNwMsBYGf3999/YsWMH1q1bh0aNGqk6HKZEo0aNsG7dOmzfvh379u1TdTiM1Qh8moCxMkhOToaDgwM6dOiAffv28VGBao6I8PHHH+Py5cu4ffs2zMzMVB0SY9UaHxlgrAwmTpyIgoICbNy4kROBGkAkEsHf3x8FBQWYNGmSqsNhrNrjZICxUuzevRvBwcH49ddf0bBhQ1WHw8qoYcOGWL9+PXbu3Ik9e/aoOhzGqjU+TcBYCZ4/fw4HBwd4eHhg9+7dfFSghiEiDBw4EOfOncPt27fRoEEDVYfEWLXERwYYU4KIMGHCBADAhg0bOBGogUQiEX777TcAwDfffKPiaBirvjgZYEyJXbt2Ye/evdiwYQP/oqzBGjRogF9//RV79uzBrl27VB0OY9USnyZgTIGnT5/CwcEB3bt3R3BwsKrDYZVg8ODBCA0Nxe3bt2Fubq7qcBirVjgZYOwNRIQBAwbg0qVLfFtaLfLixQs4ODigU6dO2Lt3L5/2YawYPk3A2Bt27NiB/fv3Y+PGjZwI1CL169fHb7/9hn379uGvv/5SdTiMVSt8ZICxYpKSkuDg4IDevXtjx44dqg6HVYEvvvgCx48fx+3bt2FhYaHqcBirFjgZYOx/iAj9+/fH1atXcfv2bdSrV0/VIbEq8PLlSzg4OMDNzQ379+/n0wWMgU8TMCb4888/cfDgQfj7+3MiUIvVq1cP/v7+OHjwILZt26bqcBirFvjIAGMAHj9+DAcHB3z00UfYunWrqsNh78CwYcNw6NAhREdHw9LSUtXhMKZSnAwwtUdE8Pb2RmRkJG7fvg1TU1NVh8TegZSUFDg6OsLFxQWHDh3i0wVMrfFpAqb2goKCcPToUfz++++cCKiRunXrIiAgAEeOHMGWLVtUHQ5jKsVHBphae/jwIRwdHfHJJ58gMDBQ1eEwFfjqq6+wb98+3L59G40bN1Z1OIypBCcDTG0REXr37o3bt28jOjoaderUUXVITAXS0tLg4OCAVq1a4ejRo3y6gKklPk3A1NamTZsQEhKCTZs2cSKgxurUqYNNmzbh+PHj2Lx5s6rDYUwl+MgAU0sPHjyAo6MjPv/8c/z++++qDodVA6NHj8auXbsQHR0NKysrVYfD2DvFyQBTO0SEHj16IC4uDtHR0TA2NlZ1SKwaSE9Ph6OjI+zs7BASEsKnC5ha4dMETO34+/vj1KlT2Lx5MycCTGBiYoLNmzfj5MmTCAgIUHU4jL1TfGSAqZX4+Hi0atUKQ4cOxcaNG1UdDquGxo0bh+3btyMqKgpNmzZVdTiMvROcDDC1UVhYCC8vL8THxyMqKgpGRkaqDolVQ69fv0arVq3QrFkznDx5EhoafACV1X78KWdqY8OGDThz5gz++OMPTgSYUkZGRti8eTNOnz6N3377TdXhMPZO8JEBphbu378PJycnfPXVV/j1119VHQ6rASZMmIAtW7YgKioKzZo1U3U4jFUpTgZYrVdYWAhPT088evQIt27dgqGhoapDYjVARkYGWrVqBSsrK5w+fZpPF7BajT/drNZbt24dzp8/j8DAQE4EWJkZGhoiMDAQ586dw/r161UdDmNVio8MsFrt3r17aN26NcaMGYO1a9eqOhxWA02ePBmbNm3CzZs30aJFC1WHw1iV4GSA1VoSiQQeHh54+vQpbt68CQMDA1WHxGqgzMxMtG7dGg0bNsTZs2chFotVHRJjlY5PE7Baa+3atbh48SICAwM5EWAVZmBggMDAQFy8eBG//PKLqsNhrErwkQFWK8XGxsLFxQVff/01Vq1apepwWC0wbdo0bNy4EZGRkbC1tVV1OIxVKk4GWK0jkUjQsWNHpKam4saNG9DX11d1SKwWyMrKgrOzM+rVq4ewsDA+XcBqFT5NwGqdlStX4tq1awgKCuJEgFUafX19BAUF4cqVK3y0idU6fGSA1Sp37tyBq6srJk2aBD8/P1WHw2qhGTNmYP369bhx4wbs7e1VHQ5jlYKTAVZrFBQUwN3dHa9fv0ZERAT09PRUHRKrhbKzs+Hi4gITExNcuHABmpqaqg6JsbfGpwlYreHn54fw8HAEBQVxIsCqjJ6eHoKCgnD9+nX8/PPPqg6HsUrBRwZYrRAVFYU2bdrg22+/xbJly1QdDlMDs2fPxurVqxEeHg5HR0dVh8PYW+FkgNV4+fn5aN++PXJychAREQEdHR1Vh8TUQE5ODtq0aQM9PT1cunQJWlpaqg6JsQrj0wSsxlu+fDlu3ryJoKAgTgTYO6Orq4ugoCBERkZixYoVqg6HsbfCRwZYjXbz5k188MEHmDVrFhYvXqzqcJga8vX1hZ+fH65fvw4nJydVh8NYhXAywKq99PR0dOrUCfv375d5rnxeXh7atWsHiUSCa9eu8VEBphK5ublo27YtNDU1cfXqVZnTBf/99x/69++PsLAwmJiYqDBKxkrGpwlYtXf79m1ER0cjKytLpnzp0qWIjo7m0wNMpXR0dLBlyxZERUVh6dKlMssyMzMRHR2NO3fuqCg6xsqGkwFW7cXHxwMAbGxshLIbN25gyZIlmDNnDlxdXVUUGWNFXF1dMWfOHCxevBiRkZFCufQzK/0MM1ZdcTLAqr34+HiYmZnB0NAQQNHpAR8fHzg4OMDX11fF0TFWZO7cuWjZsiV8fHyQl5cHADAyMkK9evU4GWDVHicDrNqLj49H06ZNhf9ftGgRYmJisGXLFmhra6swMsb+n7a2NrZs2YI7d+7IXMzatGlTTgZYtcfJAKv2iicD169fx7JlyzBv3jy0bt1aqCORSOSuKWCsqmVlZUEikQj/7+zsjB9++AFLly5FeHg4AE4GWM3AyQCr9hISEmBjY4Pc3Fx89dVXaN26NWbPni0sDwkJQevWreHh4aHCKJk68vDwQOvWrRESEiKUff/993BycoKPjw9yc3NhY2ODhIQE1QXJWBlwMsCqtYKCAiQmJqJp06ZYsGAB4uLiEBQUBC0tLcTGxsLb2xu9evWCqakpAgICVB0uUzMBAQEwNTVFr1690K9fP8TGxkJLSwtBQUGIi4vDwoUL0bRpUyQmJsocQWCsuuFkgFVrjx49gkQiQV5eHpYvX4758+ejUaNGmDx5MhwdHRETE4Pdu3fj3LlzcHFxUXW4TM24uLjg3Llz2L17N27fvo1WrVph8uTJsLS0xI8//oiffvoJ+fn5KCgowKNHj1QdLmNKcTLAqjXpuda1a9fC2dkZOjo6aNGiBYKCgrB06VLcuXMHAwcOhEgkUnGkTF2JRCIMHDgQMTExWLx4MYKCgtCiRQvo6enB2dkZa9euBcC3F7LqjZMBVq1Jd6CJiYlISUnBrFmzMGjQINy7dw+zZs2Crq6uiiNkrIiuri6+++473Lt3D4MGDcLMmTORkpKCxMREAJwMsOqNkwFWrYWFhQEounagefPmiIyMhL+/P8zNzVUcGWOKmZubw9/fHzdu3MB7772HgoICAP//WWasOuJkgFVrmZmZMDIywj///IOQkBC0atVK1SExViZOTk44ceIE9u3bByMjI2RkZKg6JMaU4gcVMcYYY2qOjwwwxhhjak6zrBUTExORnJxclbEwxlQkNzeXn/zIWC1lZmYGKyurEuuUKRlITEyEvb09T/fKWC0lFot5UhzGail9fX3ExMSUmBCUKRlITk5GVlYWtm3bBnt7+0oLkDGmekeOHMEPP/zA45uxWigmJgZDhw5FcnLy2ycDUvb29vzseMZqmZiYGAA8vhlTZ3wBIWOMMabmOBlgjDHG1BwnA4wxxpia42SAMcYYU3OcDDDGGGNqjpMBxhhjTM1xMsAYY4ypOU4GVKSwsBARERFYunQp2rZtiwcPHqg6JFbDXbt2DVu2bFF1GAw8vtVJbRl3nAyoyI4dO7BixQr4+voiPDwc/PBI1fr3338xevRouLi4oGPHjnBycsK8efMq9NhZIkJQUBDc3d2hr68PQ0NDdOzYEX/99VeJ7UJDQ9GzZ08YGxtDT08Pbdu2xc6dO0tdX0REBD788EO4ubnh9OnT5Y6XVT4e39VLZY5vqfKMu4ruE9507do1aGtrIygoqMJxlxRkqcLDwwkAhYeHl6U6K6Ps7GwCQAAoPj5e1eGorXPnzpGJiQn179+fMjMziYjowYMHZG9vTy4uLvTy5csy95Wbm0ve3t4EgPT19UlHR0f4GwOgadOmKWzn7+9PIpGIjIyMyNzcXKbNhg0bFLZ59OgR+fn50bfffkt16tQhAOTj41Pu979t2zYe31WAx3f1UJnjm6j8466i+4Q3paWlUdOmTQkABQYGljnesn5/czKgYryzUK3U1FSysLCgOnXqyO0ULly4QABo0KBBZe7vxx9/JCcnJwoLC6PCwkLKz8+nU6dO0fvvvy/8rU+ePCnT5uLFi2Rra0snTpygwsJCIiK6ffs2tW3blgCQqakp5efny61LWpeI6IsvvuBkoBri8a1alT2+ico/7iqyT1Dk008/pQYNGlRZMsCnCZhaW7FiBZKSkvDJJ5+gbt26Msvc3d1hZ2eH3bt349KlS6X2lZmZid27d+P06dPo2LEjRCIRNDU10a1bN5w9exZGRkYAgO3bt8u027p1K06dOoXu3btDJBIBAFq2bIm///4bAJCamoqUlBS59UnrAoC2tnb53jhjaqAyx7dUecZdRfcJb/r111+hq6uLPn36lDnO8uJkgKktIkJgYCAAoGfPngrrdO/eHQAQEBBQan8nT57E1KlT5XY6ANCwYUP0798fAPD48WOZZRMmTIClpaVcmyZNmqB+/fpo2LAhGjRoUOr6GWP/r7LHd0VUdJ9QXGRkJAICArBx48YqiVGqSpKBqKgo9OvXD127doW9vT1EIhFEIhEiIyPl6qakpGD27Nlwd3dHt27d0K1bNwwZMgSXL1/G2LFjsXXrVgDAxYsX0b59e6Gv4hdQ9O3bF4aGhsKyM2fOyK1n79698PT0RJ8+feDg4ICWLVti5cqVchf2EBHOnj2LL7/8Eg8fPkRsbCzc3d1Rp04dBAcHC/VevnyJmTNnYvDgwbC3t4e1tTVmzJiBV69eKdwm9+/fx/Dhw+Hq6orOnTvDy8sLFy9eLP/GLcWFCxfg4+MDb29vmJubw83NTSZuqZycHAQHB2PYsGEAgIMHD6JZs2awtrYWnmKXmJiIRYsWwd/fH/n5+Zg0aRJMTEwwcOBAmb5Onz6Njz/+GF26dIGbmxusra0xdOhQ3LhxQ269Zd2+70JUVBSePn0KAEof3Wtrawug6DG/pfH09MRXX32ldLn08aHm5uYy5a1atVJY/8WLF0hJSYGfn1+p636XeHzL4/FdpDaP74qo6D5B6vXr1xg+fDi2bt0KQ0PDqgjx/1XmOQciopiYGDIwMKCAgACh7MCBA6SlpUU3btyQqXv79m1q1KgRTZgwgXJzc4Xy9evXC+dSip8bkUgkZGNjo/CcyZ07d4Q2p0+fllk2Y8YMAkCHDh0Syn744QcCQMuXLxfKli9fTtbW1kI/Z86cIWtraxKJRASA+vbtS0RE9+/fJycnJ4qMjBTaLliwgABQy5YtKTU1VWb9Z8+eJQMDA1qwYAFJJBIiKrqApU2bNpV6TtHf35+8vb0pKyuLiIhSUlLIxcWFANCcOXOIiCg9PZ0mTJhARkZGBIAsLS3p+PHjpK+vL8Qyf/586tmzp/C+fX19afjw4aStrS3Uef78ORERzZ07l3R0dGS2bXR0NNnY2JBYLKZ169aVe/u+K9Jz5cXfz5v27Nkj1Hny5MlbrW/s2LEEgLZt21Zq3ZycHPrss89o48aNZerbx8fnnVwzwOObxzeP7//3NuOOqPR9whdffEG//fab3PpqxAWEs2bNIgD0+vVrmfLx48fL7CzS09OpadOmZG9vLwwgKYlEInyY33zTHh4eCssLCwsV7ixSUlJIQ0ODAMgM7qioKAJAdnZ2Mv2EhIQI/fTr148SExPpzJkz5O7uTrt376b8/HxycXEhPz8/mXYFBQVUv359AkCTJ08Wyh8/fkx169alLl26yG2r8+fPV9rO4sqVK6Srqyv3gd6/f7+wjuJ/vzlz5hAAMjExoXHjxlF2djatX7+eOnToQLGxsZSTk0PNmjUjAGRra0s7duygly9f0pAhQ2j48OFERLRlyxZhZ/KmixcvEgASiUR0+PBhoby07atMfHy8zBW4ZX2VZMWKFUK94l9WxR0/flyoc/369RL7K03z5s3pvffeU7ouoqLP0d9//03NmzcnTU1NGjFiRJmudn5XyQCPbx7fRDy+pd42GShpnxAQECB3cWONSgamTp1KAGj9+vUy5ZcuXaK4uDjh/+fNm0cAaO3atQr7kWaYZd1ZEP3/lbvFdxYFBQXUvXt3cnJyohcvXgjl0g+fnp6eTB9ZWVlCPwcPHpRbx/bt25UO7r59+xIAsrCwEMrGjRtHAGjXrl1y9TMyMiptZ9GjRw/y8PCQK3/+/Lmwju+//14oP3LkCAEgsVgs90tHavDgwQSAPv30U7llBQUFZGlpSQDkfhFKubm5EQBydnYWykrbvso8evSIbG1ty/0qycKFC4VYil8hXFxoaKhQ58KFC2WO90379+8nLS0tCgsLU1rnxYsXNGHCBGrfvj3p6uoK633vvfeU/rKRelfJAI9vHt9SPL7fbtyVtE+IiooiR0dHSk9PV7i+qkgGNFHJPv74Y6xZswaTJk1CeHg4Fi1aBEtLS7Rv316m3p9//gkAcHV1rewQZIjFYpw4cUKm7MqVK8KFJdnZ2TLL9PT0hH/b2NjI9Se9wlvReaCXL1/C2toaIpEIeXl5EIvFwqQSbdq0katvYGBQrveizKtXrxAaGgozMzN4enrKLbexsQERyUywIX2fderUQZ06dRT2K62jaDtcv35duOilRYsWCtsPHToUV69eRWRkJJKSkmBhYVHq9lXG0tISsbGxZa5fFsW3f2FhIcRisVyd/Px84d+KLgIqi4yMDEydOhX+/v7o2LGj0npmZmb49ddfARR9LletWoV58+bh/v37mDt3Lvz9/Su0/srE45vHtxSP74oraZ+QmZmJoUOHIigoCMbGxu8spkpPBjw8PPDLL79g2rRpCAwMxF9//YUpU6Zg3rx50NfXB1D04Y6PjwcA1K9fv7JDUKigoAA7duzA6dOn0bt3b+EPUV7SD+yxY8egq6tbYt2YmBjhgqNGjRqVP+gyiouLg0QigaenZ5lmrKsM0r8fAOjo6CisU/yincTERFhYWFR5XOVR/LOXmZmpcOAV/zJRdpFPSYgIPj4+GDt2LEaMGFHmdnp6evD19YVEIsGPP/6I/fv3V4tkgMf3/+PxzeO7IkrbJ0yYMAEjRoxQmGBWpSq5m2DSpEm4fv06OnbsiJycHCxfvhwuLi5ITEwEUHSFpJT0as+qFB0dDScnJ9y8eRObNm3CZ599VupAV0aaST58+LDUusXvDX/58mWF1lfZMVUWLS0t4d8vXrxQWKdx48YK61cXTk5Owr+fP3+usE5ycjKAol9Hpqam5V7HnDlz0KJFC8yePbtCMU6bNg0ikQjp6ekVal8VeHwX4fHN47siStsnbN26FVOnThXunin+kj4DYcSIEUJZZan0IwPSw0XOzs4ICwvDnj17MGXKFMTFxWHIkCEICwuTOWwVGRkJDw+Pyg5D8PTpU3Tr1g2NGjXCypUr37q/Jk2aIC4uDidPnlR6+EwikeD+/fsyH667d+8qvJe8MjRp0gQAEB4ejtTUVKUf6oSEBJibm8scyquo4r8Krl+/jg8//FCujvQLTFNTU+m2KqvHjx/Dy8ur3O1KOvTo6OgIc3NzPHv2DHFxcWjevLlcnfv37wNAhda9bt06ZGVlYe3ateVuK2VkZAQzM7N39qulNDy+eXxLqfv4roiy7BOktzsqkpSUhFevXqFhw4YwMTGp1Ngq/cjAsmXLZH4ZDBw4EJcuXYKBgQEuXLiAlJQUGBgYCB+e3377DYWFhXL9FBQUKOxfmgmlpqbKlOfl5Qn/pmL3Fm/atAkvXryQO39VvH55SCepWLFiBdLS0hTW+e677/Dy5UvY2dkJh6qq8qlWjRs3hq2tLXJzc7Fw4UKFdVJSUjBlyhSlh/zKq2XLlnBwcAAA7Nq1S2Ed6UDr06ePMNNWReXn5+Pu3bvlfpVELBbDx8cHAHDq1CmFda5fvw4AGDlyZLni3bJlC+Li4pQO+tDQUIWf+zelp6cjOTlZiFPVeHzz+JZS5/FdEWXdJ8TGxip9DRgwAEDROJSWVZZKTwby8vKwbds2mTIrKyvY29tDV1dXOK8o/SPdvXsXY8eOFS5+yczMxLp16/DkyROF/ZuZmQEo2glID/Hcv38fAwcOFC4AuXXrllD/2bNnAIo29M2bNwEAaWlpWL58uVAnKSlJGPjFd1yKdiijRo2CiYkJEhIS4OHhgStXrsisa+zYsUhOTkaHDh2goaGBKVOmAACCg4MRHR0t01fxx5oW38FWxPTp0wEAa9aswTfffCNsGwAICwtD165dMXHiRGhoaMi8z5J2mqXVWblyJTQ0NBAcHKzwQ7lt2zbo6upi6dKlcn2Wtu43SS+SKu+rNN9//z3Mzc2xfft2uSeYPX/+HOfOnUP//v3xwQcfyLX18/ODvb293FSiu3fvRnh4ONatWyfXJi8vDydPnkRwcLDwt7h58yb+/fdfhfGtXbsWbm5umDx5convQ3oouSzv+W3w+ObxLaWu47u4so678u4TVKIyb00gKrrVxtjYmM6ePSuURUREkI6ODs2dO1coy8nJofbt2wu3dejq6pKNjQ3p6urSggULqF69egpvoQgODhbaiMViaty4Menp6dHRo0fJ1taWAJCRkREtXryYiIj27dsnU9/a2posLS3p/PnzpKenRwCobt265OnpSUREYWFhQn1pH2/atWuXcG8zAKpfvz5ZWlqShoYG9ezZU+ae0by8PPLy8iIAZGZmRhs2bKCIiAg6fPgw9erVi7S0tAgAubm50bJly8ry51BIIpFQ//79Zd6rjY0NmZiYkFgspk2bNsnUX7x4sVBX0a0t2dnZ5OjoSADIyclJmOjkTQEBAaSpqUn29vZ07949oXzr1q2kq6tLe/fulalflu37rl2+fJmMjIzo008/pYyMDCIiyszMpP79+5Ojo6PSW7MMDQ0JADk6Ogple/bsIU1NTRKLxQpf0vd+4sQJIiq6hUt6G2Hfvn3p7NmzVFBQQOnp6bR27Vr68MMPS51nICcnR3joiYuLCxUUFJTr/Zfn1kIe3zy+idR3fBdX1nFX3n1CSWrUPAPS+26Bogk/unfvTp07d6YdO3bI1c3IyKBp06ZR/fr1ycjIiLp06SJMYKFsZ0FEFBQURHZ2dmRoaEheXl507do1IiIaPnw4BQUFCY+plFq5ciU1atSI6tWrRyNHjqRHjx4REdHSpUvJyMiIevXqRU+fPqV27drJ/HEAUPPmzenIkSNyMZw9e5Z69uxJxsbGpKurS61bt6Y1a9Yo/EDk5OTQ4sWL6f333ydtbW1q2LAhjRw5kh4/fkzOzs40c+ZMCgsLK/dO/E35+fnk5+dHLVu2JB0dHTI1NaV+/frJ3D8bFxcnzPImfWloaFDr1q0pKSmJiIj8/PzIxMREpo6pqSmNHz9e4XojIyNpyJAh1KRJE2rXrh1169aNhg8fTjExMTL1yrN937X4+Hjy8fEhW1tb8vDwoM6dO9OCBQvkJtcpbvr06WRoaEirVq0ioqIJTDQ1NWXen6JX8+bNZe57Xr16NdnY2JC2tjYZGBiQvb09DR06lI4dO1ZizElJSdShQwdhrBT/8mrXrl2pcxNIlTcZ4PEti8d3kdo+vqXKM+4quk9QpiqTARFR6cdaIiIi0KZNG4SHh1f5fcNSZmZmePnyJQIDA0uc25kx9na2b9+OoUOH8vhmrBYq6/c3P7WQMcYYU3OVfmshezuff/55ue/N3rlzJxo2bFhFETHGKguPb1ZdVctkgIiQmZmp6jBU4l3NMMaYqvD4Zqz6qZanCUJCQpCTkwNA9jYixljNx+ObseqnWiUD8fHxcHJyQp8+fYSy1atXw93dHVlZWSqMjDH2tnh8M1Z9VavTBE2bNuVfCozVUjy+Gau+qtWRAcYYY4y9e5wMMMYYY2qOkwE18OrVK+zduxfDhw/HoEGDVB2OShQWFmLv3r2lPhzo5MmT2L9//zuKirG3x+ObVQZOBmq569ev46effsLkyZPx559/Kn02eW2RkpICQ0NDueeAi8XiEh8EEhoais6dO6NHjx64cePGO46asYpRp/Gdl5eHNWvWwNnZGbq6ujAxMUGPHj0QEhKitE1GRgYWLFgANzc3eHp6wtHREX379i2xjbqqVhcQssrXtm1btG3bFg0bNhSesFabrV+/XuE97CKRCBMnTpQrj42Nxd69e5GcnIyrV6++ixAZqzTqMr7T0tLQq1cvXL16FUZGRpBIJHj16hVOnjyJkydPYs2aNXLv//nz5/Dy8oKWlhaOHz+O+vXro6CgAD/++CN69eqFJUuWYM6cOSp6R9UPHxlQE8bGxqoOocplZWVhw4YNuHHjBl6/fi3zys7ORpcuXeTa2NrawtfXF6tXr0abNm1UEDVjb6+2j++pU6dCX18fN2/exKtXr5CRkYE9e/bAwsICAPDtt9/i7t27Mm1GjBiB6Oho/Pbbb6hfvz4AQFNTE0uWLEHHjh3h6+uLo0ePvvP3Ul1xMsBqjT/++AOenp5wdnaGoaGhzEtHR0dhG5FIJPxbW1v7XYXKGCujBw8eICoqCsePH4eTkxMAQEdHB59++imOHz8OsVgMiUSC4OBgoU1ERASOHDkCc3NztGvXTq7PUaNGAShKIlgRTgbekYyMDOTl5ak6jFpLIpFg5cqViIuLw/Tp0/HXX3/h5cuXqg6LqQke31Xnn3/+wU8//aQwWW/VqhU6dOgAAHj8+LFQfvz4cQCAjY2Nwj69vb0BADExMbhw4UIlR1wzcTJQTFRUFPr164euXbvC3t5euPgsMjJSpp5EIsGmTZvQqVMn9OnTB7a2tnB1dcWWLVtk6hUWFiI0NBQjRoxAw4YNER8fj5iYGAwZMgQNGjSAsbExPvroI+FDfPPmTXzxxRdo2LAhDA0N0adPHzx8+FAuzgsXLuCrr77C4cOHkZqaiunTp6Nx48YwNDSEt7c37ty5U+73fuHCBfj4+MDb2xvm5uZwc3OTybSL279/P7p06QJPT09YWlpCJBKhTp065V5nZQoODkZCQgJu3LiB1atXY8iQIbC0tMTo0aORnJys0thY9cDju2aO7y+//BI9evRQutzKygoAYG5uLpQlJiaW2Gf9+vWhpaUFADh37lwlRFkLUBmEh4cTAAoPDy9L9RopJiaGDAwMKCAgQCg7cOAAaWlp0Y0bN2TqDhw4kABQVFQUEREVFhbS8OHDCQAFBwcL9fbv309jxowhAASAJk+eTD4+PnT69Gm6dOkSeXl5EQDq3LkzrVu3jj777DM6ffo0Xbx4kby9vQkAderUSegvJCSE2rVrJ/T3888/U4sWLcjCwoJMTEyEchMTE4qOjpaJOTAwkACQh4eH3Hv39/cnb29vysrKIiKilJQUcnFxIQA0Z84cmbpbt24lXV1dunXrFhERSSQS+v7778nExKTc27wyHT9+nGbPnk2ff/451a9fX9gWAMjS0pLu3LlTah8eHh4EgH788ceqD7ga2bZtG4/vYnh8V7/xXZKePXsSAAoLCxPKvvnmGwJATZo0UdquUaNGBIC+/vrrdxGmypT1+5uTgf+ZNWsWAaDXr1/LlI8fP15mZxEZGSkMyrS0NKH84MGDBIB69+4t17eZmRkBoEWLFsmUP3jwQOjrzUGZnJxMGhoaBICePn0qlGdmZpK+vj4BoGbNmtHFixeJiKigoIDWrVtHYrFY2AEVp2xnceXKFdLV1aUnT57IlO/fv1+Irfjf3c3Njdq0aSNTt7CwkNzd3eXetyLSL9zyvAIDA8vUt5REIqGQkBBq37690EezZs2EnWFpsXEyUPvw+K4947u47OxsMjQ0lNsemzZtIgAkEokoMTFRYduGDRsSAJo4cWKF118TlPX7m08T/I/0fN+bhwJ9fHxgYGAg/L+NjQ1cXV3RqVMnmXJDQ0MARRe7vElar1OnTjLlVlZWMDExAQC5w2D16tUTroAtfshLX18fZmZmAIoufpGeLxOLxZg4cSJmz54NADh//jwSEhJKfd9z585Fu3bthKtypaT9AsCePXuEf+fl5SE6OhoRERFCmUgkwrhx40pdF1D0nm1tbcv1km6jstLQ0ECPHj1w6dIl+Pr6AgD+++8//P777+Xqh9UePL5rz/guLiAgAGKxGJs3b5YpHzx4MMzMzEBE2L59u1y7wsJCpKenA4DctlFXPM/A/3z88cdYs2YNJk2ahPDwcCxatAiWlpZo3769TD0TExOEh4cL/09ECA0Nhb+/PwCU++lrRkZGwofyTbq6ugCA7OxsmXLpFfD6+vpybSZMmIAlS5YAKHo8rLILaICimctCQ0NhZmYGT09PueU2NjYgImRkZAhlAwYMQGRkJLp06YI5c+YIt/wMHz68xPcptXXr1jLVqyyLFy9GTEwM/v77bxw4cACTJ09+p+tn1QOPb0+55TV9fD9+/BiLFy/G7t270aJFC5llRkZG2L59O/r3748lS5bA3t4eH330EUQiEaKiojB//nxhuyu620Ad8ZGB//Hw8MAvv/wCDQ0NBAYGonnz5pg9e7bSwZ+dnY1169ZhzJgxyM3NxdixYyu0XrFYXGqd0qbQLa5Ro0aoW7cuAOD169cl1o2Li4NEIoGnpyfOnDkj94qPj0dCQgJ++eUXoc3s2bPx5ZdfIjMzE76+vnjvvffwxx9/gIjKHOO7Nn36dADAo0ePVBwJUxUe37VrfOfm5mLw4MFYs2aN0osLe/bsifDwcAwePBg//PAD3N3d8eWXX+LAgQP48ssvARQd8encufO7DL3a4mSgmEmTJuH69evo2LEjcnJysHz5cri4uMhdmXru3DnY2tqioKAAmzZtQt++faGpWX0Osujp6QEoOhRZkvz8fABQeEWzMtra2ti2bRt2794Na2trPH36FKNGjcInn3yC3NzcigddhRwdHQHU/olZWMl4fJeuJoxvIsKoUaMwdOhQDBkypMS6LVu2xObNm3Hr1i1cunQJ27dvh6+vLw4ePAgAGDNmDM8v8j+cDPxPUlISAMDZ2RlhYWHYvXs3GjVqhLi4OJkPXHR0NHr37g1XV1dMmzZNVeEqRURITU2FSCQqdUa9Jk2aAADCw8ORmpqqtF5CQoJwSE26nQYOHIjY2FgsWbIEOjo6+Oeff7Bo0aJS4xs+fDjs7OzK9dq3b19Z375C0ucRvHlImKkPHt+1Z3zPmDEDrq6u+Prrr8vVTurOnTvYtm0bDAwMMGPGjAr1URtxMvA/y5YtkznsNnDgQFy6dAkGBga4cOECUlJSAABr1qxBdna23Lm66jLhyJ07d5CVlYU+ffoIFygp07hxY9ja2iI3NxcLFy5UWCclJQVTpkwRZvArPpe3rq4u5syZI1ygc+DAgVLjS0xMxN27d8v1UnbOtawuX74MsVhc4UO9rObj8V07xveiRYtgbm4unPp708mTJ0tsn5+fjxEjRqCgoAD+/v5o1KhRmddd23Ey8D95eXnYtm2bTJmVlRXs7e2hq6srXMzz7NkzAMDevXuFw29JSUlYu3YtgKLBlZubi7S0NKGfgoICAIrPDUrPWebk5Mgtk7ZTdr5O0flOf39/aGpqYunSpTLlyvqQDqo1a9bgm2++kZmgJywsDF27dsXEiROFX9dXrlzB7du3Zfro3bs3AAjnMkty5swZUNEtrWV+ffXVVyX2mZWVhX/++UfhRCNEhGXLlmHhwoXC6QJlpIdVq9v5Ufb2eHzX3PEttWrVKmhra2PWrFlyy7Kzs7Fr1y6cOXNGafvCwkIMGzYM165dw8KFC4XrBtj/VOZ9ijXZuHHjyNjYmM6ePSuURUREkI6ODs2dO1coW716tXB/rLa2NllZWVGLFi3o9OnTQnmDBg1o2LBhRET033//CfcT//LLLzLrjIqKEtp8++23MssSEhJIU1OTANBPP/0ks8za2poAkI2NDd2/f18oDwwMJLFYTL///rvc+5s/fz4BIDs7O5lyiURC/fv3F+IQi8VkY2NDJiYmJBaLadOmTTL1bW1tycnJiR48eCCUbdiwgcRiMR07dqzEbVxVVq1aRQBIS0uLpk2bRqmpqURE9Pr1axozZgz9/PPPpfbx8uVLMjU1JQD00UcfVXHE1Ys6zDPA47vmjm8iojVr1gjxK3rhf3MKxMXFKWz/8OFD6tmzJ5mYmNCuXbvecfSqxZMOldO4ceOEAWNnZ0fdu3enzp07044dO2TqFRQU0KxZs6h+/fpkYWFB06dPF758JkyYQEZGRvT5559TRkYGzZgxgwwNDYV+NTQ0yMXFhbKysujrr78mbW1tmck37OzsKCkpiaZPn066uroyyxwcHCglJYWI/n9n8cMPP9CgQYOoZ8+e5OzsTF27dqVTp07JxBsTE0MzZ84kIyMjoS8fHx86dOiQUCc/P5/8/PyoZcuWpKOjQ6amptSvXz+6cOGC3HaytbUlAKSpqUlt27albt26kbe3N125cqWS/yJl9+zZMxo8eDCZm5uTWCymRo0a0ejRo+nHH3+k//77r8S2N27coHbt2pGxsbHM9ra0tJSZHa42U5dkgMd3zRzff/zxR5kmL/Ly8pJpl5ubS6dOnaIRI0aQtbU1LV68WGYiKXVR1u9vEVHpx0QjIiLQpk0bhIeHw9XVtbTqrIrZ2NjgwYMHCAwMLPMhNsaU2b59O4YOHcrju5rg8V05Fi9eDDMzM7Rr1w7Ozs4yTyhVJ2X9/q4+98swxhhjlWTu3LmqDqFG4QsIGWOMMTXHyUANJJFIZP7LGKs9eHwzVeBkoIZ5/Pgxnj59CgC4evUq3wbHWC3C45upCicDNcjUqVPh4OAg3J8cEBAAS0tL+Pn5qTgyxtjb4vHNVIkvIKxB1qxZgzVr1qg6DMZYFeDxzVSJjwwwxhhjao6TAcYYY0zNcTLwDty6dQtLly6Fm5sbwsLCVB1OrZKdnY39+/dXer/5+fnYtGkT7ty5U+l9M8ZYdcPJQBVbuXIlFixYAF9fX1y7dk24OKi227dvH7p164b27dvjgw8+QJcuXfDPP/9UuL9jx45BJBLJvfT19fH48WOZuj179lRYt/hLQ0MDL1++lFtPQUEB/vjjD9ja2mLMmDF4/vx5hWNmtQ8n9u9eTEwM5s+fj+7du2P06NHYs2dPlbRRe5U5tzFTTvoQnNOnT6s6lCo3ffp0EolEtG3bNqEsODiYNDQ0aN68eRXqs2vXrgrnI69Tpw69evVKqPfvv/+SSCQikUhEderUoXr16sm9RCKRwucOnDp1inx9fWnIkCFC/+rw91KHZxNUhp9//pk++eQTtfpslEVaWhrNnz+fXFxcqGPHjtSqVSv66quvZB6yVBEJCQn0ySefkLGxMS1ZskR4RsTbtnn48KHwcKmSXrXlgWX8oKJqRvrwkdq+A9m5cycBoOHDh8stGzNmDAGQeYhKWVy9epWsra0pNTWVXr9+LfPKz8+XqTtz5kyaPHmyTIJQXEJCAgGgtWvXyi0rLCwkoqIHnKjTDp+TgfJRp8S+NE+ePCEHBwdq0aIFxcfHExFRVlYWDRw4kOrVq0fXrl2rUL979+4lExMTsrS0pKioqEptM2/ePOEpp4p+LEgf+vTmEx1rqrJ+f/NpAlZpJBIJZs6cCQAYPXq03PJRo0YBAGbMmFGuyVSWL1+O2bNno06dOjA0NJR5aWr+/92xRISMjAysXbsWRkZGCvsKDg6GSCTCwIED5ZZJH2Sira1d5tiY+jE2NlZ1CNXGqFGjcPv2bfj7+8PGxgYAoKenh82bN0MkEuGzzz5DRkZGufrct28fBg8ejIKCAhw5cgSOjo6V1qagoADBwcE4dOgQcnNzkZycLPf69ttvoampiY8//rhccdd0nAywShMSEoKHDx9CX18fHTt2lFvu5uYGY2NjxMbG4ty5c2Xq8969e9i3bx8OHDiAefPm4dixY8jNzVVYVyQSYcOGDSX2t3PnTnTs2BGNGjUq0/oZY4qFhITg6NGjaNasGbp27SqzzNjYGAMHDsR///2HdevWlbnPiIgIfPHFF5BIJFi2bBmcnJwqtc25c+ewcOFCeHt7K32KYXBwMLy8vFCvXr0yx10bqHUycOrUKTg5OQkXlXl6esp8ST19+hQjR46ESCTCBx98gNu3bwvL0tLS4Ovri06dOqFbt26wsbFBjx49cPny5TKvf9CgQdDT0xPWLbVx40ZYW1sLcSl6jGlWVhYWLFiAzz//HC4uLrCwsMDYsWOFqUxV4cSJEwAAW1tbaGjIf7REIhFatGgBADh8+HCZ+vTz80NhYSGOHj2KRYsWoU+fPmjSpAmWLl1a7osx7927hxs3bmDw4MHlascYk7d582YARRfsKtK9e3cARTMplkVhYSHGjx+P3Nxc2Nra4ptvvqn0Nt26dStx/N+6dQsxMTEYNGhQmWKuTdQ6GfDy8sLJkyeFw36DBg1Cly5dhOUNGzbEypUrIRaLERwcDAcHBwBAamoq3Nzc8Ndff+Ho0aMIDQ3FrVu3kJSUhO7duyM+Pr5M69+9e7dwWL248ePH48GDB2jXrp3Cdi9fvoSHhwc6duyInTt34saNG/jpp5/w+++/o02bNnjw4EF5N0WliIqKAgBYWFgorSNddvPmzTL1+cEHH2DatGno27cv9PT0AAAvXryAr68vunXrhszMzDLHt3PnTohEInz66adlbsNqBk7s372TJ08CAOzt7RUut7W1BQAkJCSU6Rbdffv24dq1awCA6dOnK/xBURltSrJz505oampiwIABb9VPTaTWyQAANGjQAF9//TUAYNeuXXLLT5w4gQ8//BDNmjUTyrZu3Yp79+7BxMREODdtbGyM3r17IzMzU2E/yhTv903SwfSmESNGoF27dkLmDQA+Pj5wcXHBkydPMHny5FLXO3/+/FJvv3vzpWhHVtyTJ08AACYmJkrrSLeXtG5pxowZg1WrVuHw4cNITk7G5s2b0aRJEwDA+fPnMWbMmDL1AxQd/uNTBLUTJ/bv1uPHj5GSkgJAefJfvLwsyf/OnTuFf+fm5mLSpEn49NNPYWdnh65du+KXX36RO0VYkTYl2bVrF7y8vFC3bt0yt6kt1D4ZAIoGrIaGBs6dO4eYmBiZZUFBQRg/frxMWYcOHWBjYyN3nszQ0BAAKm0AKzqndfHiRRw8eFDhBXAdOnQAABw5cqTUX8xmZmawtbUt16ukX/wAkJOTAwDQ0tJSWke6rDwDVEpfXx8jR45EbGwsevToAQD466+/yrSjiY6Oxu3bt/kUQS3GiX3lJfalKZ7MK0v+i1/EW5bkPzQ0FABQr149tGzZEr/88gv27t2LI0eOgIgwZcoUdO7cGa9evXqrNspcu3YN9+/fV8tTBAA/qAgAYGNjA29vbxw8eBD+/v7Cw0IePnyIuLg44YtHys3NTeYXQ3Z2tvABBIoO+1WVv//+GwAwe/Zs6OrqyixLT0+HtbU1AODZs2cl7pwmTpyIiRMnVmpsBgYGAIrO4ymTn58PAG+Veevr62P//v1o0aIFHj9+jIMHD6J169Ylttm5cyc0NDT4FEEtN378ePj5+QmJffFD2EFBQZgyZYpM/eqQ2J8+fVpuWYcOHXDjxg0hsZeOLUWkiX15lJbYl0aa+APKk//i5aUl/9nZ2cKRhunTp8PLy0tY1qxZMxw4cAAtWrTAtWvXMG3aNGzevLlCbUoSHBystqcIAE4GBN988w0OHjyIrVu3YtmyZcLtMSNHjlR6HiopKUk4DDVs2DD07dtXOH9VVWJjYwEU7djs7OyqdF3lVb9+fQAo8ahEdnY2AMDc3Pyt1qWnp4evv/4ac+fOxaNHj0qtv2vXLj5FoAY4sX83iicnypJ/aeIPlJ78p6amCv9u1aqV3HJjY2OMGjUKy5Ytw7Zt27B+/foKtZFed/QmIlLrUwQAnyYQ9OzZE82bN0dqaip27dqFwsJC/Pnnnxg5cqTC+uvXr4erqyt69+6NVatWwcXF5Z3EKR1gDx8+fCfrKw/pLT0lTeGbnJwMAErPoZaH9F7i0u77Dg8Px71799T28J+6kV5RvnXrViH5LEti//3338PX1xcODg7o27dvlcdZPLE/c+aMzOvGjRtISEhAQkJCiYlAVXn8+DHs7OwUvq5evSok/oDy5F+67YHSk//iY1jZPB/Sozd5eXmIjY2tUBtlLl68iIcPH6r1aUROBv5HJBIJ5xv9/f1x7NgxODs7o2HDhnJ1N2/ejEmTJuG7776Dh4fHO41TevGc9EpeZUq7enf9+vVKB7uy1/fff19in9LznnFxcUrr3L9/HwBkDulVlHTH3r59+xLrBQcH8ykCNcKJ/dvLz8/H3bt3Fb6ysrLQpEkTmJqaAlCe/EsTf6DoCExJDA0NhaN2yq4vKH5ULzc3t0JtlJHeRaBuEw0Vx8lAMSNGjICenh4uXbqE2bNnY+zYsQrrLVu2DACEGbek8vLyyr1O6bnE4oe83uyv+Gx9xe/dVXY+c/369bh161aJ601OTlY62JW9kpKSSuyzV69esLS0xIsXL4TbDIt78uQJkpKS0KpVq1J3DmVx+fJlWFtbl/grTnr4j08RqA9O7N8+sbexsQEVTVcv95LeOilN6JUl/9LE387ODpaWliWur3h/yt5v8XlFpPveirR5U2FhIfbs2aPWpwgATgZkmJqaYsiQIQCAjIwMpZNpPHv2DEDRPcTSC2nCw8Oxe/duABC+NNPS0oQ2yqbfNTMzA1B0tfuhQ4cAFF2cs2jRIly9ehVA0RO4pB/qAQMGoGnTpkhLS0OXLl0QEhIi9J2eno65c+cKU3OWZP78+UoHu7JXUFBQiX1qamrCz88PQNFO+E3S7bNgwQK5C6pevXqFPn36oGPHjsIvpUePHuHgwYMKrwR+8eIFtmzZgsDAQLnzrcVdvnwZDx48KNcpguLnOsszbTKrPjixf7vEviykR1pOnTqlcPn169cB/P805KWRXvewd+9ehcul13Z06NBBSOwq0uZNZ86cwdOnT9X6FAEAfmrhmyIiIggALVmyRGmd/v37Cw+yMTY2pkaNGlGvXr0oICBAKG/UqBFt3LiRiIiys7NJV1eXAFBwcLBMX69fv6YGDRoI7czNzUlPT498fHxo2bJlQrm3tzc9fvyYiIjCwsJIT09PWGZqakpWVlYkFovJ2dm5TE/3qkqzZs0iDQ0N2r59u1AWGRlJZmZmNHfuXIVt9uzZI7yfn3/+mYiIPvroIwJAZmZmtGHDBiooKCAiovj4eOrbty+dOHGi1FgmT55MGhoa9OTJkzLHHxoaKsSycuXKMrerqWrrg4pGjRpFAKhp06bCQ6jeZGhoSACoV69elJ2dTURE169fp+bNmxMA6tmzJxGRzJiysrJS+KCiQ4cOEQDS0NCggwcPElHR2F+4cCG99957BIA++OAD4eFaOTk51LRpUwJAVlZWdPz4cSHOtLQ08vX1pW7dupFEIqnMzVKpevXqRQDo6tWrcstcXFzIwsKCMjMz5ZadOHGCHBwcaOrUqTLlEyZMIAAUEhIi12bIkCGkoaEht90r0qa4sWPHkpaWFr18+VJpnZqMn1r4Fry8vCgpKUnp8mfPntEnn3xCRkZGZGtrS+vXryeJREKvX78md3d3MjU1pUWLFhERUUBAAHl4eAhfLs2aNaNly5bR8+fPhf7u3LlDffr0IUNDQ3r//fdp3bp1JJFIaPPmzTR79myFjwK9desWDRgwgOrWrUva2tpkZ2dH8+fPVzjwVOHvv/+mTp06UevWralHjx7k5eUl7CAVef78OTk6OpKVlRXFxsYSEdGVK1fI09OTTExMSCwWCzuPVatWUVpaWqkxSCQSsrCwoM6dO5cp5sDAQHJxcSEtLS3h7yUSicjW1pamTJlSpj5qotqaDHBiX/VevHhBLVu2JHt7e+GphYWFhbR06VIyMjKiiIgIhe369esnvOfk5GShPD8/n7744guysrKi27dvC+XHjh0jLS0tWr16tVxfFWlTvG29evWod+/e5XvjNQgnA29B2a8Ixmqj2poMEHFi/y5kZGSQr68vtWzZktzd3cnT05NGjhxJCQkJStvs2rWLTExMaNCgQQqXb9++nTp27Eju7u7UuXNn6t27N506darEOCrS5ujRowSANm/eXPobraHK+v0tIir9pGhERATatGmD8PBwuLq6luMkBGOsutu+fTuGDh1aK8c3ESl9Oh1j6qCs3998ASFjrNbiRICxsuFkgDHGGFNznAwwxhhjao6TAcYYY0zNcTLAGGOMqTlOBhhjjDE1x8kAY4wxpuY4GWCMMcbUHCcDjDHGmJrjZIAxxhhTc5wMMMYYY2qOkwHGGGNMzWmWp3JMTExVxcEYU5H4+HgAPL4Zq43KOq7L9NTCxMRE2NvbIysr660DY4xVP2KxGBKJRNVhMMaqgL6+PmJiYmBlZaW0TpmSAaAoIUhOTq604Bhj1Udubi50dHRUHQZjrAqYmZmVmAgA5UgGGGOMMVY78QWEjDHGmJrjZIAxxhhTc5wMMMYYY2qOkwHGGGNMzXEywBhjjKk5TgYYY4wxNcfJAGOMMabmOBlgjDHG1BwnA4wxxpia42SAMcYYU3OcDDDGGGNqjpMBxhhjTM1xMsAYY4ypOU4GGGOMMTXHyQBjjDGm5jgZYIwxxtQcJwOMMcaYmuNkgDHGGFNznAwwxhhjao6TAcYYY0zNcTLAGGOMqTlOBhhjjDE1x8kAY4wxpuY4GWCMMcbUHCcDjDHGmJrjZIAxxhhTc5wMMMYYY2qOkwHGGGNMzXEywBhjjKk5TgYYY4wxNcfJAGOMMabmOBlgjDHG1BwnA4wxxpia42SAMcYYU3OcDDDGGGNqjpMBxhhjTM1xMsAYY4ypOU4GGGOMMTXHyQBjjDGm5jgZYIwxxtQcJwOMMcaYmuNkgDHGGFNznAwwxhhjao6TAcYYY0zNcTLAGGOMqTlOBhhjjDE1x8kAY4wxpuY4GWCMMcbUHCcDjDHGmJrjZIAxxhhTc5wMMMYYY2ru/wAYsxEEwYlQsgAAAABJRU5ErkJggg=="
+class="
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+>
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+</div>
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+</div>
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+</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[144]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">plot_tree</span><span class="p">(</span><span class="n">regr_2</span><span class="p">);</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Remedies-to-Decision-Trees-Overfit-and-Intro-to-Random-Forest">Remedies to Decision Trees Overfit and Intro to Random Forest<a class="anchor-link" href="#Remedies-to-Decision-Trees-Overfit-and-Intro-to-Random-Forest">&#182;</a></h1>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="What-makes-a-good-tree">What makes a good tree<a class="anchor-link" href="#What-makes-a-good-tree">&#182;</a></h1><ul>
+<li><p><strong>Not too small (deep):</strong> need to handle important but possibly subtle distinctions in data</p>
+</li>
+<li><p><strong>Not too big:</strong></p>
+<ul>
+<li>Computational efficiency (avoid redundant, spurious attributes)</li>
+<li>Avoid over-fitting training examples</li>
+</ul>
+</li>
+<li><p><strong>Occam’s Razor:</strong> find the simplest hypothesis (smallest tree) that fits the observations</p>
+</li>
+<li><strong>Inductive bias:</strong> small trees with informative nodes near the root</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Quick-Remedies">Quick Remedies<a class="anchor-link" href="#Quick-Remedies">&#182;</a></h1><ol>
+<li><p><strong>Minimum Leaf Size:</strong> We can setup a minimum number of leaf "size". The minimum number of samples required to be at a leaf node.</p>
+</li>
+<li><p><strong>Maximum Depth:</strong> We can also set the threshold value on the tree depth.</p>
+</li>
+<li><p><strong>Relative Impurity Decrease:</strong> A node will be split if this split induces a decrease of the impurity greater than or equal to this value.</p>
+</li>
+</ol>
+<div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">sklearn</span><span class="o">.</span><span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;gini&#39;</span><span class="p">,</span> 
+<span class="n">splitter</span><span class="o">=</span><span class="s1">&#39;best&#39;</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_samples_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_samples_leaf</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="n">min_weight_fraction_leaf</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
+<span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_leaf_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> 
+<span class="n">min_impurity_decrease</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ccp_alpha</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Quick-Remedies">Quick Remedies<a class="anchor-link" href="#Quick-Remedies">&#182;</a></h1><p>However, even if we have these <strong>on-hand weapon to avoid overfitting</strong>, it is <strong>still hard to train a single decision tree to perform well generally</strong>. Thus, we will use another useful training technique called <strong>ensemble methods or bagging</strong>, which leads to random-forest.</p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Brief-Intro-to-Random-Forest">Brief Intro to Random Forest<a class="anchor-link" href="#Brief-Intro-to-Random-Forest">&#182;</a></h1><p>Random Forest are an application of <strong>Ensemble Methods</strong> or <strong>Bagging.</strong></p>
+<p><em>[Tibshirani book, pag. 587]</em></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="Ensemble-Methods-(Bagging)">Ensemble Methods (Bagging)<a class="anchor-link" href="#Ensemble-Methods-(Bagging)">&#182;</a></h2><ul>
+<li>Motivation: solve the <strong>variance problem</strong> (you fit too much the data)</li>
+<li>Train different classifiers $h(\cdot)_{\mbf{D}_j}$ on <strong>$M$ different datasets</strong> $\{\mbf{D}_1,\ldots,\mbf{D}_m\}$ still sampled from the same generative process $
+\underbrace{\{\bx_i,y_i\}_{i=1}^N}_{\text{known}} \sim \underbrace{\mathcal{\mathcal{D}}}_{\text{unknown}}
+$</li>
+<li>Average the results of different classifiers:
+$$
+h(\mbf{x}) = \frac{1}{M}\sum_{j=1}^M h(\mbf{x})_{\mbf{D}_j}
+$$</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="How-to-create-the-ensemble">How to create the ensemble<a class="anchor-link" href="#How-to-create-the-ensemble">&#182;</a></h1><p><br></p>
+<p><div align='center'>
+<img width="80%" src="figs/ensemble.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Random-Forest">Random Forest<a class="anchor-link" href="#Random-Forest">&#182;</a></h1><ul>
+<li><strong>Bagging</strong> applied to <strong>Decision Trees</strong></li>
+<li>Each tree is very specific to a dataset $\mbf{D}_j$</li>
+<li>When splitting, each tree looks at <ins>most $K$ features randomly</ins> from the $D$ we have ($\mbf{x}\in \mathbb{R}^D$)</li>
+<li>It follows that we do NOT split on all axis and $K \ll D$ and $K=\sqrt{D}$.</li>
+<li><strong>Idea:</strong> Inject noise in each tree so they will make different errors for each $\mbf{D}_j$ so that at the end each error "clears out when averaging."</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[145]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">###################</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+<span class="n">h</span> <span class="o">=</span> <span class="mf">0.02</span>  <span class="c1"># step size in the mesh</span>
+<span class="n">plot_train_points</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">idx_plot</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># plot points of attempt idx_plot</span>
+<span class="c1">#################</span>
+<span class="n">names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;RF&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">Krange</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">27</span><span class="p">))</span>
+
+<span class="n">accuracies_rf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">accuracies_valid_rf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+<span class="n">datasets</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">make_moons</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">2000</span><span class="p">,</span> <span class="n">noise</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">figure</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># iterate over datasets</span>
+<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datasets</span><span class="p">):</span>
+    <span class="c1"># preprocess dataset, split into training and test part</span>
+    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ds</span>
+    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
+    <span class="p">)</span>
+    <span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.5</span>
+    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
+                         <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
+
+    <span class="c1"># just plot the dataset first</span>
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">RdBu</span>
+    <span class="n">cm_bright</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">([</span><span class="s2">&quot;#FF0000&quot;</span><span class="p">,</span> <span class="s2">&quot;#0000FF&quot;</span><span class="p">])</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Input data&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+    <span class="c1">#i += 1</span>
+    <span class="c1"># iterate over classifiers</span>
+    <span class="n">mid</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">Krange</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span>
+    <span class="k">for</span> <span class="n">depth</span> <span class="ow">in</span> <span class="n">Krange</span><span class="p">:</span>
+        <span class="c1">#######################</span>
+        <span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">criterion</span><span class="o">=</span><span class="s1">&#39;entropy&#39;</span><span class="p">,</span>
+            <span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+            <span class="n">max_depth</span><span class="o">=</span><span class="n">depth</span><span class="p">,</span>
+            <span class="n">min_impurity_decrease</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
+        <span class="c1">###################################</span>
+        
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">mid</span><span class="p">,</span> <span class="n">mid</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
+        <span class="n">score_test</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
+
+        <span class="c1"># Plot the decision boundary. For that, we will assign a color to each</span>
+        <span class="c1"># point in the mesh [x_min, x_max]x[y_min, y_max].</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">&quot;decision_function&quot;</span><span class="p">):</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])[:,</span> <span class="mi">1</span><span class="p">]</span>
+
+        <span class="c1"># Put the result into a color plot</span>
+        <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+        <span class="c1"># Plot the training points</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">idx_plot</span> <span class="ow">and</span> <span class="n">plot_train_points</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+                <span class="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">[:],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm_bright</span><span class="p">,</span>  <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span>
+            <span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
+        <span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="sa">f</span><span class="s1">&#39; Depth=</span><span class="si">{</span><span class="n">depth</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span>
+            <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.3</span><span class="p">,</span>
+            <span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">score</span><span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;0&quot;</span><span class="p">),</span>
+            <span class="n">size</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+            <span class="n">horizontalalignment</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># recording the accuracies</span>
+        <span class="n">accuracies_rf</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
+        <span class="n">accuracies_valid_rf</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">score_test</span>
+        <span class="c1"># print(accuracies,i,score)</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies</span><span class="p">,</span> <span class="s1">&#39;o--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies_valid</span><span class="p">,</span> <span class="s1">&#39;o--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies_rf</span><span class="p">,</span> <span class="s1">&#39;x--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Krange</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">accuracies_valid_rf</span><span class="p">,</span> <span class="s1">&#39;x--&#39;</span><span class="p">)</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Decision Trees Training/Valid Error&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Train (DT)&#39;</span><span class="p">,</span><span class="s1">&#39;Valid (DT)&#39;</span><span class="p">,</span><span class="s1">&#39;Train (RF)&#39;</span><span class="p">,</span><span class="s1">&#39;Valid (RF)&#39;</span><span class="p">])</span>
+<span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Depth&#39;</span><span class="p">)</span>
+</pre></div>
+
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Advantages-of-Random-Forest">Advantages of Random Forest<a class="anchor-link" href="#Advantages-of-Random-Forest">&#182;</a></h1><ul>
+<li>Bagging removes hyper-param related to Depth of the Tree.</li>
+<li>$K=\sqrt{D}$ so it is a fixed hyper-param.</li>
+<li>You have to tune $M$ but in general it needs to be large.</li>
+<li>DT are <strong>very interpretable</strong>; DT/RF could be used for <strong>feature selection</strong><ul>
+<li>To answer the question: <strong>which feature contribute more to the label?</strong></li>
+</ul>
+</li>
+<li>You can evaluate them <strong>without a validation split</strong> (Out of Bag Generalization - OOB)</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Feature-Importance-Evaluation-and-Interpretability">Feature Importance Evaluation and Interpretability<a class="anchor-link" href="#Feature-Importance-Evaluation-and-Interpretability">&#182;</a></h1><ul>
+<li>The <strong>relative rank (i.e. depth)</strong> of a feature used as a decision node in a tree can be used to assess the relative importance of that feature with respect to the predictability of the target variable. </li>
+<li><strong>Features used at the top of the tree contribute to the final prediction decision of a larger fraction</strong> of the input samples. </li>
+<li><p>The expected fraction of the samples they contribute to can thus be used as an estimate of the relative importance of the features.</p>
+</li>
+<li><p>In scikit-learn, the <strong>1) fraction of samples a feature contributes</strong> to is combined with the <strong>2) decrease in impurity</strong> from splitting them to create a normalized estimate of the predictive power of that feature.</p>
+</li>
+</ul>
+<p><strong>By averaging the estimates of predictive ability over several randomized trees one can reduce the variance of such an estimate and use it for feature selection. This is known as the mean decrease in impurity, or MDI.</strong></p>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<div class="highlight"><pre><span></span><span class="n">importances</span> <span class="o">=</span> <span class="n">forest</span><span class="o">.</span><span class="n">feature_importances_</span>
+<span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">([</span><span class="n">tree</span><span class="o">.</span><span class="n">feature_importances_</span> <span class="k">for</span> <span class="n">tree</span> <span class="ow">in</span> <span class="n">forest</span><span class="o">.</span><span class="n">estimators_</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+<p><div align='center'>
+<img width="40%" src="figs/mdi.png" /></p>
+<div/>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Out-of-Bag-(OOB)-Evaluation">Out-of-Bag (OOB) Evaluation<a class="anchor-link" href="#Out-of-Bag-(OOB)-Evaluation">&#182;</a></h1><ul>
+<li>When training a random forest, each tree is constructed using a <strong>bootstrap sample</strong> from the original dataset</li>
+<li>This means that each tree is built using a subset of original data, and some data points are left out</li>
+<li>The key idea is that the data points that were not used in the construction of a particular tree can be used as a validation set for that tree</li>
+<li>We can obtain an unbiased estimate of the tree's performance without the need for a separate validation set
+OOB Evaluation:</li>
+<li>Removes <strong>the need of a validation set</strong></li>
+<li>It gives you a <strong>quick way to estimate the number of ensembles</strong></li>
+<li>Quick good hint of how the forest <strong>generalization error</strong> could be (without doing K-fold cross-validation)</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[147]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+
+<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">123</span>
+
+<span class="c1"># Generate a binary classification dataset.</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+    <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">n_informative</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># NOTE: Setting the `warm_start` construction parameter to `True` disables</span>
+<span class="c1"># support for parallelized ensembles but is necessary for tracking the OOB</span>
+<span class="c1"># error trajectory during training.</span>
+<span class="n">ensemble_clfs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=&#39;sqrt&#39;&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="s2">&quot;sqrt&quot;</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=&#39;log2&#39;&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="s2">&quot;log2&quot;</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=None&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Map a classifier name to a list of (&lt;n_estimators&gt;, &lt;error rate&gt;) pairs.</span>
+<span class="n">error_rate</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">)</span>
+
+<span class="c1"># Range of `n_estimators` values to explore.</span>
+<span class="n">min_estimators</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">max_estimators</span> <span class="o">=</span> <span class="mi">150</span>
+
+<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Record the OOB error for each `n_estimators=i` setting.</span>
+        <span class="n">oob_error</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">clf</span><span class="o">.</span><span class="n">oob_score_</span>
+        <span class="n">error_rate</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">oob_error</span><span class="p">))</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+
+<span class="c1"># Generate the &quot;OOB error rate&quot; vs. &quot;n_estimators&quot; plot.</span>
+<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf_err</span> <span class="ow">in</span> <span class="n">error_rate</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">clf_err</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;OOB error rate&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
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"
+class="
+"
+>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[148]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
+<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
+<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+
+<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">123</span>
+
+<span class="c1"># Generate a binary classification dataset.</span>
+<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
+    <span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">n_features</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+    <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">n_informative</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># NOTE: Setting the `warm_start` construction parameter to `True` disables</span>
+<span class="c1"># support for parallelized ensembles but is necessary for tracking the OOB</span>
+<span class="c1"># error trajectory during training.</span>
+<span class="n">ensemble_clfs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=&#39;sqrt&#39;&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="s2">&quot;sqrt&quot;</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=&#39;log2&#39;&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="s2">&quot;log2&quot;</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+    <span class="p">(</span>
+        <span class="s2">&quot;RandomForestClassifier, max_features=None&quot;</span><span class="p">,</span>
+        <span class="n">RandomForestClassifier</span><span class="p">(</span>
+            <span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Map a classifier name to a list of (&lt;n_estimators&gt;, &lt;error rate&gt;) pairs.</span>
+<span class="n">error_rate</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">)</span>
+
+<span class="c1"># Range of `n_estimators` values to explore.</span>
+<span class="n">min_estimators</span> <span class="o">=</span> <span class="mi">15</span>
+<span class="n">max_estimators</span> <span class="o">=</span> <span class="mi">150</span>
+
+<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">:</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+        <span class="c1"># Record the OOB error for each `n_estimators=i` setting.</span>
+        <span class="n">oob_error</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">clf</span><span class="o">.</span><span class="n">oob_score_</span>
+        <span class="n">error_rate</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">oob_error</span><span class="p">))</span>
+        
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+
+<span class="c1"># Generate the &quot;OOB error rate&quot; vs. &quot;n_estimators&quot; plot.</span>
+<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf_err</span> <span class="ow">in</span> <span class="n">error_rate</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">clf_err</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;OOB error rate&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+
+<div class="jp-RenderedImage jp-OutputArea-output ">
+<img 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+<h1 id="Application:-Human-Pose-Prediction-in-Xbox-Kinect">Application: Human Pose Prediction in Xbox Kinect<a class="anchor-link" href="#Application:-Human-Pose-Prediction-in-Xbox-Kinect">&#182;</a></h1><p><center>
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+<p><center>
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+<h1 id="Decision-trees-are-in-XBox:-Classifying-body-parts">Decision trees are in XBox: Classifying body parts<a class="anchor-link" href="#Decision-trees-are-in-XBox:-Classifying-body-parts">&#182;</a></h1><h3 id="Trained-on-Millions-of-synthetic-images">Trained on Millions of synthetic images<a class="anchor-link" href="#Trained-on-Millions-of-synthetic-images">&#182;</a></h3><p><center>
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+<p><a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/BodyPartRecognition.pdf">Link to the Microsoft paper</a></p>
+<p><em>To keep the training times down we employ a distributed implementation. Training 3 trees to depth 20 from 1 million images takes about a day on a 1000 core cluster.</em></p>
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+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Microsoft-Kinect-Research-Presentation">Microsoft Kinect Research Presentation<a class="anchor-link" href="#Microsoft-Kinect-Research-Presentation">&#182;</a></h1><p><a href="https://www.youtube.com/watch?v=QPYf6pXe_4Q&amp;t=24m39s">Link to video at the right time</a></p>
+
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[149]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+     <div class="CodeMirror cm-s-jupyter">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Audio</span><span class="p">,</span><span class="n">Image</span><span class="p">,</span> <span class="n">YouTubeVideo</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
+<span class="nb">id</span><span class="o">=</span><span class="s1">&#39;QPYf6pXe_4Q&#39;</span>
+<span class="n">start</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">minutes</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">seconds</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">total_seconds</span><span class="p">())</span>
+<span class="n">YouTubeVideo</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="nb">id</span><span class="p">,</span><span class="n">width</span><span class="o">=</span><span class="mi">1280</span><span class="p">,</span><span class="n">height</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="n">start</span><span class="p">)</span>
+</pre></div>
+
+     </div>
+</div>
+</div>
+</div>
+
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+
+
+<div class="jp-OutputArea jp-Cell-outputArea">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[149]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+
+        <iframe
+            width="1280"
+            height="1024"
+            src="https://www.youtube.com/embed/QPYf6pXe_4Q?start=1440"
+            frameborder="0"
+            allowfullscreen
+            
+        ></iframe>
+        
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Advantages-of-Decision-Trees">Advantages of Decision Trees<a class="anchor-link" href="#Advantages-of-Decision-Trees">&#182;</a></h1><ul>
+<li>The decision is <strong>highly interpretable</strong> (you can explain the decision to a doctor).</li>
+<li>Learn which <strong>feature correlates better w/ $y$</strong> </li>
+<li><strong>Do not need pre-processing</strong> of the data</li>
+<li><strong>Complex</strong> decision boundary that adapt to data density</li>
+<li><strong>Axis-aligned yet complex/segmented</strong> decision boundaries</li>
+<li>Inference is <strong>fast</strong>: test attribute and split</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="Disadvantages-of-Decision-Trees">Disadvantages of Decision Trees<a class="anchor-link" href="#Disadvantages-of-Decision-Trees">&#182;</a></h1><ul>
+<li>Suffer for <strong>Variance problem</strong> (overfit)</li>
+<li>Sensitive to the training set <em>(variance)</em></li>
+<li><strong>Greedy approach</strong> does not yield the global optimal tree</li>
+</ul>
+
+</div>
+</div>
+</div>
+</div>
+</body>
+
+
+
+
+
+
+
+</html>
diff --git a/AA2324/course/09_decision_trees/09_decision_trees.ipynb b/AA2324/course/09_decision_trees/09_decision_trees.ipynb
new file mode 100644
index 0000000..856176d
--- /dev/null
+++ b/AA2324/course/09_decision_trees/09_decision_trees.ipynb
@@ -0,0 +1,4249 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Artificial Intelligence and Machine Learning\n",
+    "\n",
+    "## Unit II\n",
+    "\n",
+    "\n",
+    "## Decision Trees and Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       ".output_png {\n",
+       "    display: table-cell;\n",
+       "    text-align: center;\n",
+       "    vertical-align: middle;\n",
+       "}\n",
+       "img[alt=regression] { width: \"50%\"; }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import HTML\n",
+    "\n",
+    "HTML(\"\"\"\n",
+    "<style>\n",
+    ".output_png {\n",
+    "    display: table-cell;\n",
+    "    text-align: center;\n",
+    "    vertical-align: middle;\n",
+    "}\n",
+    "img[alt=regression] { width: \"50%\"; }\n",
+    "</style>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": false,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## My own latex definitions\n",
+    "$\\require{color}$\n",
+    "\n",
+    "\n",
+    "$$\\def\\mbf#1{\\mathbf{#1}}$$\n",
+    "$$\\def\\bmf#1{\\boldsymbol{#1}}$$\n",
+    "$$\\def\\bx{\\mbf{x}}$$\n",
+    "$$\\def\\bv{\\mbf{v}}$$\n",
+    "$$\\def\\bz{\\mbf{z}}$$\n",
+    "$$\\def\\bmu{\\bmf{\\mu}}$$\n",
+    "$$\\def\\bsigma{\\bmf{\\Sigma}}$$\n",
+    "$$\\def\\Rd#1{\\in \\mathbb{R}^{#1}}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "# plt.style.use('seaborn-whitegrid')\n",
+    "\n",
+    "font = {'family' : 'Times',\n",
+    "        'weight' : 'bold',\n",
+    "        'size'   : 12}\n",
+    "\n",
+    "matplotlib.rc('font', **font)\n",
+    "\n",
+    "\n",
+    "# Aux functions\n",
+    "\n",
+    "def plot_grid(Xs, Ys, axs=None):\n",
+    "    ''' Aux function to plot a grid'''\n",
+    "    t = np.arange(Xs.size) # define progression of int for indexing colormap\n",
+    "    if axs:\n",
+    "        axs.plot(0, 0, marker='*', color='r', linestyle='none') #plot origin\n",
+    "        axs.scatter(Xs,Ys, c=t, cmap='jet', marker='.') # scatter x vs y\n",
+    "        axs.axis('scaled') # axis scaled\n",
+    "    else:\n",
+    "        plt.plot(0, 0, marker='*', color='r', linestyle='none') #plot origin\n",
+    "        plt.scatter(Xs,Ys, c=t, cmap='jet', marker='.') # scatter x vs y\n",
+    "        plt.axis('scaled') # axis scaled\n",
+    "        \n",
+    "def linear_map(A, Xs, Ys):\n",
+    "    '''Map src points with A'''\n",
+    "    # [NxN,NxN] -> NxNx2 # add 3-rd axis, like adding another layer\n",
+    "    src = np.stack((Xs,Ys), axis=Xs.ndim)\n",
+    "    # flatten first two dimension\n",
+    "    # (NN)x2\n",
+    "    src_r = src.reshape(-1,src.shape[-1]) #ask reshape to keep last dimension and adjust the rest\n",
+    "    # 2x2 @ 2x(NN)\n",
+    "    dst = A @ src_r.T # 2xNN\n",
+    "    #(NN)x2 and then reshape as NxNx2\n",
+    "    dst = (dst.T).reshape(src.shape)\n",
+    "    # Access X and Y\n",
+    "    return dst[...,0], dst[...,1]\n",
+    "\n",
+    "\n",
+    "def plot_points(ax, Xs, Ys, col='red', unit=None, linestyle='solid'):\n",
+    "    '''Plots points'''\n",
+    "    ax.set_aspect('equal')\n",
+    "    ax.grid(True, which='both')\n",
+    "    ax.axhline(y=0, color='gray', linestyle=\"--\")\n",
+    "    ax.axvline(x=0, color='gray',  linestyle=\"--\")\n",
+    "    ax.plot(Xs, Ys, color=col)\n",
+    "    if unit is None:\n",
+    "        plotVectors(ax, [[0,1],[1,0]], ['gray']*2, alpha=1, linestyle=linestyle)\n",
+    "    else:\n",
+    "        plotVectors(ax, unit, [col]*2, alpha=1, linestyle=linestyle)\n",
+    "\n",
+    "def plotVectors(ax, vecs, cols, alpha=1, linestyle='solid'):\n",
+    "    '''Plot set of vectors.'''\n",
+    "    for i in range(len(vecs)):\n",
+    "        x = np.concatenate([[0,0], vecs[i]])\n",
+    "        ax.quiver([x[0]],\n",
+    "                   [x[1]],\n",
+    "                   [x[2]],\n",
+    "                   [x[3]],\n",
+    "                   angles='xy', scale_units='xy', scale=1, color=cols[i],\n",
+    "                   alpha=alpha, linestyle=linestyle, linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Recap previous lecture\n",
+    "- Supervised Learning with $k$-NN\n",
+    "- k-NN strengths and limitations\n",
+    "- A bit of theory of learning Empirical Risk Minimization\n",
+    "- Usage of the validation set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Today's lecture\n",
+    "## Another \"non-parametric\" model\n",
+    "### Decision Trees\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# This lecture material is taken from\n",
+    "- Information Theory part  - (Entropy etc) is taken from __Chapter 1 - Bishop__.\n",
+    "- Decision Trees are very briefly covered in __Bishop at page 663__.\n",
+    "- [Cimi Book - Chapter 01](http://ciml.info/dl/v0_99/ciml-v0_99-ch01.pdf)\n",
+    "- [CSC411: Introduction to Machine Learning](https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/06_trees_handout.pdf)\n",
+    "- [CSC411: Introduction to Machine Learning - Tutorial](https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial3.pdf)\n",
+    "- [Cornell ML course](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote17.html)\n",
+    "- [Cornell ML course Bagging](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote18.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# From $k$-NN to Decision Trees\n",
+    "\n",
+    "#### Non-parametric models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Recall $k$-NN \n",
+    "\n",
+    "- Denote the set of the $k$ nearest neighbors of $\\mathbf{x}$ as $S_{\\mathbf{x}}$. Formally $S_{\\mathrm{x}}$ is defined as $S_{\\mathrm{x}} \\subseteq D$ s.t. $\\left|S_{\\mathrm{x}}\\right|=k$ and $\\forall\\left(\\mathbf{x}^{\\prime}, y^{\\prime}\\right) \\in D \\backslash S_{\\mathrm{x} \\prime}$\n",
+    "$$\n",
+    "\\operatorname{dist}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime}\\right) \\geq \\max _{\\left(\\mathbf{x}^{\\prime \\prime}, y^{\\prime \\prime}\\right) \\in S_{\\mathbf{x}}} \\operatorname{dist}\\left(\\mathbf{x}, \\mathbf{x}^{\\prime \\prime}\\right)\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "- (i.e. every point in $D$ but not in $S_{\\mathrm{x}}$ is at least as far away from $\\mathbf{x}$ as the furthest point in $S_{\\mathbf{x}}$ ). \n",
+    "\n",
+    "- We can then define the classifier $h()$ as a function returning the most common label in $S_{\\mathrm{x}}$ :\n",
+    "$$\n",
+    "h(\\mathbf{x})=\\operatorname{mode}\\left(\\left\\{y^{\\prime \\prime}:\\left(\\mathbf{x}^{\\prime \\prime}, y^{\\prime \\prime}\\right) \\in S_{\\mathbf{x}}\\right\\}\\right)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Ambiguous cases based on the distance and neighbours\n",
+    "\n",
+    "\n",
+    "<center>\n",
+    "<img width=\"40%\" src=\"figs/nn.png\" />\n",
+    "<center/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Over or Under Fitting\n",
+    "<center>\n",
+    "<img width=\"60%\" src=\"figs/over_under_fit.png\" />\n",
+    "<center/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# $k$-NN has irregular and non-linear decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What is the the training error of $k$-NN? 🤔"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- In $k$-NN there there is no explicit cost/loss, how can we measure the training error? \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- What we **can do is to <ins>classify the train with respect to the train</ins>, given a fixed inductive bias (configuration of $k$-NN used).**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- So classify the training points as if they were validation points, given a fixed $k$-NN configuration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What happens when you classify the training set with $k=1$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## Ideas?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## let's see with an example..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "h = 0.02  # step size in the mesh\n",
+    "K = 1  # Neighbors\n",
+    "\n",
+    "names = [\n",
+    "    \"Nearest Neighbors\",\n",
+    "]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(K),\n",
+    "]\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1\n",
+    ")\n",
+    "rng = np.random.RandomState(2)\n",
+    "X += 2 * rng.uniform(size=X.shape)\n",
+    "linearly_separable = (X, y)\n",
+    "\n",
+    "datasets = [\n",
+    "    make_moons(noise=0.3, random_state=0),\n",
+    "]\n",
+    "\n",
+    "figure = plt.figure(figsize=(12,8))\n",
+    "i = 1\n",
+    "# iterate over datasets\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # preprocess dataset, split into training and test part\n",
+    "    X, y = ds\n",
+    "    X = StandardScaler().fit_transform(X)\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y, test_size=0.4, random_state=42\n",
+    "    )\n",
+    "\n",
+    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
+    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "    # Plot the training points\n",
+    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n",
+    "               cmap=cm_bright, edgecolors=\"k\")\n",
+    "    ax.set_xlim(xx.min(), xx.max())\n",
+    "    ax.set_ylim(yy.min(), yy.max())\n",
+    "    ax.set_xticks(())\n",
+    "    ax.set_yticks(())\n",
+    "    i += 1\n",
+    "    # iterate over classifiers\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        score = clf.score(X_train, y_train)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(\n",
+    "            X_train[:, 0], X_train[:,1],\n",
+    "            c=y_train, cmap=cm_bright, edgecolors=\"k\"\n",
+    "        )\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name+f' K={K}')\n",
+    "        ax.text(\n",
+    "            xx.max() - 0.3,\n",
+    "            yy.min() + 0.3,\n",
+    "            (\"%.2f\" % score).lstrip(\"0\"),\n",
+    "            size=15,\n",
+    "            horizontalalignment=\"right\",\n",
+    "        )\n",
+    "        i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "hide_input": false,
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "\n",
+    "h = 0.02  # step size in the mesh\n",
+    "K = 1  # Neighbors\n",
+    "\n",
+    "names = [\n",
+    "    \"Nearest Neighbors\",\n",
+    "]\n",
+    "\n",
+    "classifiers = [\n",
+    "    KNeighborsClassifier(K),\n",
+    "]\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1\n",
+    ")\n",
+    "rng = np.random.RandomState(2)\n",
+    "X += 2 * rng.uniform(size=X.shape)\n",
+    "linearly_separable = (X, y)\n",
+    "\n",
+    "datasets = [\n",
+    "    make_moons(noise=0.3, random_state=0),\n",
+    "]\n",
+    "\n",
+    "figure = plt.figure()\n",
+    "i = 1\n",
+    "# iterate over datasets\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # preprocess dataset, split into training and test part\n",
+    "    X, y = ds\n",
+    "    X = StandardScaler().fit_transform(X)\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y, test_size=0.4, random_state=42\n",
+    "    )\n",
+    "\n",
+    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
+    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "    # Plot the training points\n",
+    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n",
+    "               cmap=cm_bright, edgecolors=\"k\")\n",
+    "    ax.set_xlim(xx.min(), xx.max())\n",
+    "    ax.set_ylim(yy.min(), yy.max())\n",
+    "    ax.set_xticks(())\n",
+    "    ax.set_yticks(())\n",
+    "    i += 1\n",
+    "    # iterate over classifiers\n",
+    "    for name, clf in zip(names, classifiers):\n",
+    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        score = clf.score(X_train, y_train)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        ax.scatter(\n",
+    "            X_train[:, 0], X_train[:,1],\n",
+    "            c=y_train, cmap=cm_bright, edgecolors=\"k\"\n",
+    "        )\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(name+f' K={K}')\n",
+    "        ax.text(\n",
+    "            xx.max() - 0.3,\n",
+    "            yy.min() + 0.3,\n",
+    "            (\"%.2f\" % score).lstrip(\"0\"),\n",
+    "            size=15,\n",
+    "            horizontalalignment=\"right\",\n",
+    "        )\n",
+    "        i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# When $k=1$ we perfectly classify the training set! 100% accuracy!\n",
+    "\n",
+    "It is easy to show that this follow by definition **(each point is neighbour to itself).**\n",
+    "\n",
+    "but will this hold for $K \\gt 1$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# We record the training accuracy in function of increasing $k$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 14 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "###################\n",
+    "h = 0.02  # step size in the mesh\n",
+    "plot_train_points = False\n",
+    "idx_plot = 1  # plot points of attempt idx_plot\n",
+    "#################\n",
+    "\n",
+    "names = [\n",
+    "    \"Nearest Neighbors\",\n",
+    "]\n",
+    "\n",
+    "Krange = list(range(1, 27, 2)) + [48]\n",
+    "\n",
+    "accuracies = np.zeros_like(Krange, dtype=np.float32)\n",
+    "accuracies_valid = np.zeros_like(Krange, dtype=np.float32)\n",
+    "\n",
+    "datasets = [\n",
+    "    make_moons(n_samples=2000, noise=0.3, random_state=0),\n",
+    "]\n",
+    "\n",
+    "figure = plt.figure(figsize=(20, 20))\n",
+    "i = 1\n",
+    "# iterate over datasets\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # preprocess dataset, split into training and test part\n",
+    "    X, y = ds\n",
+    "    ######### Watch out here ################\n",
+    "    X = StandardScaler().fit_transform(X)  # in theory this is an error!\n",
+    "    # you want to estimate the scaling of data on the training set and then apply to valid and test\n",
+    "    ##########################################\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y, test_size=0.4, random_state=42\n",
+    "    )\n",
+    "    # validation from training (overwrite X_train)\n",
+    "    X_train_entire, y_train_entire = np.copy(X_train), np.copy(y_train)\n",
+    "    X_train, X_valid, y_train, y_valid = train_test_split(\n",
+    "        X_train, y_train, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
+    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    ax = plt.subplot(len(Krange) + 1, 1, i)\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "    # Plot the training points\n",
+    "    # ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n",
+    "    #           cmap=cm_bright, edgecolors=\"k\")\n",
+    "    ax.set_xlim(xx.min(), xx.max())\n",
+    "    ax.set_ylim(yy.min(), yy.max())\n",
+    "    ax.set_xticks(())\n",
+    "    ax.set_yticks(())\n",
+    "    #i += 1\n",
+    "    # iterate over classifiers\n",
+    "    mid = len(Krange)//2\n",
+    "    for K in Krange:\n",
+    "        clf = KNeighborsClassifier(K)\n",
+    "        ax = plt.subplot(mid, mid, i)\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        score = clf.score(X_train, y_train)\n",
+    "        score_test = clf.score(X_valid, y_valid)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        if i == idx_plot and plot_train_points:\n",
+    "            ax.scatter(\n",
+    "                X_train[:, 0], X_train[:, 1],\n",
+    "                c=y_train, cmap=cm_bright, edgecolors=\"k\"\n",
+    "            )\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(names[0]+f' K={K}')\n",
+    "        ax.text(\n",
+    "            xx.max() - 0.3,\n",
+    "            yy.min() + 0.3,\n",
+    "            (\"%.2f\" % score).lstrip(\"0\"),\n",
+    "            size=15,\n",
+    "            horizontalalignment=\"right\",\n",
+    "        )\n",
+    "        # recording the accuracies\n",
+    "        accuracies[i-1] = score\n",
+    "        accuracies_valid[i-1] = score_test\n",
+    "        # print(accuracies,i,score)\n",
+    "        i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Let's see the training error in function of $k$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "_ = plt.plot(Krange, 1 - accuracies, 'o--')\n",
+    "_ = plt.title('Training Error')\n",
+    "_ = plt.xlabel('K')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Let's see how it generalizes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "- We are going to select the best $k^{\\prime}$ from validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- Then re-train with the best $k^{\\prime}$ from validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- Now everything is fixed and locked and we can test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "########## Plotting the trends ###########\n",
+    "# Plot training and validation accuracy in function of $k$\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "_ = plt.plot(Krange, 1-accuracies)\n",
+    "_ = plt.plot(Krange, 1-accuracies_valid)\n",
+    "########### Selection on Validation ###########\n",
+    "# Select best K on validation and then testing with best K\n",
+    "accuracies_valid, accuracies\n",
+    "idx_best = np.argmax(accuracies_valid)\n",
+    "K_best_valid = Krange[idx_best]\n",
+    "############## Testing part ####################\n",
+    "# retrain with k_best and test\n",
+    "clf = KNeighborsClassifier(K_best_valid)\n",
+    "clf.fit(X_train_entire, y_train_entire)  # train on training with Kbest\n",
+    "score_test = clf.score(X_test, y_test)  # only now testing\n",
+    "_ = plt.plot(K_best_valid, 1-score_test, 'ro')\n",
+    "_ = plt.legend(['train', 'validation', 'test'])\n",
+    "_ = plt.xlabel('K')\n",
+    "_ = plt.ylabel('Error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Remember to estimate scaling on the training set only!\n",
+    "\n",
+    "- In theory this is part below is an error.\n",
+    "- I took the code from sklearn documentation but in practice you have to estimate the scale parameters **ONLY** in the training set.\n",
+    "- Then applying it directly to the test set. \n",
+    "- If you work in inductive settings, you cannot do it jointly like the code above.\n",
+    "\n",
+    "```python\n",
+    "    ######### Watch out here ################\n",
+    "    X = StandardScaler().fit_transform(X) # in theory this is an error!\n",
+    "    # you want to estimate the scaling of data on the training set and \n",
+    "    # then apply to valid and test\n",
+    "    #########################################\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Why showing training errors vs number of $k$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## $k$-NN, lessons learned\n",
+    "- We also showed how $k$-NN overfits with $k=1$ and studied smoothing regularization effect.\n",
+    "- We also found a way to **measure the cost function** for $k$-NN, though there is no explicit learning.\n",
+    "\n",
+    "## Towards Decision Trees\n",
+    "- The decision that $k$-NN takes when $k \\gt 1$ is related with the concept of **impurity of a cluster** of datapoints\n",
+    "- This notion of **impurity** is key for understanding **Decision Trees**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Impurity\n",
+    "\n",
+    "- We will play a game and we will try to classify each of the upcoming data points with $k$-NN!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Game\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_00.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Ready?\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_01.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Go!\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_02.png\" width=\"50%\"/>\n",
+    "</center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_03.png\" width=\"50%\"/>\n",
+    "</center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img src=\"figs/impurity_04.png\" width=\"50%\"/>\n",
+    "</center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img src=\"figs/impurity_05.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img src=\"figs/impurity_06.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# 3 blues vs 3 reds $\\rightarrow$ Tie! $\\rightarrow$ high uncertainty!\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_07.png\" width=\"50%\"/>\n",
+    "</center>\n",
+    "\n",
+    "## How do we measure impurity?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Impurity Functions\n",
+    "\n",
+    "Data: $\\mathcal{D}=\\left \\{ \\left ( \\mathbf{x}_1,y_i \\right ),\\dots,\\left ( \\mathbf{x}_n,y_i \\right ) \\right \\}, y_i\\in\\left \\{ 1,\\dots,K \\right \\}$, where $K$ is the number of classes\n",
+    "\n",
+    "We will cover 3 impurity functions:\n",
+    "\n",
+    "- Misclassification\n",
+    "- Gini Index\n",
+    "- Entropy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Misclassification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Misclassification\n",
+    "\n",
+    "Let $S_k\\subseteq S$ where $S_k=\\left \\{ \\left ( \\mathbf{x},y \\right )\\in S:y=k \\right \\}$ (all inputs restricted to have labels $k$)\n",
+    "<br>\n",
+    "$S=S_1\\cup \\dots \\cup S_K$\n",
+    "<br><br>\n",
+    "<u>Define</u>: \n",
+    "\n",
+    "$$p_k=\\frac{\\left | S_k \\right |}{\\left | S \\right |}\\leftarrow \\textrm{fraction of inputs in } S \\textrm{ with label } k$$\n",
+    "\n",
+    "$p_k$ is the probability of picking a point with label $k$, then the **Misclassification** is:\n",
+    "\n",
+    "$$ H(S) = 1 - \\max_k(p_{k}) $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What is the misclassification of this set?\n",
+    "<br>\n",
+    "- Just consider the colored points.\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_06.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img src=\"figs/impurity_06.png\" width=\"50%\"/>\n",
+    "</center>\n",
+    "\n",
+    "- We have 4 points in total.\n",
+    "- 3 reds and 1 blue\n",
+    "- $p_{\\text{red}}=\\frac{3}{4}$ vs $p_{\\text{blue}} = \\frac{1}{4}$\n",
+    "\n",
+    "then Misclassification $ H(S) = 1 - \\max(p_{k}) = 1-\\max\\{\\frac{3}{4},\\frac{1}{4}\\} = \\frac{1}{4} = 25\\%$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What about this\n",
+    "We have 3 classes now\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_09.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img src=\"figs/impurity_09.png\" width=\"50%\"/>\n",
+    "</center>\n",
+    "\n",
+    "- We still have 4 points in total.\n",
+    "- 2 reds and 1 blue and 1 green\n",
+    "- $p_{\\text{red}}=\\frac{2}{4}$ vs $p_{\\text{blue}} = \\frac{1}{4}$ vs $p_{\\text{green}} = \\frac{1}{4}$\n",
+    "\n",
+    "then Misclassification $ H(S) = 1 - \\max(p_{k}) = 1-\\max\\{\\frac{2}{4},\\frac{1}{4},\\frac{1}{4}\\}=50\\%$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_style": "split",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Which one is better?\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_08.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_style": "split",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "# Which one is better?\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/impurity_09.png\" width=\"50%\"/>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# The lower the misclassification, the better it is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# This works for all impurity metrics. \n",
+    "## The lower they are, the better it is!\n",
+    "### It is similar to a cost/loss function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Plot Miclassification function for binary case\n",
+    "\n",
+    "```python\n",
+    "pk = np.arange(0, 1.1, 0.1)\n",
+    "missclassification_binary = 1 - np.maximum(pk, 1 - pk) #runs over K\n",
+    "_ = plt.plot(pk, missclassification_binary)\n",
+    "```\n",
+    "\n",
+    "{{import numpy as np;pk = np.arange(0, 1.1, 0.1); missclassification_binary = 1-np.maximum(pk, 1-pk); _ = plt.plot(pk, missclassification_binary);_ = plt.xlabel('p');_ = plt.ylabel('Miclassification')}}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Gini impurity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Gini impurity\n",
+    "\n",
+    "Let $S_k\\subseteq S$ where $S_k=\\left \\{ \\left ( \\mathbf{x},y \\right )\\in S:y=k \\right \\}$ (all inputs restricted to have labels $k$)\n",
+    "<br>\n",
+    "$S=S_1\\cup \\dots \\cup S_K$\n",
+    "<br><br>\n",
+    "<u>Define</u>: \n",
+    "\n",
+    "$$p_k=\\frac{\\left | S_k \\right |}{\\left | S \\right |}\\leftarrow \\textrm{fraction of inputs in } S \\textrm{ with label } k$$\n",
+    "\n",
+    "$p_k$ is the probability of picking a point with label $k$, then the **Gini Impurity** is:\n",
+    "\n",
+    "$$ G(S)=\\sum_{k=1}^{K}p_k(1-p_k) $$\n",
+    "\n",
+    "In words, we aggregate over classes and for each class $k$, we multiply:\n",
+    "- probability of picking $k$ aka $p_k$\n",
+    "- by its inverse aka $(1-p_k)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Gini impurity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "pk = np.arange(1e-9, 1.0, 1e-2)\n",
+    "missclassification_binary = 1-np.maximum(pk, 1-pk)\n",
+    "kwargs = {'linewidth': 3}\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "_ = plt.plot(pk, 2*pk*(1-pk), **kwargs)\n",
+    "_ = plt.xlabel('p')\n",
+    "_ = plt.ylabel('Miclassification')\n",
+    "_ = plt.legend(['Gini'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Entropy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Entropy: Intuition\n",
+    "\n",
+    "In which of the two series there is more __confusion__?\n",
+    "\n",
+    "#### Sequence A\n",
+    "\n",
+    "|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+    "\n",
+    "#### Sequence B\n",
+    "\n",
+    "|0|1|0|1|0|0|1|0|0|1|1|0|0|0|0|1|0|1|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Sequence A\n",
+    "\n",
+    "|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+    "\n",
+    "{{ import matplotlib; import matplotlib.pyplot as plt;seqA = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]; _=plt.bar(*np.unique(seqA, return_counts=True));}}\n",
+    "\n",
+    "```python\n",
+    "plt.hist(seqA, bins=2, density=True) # OR\n",
+    "plt.bar(*np.unique(seqB, return_counts=True))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "#### Sequence B\n",
+    "\n",
+    "|0|1|0|1|0|0|1|0|0|1|1|0|0|0|0|1|0|1|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+    "\n",
+    "\n",
+    "{{ seqB = [0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1]; _=plt.bar(*np.unique(seqB, return_counts=True));}}\n",
+    "\n",
+    "```python\n",
+    "plt.hist(seqA, bins=2, density=True) # OR\n",
+    "plt.bar(*np.unique(seqB, return_counts=True))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Entropy of A is lower than Entropy of B\n",
+    "**A**\n",
+    "<div align='center'>\n",
+    "<img width=\"30%\" src=\"figs/ent_a.png\" />\n",
+    "<div/>\n",
+    "    \n",
+    "**B** \n",
+    "    \n",
+    "<div align='center'>\n",
+    "<img width=\"30%\" src=\"figs/ent_b.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Definition of Entropy \n",
+    "\n",
+    "If $X$ is a discrete random variable that can take values $\\{x_1,\\ldots,x_n\\}$ with probability $\\{p(x_1),\\ldots,p(x_n)\\}$ then:\n",
+    "\n",
+    "$$\n",
+    "H(X) \\doteq -\\sum_{x \\in X} p(x)\\log_2 p(x)\n",
+    "$$\n",
+    "\n",
+    "- How surprised are we by a new value in the sequence?\n",
+    "- How much information does it convey?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Entropy\n",
+    "\n",
+    "> Let's say that you have a **unfair dice** that always return **`6`**. How would you be exicted to play a game with your friend using this dice?\n",
+    "\n",
+    "<center>\n",
+    "<img src=\"figs/dice.png\" width=\"30%\"/>\n",
+    "</center>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# No excitement! I always know the answer beforehand!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Where the $\\log()$ is coming from?\n",
+    "\n",
+    "- We consider a discrete random variable $X$ and we ask how much information is received when we observe a specific value for this variable.\n",
+    "- Entropy tells the **\"degree of surprise\"**, so it must be related to probability of event $X=x$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- $p(x) = 1$ we want $h(x) = 0 \\rightarrow$    **[Boring] NO surprise, a very common event happens**\n",
+    "- $p(x) \\rightarrow 0$ then $h(x)=\\infty \\rightarrow$  **[Excitement] Lots of surprise, a rare event happens**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "- We also want that if two events are **independent** $p(x,y) = p(x)p(y)$\n",
+    "- we want $h(x,y) = h(x) + h(y)$ **we sum the surprises** \n",
+    "\n",
+    "$$ \\log\\big(p(x)p(y)\\big) = \\underbrace{\\log\\big(p(x)\\big)}_{h(x)}+\\underbrace{\\log\\big(p(y)\\big)}_{h(y)} $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### A good choice is $-\\log()$: it satisfies all the above properties\n",
+    "\n",
+    "- You can think of $-\\log(\\mathbf{x})$ or else $\\log(1/\\mathbf{x})$\n",
+    "- We want the \"inverse\" of the probability and then $\\log()$\n",
+    "```python\n",
+    "pk = np.arange(0.1, 1.1, 0.1)\n",
+    "_ = plt.plot(pk, -np.log(pk))\n",
+    "```\n",
+    "\n",
+    "{{import numpy as np;pk = np.arange(0.1, 1.1, 0.1); _ = plt.plot(pk, -np.log(pk));_ = plt.xlabel('px');_ = plt.ylabel('-log(px)')}}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Back to Entropy\n",
+    "\n",
+    "Now suppose that a sender wishes to transmit the value of a random variable $X$ to a receiver. \n",
+    "\n",
+    "The average amount of information that they transmit in the process is obtained by taking the **expectation with respect to the distribution p(x) and is given by:**\n",
+    "\n",
+    "$$\n",
+    "H(X) \\doteq E[h(x)] = \\sum_{x \\in X} p(x)~h(x)=  -\\sum_{x \\in X} p(x)\\log_2 p(x)\n",
+    "$$\n",
+    "\n",
+    "> We can take advantage of the nonuniform distribution by using shorter codes for the more probable events, at the expense of longer codes for the less probable events, in the hope of getting a shorter average code length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Measuring the \"divergence\" between two [discrete] distributions\n",
+    "\n",
+    "\n",
+    "**Objective**: Estimate a sort of \"distance\" (or better **divergence**) between two distributions **$p(x)$ vs $q(x)$**.\n",
+    "\n",
+    "- We have an **unknown distribution** $p(x)$, \n",
+    "- We want to model it using an **approximating distribution** $q(x)$. \n",
+    "\n",
+    "If we use $q(x)$ to construct a coding scheme for the purpose of transmitting values of $x$ to a receiver instead of $p(x)$, then the **average additional amount of information** required to specify the value of $x$ as a result of using $q(x)$ instead of the true distribution $p(x)$ is given by:\n",
+    "\n",
+    "$$ \n",
+    "\\underbrace{H(P,Q)}_{\\text{q not p, so extra}} - \\underbrace{~~H(P)~~}_{\\text{best we can do}}\n",
+    "$$\n",
+    "\n",
+    "**Idea**: if you use **q** instead of **p**, but the underlying process is governed by **p**, then you need to pay an extra price in transmission a bit more of information. \"The bit more\" is the equation above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Measuring the \"divergence\" between two distributions\n",
+    "\n",
+    "$$ \n",
+    "H(P,Q) - H(P) = \\underbrace{-\\sum_{x \\in X} p(x)\\log q(x)}_{\\text{cross-entropy}} - \\underbrace{\\big(-\\sum_{x \\in X} p(x)\\log p(x)\\big)}_{\\text{entropy}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Measuring the \"divergence\" between two distributions\n",
+    "\n",
+    "$$ \n",
+    "H(P,Q) - H(P) = \\underbrace{-\\sum_{x \\in X} p(x)\\log q(x)}_{\\text{cross-entropy}} - \\underbrace{\\big(-\\sum_{x \\in X} p(x)\\log p(x)\\big)}_{\\text{entropy}}=\n",
+    "$$\n",
+    "\n",
+    "$$ \n",
+    "= -\\sum_{x \\in X} p(x)\\log \\Big( \\frac{q(x)}{p(x)} \\Big) =  \\sum_{x \\in X} p(x)\\log \\Big( \\frac{p(x)}{q(x)} \\Big) \n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Relative entropy or Kullback-Leibler (KL) divergence\n",
+    "\n",
+    "$$ \n",
+    "KL(P||Q)= \\sum_{x \\in X} p(x)\\log \\Big( \\frac{p(x)}{q(x)} \\Big)\n",
+    "$$\n",
+    "\n",
+    "**Note that:**\n",
+    "\n",
+    "- $KL(P||Q) \\neq KL(Q||P)$ so it is **NOT a distance metric**, but thankfully the following holds:\n",
+    "- $KL(P||Q) = 0 \\longleftrightarrow p = q$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Learning a decision tree is about <ins>reducing impurity</ins>\n",
+    "\n",
+    "- So we want to reduce 1) Misclassification or 2)Gini Impurity or 3) **? What else ?**\n",
+    "\n",
+    "- **Know your enemy**: the uniform distributions $\\mbf{x} \\sim\\mathcal{U}$\n",
+    "- If we have $K$ classes then $\\{q_1,\\ldots, q_k\\} = \\{1/K,\\ldots, 1/K\\}$\n",
+    "\n",
+    "We want find a function $f(p)$ so that $f(p)$ is very distant from uniform distribution $Q$, so we can use KL divergence for this:\n",
+    "\n",
+    "$$\n",
+    "KL(P||Q)= \\sum_{x=1}^X p(x)\\log \\Big( \\frac{p(x)}{1/K} \\Big) = \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)-p(x)\\log\\big(1/K\\big)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "$$\n",
+    "KL(P||Q)= \\sum_{x=1}^X p(x)\\log \\Big( \\frac{p(x)}{1/K} \\Big) = \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)-p(x)\\log\\big(1/K\\big) = \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)+p(x)\\log\\big(K\\big)\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)+ \\sum_{x=1}^X p(x)\\log\\big(K\\big) =  \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)+ \\log\\big(K\\big)\\underbrace{\\sum_{x=1}^X p(x)}_{1}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "$$\n",
+    "KL(P||Q)=  \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)+\\text{const.}\n",
+    "$$\n",
+    "\n",
+    "We want to be far away from Q so let's maximize the divergence:\n",
+    "\n",
+    "$$\n",
+    "\\max_P \\sum_{x=1}^X p(x)\\log\\big(p(x)\\big) \\rightarrow \\min_P -\\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)\n",
+    "$$\n",
+    "\n",
+    "So this tell us find the **$P$** so that we minimize the Entropy:\n",
+    "$$\n",
+    "\\min_P \\underbrace{-\\sum_{x=1}^X p(x)\\log\\big(p(x)\\big)}_{\\text{entropy}} = \\min_P H(P).\n",
+    "$$\n",
+    "\n",
+    "Which is equivalent of saying: **in order to find a good tree, you should minimize the entropy!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Artificial Intelligence and Machine Learning\n",
+    "\n",
+    "## Unit II\n",
+    "\n",
+    "\n",
+    "## Decision Trees and Random Forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Today's lecture\n",
+    "## Another \"non-parametric\" model\n",
+    "### Decision Trees and Random Forest\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# This lecture material is taken from\n",
+    "- Information Theory part  - (Entropy etc) is taken from __Chapter 1 - Bishop__.\n",
+    "- Decision Trees are very briefly covered in __Bishop at page 663__.\n",
+    "- [Cimi Book - Chapter 01](http://ciml.info/dl/v0_99/ciml-v0_99-ch01.pdf)\n",
+    "- [CSC411: Introduction to Machine Learning](https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/06_trees_handout.pdf)\n",
+    "- [CSC411: Introduction to Machine Learning - Tutorial](https://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial3.pdf)\n",
+    "- [Cornell ML course](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote17.html)\n",
+    "- [Cornell ML course Bagging](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote18.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Motivation for Entropy\n",
+    "\n",
+    "- Maximizing the divergence of your tree from the uniform distribution _[of the classes]_ boils down to **maximize the KL divergence between a distribution P and Uniform on the classes**.\n",
+    "- Maximizing KL wrt to Uniform  $\\rightarrow$ **minimize the entropy of data class-label.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Impurity functions review\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "pk = np.arange(1e-9, 1.0, 1e-2)\n",
+    "missclassification_binary = 1-np.maximum(pk, 1-pk)\n",
+    "kwargs = {'linewidth': 3}\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "_ = plt.plot(pk, missclassification_binary, **kwargs)\n",
+    "_ = plt.plot(pk, 2*pk*(1-pk), **kwargs)\n",
+    "_ = plt.plot(pk, -1/2*pk*np.log2(pk)-1/2*(1-pk)*np.log2(1-pk), **kwargs)\n",
+    "_ = plt.xlabel('p')\n",
+    "_ = plt.ylabel('Miclassification')\n",
+    "_ = plt.legend(['Misclass', 'Gini', 'Entropy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# From Impurity of a Set to Impurity of a Tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Impurity of a Tree with a recursive definition:\n",
+    "\n",
+    "$$H(\\texttt{Tree};S)=\\frac{\\left | S_L \\right |}{\\left | S \\right |}H(S_L)+\\frac{\\left | S_R \\right |}{\\left | S \\right |}H(S_R)$$\n",
+    "\n",
+    "where:\n",
+    "\n",
+    "<ul>\n",
+    "<li> $\\left ( S=S_L\\cup S_R \\right )$</li>\n",
+    "<li> $S_L\\cap S_R=\\varnothing$</li>\n",
+    "<li> $\\frac{\\left | S_L \\right |}{\\left | S \\right |}\\leftarrow \\textrm{fraction of inputs in left subtree}$</li>\n",
+    "<li> $\\frac{\\left | S_R \\right |}{\\left | S \\right |}\\leftarrow \\textrm{fraction of inputs in right subtree}$</li>\n",
+    "</ul>\n",
+    "\n",
+    "Note: you can replace $H$ with ones of the Impurity function we have seen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Why Misclassification is a bad loss function\n",
+    "\n",
+    "- Different splitting results give the same loss value if we use classification loss type. \n",
+    "- New split regions do not reduce the original loss.\n",
+    "- The misclassification loss shape is **not concave** (here for a binary classification problem)\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"50%\" src=\"figs/missclass_split.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "<div align='center'>\n",
+    "<img width=\"50%\" src=\"figs/missclass_split2.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Misclassification is a bad impurity function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "pk = np.arange(1e-9, 1.0, 1e-2)\n",
+    "missclassification_binary = 1-np.maximum(pk, 1-pk)\n",
+    "kwargs = {'linewidth': 3}\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "_ = plt.plot(pk, missclassification_binary, **kwargs)\n",
+    "_ = plt.plot(pk, 2*pk*(1-pk), **kwargs)\n",
+    "_ = plt.plot(pk, -1/2*pk*np.log2(pk)-1/2*(1-pk)*np.log2(1-pk), **kwargs)\n",
+    "_ = plt.xlabel('p')\n",
+    "_ = plt.ylabel('Miclassification')\n",
+    "_ = plt.legend(['Misclass', 'Gini', 'Entropy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree\n",
+    "\n",
+    "- Learning the simplest (smallest) decision tree is an **NP complete problem** _[Hyafil & Rivest 1976]_\n",
+    "- Like in K-means we proceed with a **greedy approach** by minimizing the entropy of the entire tree $\\min_P H(P)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree: \n",
+    "## Simple, greedy, recursive approach, builds up tree node-by-node\n",
+    "\n",
+    "- Resort to a **greedy heuristic**:\n",
+    "    1. Start from an empty decision tree\n",
+    "    2. **[Greedy Step]** Choose _A) a dimension among axes B) **BEST** splitting value that minimize the Impurity function on the chosen axes_\n",
+    "        - When Impurity is Entropy we **minimize the entropy** also know as **maximizing the Information Gain**\n",
+    "    3. Once A) and B) are chosen save them as \"parameters\" of the model\n",
+    "    4. Apply **recursion to the sub-problem**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree: \n",
+    "## Simple, greedy, recursive approach, builds up tree node-by-node\n",
+    "\n",
+    "    \n",
+    "- **Termination**:\n",
+    "    1. if no examples – return **majority** from parent (Voting such as in k-NN).\n",
+    "    2. else if all examples in same class – return the class **(pure node)**.\n",
+    "    3. else we are not in a termination node (keep recursing)\n",
+    "    4. **[Optional]** we could also terminate for some **regularization** parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Information Gain\n",
+    "\n",
+    "- To minimize the entropy, we practically maximize the information gain, given **we chose an attribute**.\n",
+    "- We pick the maximum over information gain\n",
+    "\n",
+    "$$IG(Y|X) = \\underbrace{H(Y)}_{\\text{prior entropy in the node}} - \\underbrace{H(Y|X)}_{\\text{new, conditioned on the split}} \\qquad \\forall~X~~\\text{splitting attribute}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree for Classification: Toy Sample\n",
+    "\n",
+    "\n",
+    "<img width=\"40%\" src=\"figs/data_01.png?2\" />\n",
+    "\n",
+    "[Taken from wei2624.github.io](https://wei2624.github.io/MachineLearning/sv_trees/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "## Which axis and value do we split?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Learning a Decision Tree for Classification: Toy Sample\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_02.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# The current decision tree\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_03.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_02.png\" />\n",
+    "<img width=\"30%\" src=\"figs/split_00.png?1\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Region 1 on the left is pure so we are done on $R_1$\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_02.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "# Let's split $R_2$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Region 1 on the left is pure so we are done on $R_1$\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"35%\" src=\"figs/data_02.png\" />\n",
+    "<div/>\n",
+    "    \n",
+    "# First try all values on horizontal axes for $R_2$ (right)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_02.png\" />\n",
+    "<img width=\"30%\" src=\"figs/split_00.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Now let's try all values on vertical axes for $R_2$ (right)\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"35%\" src=\"figs/data_01.png\" />\n",
+    "<img width=\"30%\" src=\"figs/split_01.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Much better gain in split on vertical axes  at value $\\approx 2.7$\n",
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/data_04.png?2\" />\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# The Final Decision Tree\n",
+    "\n",
+    "<img width=\"40%\" src=\"figs/tree_02.png?2\" />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_style": "split",
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Test point to classify?\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"80%\" src=\"figs/data_04.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "cell_style": "split",
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "# The Final Decision Tree\n",
+    "\n",
+    "<div align='center'>\n",
+    "<img width=\"80%\" src=\"figs/tree_02.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import neighbors, datasets\n",
+    "# import some data to play with\n",
+    "iris = datasets.load_iris()\n",
+    "# we only take the first two features. We could avoid this ugly\n",
+    "# slicing by using a two-dim dataset\n",
+    "X = iris.data[:, :2] # Nx2 \n",
+    "y = iris.target      # Nx1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# The data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(*X.T, c=y, cmap='jet');\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=1)\n",
+    "clf = clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "# Plot the training points\n",
+    "plt.scatter(*X.T, c=y, cmap=plt.cm.RdYlBu);\n",
+    "# for i, color in zip(range(n_classes), plot_colors):\n",
+    "#     idx = np.where(y == i)\n",
+    "#     plt.scatter(\n",
+    "#         X[idx, 0],\n",
+    "#         X[idx, 1],\n",
+    "#         label=iris.target_names[i],\n",
+    "#         cmap=plt.cm.RdYlBu,\n",
+    "#         edgecolor=\"black\",\n",
+    "#         s=50,\n",
+    "#     )\n",
+    "\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=2)\n",
+    "clf = clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "# Plot the training points\n",
+    "plt.scatter(*X.T, c=y, cmap=plt.cm.RdYlBu);\n",
+    "# for i, color in zip(range(n_classes), plot_colors):\n",
+    "#     idx = np.where(y == i)\n",
+    "#     plt.scatter(\n",
+    "#         X[idx, 0],\n",
+    "#         X[idx, 1],\n",
+    "#         c=color,\n",
+    "#         label=iris.target_names[i],\n",
+    "#         cmap=plt.cm.RdYlBu,\n",
+    "#         edgecolor=\"black\",\n",
+    "#         s=50,\n",
+    "#     )\n",
+    "\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=3)\n",
+    "clf = clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8NmzYgCVLlrQVnCWi7Ghqkqirj68Z5nBIlJUCNltuw4/dJlBcZGw3RUTUn1gumA0fPrzLUvcXX3wx7utp06Zh2rRpvdktogGlvkGioaHr8UgE2LIVqKyUsOc4nBER9Uc5n/xPRNai64lDWUc7dvROX4iIBhoGMyKKU1+fvk00CsRiLBZGRJRtDGZEFCccMdcuYrIdERGZx2BGRHHMLmpMsa0tERFliC+tRBQn32uuncPRs/0gIhqIGMyIKI7HI9KOhuXnW6OmGRFRf8NgRkRdDK5IfkrT7QaKixjKiIh6guXqmBFRYuGIREO9Uek+Lw/Iz++5cGSzCQwdItHUDDQ1GfdpU4HCQsDp7NlQJqVEOIy23QacTlbzp4FJj2rQQjEICChuGxRb5mMpUkpogSikLqHYVShOlX9XFsVgRmRxmiaxZWv83pChMLCrTqK0xDj12BMURcCXD/h6cVPw5oBEXZ0RylqpKlBcJJGXxzcRGhj0mI5wdTO0QDTuuM3ngLPMA9HNaQTR+hDCtUFAb38REXYFrnIPVLc9K32m7OGpTCIL0zSJzVsSb9gNADtrgWCwf9QTaw5I7NwZH8oA4+sdO4FAoH88TqJUpC4R3OzvEsoAIOaPILi1scvuOKlE6kII7wjEhTIAkFEdwc2N0IJd74dyi8GMyMIa/Onb7Kzt+X70NCkl6nalbrOrDt16QyLqi6INIcionvR6PRiD1mwuTElNIlIbSNkmvDPYrf5Rz2MwI7Kwpqb0bfTkr+F9RigMaGkeh6YB4XDv9IcoV6IN6X/Jo35zfwix5giQ5rOMHopBj2qpG1GvYjAjsjCzA0Sa1rdHkjqfvkzarh+EUKJUpIm/ZRkz94eQ7XbUOxjMiCzM7KIpVe3bE+NV1WQ7vmJRPydM/C0Lk6szs92Oegd/GkQW5jVRhb8/bI3kcqYPZ62lM4j6M3tB+l9yu8/cH4LN4wDS5DzFZYNiN/nJiHpFP3hJJ+q/Cnzp25SW9Hw/epoQAkVFqdsUF7GeGfV/9gIXhD35W7PitkH1mCtxIVQBR0leyjbO0tTXU+9jMCOyMFUVGDok+SnN0hLA7e4fYcWTJ1Ba2nXkTFWBslKwjhkNCEIRcA/1JQxfNp8D7sr8bn1AcRS54CzLAzrVPhN2Be6h+VDdLGdqNbv1EwmFQqitrYXT6URpaWm2+kREHaiqQNVQCb8faGypwu9yAiUlme9XGY1JNDcBMc04FerxAE5H/G21VuAPtJRAstuMdjZbzwUkT55AnpuV/2lgU2wK3JX5Wav8by90wVbgZOX/PqLbwWznzp149NFH8eKLL2L16tVtdYUKCwvx4x//GD//+c9x7LHHZr2jRANVLCZRXQPEYu3HAkEguAUoLeleRXwpJRoautZHa2wE8twSpaVGCNJ1iZodXctT1DcARYUSPl/PvaALIeBy9djNE/UZil3N2vwvIYQx54wsr1vBbNGiRbj55psxZswYnHTSSbj44ovh8/kgpUR9fT2+/fZbXHbZZZgwYQKeeOIJ+HwmJsgQUVJSdg1l7dcZFfEHV0g4HOaCUlNT8qK1gSBQu8s4PbpjZ/KaYXX1gGqT8PDUIhFR1pkOZvPnz4emafj666/TBq4333wTM2bMwOOPP85wRrQbAoHEoayjBr8xByud1tGyVJqbAU+eRCiU5j4bjBE2ngohIsouUyes//73v+PII4/EL3/5S1NB64QTTsDjjz+Oe+65Z7c7SDSQBUzslhJIveNKm0jEXIFWf2P6NtGo+aKwRERknqkRs+OOO870yFddXR2Kiorg8/lwww037FbniAY6abIgt5TpR6/M7iKQ7Xb7nTjYXMMBwDttNMQhk3LdjYzVeo7DvdPfynU3iPo1U8EsUSiTUmLnzp0IhUJtCwCklPjDH/6AefPmJf0+IjLP7gCCaU4r2m3mVi3azZU+gtORfk9KIcxV69/vxMHIv/NSc3c8ALxafyLuvbYvB5sPct0Bon4vo3IZjz/+OGbPno3GxvhzHq2f2luDGRHtnnwv4E8yWb+tTb6521JVoxRFqtOjNhUoKACamlNvju71mCvV4Z022lznBoBaz3F4a+nGXHeDiCwuo2B200034eKLL8app54Ke4eP4Zqm4c9//nPWOkc00NlsAsVFErvqEl/vcpnbtqlVUREQjiSeHyYEUFpqBK7SEqNcRiJ2G1BQaP4+iYjIvIyC2aBBg/DAAw8kvG7UqFG70x8i6iQ/X8Bmk2jwt59iVFXAl2+MlnVnZaTNJjC4writpqb2eWJ5ecZImcNu3JbbLVBRbrQLtoywKYoRAgt8mRe2JSKi1DIKZtdddx0++ugjTJrUdRLrd999h6FDh+52x4iyRdclmpuNqvmt1eQ9HuM0YU8FDCklAgHjPqNRYzSq9T47Vs7XdYnGJiMk6brRt3yv0bZj39xuAbfbaA8Yt5dpqQpVFSguMgrF6roRuBLdltMpMKjMeCy6NHZ0YXkM6gv0mI5oQwgxf8SodO9QYS9wwpbv4O8wWV5Gweyiiy7CtGnTsMcee8Qdj0ajePfdd7Fp06asdI5od2m6RHW1EY5a6TpQX2+EofJBMutbDEkpsWNn+0hTK7/fqLBfUW4UhI3FJLZXx59W1HVgV50xx6t8kOwSHLMZJIUQpibwCyGg8r2M+ggtHENwc6Oxj1gLPRRDOBRDrDECV6WX4YwsLaNgdtppp2H58uWor6+H19v+Sx4KhVBXl2QyDFEO7NoVH8o6isWA2lqgvDy79+n3dw1lraQEanYAQyoldtYmrwUWiRgBrbQku30j6s+klAhtbYoLZR1pgSiiu0JwlLh7uWdE5mUUzD755BN88cUXGDlyZJfrXnrppd3uFFE2aJpMW3w1FAaiUQm7PTufoKWUaQu0apoxcpauJEVzs3G6UeVwFZEpWiAKGUtd/C9SH4K92MVRM7KsjLaqP+WUUzBkyJCE1x1//PG71SGibAlHTLZLE5C6I6alLjPRKtmIWmeRJKN9RNSVFkyzfxkA6BIyarJyM1EOZBTMfvOb3+A3v/kNNm7cGHfZsGEDHnzwwWz3kSgjufg8bPo+TTbkZ3qibuAfDPUDGZ3KvPDCC7FixQrcd999ccdbC8z+5je/yUrniHaH02muncuVvftUVeOSbh/JvDyk3ShcCMDhyF7fiPo7W54d0V2p/7CETUDYMxqTIOoVGQWz6dOno7GxEYccckjc8VgshmeeeSYrHSPaXYoi4PVKNDUlb5PnRlZXZQohUOBLXhAWMAq0ej3G6cxUpzR7spwHUX+kuGxQHCr0SPJPRvZCN+eXkaVlFMz+53/+B4qiID/BXjBjx47d7U5lYt26dQkXI9DAVlxkrL5MNDrlcAAlPbDq0es15oYlCoSqCpQNMgJcSbFETU3ieWRuF1BYmP2+EfVnQgi4Kr0Ibm5MuAjA5nPAXmhyKJ0oRzIaz/V6vXj++eexffv2tmOvv/46tm/fjsrKym7fXjQaxc0334zBgwejtLQU1113HfQ0M6gnTpwIIUTbZeHChd2+X+r/hDCKpJaVAm63sZG3y2WUoago75kRKSGMAq7l5cYpS7vdOK1aXARUDgbsLSN0qipQUWH0xeEwQpvTCaO/ZV2Lueq6RDAo0dwsEYslLgfQ2i4SkYhGJaRM3i6bpDTuL9KL90mUiGJXkTesAM5BeVDcxgia6nXAPSQfzkEejpaR5WU0Ynb99dfj4YcfxqBBgzBt2jQAwEknnYSf/vSnuPXWW7Hvvvt26/buuOMOTJw4Eddddx0WL16MmTNnYp999sHll1+esP1//vMf7LfffrjmmmsAGG9gU6ZMyeSh0AAghEBenhGSevM+XU7AlebDeWOTUey2NctoGrCz1hjJy2sptaTrErW70KX0h90uMais/VSsrks0NBi32Xp7qgoU+CS83p6p2t9aHsTvb1+NqihAvleioCDrd0dkilAE7AUu2AuyOIGUqJdkFMzWrVuH7777DsOHD287pqoqzj33XFx55ZX48MMPTd9WfX09jjjiCEyePBkAcMUVV+Dvf/87vvjii6Tf8/jjj2P+/PkoKyvLpPtEltDQIFHf0PW4rgM7dgClJRIej0B1jVFwtrNoFNi6DagcLKEoSNhO04xCtbGYsYF5NkkpUVsLNHcKjLoOY1/PCKBrOkxsLkBERC0yOpV5yCGHxIWyVrW1tfi///u/bt1WYWFhWyhrpWkaTjjhhITtq6ursWjRIgwfPhxTpkzBa6+91q37I7ICXU8cyjoyRslkwlDWSkqjXVNT4vDWyt8IRCLZPcUYCncNZXHXh4BNH+zI6n0SEfV3GQWz5uZmbN68Oe7Yxo0bceedd2LcuHG71aE33ngDhxxyCE499dSE19fV1WHWrFmYOHEi3nrrLZx22mmYPXv2bt0nUW9LtzsAYISuVKs7W4VCxunLdFKtTs1Ek4nHsG7JluzeKRFRP5fRqcwbbrgB06ZNw8iRIzF06FD88MMPWLx4MRRFwbPPPptRRzZs2IC5c+fiySefhM/nw/HHH4+jjjqqS7sxY8bgzjvvBAB8/fXXuPDCC3H33XfjxBNPxHHHHZfwtsPhMMIdyrv7/f6M+kiULVGTuxKkq4fWKmai4HnURJvuMHN7jVvS7IlFRERxMhoxKysrwzvvvINDDz0Ua9asQXNzM66++mqsXr0aRxxxREYdqaqqwr333ovnn38eDocD559/PiKpzs0AGDduHJYtW4aioqKUgXDevHkoKChou1RVVWXUR6JsUUxOvDK7aFQx8Zdspk13mLk9uyejz35ERAOWqVfN9evXd5lT5nQ6MXPmTMycOTPp923ZsiXpnpqdKYqCwsJCnH322XA4HPjJT36Cb775Bvvvv3/K7ysuLsYZZ5yBjRs3Jm0zZ84cXH/99W1f+/1+hjPKqQKfuVOLeZ707Ww2o+5ZutOZniyvSvXkpd9ndPhxFdm9UyKifs7UZ+gtW7bg6aef7tYNb968GU899VRGnWpdDGB2eX9RUVHKAOh0OuHz+eIuRLlkswk402y35PUARYXG1kypFBcB+b7U7ex2o45bNnk8RjmOZBQFGHFS9+saEhENZKaC2aRJkxCNRnHFFVekHJkCjNGohx56CLfffjtuvPHGjDq1c+dO7Lnnnthnn31MtV+xYgXOOeecjO6LKFcGDUq+F6bbDZSUCCiKwOCK5KGruAhwuwXsNoHyQYmDktMJlA/Kfh0zRRGoKDdCX2c2m1HA11XAzT6JiLrD9ASQyy67DBUVFZg0aRKKiopwwAEHoKKiAk6nE4FAAHV1dfj666/xzTff4KabbsKCBQugmJiEUltbi3vvvReXXHIJ9t57bwDA/fffj4ULF7Z9/4wZM7Blyxa88cYbmDlzJioqKjB79my4XC4sXrwYI0aMwEknnZThU0ADgVE1H4hpgKoYxWZ3p+p/U7NEoNn4v8cDeDzdv63W0BUIGHtr6roRrEpKAJez/fbsdoE9qgC/X7YVj3U6gOJiY/eAVk6nQOVgo+BrOARAGNtDefIyf5yRiERDS/FYhx0oKIh/3mw2gcEVEuGwUT4DEnC6jMK6fa7CusMNKDZA6kAkaPyboR82avh4hQZNA/YZrWD8uK6JWUoJLRiDHo4BQsDmsUOxs+ob0UDXrZm5p5xyCtasWYOnnnoKixcvxrJly1BXVwev14tRo0bhpJNOwiuvvNKtbZk0TcM//vEPPPjggzjzzDMxcuRIXHjhhTjooIPa2tTV1aGmpgaAsR3UPffcg+eeew6nnnoqxo0bhwULFnTnYdAA09gkUVfXXg0fMMpQFBRI+PK7FyBCIYmaHfG3FQwBtbskygcZ4cgsXZfYsaMl0LSIxYDqasDjkSgtaa/ov7M2fsPzQBCIVANlpRIOh9EuEpHYsTN+hWYoBDS7JUpLuhdEdV1i+/b4lZehkFHmo8AnUVjYfltCCLhcxlZXfZLdDXiLjRUZrT9YD4BQIxCo79ZNNTRK/OquIP79fxqEMEY6dR0YMUzBnN+21xfRQjGEtjXF7ecY2QGoXjtc5V4Ibl5PNGAJOQA3tvP7/SgoKMCbQ0fCY3Z5HPVJTc1GdfpkigoBn8/cm2AsJrFla+o2Qyrbt0hKp7paxoWyzvLzjf5V1ySfZC+Esf8mAGzb3r4tUmcOh3Fq0WwI3bJVpizBYfZ5O+yxYyAOmWTqPnPC5gR8g4z/d35upOxWOIvGJC65PoBvv9ehdfo5qArgzXdg9Fn74Mt/fY/AxgYgySuv6rbBNSS/7404ElFKsWgAy1+4DA0NDSnnumd5AT2RdUgpUV+fuk19gzE6ZMauXenb1KW5v1axWOpQBgCNjcYoWaqVj1Ia+1T6G5OHMsDYFSAYMte3UCh1KAOQdteCPiOv0Pg3UQgSAnDlA8Lcy+R7H8ewel3XUAYAmg40Nkax9b/ViNQFk4YyAMbpzVCWi84RUZ/BYEb9VjicvkCrlMYpOjPMBJuOpxtTMVvj2Ey7pmaguTl9OzNtAGOfy3SkBMJZ3uKp1ykqYHemX/bqNFdn5B/vRFPWdtN1iZrVOxBrTF9dOOo3WYGYiPodBjPqt1KNIHWUaIQjU2YnBpit6G+mb1Kae6xmnw+z7WJRc+0sy+RIGIS56Q71DTLtcxcLaylHy1pJsz8EIup3GMyo37KZXNpitp2ZKT9mq+vbEpSYSCRRKYrOVBWwmcgO2X4+HE5z7SxL18wlad3cacWhg5WUdd2EAJz5Dgg1/S8SV2cSDVxZD2Z/+ctfsn2TRBlxOETaYKOqRmkHM7ye7LQBjMr/6QgBFBaYuE8v4M030c5k34oK07dRVcBucpGDZbWVxUgSzqQEIIGwuf0+f3KiPe1I6OD9y2ErSP8LZ/f19dRLRJnKKJh98cUXmDZtGsaMGYO99tqr7VJVVYULL7ww230kylhJcfrrza5+KyxMPWqmKEadLzMURcCXJkwVFxvh0utN3sZuA3z5QL439eia12O+lIfNJpCXZlpVSYmpm7K+QL0RwDqHMymNH3ZzHUydewQwfpyKUycnHm5UFGDkqEKU7zMIjkIXhD35S6+9yAXFwREzooEqox2GzzrrLBQWFuKMM86A09n+yS4ajeKNN97IWueIdpfTKVBRLlFXH7+60eEwRoZcLvOjPooiMHSIRHU1EOk0v8rhMKrrd6dWWFGRgKLILischTBCmbelaG1xkRHAWgu9tvK0bNnUep8V5Uah2o6T/BUF8PmQNgR2VlYqsGuX7LL/pqIApSWAuxvPm6XpMaBhO+ApAuyu9uStxYBgvTGiZpIQAr+51oU9KiN49pUIGlrKljkdwNTj7bjgqsNxzz9qIVQFeUN9CO8MxC0EEKqAvdgNu4kRNSLqvzIKZrt27cKXX34JR4L9ZM4666zd7hRRNhnhzKgzpcVaTsPZMwsWiiIweLBR7qJ1BaY7D7CZmDeUiM8H2B1AQ4Mx5clmN05zdvi8AyEEvF5j1KY5gLbq+vn58ZX/FUWgtAQoLpKIRAEBIzBmWg+ruFigsNB4nJpm9Kk7BXT7DD0GNO4wVmm2Vv7XMlvZoKoCl/3UiYvOdGDN9zo0DRgxXIE3T6A2r31IU9gUVI3yokBEsGtHBDabguLBTqyvVWGFShnRxjCiu4LQY3rLrgQOOErdUFROSybqaRkFs9NPPx2bNm3CiBEjulyXn9/Nj+ZEvcRuE7Bn9Bvflc0msLu/6rpu7CLQcSQvphnlO/LygNISCSEEIhGJ6pr40bJI1KhzVlwske+ND0uKIkzPm0tHUQQ8Juem9Xm6ZlyywG4X2Hd08tORYyt0fPZhPRRhrLwVAvj+uxCKilSUjyhAdY7qxOm6jtCWRuihjs+DRMwfRswfhqvKB5srS39ERJSQqb+w1157DdFo+yfIo446CldffTV++tOfxrWLxWJ4/vnn8c9//jO7vSTqh2p3JS8eGwgADTbA5+sayjratQuw22S3TslSbu01SOLTD+oBAFrL9LXWKW4NDRqUH/ywleYjpvf+zzSyI9AplMULbfYjb69CU/sgE1FmTAWzhx56CG+99VaX40uXLu1yjNuIEKUXi0kE0iz28zcaZ9fSlbTyN/bhfSoHoEhdCIqS+Oeq60BtbQwHjZNYs713X0t1XUcsXWFbCcT8YTgK3b3TKaIByNTHnqlTp2LLli3QdT3lJRKJ4M477+zpPhP1eWZ2EZASCJio1h8MGttPUR8gdaxZHUwZthUF0AK9X71XD5s7jWtm5wIiypypYPaLX/wCgwcPbvv6vffeS9juiy++wPHHH5+dnhH1ZyZzFONWPyOlqZq2MheF/83+svGXkqhHmQpmaqdy1s8++2zCdvvssw+uvPLK3e8VUT9ntmq+mUn8u7PyknqZoqKqypGyHp6UgNNjcmuILFJc5mqnKZz8T9SjTP+FNTc347rrrsN3332HNWvW4Ljjjou7XkqJ77//nqdUiExwOgQcDolIirNCeXlGOQ1/Y+rb4kLovqV8eB42bUr+g7fbBTbU937QVhQFiktNOfkfABylnF9G1JNML63xeDx44IEHUFRUBK/Xi2HDhsVd9txzT5x55plYsmRJT/aXqN8oLUm+t6bdZhSWVVWB0tLkt+HxAJ40VfrJWr7ZLjB+glGDpOPPX1WNy7iJhWgO52YE1FWZn/JdwVGWxxWZRD2sW2PSHo8HL774It56662Ec8kikQhsZndAJjKhdQQ2W6fqpJRpb0vXjfvsThX/TNjtApWDJRobgaYmo56VzWbsfZnvbb9/T56AvULC7wcCLUVtHXZjpCwvj6cx+x6BdY0OHHSEHfXbQ9i8KQy7XWDvMXloUhxYV534uxQhocvUP+vd/XtRVAV5exYiUhO/K4HiUOEodcPm6VpU3Ox9mm2nCEDP0omXbL9+EPWGbqcoRVGSTvD/4IMPEAwGceqpp+52x2hgC4aMIBJqWb1ot0v48o0Rou6+yGqahL8l/Og6IISE12tsU2TrsBF3c7OxdVP7RtQSbhdQWtpzIU1Ko0+tb0S6blw6zwhwOFKPnFESjjzAnQ/YnMaTGg0DIT8QNbEstkcJrKkWgMiDfQ9jyHNtgqKyXhdQlR/Dmi+b4fdr8HgUjN3Pg+qQDXXN7b+TWjiGyK4QtCYjTAmbAnuhE/YCF0Q3f3cVRYGrwgtUpG6nRzVE60KI+sOANLaUsvmccBS5IDrsEKAFo4jUhaA1GytNhUOBo9AFm8/Z9rcsBHDikW6cdYIHI4fZoWkSn64K4y9vNGPl6u6vAtXDGiJ1QcSaIkbfbAL2Ahfshd1/Poh6m6lgtv/+++Orr74ydYNnn302gxntlsZGY8/HjqJRoyBrKAyUFKcf9WoVi0lsr+4Ytoz358ZGY0/JinIJu12gvkGiIcEbYzAEbN4CDB0isx7OIhFj382OowO6Dvj9Rt8GV8i4LZeomzxFgCu/PeUKAdidgGOQsTl5KM3kvRwr9EiEtzTi/76KtZXXaG7WseLTRng8Kgbt7UONXyDWHEFoa/ympjKmI7IziFhTFO4h+VkPI1o4huDmxrhfXqlJROtCiDVG4K7yQbEpiPrDCFfH13yRER3hmgBigShcFV4oisAtVxTg+EnutptTVYEf7efEIQc4cc+fGvD6u+b3LNWCUQS3NMatHpUxiUitEdTcQ30MZ2RppoLZaaedhp/97GcoLS2FlBKvv/46iouLceihh8at2HzrrbcwZsyYHuss9X/RaNdQ1lFzM+B2m59XtWtXfCjrSNeBnbVAWWniUNZKSqBmB1BRbu4+zZBSYmdt8lM2mmb0vawse/c5oNjdRigDELcEsvX/niJj1CzDPTF7Q0EsjG92xbrUPNM0oLlZQ2l9ABJ5CG1vSnwDAPRQDNG6EBwl2ZuwL6VEeHtz0l9eGdMR3tEMZ5mnSyjrSGuKIuaP4OSTCzHlCOMPuuPnkNb9Z6+/tACffRnB9p3p66xJKRHa1pS0pIce1hCpDcJZxomZZF2mgtkFF1wQF7i+/vpr3HXXXV3aXXjhhRwto93SmPw9pr1No7lgFovJtIVcIxFjJC6dcNiYe5atUbNIxBgFTCUQBGKazHiD9AHN5TUSdbKRVSmNNs0pPgXkULFX4usVgaQlwzQN+P67MCr2VNGcpuZZtCEEe7Era/Os9JAGPZI6JGlNUURt6U8XR+tDOHNKHjRdQk32tyWBqcflYeEL6Uc4taYopJZ6glq0wQiqHDUjqzK1vKbzKFiyVTmNjY346KOPdr9XNGClKh/RnTaAsdG3GekCUnfbmWH2MURZZD0zNkfyUAYY16ldJ7JbRXGeuTqueiiWto3UZNqw0h16OP19AoAWSv8Ho0c0jKiyJQ9lME5r7j3c3HRozUzfpDGqR2RVGa17bm5uxssvvxx37Pvvv8d5552HESNGZKVjNDCZ+VBv9oN/tj8PZ3Nhl+nHwA/1PUNKWLmEvdnYYHbUJ6u/RyZvzPQ80DQPVtdT1/vL5D6z/uJAlEUZBbN58+Zh4cKFqKysxOGHH46xY8di1KhR+PTTT/GHP/wh232kAcRtYiqMmTYA4HSaew/xetK3EcJYGZktZjYdV4TxGCgDkUDXpa1d2pifUN7bttQJuN2pf99UFYg60+8QoDjVuFWSu0s1syuBIqDmpx+RVD12fPR5GLEUI3pCAMtXmltFa6Zvwq5A2FiLjawro99Or9eLpUuX4sknn8SRRx6JY489Fvfffz/WrVuHiRMnZruPNIB4PcmLrrbymax0rygibVuvx6iun06+19x9mmWzibTz5Hw+1l/KWLBlPlKicCalsRll2MQO8TkSjgL7HZif9IOFEMD4g7wIK3YoztRbKTmKs1upX7EpsKUJXY4iF+z5Tghb6t9fR5EbLyxpgkB7/cCONE2itl7HW8tNBjOXDYo79WlPR7Gbf1dkabtVDXbKlCmYMmVK3LHVq1dj7Nixu9UpGrgURaB8kER1DbqsRgOAstLujVwVFAAxzVjN2ZnbDRQXG+FncIXEtu2Jb8PtAoqKsv9CXlxsPMZECxTMBkZKQo8BjTuA/NKuZyylDvhrcrRTuHmrd6gYf5AXKz5rgqoaE/5b/91nvzx877dDCAFXZT5CWxoTTsh3lObB5s3+XDrnIA+kJqEFus4js/kcsBcZiw3cQ3wIbmlMOKfLWeGB6rbh2/Ux3PZwPX49sxC2lh+WlMbcstoGHTfM34VQ2PxpZ/dgL4JbGqGHEzwfxW7YfRyGJmszFcxWrFiBsrIyDB06FJqm4bnnnku4J2Y0GsXzzz+PZcuWZb2jNHA4HAJDKiUCgZbQIo1Nv70edLuulxACpSVAvtcoMhuLGW9uvvzWU52i7T6rhhplMwJB4z3bZgMKCwGXq+t9alr7vBeHE0knLweDEuGwMQro9cYXqlUUgbIy4/rmgPGGa7MZjzObp00HrGgIqNsKOD1GgVlI41g4ACvPL2slIbC2wYFREwthC0TQ3KzB7VYh8h34oVa0/e4qNgXuPXzQmqOINUUgdQnFqcLuc0Kxm9uYPJGxeygYVCgQjUl8ulZDNNr+OykUAVel1yjH4Y9AajoUuwKbzwnV2f62ojhU5A0rgGwOQQQi0CWgum3QPW4oHU4nvv9pCGddU4OTj3ZjzF52xGLAf/4bxrufBE0v4mnrm6rAXeWDFogi1tjyfDhang9H5s8HUW8xFcxaR8aeffZZqKqKxx57DB9//HHCthwipmxQFAGv1wgzuysUMmqjdVxVGY0CRYXGlkYd77OoCCgqSn5bum7cVucROK9HoqioPXgFghI7d8afSaurB/LyJEqK29sJIeBymZtzRhmQekshWWsXk00mFohi3YZA3GiYsIfg7DQSJoSAzevIyujYvsMVhGoC+PStQNuxoiIVk39chGUrtQ7V+gVUtx2qO8W8LqmjIBzA1u0dZu83avAVRJE3xIemaHs4a2jU8ZfXs3N6WQgBm8eRcAspIqszNcfsv//9b9yk/ksvvRQ7duyArutxl3A4jN/+9rc91lmi7gqFjNOinUtdxGLAjp1AIGB+5ERK47YSnRZtajaK0EopEQxK7NiReHpTIADs2NHNB0EDUiwQTXiKUkZ1hLY1GdsNZdnYPRR8uXwXVn0ZiDteV6fh7y/txOT9uvfB21HXhK2bu/bT36Cj5pt6uNX0RWOJBhpTwayyshL5+e2zqC+44AIUFhZ2aWe323HllVdmrXNEu0NKiV1pisfu2oWEp+UTaW5OXX8sHDaCV7qCtaGwsR0TUTJSSoR3pB49Ctc0m/7dNcsdDSMY1JPulvH6q7swpNRcOCtxxLCrNnldMV0H7E3WXRlLlCsZrcq87rrrUFNTk/C64uLi3eoQUbZEokA0Tb1JTW/fKD2dJhO7Evgbk28B1VF9vbn7pIFJD2uQkdSLE5JNvs9UsQ/46KPGlL+/UgJjK83dXmBH+tC1bQsrKBN1llEwq66uxvz583HZZZfh1VdfhZ5o+RxRjmnmCpQjZvJsipl2sSzfJw1MZivTZ7OCfXmhkvZDhaIA4aC5X96QiXZSgu8fRJ1kVC5j0aJF8Hg8iEajeOWVV3DRRRdh2LBhuPTSS1n5nyxDNbkAy2ztTVVJPxqmqonLfGR6nzQwCZOrj7NZOLbWr0OI1HV5dR1wOM3dp8OpAI3p/xiSbfFHNFBl9Bfhbim9brfbcfrpp+P000/H0qVLMWrUKPziF7/IageJMuVwpA9nimJ+JwEzK0R9+ekL5AJGfTWiZBSXLX11ekVAzTNRhd+kmnpg4sGelL+/UgIb6sy9beSXp19qXD44e/0n6i8yCmZ33HEHPvzwQ8ycORODBw/GJZdcgvHjx+ODDz7AI488ku0+EmVECIHiFKUvAKNOmdkSLx6PUWcsGbvdKL+R7j7ttsS10YhaCSHgLEu9NYSz1G16r0yzXGV5UFWRNJydcGIh1m0xd+qxJmSDx5v8LUYIQClKs/0F0QCUUTC79dZbcfTRR+P555/H/vvvj/Xr1+NPf/oTDj/88Iw6EY1GcfPNN2Pw4MEoLS3Fddddl3LeweLFi3HBBRdg7ty5OPfcc7FmzZqM7pf6v7w8o8Bs5zcaIYzK+/le829siiJQUZ645pjLBZQPMtp4PCJpLTSHA6io6MYDoAHL5nXAWeHpelpTEXAOyoO9IPvF7/7vWw3HnFyCwZ1GshwOgVOnFmH5d935e1Fgr/ShuKTrpxmHQ2DIOB8aoyz4StRZRnPMjjrqKDz88MPYd9998eqrr+LnP/85fvSjH+GKK66AL4N9ZO644w5MnDgR1113HRYvXoyZM2din332weWXX96l7Ycffojp06dj7dq1KCwsxEcffYTJkyfjq6++yui+qfdFoxLNzcZ8FZvNGInqXNFfSolQyFgxKQE4HcZoVCYFjD0egbw8iWDIWBCgqsbpy0xuS1UFykqNIrOtqzmNLZviq/r78gXy3O2FbYUACguMoNhZNCrRHAB0DVBtme1w0FPyRlei5NQfwZbvRvCHaux45RNo/kD6b0xIATyFgM3Rvldlov0qFdWo1i9U40mJNBv/ZiAalXjnUxWrvtGgKAKHjFdw6P5a3M8KAOr9EkvejmLzdh35HoETjrZhrz26hoYftgj880OJhkaJoRUKTjpKoqgHX3bs+U7YvA5ogShkTIdQFah59oQjZeHaAKINYUACil3AWZ4PtdM+mqoCHHagEweMcUBKYOXqCD5eGUbHbSqXf63DXejBkSN11O2KwulUIN0OvPtN1/vUI5pRXV/TIewKbPnOuIr+MakAxT5UlOtwxSKALgGXDTvDdtRnuCBTj+mINYYho8bzYct3JKzor4VjRq03TUI4VNjzHVmdk5cpKSX0UAyx5iggJRSHDbZ8R5efqZQSsaYI9FAMgICaZzN+9izi3u8JmUEhnC1btmDIkCFtX69fvx4///nP8e9//xszZ87EvHnzTN9WfX09PvvsM0yePLnt2EknnYSRI0fi97//fZf2kydPxh577IEnnnii7dioUaNwxRVXYNasWabu0+/3o6CgAG8OHQmPwk9svUVKidpaY/uhzoqKjDADALGYRM2OrkVhFQUYVAY4nbl7YWpulthZm/i6stL24NXULFtqpMW3cbuA0lIjxLXWWWtKkE0KC3K7ibnismPvBy9FyYkTIGMapJQQqgIZ1fD9r55DzQv/NnU7hz12DMQhk4C8AsDVKcEIYQSuhm3tKybyCgFXgp3ng34g2NCtx/DVOgX/e3sTams12GzGz0LTgOHD7HjwNheGlhvtXnojgnsWhBHTjOAipVFGZcpRNtx2vQtOh0A0KjH3YYkly5qhqkbXW/euvO5KH847Of3LaK3nOMx9eTtW/mNttx5HOlpYQ3Bj4udGcduQN9R43vcaasO8G4pRUaoiGjP6a7cJbK2J4eZ76rBhq7GkeEyFxKr/1CMSlVDV9udtn/3yUB1zIRRrqbNW04yYv2u6cpS42/bKzLZIXRCRnV3LcNh8DjgHeSCEgNQlQtuboDV3egERgLPMA3tB7vbKlJqO4NamlrDVgQBcFd62nRu0UAyhrY2QWvzvlbArcA/J362ttih3YtEAlr9wGRoaGlIOJGX08WHjxo1oamrC008/jeOPPx4jR47Exo0bceutt5oOR60KCwvjQhkAaJqGE044oUvburo6vPfeezjwwAPjjh9wwAH461//2v0HQr2qdlfiUAYAdXVG6NF1ierqrqEMMN67q2vQ9qbS2yKR5KEMMHYSiEaNyv+1tYlXtwVDaLuNuvrEoQwA6hvM1U3rKSPvvRjFx48HAAibCsVug1AUCIcNI+/+GYp+vL/5G3N620OZEO0XABAKUDDY+L/bZ4Syjm1aL3kFiQNbEtt2Clx5cyPq6oyRtlisfUXtps1RXHFzEMEQ8NaHUcx7JIxozPh5xTQjlAHAvz6I4f89ZAyLzn9c4h//Mn5YmmbcnpTGv/c87Mc/Psjdh4VkoQwA9GAMweomFPoUPPDLEpQWGS/5dpuA3Wb0eVCJigd+WYx8j8BegyQ++6AO4Yhse3ytz9vqrwKosBnPR3hnIGEoA4BIbRCxhnAWH6Eh2hBOGMoAIOaPILzDeHEJVScIZQAgjaK8PbFjghlSSmNz9c6hDAAkENrWBC0Ugx7VENzs7xLKAGPXh+DmRkg9N6+B1DsyCmY//vGPUVpail/84heoqqrC+++/j6+++grXX389SktLd6tDb7zxBg455BCceuqpXa5btWoVYrEYqqqq4o4PHToUK1euzHoVbMqeWEwm3Mqoo/oGI7ilqvElJdDoz27fzNpVZ65NfZqBnWDQ2CqqMc32jfUN5nclyCb3yAqUnvqjhKd9hBCQmo6q/+3695lUXkHrN3e9Toj2U5duX+I2bR0rAGAuAL2wRCIc0hOWLtE0YHt1DEveB/6wKJL0LnUJ/OPtGFaslli8pDllGYk/PB3Myc8qvDP9aWXNH8HUY/Pg9QjYEpwit6kChfkKTj46D4HqYNKJ/7oOfLkqgEH5OmL1qYNXZFd2nw8pJSK7Uj/WWEMYsUAUWlPqoruRXbnZbUALxqCHU5+Sj+wKtp2OTka2nMql/iujYFZZWYmHHnoI27ZtwxNPPJHxpP+ONmzYgMsuuwynnXYaHn74Ybz//vtd2lRXVwMAPB5P3HG3241oNIra2sTDGeFwGH6/P+5CvStg4rUwFgOaTYwSJRt162lhE6+FoVDqbZtamfkV1HUgnIMP98UnHAiZIh0LVYF3v2FwVKZZfgoYqx2EkjpwSdkSytKVh1AAu7nTUG++HWwb+UpECOC1ZRF8v1FPGbiEABb9LX11/c1bovhuc+/PX4r6zb1B71UhkXIBpwAO3seGdetCKevwqSogG9NvlSE1mXhkKEN6WIM0MVIerU/fNz2sQY/2foVnMyN1WnMUMRM/02gjd0zozzJ6JXnrrbcwY8YMeM0UdjKpqqoK9957L55//nk4HA6cf/75iHR6h4u2nN9SOxWnsqWqYQBg3rx5KCgoaLt0HnGjnidNFvc2M0LfHwZGzZ6JMPu8ZZOa5zR1qkT1mFgVaHOkDmVtTJ4KNDlvKRBMs52RBJqbzRQ/NT5UmKlNFzS5tVdWmd3ntVFPOedLEcLUm4EQ5nfUyOrvrsk/GNOn+HKx2YDZx2CmGU9l9msZBbNhw4Zlux9QFAWFhYU4++yz8fjjj2Pz5s345ptv4tqUlZUBAAKB+CGTUCgEu92OoiQ1CubMmYOGhoa2y6ZNm7Lef0rNbrKOpMNh4rYyWku8+8y8OStpBodamXmcgPnnLZuC322HSDO5WA9FEd6aZrd2wNjV3cw7jWZyz0eTqWDPYfaUPy9VBUbvbYctzRzqmAaM2svcVkVDB/X+m2XaIrQtXD47YgnmLLWKaRL1TTJtfb1YDHB5zf0BKo7sjSCKBKsuE+m8CjXxjRmT6HtbopWjnQlVmHrezNwW9V25XzucQOtigM6f8Pbff38IIbB169a44zU1NZgwYUKXkbRWTqcTPp8v7kK9y+1OH2zy3Ebl/HTyzc8Bzyqzlf+9ntRthAAKfEgbClwuwGbr/UnltUv+D1pTKOnog4xpqPnbx9CbzZxG04FYOHk4az3eXAfEIqnbRcOmA9w5U50pT8lpGnDuqTaccLQt6fZYQgAF+cCM8xQUF6lJA7eqAj8+Og9FOdjNwVWe5pcNAFSBtz4OJ5xf1sqmCiz5IIR9D/CmfD5cLoHaqD1tIFTzbFldOajYFKie1J9ShE3AXuxO+65my3dmvTCvGTZf+tPw9gKXqfp09sLs17Aj67BkMNu5cyf23HNP7LPPPnHHy8vLccQRR2DlypVxx1evXo3TTz+9F3tI3SWEUeg1GVUxSmY4HAKpcrPLZdQ9y4UCX+otnlTVCI0FBal3CCgpNuqUlaRYJ6Mo6XcQ6Cl6KIp1s54EpOwy10zGNIS37sKmexabv8HGnQBk19AlpfFuH24G9BjQVJu8nZRAs4kRuhZTDtdxzJF5XcJU69cXnJOP/faWuPpSJ0pLRJcwoiiAIoDbZ7nhcSuYO9trHOvUTlWBokIV/3tpboZxVZcdiit1AHJXerF8ZRhvfhAwamh1eH71lvD92jsBfPZlBJsDNgwq7zra2Pr1uB8VIqwJuCo8yc8+KwLOsuz/kTrL8lLuIeos90JRFbjKk3+CEjYFjhKTe7BlmWJTUu7moDhV2ItcsOU7UoZQe6ELqitHpw2oV+Q8mNXW1uKWW27B2rXttX3uv/9+LFy4sG1z2xkzZuCUU04BAMyfPx+LFy9um8C/YcMG1NTU4Iorruj9zlO3uN2JK+d7PUY1/NbRocICI7x0PGWpKMbxQWU5rO2lCFQOTry3Zp4bqBxstFFV43Hme+NPazqdwKBBRsFbAHA5BQZXdL09Tx4wuAKw23NXgmHXmyvx1U/vQ8PH37Yd0wJhbH/uA3wxbT6itWmWlHYkdaBua9cRMSmBQH174NKiQMN2INLh9KeUxtcN282f7oQRfH83W8FV0/NRWtIeXKqG2vHrWT5c9zPj9suKFTx9fx5+coIdzg6nlyceoGLhXXk4cqLxS3jYATr+dJ8Phx7sbvuZOhwCU0/0YtGDHpSX5G7OT15VAdT8BOfGVQF3VT5Ul/EmP+/xBvx+kR81te1Didt3arj/yQbc+4SxlLgpJCBLvZgwMT/utObIvV3Y97AifFttHFPddrirfF0ChC3fgbw9fD1yqk2xq3BX+WDr9FhVj9EXW8u+oTavA+6h+VDcHV5ABGAvcCKvyhdXALe32QtdcA32Qul4ylURsBe54B7qg1AEhBBwDfbCUeKOC6LCrsA5yANHaW6CJfWejArMpvLJJ59g4sSJptvX1NTghBNOwLfffoszzzwTI0eOxCmnnIKDDjqorc3ZZ5+N9evX49NPPwVgbMn0l7/8BePHj8eGDRtwzTXXYOzYsabvkwVmc0/XJXS9ZWQiwWmFaEyi0d++mtPpMAqu5rK4bEe6LttqrdntiR8DYCzz1zQjoKWq5p/u+cglW0EeVK8LkZ1+yHD3Vtq1FZgFALvLqGlms7cHrlBT11niTo/RTlGNkbRkOwSYpGkSO+oVqAIoLUoyCd7hRgh5qGtQ4MkDfM4QEG6KD5JCAE4PGiMuNDYLFPl0uEUAiHRacqyoRs01u9sYVYqGgVATal1HxBWY1YJRROpD0EMaoBiBwl7gigsONhU47lA3TjnGjUElKnbV61j6QQD//CiYcMWuFoxAj0kobhvUBOfK9xxqw+nH5+Hg/ZyABP7zRRivLAu0FZftyK5K5LsFIjGJplDy30mp6ZC6UYC4t04RSl0auw0oImU1/3R90yMaovUhxAItC8vcdtgLnVCdPT8iJWO6UbjZpiT9sCmlhIzpgBAQqmDV/z7ObIFZU8Hs7rvvRjCYvt6BrutYsmQJPvnkk+71tpcxmFlbMGhU/k+kqBDw+fji1Fe0BTNPkRFWWk9fto2I6YC/xhgNEwLwlQNqyyhMazshjHlq/pqeWZLrLQWceV37pmuAv9r4V1GNvrW+XnTsWyTQcroWRvjML2tvA7S1a46W4JYXwlix5FtEaoOI1iVYxikA95B8Y0TKKXD3TcXYb5QDmiahqgK6LiEE8MOmGK67sxYNTeafjxOPdOOmywugS7TNN4tpEgLAnX+ox7+W52JZae7EGiMIbU9cn6en9iKlgc1sMDP1seCDDz7A+++/n3TVYytN07Bt27bu9ZSog5iWPJQBRrV8hyP96jGyEKenvWp/W8X/1p+fAvgGAXVbAE+xEco6jgq0/l91GNc3pdh6IRNuH+BwJ+6bohohq2G7Ed4UNXHf7G6j+G2osWso6/B/j70WIwa58VlTNHEoAwAJBLc2wbNnIa66oADjRhghtXW0tXU0ddgQG26aUYhb7jNR9RjGdkw3XV4AIQBbh5EjmyqgS4lbrizEmvU7sGlb79f3ygU9qiUNZQAQrglAcdo4l4tywtRv3bRp0/DUU0+lDWYA8Nhjj+12p2jgMrMNkb+x6zw1srCOI2WdCWFsVu70Ao685LVGhGi5vi67BbJat4BKdp82hxEsUxW2FSJ+u6ikp6WAY8ZE8ExdmrMPuoQ9EsEJR7qTnv5WVYHDD3RicJmKbTvSh6nTp+QZI2UJTucpQiAGidMne/DQMwOj+HY0zc4FRpsQ1Irs1eokMsvULMjzzjsPBQXp14LX1tbiiCOO2O1O0cAVMnE2xUwbsgibPX2RWSmNUat082eEAGxZ3IBatbefmkzZt7z0p1AVxTiNmeIxCAHsWZJ+Wx4AcEJr28sy+e0J7D/GXEG8H+3rTFsuY8I+Jovr9QNaMP1CEi1gfrEJUTaZGjHLy+u6xLempgbfffddWzV+wNgy6Ve/+hXWrFmTvR4SERERDRAZnUC///77ccMNN7R93bp+QAiBgw8+ODs9owHJ5Uq/JyVPY/YhsahRJqPz3LGOhDBWNqYZcYKUxiKAbNGi7RP7k2md3O9IU6JA14FoKOXooJTAD7U2KE417ahZCCqiMZly1ExKiS++Mbdn4mdfhnHy0WrSUbOYJvH5VwNn/0XVbU/7M1DzcrDtBhEyrGP273//GytXrsQ333yD+fPn44cffsD333+P+++/H3/5y1+y3UcaQMxW16c+JNSYPHBJaYSjcFN8/bJE7SKB7G8eGmpMfZ+xiFGqI5pm94JQo3Fp/ToBIYB3v3HAUZQm5CkCMYcDb34QhJZkG6WYJvHvFWFT88sA4JV/BqCI9g/RHenSWJn5yr8yL0nS19gLTVThZ3V9ypGMgtmkSZOw3377Ye+994aqqigtLcXw4cNx4YUX4uqrr852H2kAsakCg8qSX19UCK7I7GvCzV1DS2s1/9ZyGYBRaFaLtl/Xsb0W6Vblf9OC/vY6ZJ37pmtAY8sS4aadxteJ+hYNAsEG47G0tu9cSBdAc7QE39WoUL122IuSvOkLo1K/UAQeXuTH198ZU0VaA5qmS0gpsXFLDL97vN70w/x+cwy/W9gAXSJuz8yYJiF14I4/1A+YFZmAUazWlWJiv3NQHldkUs5k9Ju3atUqzJkzB1OmTMFZZ52Fn/zkJ7j66quxdOlSvP/++9nuIw0wbrdAZaVEU2N8gdn8fOsUmKVuaq4DIgFEFS921gu4nECRJxJfYFZKozRFNwrM7qrXEQ4DpcUi850SmnYCEbdxn6rN6E84EF9gVteAhm0tffMYK0m1aMtIX4dVltEQUL8V0unFTr8Dmi5RVqBBjTYj5BoJYDuEEHCW5sHmsSNcF2wpMCtgz48vMBsMS1x3Ry2OPdSNI8bbEQ1pcLpV/OfLKJb9O3GB2QKvgMupYFeDhminmrFLPwhizQ9RnD45DxP2NQrMfroqjL//K3GBWbM8boF8j4L6Rh2hcO52QOguW74Dec4CRBtCiDWnLzDbWuxVCGF68/jdxQKzA1NGweymm27ChRdeiMrKShx77LE4++yzceaZZ0LTNFx00UXZ7iMNQHabQFGRsX8m9X1NAYmFzzXglaU70Rwwju03RsH085w44uAOL0N2l1ETrLVmmKICig3QYnHzy977OIY//iWMr9caoc7rAc482YHpP3Ugz53Bm1ck2LWCf2eOPKNvQonvWywG6MYbu5QSr/8zhCdfaMD6zUZIKS4UOG+aHadc1H4aVuoSseYo9KAG6BLQJLTmKFSnDYq3fXVkyB/BK8/U4ZUnOwQeVcBZlgd7fvvpuB/t68DPTs/H/qON720O6njj3QCeeqUJTYH27/1hcwz3PZmdkhgj97DhkjPzcfiBTiiKQDQm8dbyIP78chO27+wbo2+KQ4WzzANnilF6qUlEdgUR9YeAlh+h4lThKHHD5umZlaxSSkTrQojWhyBbRjiFXYGjyA2bz8GA1s9lbUumrVu3oqamBuPHj8/GzfUoVv4n6h2HPXYMmvc7HNNvDOC7DTr0DlPEFGFkkl9f68RPTnAYE+y9LTu7d3zjaX2JatwJRIN44fUIfvdouO37225PAUbtpeCPd+XBne3T3S4fkFeQvG8N1YAWwSNPhfHEXyNxGwi0fsuPJg6C4+DhWPmPtQhu9iedfO4ozYOjyIVYcwShrckL+zkHeWAvcGLKJDduubIAuh6/7ZemSWzaHsMvbq+NC2fZsO/edtw3pwSqirgFBTFNojkg8fPbdmJLdd8IZ6lITRo/q0jix9L6M8jqfUqJ0LYmaM2Jy3XYC10pN0Mn6zJb+T/j8diNGzfirrvuwlVXXYU77rgDGzdu7BOhjIh611MvRrqEMqA9VM17JIy6Bh3wlhgHOo8GtH7tLcGOXTru/kM47vvbbk8Hvv1ex6K/9cDqwkShrOPX+aVY852GJ/5q3Hfnj7tSAp/+pwY13+xEtD6UckVgZGfAqEy/LXW15XBNM/JcEjdcVgCJrnuxqqpAVYUNF07LbpFUIYBbriyErVMoA4yvPXkC1/0s+ZtOXxKpCyYNZYDxM5BadhekxBojSUMZYBS+1UKZn3om68voVOarr76Kc889F4qiYOTIkXA4HLj33ntx+OGH49lnnzVVjJZoINvvxMHwThud6270OP1Hh+Pl+5q6hLKONA14/V2BC/dK8TlRCEAIvPqvNPenAy+8HsX087J4usddkLqMR8tpzZeXxqCqxuNJ1mzrF9sRbUhfITm8MwCYGOQaXiRhtxnV+xNRVYFTj83DwhcaEcvSANb4sQ4MKU/+1mFTBQ7ez4mKUrXPnNJMREqJaIOJHQL84fQrbbvB1H1yV4J+LaNgNmvWLBx77LF45plnUFJifMqNxWJ47LHHMHPmTDz77LNZ7SRRf+OdNtrY3Luf8zfoaGhM3UZRgB82mTvVtn5L+na76iUCQcCTrbM9NkfyLaVaCYHvN8qkoQwwbiJQG2ybM5SKmd0BACDoj0LTjecwGW+egqICBTt2ZWdkZ1ilDbqUScMgYNS0HFrRt4MZdNl1WDZRs0h2R8xSjdB1pw31XRmdyqyrq8Njjz3WFsoAwGaz4eqrr4ae6qMxEQ0oLqeAmXErT5650S23S6TduUlRAEc252SbrJ3myUvfN9Vu8iU3wZ6WidjsiqnnNxjK3hyzYCh1KGtr14dWaCZkcsRVZHmBpqnbM/n7QX1TRr9S06dPh9+feGXPoEGD4r7+29/+lsldEFE/4HYJHHaQmnJER9OAHx8iu07M6kxKTD5MTzkqparAUYeoafeZ7JZgmiG/ltpmPz5cSfkQFEWgbHQpVG/6ivKOZHXOOom5HbCleKyaJvH5V+GsTv5fvjKEaCz57UkpUVuv4Zvv+vZek0IRUPPSn1SyebO7MtOWb6L4bf7A2dd0IMroVGZxcTEuueQSTJ06NW4ex7p167Bz507MnTsXABCNRvHKK6/gjDPOyE5viajPueynDnz8eeJSFKoCHLCPigPGCaMGWKptmaJBHHyAin1HK1i9VkfnOdet33bpudldJQctYpTrUG2J+yYEEGzElKNs+ONfBLbXyC59M0bxFAwZX4H6XUEEm5KHFsWpwuZ1IOZJPQlccan4YZvEp6tCmDDO2WXyv65LCAE8/ffUiwi6y98k8fKbzTjnZE/CkTMhBJ78W1OX56AvchS7EQwkD+aK2wYly4Vo7QVOYx5ikudP2BRT4Y36rox+o7766iusW7cOTz75ZMLrV69eDQDQNA1bt27NuHNE1PeN38eG393iwm/uDSEYAmw2Y5BJ04AJ+6m4+5du4wNe4w7AV27M6eosFgYad0IIgQdvz8MN/y+IFV9qUFVAAIhpgNsF3DHbjX1G9UAJnIZtQOFgY8/PzsNikQAQqIPLKbBgfh6uvTWI7zbocX0ryBe49c7D8dKaCFSXDa5KL0Lbm7vMYVJcKtyD840itBUehLY0GgVoOxEOBa4hxt5kv3moHrddVYhDDnAZlfylMXIYiUrMf7wBK1Znf5Xq439thMspMO3HedB142EoivHU/PnlRrz6diDr95kLqtsO12AvQtubuizGUNw2uAd7s15TTLGrcA/xIbS1sct8RGFX4B6SD8FTmf1aRnXMli5dioMOOghlZSmq8rV49NFHMXPmzIw611NYx4xy7bDHjum5yf+qvT3cRENG1XoLCAQlln4g8f1GCZcTOPYQgX1GJTjHqTqM8hSKavQ9UG9U2e9ASoml7+t4+uUowhGJfcfYcNMMBZ683fh7tjsBxW7MKYsEkXBZpM0F5PmMiUBazOibHl+6QEqJT1ZqWP55DJoG7DtaxXGH2+Av/DHmvrwdK/+xFgCga7pROiMYA4SArcAJm8fe5Y0+FowiUhOA1HQIVYG91A17gsKmo4bbcNTBbrhdAuu3xPCvfwezOrcskcpBKiYf7kZhvoLqWg3LPgpiV0M/GCrrROoSscawMeleCNi8jh7fsklKCa0pCi0UBWCcVlXzuv5+UN9hto7ZbhWYDYVCcLlciEajWLlyJQ4++OAubTRNg6paK/wwmFGu9UgwU2xGLTC7s30VoZTGdkbNdTBVf6GnqHajbx1XOLZu/h2o79ZNBQI6Lp4dwXffx4c1h11g7k1eHH94N/tmdwKekpbtmFr7phv7aAazUyUfAGo9x7UFMy0cQ3h7c5fVdTafA84yT9uISLQxjMiOQPzIiSLgLHXDXsBNton6kh4tMLtp0yYceuihOOGEEwAAdrsdzc3NuPbaaxGNxr9YWi2UEfVLQgEKOpwGbP1ULYSxt2N++tHtHqPYjL6p9q59c+UDnuJu3dxZvwh3CWWAceru5v/XiE++7MaIgs0B5A8yRufi+qYAeYVGDbMs06MagpsbE5Y8iPkjCG1vgpQSsaYIwtubu5bX0CXCNQFT9dCIqO/JKJhdffXVUBQF5eXlbceOOeYYjBo1Cr/5zW+y1jkiMsmd376HY2dCAA6XMbE+F/IKAIjkfXO1bB5uwpIPgOrq1FXP5/8+zZ6XcX0rbO9HIm5f1ushROtCKetjac1RaKEYwjtSz9MK7wxCmqizRUR9S0avOA0NDfjoo48wYsSIuOOjRo3CokWLstIxIuoGpzd13SUpjZGzXHDkZa1vi15KXxV9w6YoImaKfipq6lWgrRzZ25dQSomo30Rl97oQZCzNY9AltEDfLklBRF1lFMzGjRuXcALiiy++iMbGNDV/iCj70o3qCJH9SphmJBvF69LO3JSHxiZzE8vr/CYeq9nnI4vzUKUmTU31M7v/Yrb3aSSi3MvolXrMmDH4+9//DiklIpEIVq5ciZkzZ+KPf/wjzjrrrGz3kYjS0bXUBVqlzM3qTKmbq5xvsm/FReZCUkmhyfs0s/ZJz96G0UIVpqq2C5u5xylsOQjbRNSjMp5jtnnzZvzpT3+C2+3GhAkT8Oc//xlXXXUVfv/732e7j0SUTjhNEVEh0rfpKeHm9AHIZN9mnJ++4vnovR2wmQksUjfKiaTsmzTqlGWJEAL2gvTFQR1FLghH6scgVAE1L/0uAkTUt2T8ceuqq67Ctm3b8NVXX+Hzzz/Hzp078dBDD8Htdmezf0RkRqjRqKuVKGS0lsyIZb/QqClBf8vIWZIAFPKbHjGbdKARvJJRFOC2/+1GVfRAPYAE20G1ft1cb25UrRschS6IFNso2XxOqC4bnGWp5905B3lY04qoH8oomH3xxRf44osvUFdXhzFjxiAQCOCiiy7Ctddei4aGhmz3kYjSkRLwVxujOx2DRGs9rqba3PVN14CG7V1Hp3TNqK8W6N5rxtP32nH4IV0/ABYVqfjjvT6MGt6NG9OiQEO1sbNA5z431fbIKKOwKXBXFUD1dBrtUgQcJW44BxmLDWx5driG5ENxxJ/WFHYFrsHerO/RSETWkFHp4iOOOAILFizA3nvvjS+++AKTJ0/Gj370Ixx99NG4/fbbcd9992W7n0SUjtSNMCHq2gu5xiLo+cKyLbXS7C0jVbGIEWg6h7DGHcZE+tZtjTqHoVY2h3F7QgWkBoSajf0qW6+2Kfj9rQqaoqVY/C+JQEBi4gQbDhgRyWwenRYF/DVGvTXVZjyPCUYXWyv6v/leDP4micpygZ+cYMdee3R/cYBiU+CuzIce1dqqyasuW5etdmx5dqh7+KBHNMiYUflfcaocKRsApKYj6g9DC8UgWir/2/Kd3I5pAMgomN14440477zzAAAzZ85ESUkJlixZAq/Xiz/+8Y9Z7SARdVPr3KneYHMYxWs7rnB05Bm1yxp3ANEEI1GpwpO3xAhlHUOdK984Fds66qeogG8QvKod55/fKXQ27zLaZkKPJZ3o3xSQuP72IP5vlbE/p6YZ+1E++0oUF51pxzWXOjMKS4pdhWJPHeyEEFCdNoD7Vg8YseYIQtvi9+eMNUUQrg3CXZnf49tBUW5ldCrTZjN+KR577DH8+9//xgMPPACv1wsA+Oijj7LXOyKyLqEYVfNbS2J0vEC0VNTvxhtIXlF7zbC420JL2Cs0/t/xdju3a92SKstuvSeIFV8ZgVJryZWt/z79chR/fY31xCg79IiG0Naum6YDADSJ4JZGlknp5zIKZqNHj8awYcNw1VVX4aabbsKZZ56JFStW4Morr8TTTz+d7T4SkRW5vPGhqKPWYy6vudsSSvvtJby+ZYcAuxuw2ZPfp5SAK/kedJnYsFnHux9r0FO8Fz75QgRa562TiDIQqU8z2q2bK1JMfVdGweyMM87A2rVrUVdXh3nz5gEAKisrMWfOHHz//fdZ7SARWZQ9zQpsIcxXzbc70xeibQ1vqVZJCgE4srsy/KPPYmm7tmOXxLr1HMWg3ac1pV89HWviCG1/lvGJaofDAYejfVVQx30ziWgASDZa1rmNuRszf5+mby87I1iRqGwbjEvdLit3RwOcqeosWS7hQtbCstFElJlYJP1uA8lWXnammayx1nkxQaL71GLI5krU0SPUlKcxAcBuB4YP5csp7T7VmX6VLyf/9298JSGizIQaU49gCQGETNYB02Kpq/BLCUSCxn22fp2qX1l0yHgVleUCSpJXS1UBTj7WhnwvyxjQ7rMXutK2sZnYPYL6rn4TzNatW5frLhANLFrUqIwPdCpq2/L/oL97ZTuadiXeIaB1n8/mluubdia+TymN+8tyMFMUgd/NccPlMEJY3HUC2GOIwHWXpX8zJTJD9dhTBi9Hqdson0L9liWCWSAQwA033IDBgwejvLwcV155JZqbU9cimjhxIoQQbZeFCxf2Um+JqE3IbxRnjYbbw1EsAjTubNnuqBv0GFC/3QhWrbXOdM34umF7+7FIsOsuB1rM2EWgcUfWHlpH40apeO5hD35ygh2ulvfM0iKBy8934Mn7PfDlc7SMskMIAWdZHpzlHigdTmuqbhtclV44irjtYX9nidh9ySWXYOTIkXj00Ufx6quvYsGCBQgEAklLb/znP//Bfvvth2uuuQaA8Ys8ZcqU3uwyUUoxKPjYPQ4f5O2HHbYiOGQUBwW/xTGBlSjTMti2TLUDbp+xElLACD+hpqxusJ0xKY2RrNZ5XVJv+boTp8dYVdla+T8SaN/js0V9QwwvLKrG3/8ZRV29REmRUV3/nFMd8eEnFmkpONt7W01VVSq45WoX5lzlhKYDNnV3wpjE6HKJ+u0hbN4Uhs0mMGpsHpoUB7bVZa3LmfVMSsT8YUTqw5BRY1cCe74D9kJXl+2hqGcIIWD3OWH3OSFbPnxwt4eBI+fB7Msvv8Rxxx2HK664AgBw+umno6amBs899xwWLlwIp7PrkO7jjz+O+fPno6ysrLe7S5RWFCr+UDQV3zqqjLWBQiAqbPgwbz987B6Lq+r+jsO6c4MON+AtNf7f+uJscwL5LiOcNe/K8iPoBpcX8BQbQau1b3aX0edAAxBsCaHe0vYyFkIY4dLpNS7+GiAWRvVOHZfOCqCmVrZNtt9WI7FgUQSvLoviT/fkoaw494P8QgjYdiufSIzMj+D/PmyGogC6DoTDEl+sMObj7XdYEdZV5+ZNWOoSoa2N0IIddkCQEtGGMKL+MNxD8qG67clvgLKOgWzgyfmrnN/vx6WXXhp3bMqUKdA0DX6/v0v76upqLFq0CMOHD8eUKVPw2muv9VZXiUxZ5v0R1jqGAkJAdnhR1YWCqLBhYeGpiGpmy0MoXUNZx/+7vOZrhWWbajeq9XfsT8f/5xUYAdKVb4SyzuU1Wv+fXwZA4Lb7QtjRIZS10iWwvUbijod6aZupHjamQmLl58ZUjY6PVdOMy9ef1MPjzE05hEhdMD6UdSSB0LamthEcIuoZOQ9mhx9+OOz2+E9guq6jsrIy4YhYXV0dZs2ahYkTJ+Ktt97CaaedhtmzZ/dWd4lS0qDg/bz9IUXiPy0pFDSqeXj3G5PV6Vsr5yf71Cwl4M7PoKdZkK6qv5RGKHOl6F9LWNu4w4FPVmpIttOMpgMffqJhW3XfL+JavT6QcjFrNCoxrLD3w4+UEtH61OVIpCYRM1EAlYgyl/Nglsjbb7+NG264IeF1Y8aMwZ133ol33nkHq1atwoQJE3D33Xfj7bffTnp74XAYfr8/7kLUE+pUL5qV1JNzVanh6y0mR7lsjtTXCwGoadr0FFuaav1CGBX9VVvawrBfrU1/dxLA6nUpNkDvC3QNmzZF0m5eEG7u/Wq1MqYbw5Np6KE+/jMgsjjLBbPPP/8c9fX1uOqqq9K2HTduHJYtW4aioiI8++yzSdvNmzcPBQUFbZeqqqpsdpmojZpo0nsnEoBNNTkiYumzRiY6Z/K0l2ry+bDZ+vh8GyFMbV6QZMDVGvr4j4DI6iz15x8MBvHrX/8aL730UpfTm8kUFxfjjDPOwKZNm5K2mTNnDhoaGtouqdoS7Y5CvQmDYnUpA4kuVBw20mStrWgw9fVSpm/TUyLB9JX/o8H2UhrJCIGD9wXUNBPq7XbgwH36+KpAoWD0WHfSYrWAMe9Mzev9CfbCpkDY078l2Dyc/E/UkywVzGbNmoW77rqr2/tuFhUVYciQIUmvdzqd8Pl8cReiniAAHN/8WdJTd4rUMTRagwP3SF2nr004kLjoakfB7BZUNS3UBEAm7ltbkdlGo9ZZqjlyWhRFnghOO94GJUkzIYAzTrT3i+r6jiJX0i2eFAUoKbHhu5ref5xCCDiKU5+GV5wqFG4HRNSjLBPM7r77bpx77rnYZ5992o6Zrea/YsUKnHPOOT3VNaJuOSS4GlOaPgVgBDEAEC3/lmoNuKLuNfN7cUMa5SRaw1lr4Gn9f/Mu8/tRZpvUAf8OtIWzjhfAKDKrx4yRtc47BLT+q2vG4wNwwxUuHHKgMSLWWmG/dRTtyIkqrpveP7ah+b5G4OAjC43pgS2Ps/X3obBAReGePsT03ARQW74D9qLEuxgIuwJXZT7LNxD1MEt89HnuueewatUq7LnnnnjppZcAAA0NDdiwYQPmzp2LGTNmYMuWLXjjjTcwc+ZMVFRUYPbs2XC5XFi8eDFGjBiBk046KcePgsggAExtWo4JoW+xvHgCql1lcOth7L9zFQ4Ifgc7ujl5WosCdVuNAq2ttcC0lgKzeo4nYsfC7X1zeoxjrXtadpxvF/ID0YBRt0y1A2jZ+zIcQOtcNZdT4KG5bnyyUsPrb0VRs1Oiokzg1Ml2HHyA2q8CwertCoZPKEapI4bGuihUm0BekQPf1yqozqD+cLYIIeAszYMt34FYQxhaRINQBGz5Dtg8DohkQ5pElDU5D2bvv/8+LrnkEkQiETzzzDNx1y1btgyAUSKjpsb4VO31enHPPffgueeew6mnnopx48ZhwYIFvd5volScQ0tw3G/OxlmTD4BoGRYJbTgQG+97FTv//kkGtyiBcJNxsZr80vgVmqodcLiAhh0AOhbqiqXdpklRBA6dYMOhE3L+0tTjdviBHbABsAEagO257lE71WmDOqj//wyIrEjIAVgt0O/3o6CgAG8OHQmP0scnE5PlOCqLsP9rt8Be6IHoUCJe6hJCEfj+1uex5yES4pBJOexllhRWAoradQ5Z6zZNdVty0y8LqvUch7kvb8fKf5ioDUJE/U4sGsDyFy5DQ0NDyrnulpljRtRf7HH9abB1CmUA2k4DDf/lWYA9R7XHssntS16jTAgjsHmKe79fRER9GIMZURYpeU6UTpsIJcVmisKmAJXDerFXPcTpTV8uI1fbRRER9VGcRECURY4yHxRH6j8rGdMh8tJsZ9QXKEr6yv9ERNQtHDEjyqJYQyBtG6EIIJKjEhfZJJPUMOt4vbW3LiAishwGM6IsitU3o/79ryBjyctYCJsKbNvYi73qIRETOw5EQz3fDyKifoTBjCjLNt73mjGYpHUt7y51HdXPfwgELFj2ors6F43tqPVYc12vdYeIqD9gMCPKsqYVP2D1Jb9HtNbYKknGNEhdQmoatj/zPr7/5bM57mG26ED9tvZCsnGnNiXgr859AVwioj6Gk/+pT5NSIhgEIhFjrrnbDTgcuZ903vDBanx26M0oOmZfuEeUQ2sKYdey/yK6w9+1sVCMqvlCadnCKJB67paV6DGjVpnLBzhbVmBGgkAwh+Xr+whVBQ4/0IW9h9kQiQL/XhHC95tiue4WEeUYgxn1WaGQxI6diNsQur4BcLkkykqNKvI5pemoe+sL1L2Vok1eIeDKjz/mKQYCdS2bhFucogL5ZYDN0R4m8xxGmYymHUa1f+pi/9EO3HZ1IUoKVURjEooALj8nH//5bwhzH6lHU6CPBHMiyjqeyqQ+KRKRqNkRH8pahUJATY0xmmZp7gIjlAnR9eIpBhyeXPcwDQH4ylv2vkR73wGj8Kyv3BgFpDjDh9hwz03FKMw3nhu7TUBVjeftR/s6MW8Wi/ISDWR81aQ+ye9PfbYvHAFCVq5IYbMblfOT1fqSEsgr6N0+dZfTk3g7JqAlpCmAqx/Ua8uy8071QFXRFsY6UlWB/Uc7MGGffrAzBBFlhMGM+hwpJZrTlwtDoLnn+5KxQZXpi7OqNuMUoVU5TVT1d1p91K+3SRx3qBu2BKGsVSwm8eND3b3YJyKyEgYz6rd0K5/JtDvMTfC38qlAYaLyv5X7nwOqAjjsqec+CgXw5OV+AQsR5QZfNanPEUJANfGba7Py0pbmRnNbFll58rwWTV/5X4v2Xn/6AE0X2FmXuoSIlMCWapYZIRqoGMyoT/Lmm2hj5bNoO6uN0JUs2EhpVM3XLRzMwk3pR8z6wsrSXrb4rQC0FMO5qgK88a6Jc/VE1C8xmFGf5MsH7Pbk1xcUAPY0p4xySwLNu1r+2+lNurVQq9Wr5kfDRvBKtGemlEY9swgDRmcvLm3GD5ti0LT450xvCWtPvNyErTUcMSMaqBjMqE9SFIGKciDfGz9oY7MBJcVAYYGVQ1mLaMiojh/rsHy0NdA0bO8bpwGbdwGB+vbq/4BR7T/oBxp35KxbVhYMSVzz/2qx+K0AguH2521rjYY7F9Tj6b9zlJFoILPyLByilBRFoLgYKCyUiMWMgGazGXPQ+oxYBPDXAEIFFMUINTJBcTYrCzUaF8UGCFh7XlwCUkp8+qWCV96MYf3GGHw+BScd68CJR0i4nBncYGuZELvbeD6iEeP56aA5KPHg034s+GsjBpepiEQl55UREQAGM+oHFEXAYeGqEqZIDdD6+BuzlefDJaFpErc/LPHGm36oqvEjEAL47PMQnnrBjgXz3BhU3I3lvTYn4CsD0LHYrgNweeGMdd2mKhSW+GFz33veiKjn8FQmEQ1Yz7wq8MabRsG71lzcOl1uy9YobrwzbH4HCaF0DWVA244IHtsODC/t4+GbiHocgxkRDUjRmMSzLyVfnKBpwJdfh/HlOpMvk04PuoSyTo4ZE+lmL4looGEwI6IBacM2BbvS1BRTFeDTL0zeoN2V8mohgDGDOWJGRKkxmBHRgKSbyUgCKWuOxbdNPVrW2oSIKBUGMyIakIZV6vB6Ur8EahpwwBiTaSoaTrkTgpTAdzVqd7pIRAMQgxkRDUhOh8DZ0zxJR7FUFRi2hx0H72uyfEm4pf5YknAmBPDeNymqIhMRgcGMiAawy8+RmHiQMTesY0BTFKDAp+LeX7vN18XTNaBpp/H/juGs5f+BaDHWbGeFIiJKja8SRDRgOR0CD/7GhqUfFuDlN0LYtDmG/HwFJ0924awTgOKCbtQwA9p3bXB5AYcbgGjZuqoRQedIANt74mEQUT/CYEZEA5rdJjD1GB1Tj3EAyEKlYi1q7HPaea/TTHYRIKIBh6cyiYiIiCyCwYyIiIjIIhjMiIiIiCyCwYyIiIjIIhjMiIiIiCyCwYyIiIjIIhjMiIiIiCyCwYyIiIjIIiwRzAKBAG644QYMHjwY5eXluPLKK9Hc3Jy0/eLFi3HBBRdg7ty5OPfcc7FmzZpe7C1RD7A7AUceYGMVUiKigcwSlf8vueQSjBw5Eo8++iheffVVLFiwAIFAAE8//XSXth9++CGmT5+OtWvXorCwEB999BEmT56Mr776Cj6fLwe9J9oNDg/gKQQUtf1Ya+X4aChn3SIiotzI+YjZl19+ieOOOw533HEHTj/9dPz5z3/GySefjOeeew7hcLhL+9tuuw1Tp05FYWEhAGDSpElwu91YuHBhL/ecaDc5PUB+CSA6/RkqNiC/jKNnREQDUM6Dmd/vx6WXXhp3bMqUKdA0DX6/P+54XV0d3nvvPRx44IFxxw844AD89a9/7fG+EmWNogB5RYCUgBDx17V+7Snq/X4REVFO5TyYHX744bDb7XHHdF1HZWUlysrK4o6vWrUKsVgMVVVVcceHDh2KlStXQkrZ4/0lyoqySiOcdQ5lrYQAbA5AtSe+noiI+iVLzDHr7O2338YNN9zQ5Xh1dTUAwOPxxB13u92IRqOora1FaWlpl+8Lh8Nxp0U7j8QR9TqXO/FoWWeKasw5IyKiASHnI2adff7556ivr8dVV13V5bpo1HiDUlU17rjNljpfzps3DwUFBW2XziNuRL0uFEwfygBA13q+L0REZBmWCmbBYBC//vWv8dJLL3U5vQmg7dRmIBCIOx4KhWC321FUlHhOzpw5c9DQ0NB22bRpU/Y7T9QdO7YaoSvZ6XcpgViEo2VERAOMpU5lzpo1C3fddRfKy8sTXr///vtDCIGtW7fGHa+pqcGECRO6jKS1cjqdcDq5wo0sRNeBQD3gLel6SrM1rDXX5aRrRESUO5YZMbv77rtx7rnnYp999mk7tm7durg25eXlOOKII7By5cq446tXr8bpp5/eG90kyp5wM9BYC0g9/rgeAxp3ALGu5WKIiKh/s0Qwe+6557Bq1Srs2LEDL730El566SX86U9/aiswO2PGDJxyyikAgPnz52Px4sVtE/g3bNiAmpoaXHHFFTnrP1HGIs1A3RbAXw007gQatgP121hclohogMr5qcz3338fl1xyCSKRCJ555pm465YtWwbAqF9WU1MDwCiv8eijj2LGjBkYP348NmzYgCVLlrQVnCXqk6IcHSMiIgsEs6OOOiphhf+OXnzxxbivp02bhmnTpvVkt4iIiIh6nSVOZRIRERERgxkRERGRZTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRfSbYLZu3bpcd4GIiIhot1gumH300Uem2k2cOBFCiLbLwoULe7hnRERERD3LlusOtPrwww9x8803Y/PmzVi/fn3Ktv/5z3+w33774ZprrgEACCEwZcqUXuglERERUc+xRDBrampCVVUV9t13X2zevDlt+8cffxzz589HWVlZL/SOiIiIqHdY4lSm1+vFsGHDUFFRkbZtdXU1Fi1ahOHDh2PKlCl47bXXeqGHRERERD3PEsGsO+rq6jBr1ixMnDgRb731Fk477TTMnj07190iIiIi2m19LpiNGTMGd955J9555x2sWrUKEyZMwN13342333476feEw2H4/f64CxEREZHV9Llg1tG4ceOwbNkyFBUV4dlnn03abt68eSgoKGi7VFVV9WIviYiIiMzp08EMAIqLi3HGGWdg06ZNSdvMmTMHDQ0NbZdUbYmIiIhyxRKrMndXUVERNE1Ler3T6YTT6ezFHhERERF1X58fMQOAFStW4Jxzzsl1N4iIiIh2i6WCmaZpkFJ2OT5jxgyccsopAICZM2di7ty5CIVCAIDFixdjxIgROOmkk3q1r0RERETZZolgFolE8Prrr+P111/Htm3b8NRTT2HDhg1t19fV1aGmpgaAUfPsnnvuwfjx43HDDTegtrYWCxYsyFXXiYiIiLJGyERDVP2c3+9HQUEB3hw6Eh5FzXV3aAA67LFjIA6ZlOtuUC+q9RyHuS9vx8p/rM11V4goB2LRAJa/cBkaGhrg8/mStrPEiBkRERERMZgRERERWQaDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWQSDGREREZFFMJgRERERWYTlgtlHH32Uts3ixYtxwQUXYO7cuTj33HOxZs2aXugZERERUc+y5boDrT788EPcfPPN2Lx5M9avX5+y3fTp07F27VoUFhbio48+wuTJk/HVV1/B5/P1XoeJiIiIsswSI2ZNTU2oqqrCvvvum7btbbfdhqlTp6KwsBAAMGnSJLjdbixcuLCHe0lERETUsywRzLxeL4YNG4aKioqU7erq6vDee+/hwAMPjDt+wAEH4K9//WtPdpGIiIiox1kimJm1atUqxGIxVFVVxR0fOnQoVq5cCSlljnpGREREtPssM8fMjOrqagCAx+OJO+52uxGNRlFbW4vS0tIu3xcOhxEOh9u+bmhoAAA063oP9pYoOX8gAtEYzHU3qBc1ao2IBJoQiwZy3RUiyoFY1HjNTzeI1KeCWTQaBQCoqhp33GZL/TDmzZuH22+/vcvxM7Z+n73OEXXH2esA/DnXvSAiol7W2NiIgoKCpNf3qWBWVlYGAAgE4j9xhkIh2O12FBUVJfy+OXPm4Prrr2/7Wtd17Nq1CyUlJRBC9FyH+wi/34+qqips2rSJK1tzhD8Da+DPIff4M8g9/gx6hpQSjY2NqKysTNmuTwWz/fffH0IIbN26Ne54TU0NJkyY0GUkrZXT6YTT6Yw71rqqk9r5fD7+EeYYfwbWwJ9D7vFnkHv8GWRfqpGyVn1q8n95eTmOOOIIrFy5Mu746tWrcfrpp+emU0RERERZYqlgpmlawklxM2bMwCmnnAIAmD9/PhYvXgy/3w8A2LBhA2pqanDFFVf0al+JiIiIss0SpzIjkQj++c9/4vXXX8e2bdvw1FNP4ZhjjsGwYcMAGPXLampqAACHH344Hn30UcyYMQPjx4/Hhg0bsGTJEp6a3A1OpxO33nprl9O91Hv4M7AG/hxyjz+D3OPPILeEZPEvIiIiIkuw1KlMIiIiooGMwYyIiIjIIhjMiCwoGAxiy5Ytue4GERH1MgYzAmBUIt5jjz2wfv36XHdlQHrjjTcghGi7FBcXw+1257pbA9Lbb7+NWbNm4bHHHsOaNWty3Z0BY9SoUXF/A62XRx55JNddG1Cam5tx8803Y/78+fj1r3+NCy64ANu2bct1twYUS6zKpNy7+eabsWnTplx3Y8B69tln8cwzz7R9PXjwYBQXF+ewRwNPIBDAjBkz4Ha78cADD3TZk5d6zocffohDDjkE9957L+x2e9vxc889FyeffHIOezbw/OIXv8D48eNx3XXXAQCeeuopXHLJJVi6dGluOzaAMJgR3nnnHTgcjlx3Y8Bau3YtKisrccEFF+S6KwOWpmk455xzYLPZ8Mwzz3Crtl7m9/vx1FNPQVHaT+KsXLkSVVVV2HPPPXPYs4HnlVdewU9+8pO2rw8++GBcffXVuevQAMRTmQNcIBDAE088wT+8HHr44Ydx7733YuTIkbjxxhvbavZR73n44Yfxr3/9Cw8//DBDWQ6cfPLJcaEMABYvXoypU6fmqEcDV3l5ORYsWABd1wEAn376KY488sgc92pgYTAb4ObOnYtbbrmly4si9Z5x48Zh+vTpaG5uxj333IMJEyZg7dq1ue7WgKHrOubNm4eRI0fi9ttvx7hx4zBq1Cg8+eSTue7agMZglhu/+tWvsHTpUvzP//wP3n33XTz99NNYsGBBrrs1oPDdeABbvnw5iouLMXbs2Fx3ZUC74oorsHDhQmzatAl33XUXtm3bhssuuyzX3RowPvvsM1RXV2Py5MlYuHAhvv76a5x33nm45JJL8M9//jPX3RuQNm3ahC1btuDQQw/NdVcGnIsuugj33HMPXnjhBZx44ol44IEHMHTo0Fx3a0BhMBugwuEwHnnkEVx//fW57gq1sNlsuPHGG/GrX/0KH3zwATZu3JjrLg0IGzZsAAD87Gc/azv2q1/9CkVFRVwRmCOvvvpqwtOb1PM0TcOmTZswa9YsOBwOHH300VixYkWuuzWg8Ld+gLrzzjsxe/Zs2Gxc/2E1l19+OQBg8+bNOe7JwNA6l6bjAhi73Y6DDz4Y3333Xa66NaDxNGbuzJo1C/n5+bj77rvxySefoLCwEKeddhpCoVCuuzZgMJgNUM888wwmTpwIl8sFl8uF0aNHAwBGjx6NKVOm5Lh3A1tRUREAYMiQITnuycAwatQoAOhS0LewsJAlS3LA7/dj+fLlfB3KgXA4jEcffRRnnnkmAGDMmDF49dVXsXnzZrzzzjs57t3AweGSAWrZsmWIRqNtX2/ZsgWTJ0/GkiVLsPfee+ewZ7RixQpMnDgRw4YNy3VXBoTx48djxIgRePfdd+PCwLZt23DUUUflsGcD0z/+8Q9MmjQJXq83110ZcMLhMGKxWNsoMgDsu+++KCsrg5Qyhz0bWDhiNkCNGDECY8aMabuMGDGi7fgee+yR494NHMuXL8eZZ56JTz/9FIBRvuTuu+/Gn/70pxz3bOAQQuCxxx7DX/7yFzQ2NgIANm7ciLVr13IOZg7wNGbu+Hw+TJkyBS+++GLbsQ0bNsDj8bBkRi8SkjGYAKxfvx577rknfvjhBwwfPjzX3RkwVq9ejTPPPBPfffcdzj77bAwbNgyXXXYZ9tprr1x3bcD55z//iSeffBJDhw7F5s2bceONN+LAAw/MdbcGlFgshrKyMvz3v//lB8Qc2bVrF+bMmYO8vDwMHjwY69evx7XXXts23YV6HoMZERERkUXwVCYRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkRERGRRTCYEREREVkEgxkR9VnNzc344x//iKlTp+J3v/tdwjabNm3C7373Oxx22GH44IMPermHBr/fj3nz5uHwww/H559/ntFtvPHGG6itrc1yz4ztp95+++2s3y4RZYbBjIj6LKfTiTPOOAPvv/8+gsFgwjbl5eWYOHEiPv74Y2ia1qv9a2pqAgDk5+fjmGOOwfLly+M2iDbrt7/9Lex2O0pKSrLdReyxxx7YtWsXnnzyyazfNhF1H4MZEfVZNpsNxcXFKCoqStrG4XBgzz337MVeGbZs2YJbb70VgLFR+uDBgzO6nT/84Q+QUmLKlCnZ7F6cs846C8uXL8c777zTY/dBRObYct0BIqL+JhAI4Oyzz8aoUaN263Y2b96M2267DWvXrs1Sz5K7/vrrccopp+Crr76C0+ns8fsjosQ4YkZEWfPyyy/jjjvuwNy5c5Gfn4+VK1e2XffQQw9h5syZOOaYYzBt2jRs3LgRAPCPf/wDF110EX73u9/hpptuQklJCUaOHInFixe3fe+aNWtw6aWX4v7778dZZ52F++67b7f7+u233+Laa6/F+eefj/Hjx2PBggUAgB07dmD+/Pk44IADsHbtWkybNg0ejwc//elPIaVs+/5gMIjrr78ed9xxBy6//HJ88sknbdf9/e9/xw8//IB///vfmD59Or744ou262pqanDmmWeioKAABx54IDZt2pS0j7///e/xox/9CPn5+XHHv/vuO1x//fW49dZb8eMf/xi//e1vAQDV1dW46667cMghh+Drr7/GySefDI/Hg7POOgvRaBRz585FVVUVysvL455fABg9ejSklPjLX/6S+ZNKRLtPEhFlQSgUklVVVW1fL1q0SK5YsUJKKeVDDz0kX375ZSmllJqmyUMPPVQefPDBUkopq6urpc/nkwceeKBcuXKl/P777+WJJ54oHQ6H/OGHH6SUUh588MFy9uzZUkop//vf/0ohhNy2bVvbfQ0bNkzeeuutSfv2ww8/SADynXfekVJK2dzcLKdNmyYjkYiUUsolS5ZIAPKNN96Q4XBYPvLIIxKAvO666+TatWvlq6++KgHIV199te02r7nmGnnTTTdJKaXcunWrtNvt8thjj5UPPviglFLKo48+Wv7sZz/r0ofzzz9fbt26Ve7atUtWVVXJq666Kmm/R40aJW+55Za4YzU1NXLixImyvr5eSinlO++8IwHIpUuXyp07d8qrrrpK2u12OX/+fBkOh+V7770nAchp06bJL7/8UkajUfmTn/xEjhs3rsv9nXXWWfLkk09O2h8i6nkcMSOirAiFQti8eTMeffRRAMBPfvITDB48GNFoFLfffjs+/vhj3HzzzbjlllswZMgQOJ1OBAIBDBo0CEVFRZg6dSoOOOAA7LnnnnjmmWegqiqef/55AMD555/fNsfK7XZDSonvvvsu474uWLAANTU1+PWvf42bb74Zy5Ytw6RJk/D999/D4XBg3LhxAIBrr70WI0eOxNSpU1FRUYGvv/667Taee+65trlrgwcPxsEHH4xjjjkG11xzTcr7nj59OgYPHoyioiJMmjQJa9asSdguEAjg22+/RUVFRdzxu+66C8cffzwKCgoAAMcccwwWLVqEQw89FCUlJTjooIMQjUZx0003weFw4KijjkJxcTHGjx+PffbZBzabDSeffDK+//77LvdZXl6Ozz77zPwTSURZxzlmRJQVBQUFuPHGG/GLX/wCzz77LB599FEccMABWL16NWpra3H77bfD7XYn/X4hRNv/S0tLMXLkyLbTnddeey02bNiAe++9t21lZTQazbivH3/8MY488kjMnz/f9Pc4nc64lZ9SSvzwww9tX1dVVaGurq5b/XA6nYhEIgmvq6+vb2vT0VtvvYUZM2bEHTv//PNT3o/X64372m63IxQKdWnn8/mwc+fOdN0moh7EETMiyprf/e53eOGFF7B+/Xoccsgh+PDDD9sCwOrVq7u0TxZKAGM1ZetKxscffxyXXnopZsyYgXPOOWe3+xkKhbrdn84uvvhiLFq0CLt27YKu61izZg0uuOCC3e5bq9YwFQgE4o5Ho9G4QGhGx9CbipSSE/+JcozBjIiyYvv27fj8889x9tlnY/Xq1Tj44INx7733Yu+994bT6cTjjz8e1/6dd97BihUrEt5WNBrFd999h+OOOw5btmzBzJkzcfXVV3eZBJ+p/fbbD8uWLYsLOJqm4YEHHjB9G/PmzcOBBx6IOXPm4PHHH8cTTzyBgw46qO16IUTcYoHu8vl8KC8v7zIKN3bsWDz55JNoaGhoO9bU1IR//etfGd9Xq/r6euy99967fTtElDkGMyLKilAo1Lay0efz4ayzzsLQoUPh9Xpx+eWXY8GCBbj88svxz3/+Ew8//DD+9re/4ZBDDmn7/nXr1rX9/w9/+AMOO+wwTJo0CTU1NdA0DUuXLsWuXbvaVhNWV1djx44dAIxQlapwa+vpz9Y2V155JQDgqKOOwlNPPYWlS5fivPPOw0knnRTXvmOwikajcffx85//HPvuuy+OPfZYVFRUIBAIxJ3qLCwsxDfffANN0/D555+3nXqNxWJtbWKxWMpTsieccELcvDYAuPHGG7Fz504ceeSRePLJJ7Fo0SJcfPHFOOyww5L2PRQKxd1P6+PoHBy//fZbHH/88Un7Q0S9IIcLD4ioH2lddXjRRRfJxx9/XF577bVy165dUkpjFeQll1wi8/PzZWVlpZw9e7YMhUJt3zts2DB5wgknyKuuukqee+658qc//Wnb9+q6Li+88ELp8XjkSSedJNetWycnTpwoR44cKVeuXCkfeeQRqSiKPOigg+Ty5cu79Gvjxo1y5syZEoC88MIL5bp166SUUi5dulSOHTtWut1uedhhh8mPP/5YSinljh075AUXXCAByFtvvVU2NDTIBQsWSCGEnDhxovz222+llFLecsstsrKyUqqqKgFIALKioqJtJembb74py8rK5NFHHy3Xr18vr732WglA/vSnP5VbtmyR7777rhw6dKj0er3yzTffTPicfvLJJ3Lw4MFS07S44y+//LLce++9pdPplCeccIJcv369lFLKtWvXyhNOOEECkI899pj0+/3ysccekwDkvvvuKz/77DO5evVqOWXKFAlA3nfffbKpqUlKKWUgEJBer1euXr06o58/EWWHkHI3xtqJiLJg+PDhuPjii3HbbbfluiumhEIhXH/99XjwwQdht9sBAOFwGEuXLsXatWtxww03ZO2+pk+fjqlTp2LatGlZu81EFixYgDVr1mSlRhwRZY6nMomIuumXv/wl3G53WygDjNWT++23HwYNGpTV+3rwwQfx4osvxs0py7aNGzfi/fff79YqVSLqGQxmRJRzmqbtVvmL3tbc3IzFixfj008/ha7riEaj+PDDD3HfffdlZdVoRx6PB7///e/xwAMPwO/3Z/W2AWDTpk3485//jCeeeAIOhyPrt09E3cNTmUSUM7FYDAsWLMDVV1+NMWPGYO7cuTjrrLNy3a20Ghsb8b//+7949dVXEYvFMHbsWFx88cW4+OKL40bRsqn1pdps6QuzdF2HovAzOpFVMJgRERERWQQ/JhERERFZBIMZERERkUUwmBERERFZBIMZERERkUUwmBERERFZBIMZERERkUUwmBERERFZBIMZERERkUUwmBERERFZxP8H/U9vVLViMeYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "# Plot the training points\n",
+    "plt.scatter(*X.T, c=y, cmap=plt.cm.RdYlBu);\n",
+    "# for i, color in zip(range(n_classes), plot_colors):\n",
+    "#     idx = np.where(y == i)\n",
+    "#     plt.scatter(\n",
+    "#         X[idx, 0],\n",
+    "#         X[idx, 1],\n",
+    "#         c=color,\n",
+    "#         label=iris.target_names[i],\n",
+    "#         cmap=plt.cm.RdYlBu,\n",
+    "#         edgecolor=\"black\",\n",
+    "#         s=50,\n",
+    "#     )\n",
+    "\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=4)\n",
+    "clf = clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "# Plot the training points\n",
+    "plt.scatter(*X.T, c=y, cmap=plt.cm.RdYlBu);\n",
+    "# for i, color in zip(range(n_classes), plot_colors):\n",
+    "#     idx = np.where(y == i)\n",
+    "#     plt.scatter(\n",
+    "#         X[idx, 0],\n",
+    "#         X[idx, 1],\n",
+    "#         c=color,\n",
+    "#         label=iris.target_names[i],\n",
+    "#         cmap=plt.cm.RdYlBu,\n",
+    "#         edgecolor=\"black\",\n",
+    "#         s=50,\n",
+    "#     )\n",
+    "\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Let's try our \"problematic\" dataset (for PCA and k-NN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "N_samples = 50\n",
+    "# samples points for class 1\n",
+    "X_1 = np.random.uniform(50, 200, N_samples)\n",
+    "X_1 = np.vstack((X_1, (1,)*N_samples))\n",
+    "# samples points for class 2\n",
+    "X_2 = np.random.uniform(50, 200, N_samples)\n",
+    "X_2 = np.vstack((X_2, (2,)*N_samples))\n",
+    "X = np.concatenate((X_1, X_2))\n",
+    "# data\n",
+    "X = np.concatenate((X_1, X_2), axis=1)\n",
+    "# labels\n",
+    "labels = X[1, ...]\n",
+    "# Plot also the training points\n",
+    "plt.scatter(\n",
+    "    x=X[0, ...],\n",
+    "    y=X[1, ...],\n",
+    "    c=labels,\n",
+    "    cmap='jet',\n",
+    ")\n",
+    "# Code below wants Nx2\n",
+    "X = X.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=2)\n",
+    "clf = clf.fit(X, labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z/RujIYMd22m3hz6uhzK/VlGRf3362l5fQj0ua6qPUOoqKy2J6BhWVNPj6al9T8ebv8fg4EHlKi7M1RUDam7/V6zd234JpN6KdUTq2IrE6yScKtcb6DnJeX41aZu8idS+KSk6ruKCox7/lrr1M9jxd/dk3HBN94dxweybb77R0aNHNXny5Crz7Ha7JKlOnTpu0+vWrf4zDHPmzFFycrLrp/IVN09atHBcevT24dAWLeS6Qteype+2LMtSmb1QZfZCWZYV0DoVBbJ+ebmUkuL4fd8+x++e1iso8D7PU5sFBb/WV3LiSED1SAp4HU/7wNc8T2NXUaDjeOhQ4GMU6rhK/q/va3t9CfW4rKk+QqnL+f+RGsOKano8PbXv6XgL5BiUpMaNa6beiqrbL4HUW7GOSB1bkXidhFPleoM5J1WcZsI2eROpfeM8N/v7mjp0KPA+jApmJ06c0P3336+33nqrytubklxvbRYWug9oUVGRYmNjleJMIZXMnDlTeXl5rp+srCyftRw86Ei3lS7Ouc23nfxo3YEDvtuy2WyqE1tfdWLry2azBbRORYGsHxPz6wcAWrVy/O5pvQYNvM/z1GaDBr/WF5fQOKB6JAW8jqd94Guep7GrKNBxbNo08DEKdVwl/9f3tb2+hHpc1lQfodTl/P9IjWFFNT2entr3dLwFcgxK0pEjNVNvRdXtl0DqrVhHpI6tSLxOwqlyvcGckypOM2GbvInUvnGem/19TTVtGngfRgWz6dOn6/HHH1daWprH+V26dDk58O4jkpOTo27dulW5kuYUHx+vpKQktx/p1ys4lZWXOz5ZccEFUqUPhf46/90YNW3dXU0zumvFuzHVt3Vy2Tp141SnbpyatD4noHUq8nf9995z1P/++1JqquPnggsc21V5vfh4x7z33qt+TN57z/GBgtjYX+uLq5eoJq3P0XIP7VZet149KTWjh2sdf7bhwgur7gNf87yNXUWBjOOFF0pJSf6N0fIVju2MjQ19XJtmdFe9ev716Wt7fQn1uKypPkKpq07duIiOYUU1PZ6e2vd0vPl7DK54N0bx9VP0/r9qbv9XrN3bfgmk3op1ROrYisTrJJwq1xvoOcl5fjVpm7yJ1L6Jq5eo+AaNPP4tdetnhePvbsV7/L0tX5kxwWzu3LkaNWqUTj/9dNe0Xbt2uS2TlpamCy64QJs3b3abvn37dg0dOjTgPl98sepAOT9RkZXleJ6O9/nlatnxSp3S4UplZZXr2Wd9L+sUzDoV+bO+8xEVWVnSH/7g+P/hwx1vaXpa7+qrpcxMz/Mqtnn11VXrO6XDldqXVf26mZlSYaHc1vG1DZmZjv4CnVfd2FXk7zgOG+b/GO3Lcmync5nhwx1tBDuuhYX+9enP9oZjPPwd23D2EUpdkR7Dyn3X5Hh6at/T8ebPMZiVVa70M4bW+P6vWLu3/eJvvZXriNSxFYnXSThVrtffc5LzvGfiNnkTqX3T6vShyvLxNy8rS7r2WvfpCxb4136tPy5DkhYvXqxVq1Zp8ODBrml5eXnKzMzU7NmzNX78eO3fv18rV67U559/rhEjRmj79u1KSkpSZmamevfurU2bNqlRo0Z+9efpOWYtWjguPXp7jlnl+e7PMVujXRv/z69lnYJZx9P6zueYOdev7jlmLVpIa9a4P8+o4nqVn2NWuc3qn7e1Rru++r9q2636HLNqxqDS88L8nhfwc8w811DxeT59+wbWtyS3Nis/MyiYcZV+fdZT5RrD+xyz0I7LmuojlLoiPYbhqjug9lv9+pr19Br39rqv+hyzmt//brV72S+Vn2Pmbx2ROrYiOU7hULleb+ckr+c9A7fJm0jtm+3r/qZf9nzu9vf14MGqzzHz9LfL+OeYrVu3Tn379lVJSdVPSKxZs0Z9+vTRiBEjtGfPHn399deSHF/J9Prrr6tr167KzMzUbbfdpo4dO/rdpzOYndFnprJ3rtWhvV+7ns7btHV3tex4peM5Ujk7dGD7Sq/zKwpk2VDWqW59b+Lrp6jkxNEKT3fuoFL7CRXk7lHF1RqldXQ9+f/Xmn79BgFf9eXl7HA8+T97e5V2M87y/HT16sZAUlDzAn7yv4caktM6SJZOfotB4H172jfu+yHAcd20REezt7um1eiT/0M8Lmuqj1DqivQYhqvuYNqvqOKT/5ObdZBscnvyv6c6IrH/K/blbb80zah8LvKvjkgdW5Ecp3DwfU6q/rxn4jZ5E6l9c2DHGmVtfUfFhbmuafH1U9S0TS+VFORU6T/t1Mv03YdzzA9mtcEZzHpe85LqxtV3fAzaXui4udPD+86+5ge7bCjreFq/vKxcpfbjiktorDp149za9NSH43u88mSzWYqt18it74rLSwqovuraDWYMgp0XKG9jFErflZcJdVztRUdlWTbFJSTX+L0e4RzbcPYRSl2RHsPKfdfkeDrbl62uZJV6Pcb8rSMS+79iX972S7B1ROrYiuQ4hUN156RAjxHTRWo7HN9Pe0RxCY0VVy/Ra/+lJYX6fMlNBDNPKgczAACAmuRvMDPm5n8AAIBoRzADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMETdUFYuKirS4cOHFR8fr6ZNm4arJgAAgKgU8BWzQ4cOafbs2ercubMSExPVunVrpaWlqUmTJho5cqQ+/vjjmqgTAADgNy+gK2YLFy7UPffcow4dOmjAgAEaO3askpKSZFmWjh49qh9//FF//OMf1a1bN7300ktKSkqqqboBAAB+c/wOZo8++qjKysr0/fff+wxcH3zwgcaPH68FCxYQzgAAAPzk11uZy5Yt04UXXqg//elPfgWt/v37a8GCBXriiSdCLhAAACBa+HXF7NJLL/X7yldubq5SUlKUlJSkO++8M6TiAAAAoolfwcxTKLMsS4cOHVJRUZEsy3JNe+GFFzRnzhyv6wEAAMCzoB6XsWDBAt199906duyY23TLsmSz2VzBDAAAAP4LKpjNmDFDY8eO1cCBAxUbG+uaXlZWppdffjlsxQEAAESToIJZs2bN9OSTT3qc1759+1DqAQAAiFpBfSXT1KlTtWHDBo/zdu/eHVJBAAAA0SqoK2ZjxozRkCFD1Lp1a7fpdrtdn3zyibKyssJSHAAAQDQJKpgNHjxYX3zxhY4eParExETZbDZJju/OzM3NDWuBAAAA0SKoYLZx40Zt2bJF7dq1qzLvrbfeCrkoAACAaBTUPWZXXnmlTjnlFI/z+vbtG1JBAAAA0SqoYPbAAw/ogQce0N69e91+MjMz9dRTT4W7RgAAgKhgs5yP7Q/A2WefrU2bNrnuLXNyPmC2rKwsbAXWhPz8fCUnJ6vnNS+pblz92i4HAAD8xpWWFOrzJTcpLy+v2m9GCuoes3HjxunYsWM699xz3TstLdU//vGPYJoEAACIekEFsz/84Q+KiYlRw4YNq8zr2LFjyEUBAABEo6DuMUtMTNSSJUv03//+1zXtvffe03//+1+1bNkybMUBAABEk6CC2bRp0zRx4kR99dVXrmkDBgzQlClT9N1334WtOAAAgGgSVDDbtWuXdu/erSFDhrim1alTR6NGjdLEiRPDVhwAAEA0CSqYnXvuuWrTpk2V6YcPH9Z//vOfUGsCAACISkEFs4KCAu3bt89t2t69e/XII4+oU6dOYSkMAAAg2gT1qcw777xTQ4YMUbt27dSqVSv9/PPPWr58uWJiYrRo0aJw1wgAABAVgrpilpqaqo8//ljnnXeeduzYoYKCAk2ZMkXbt2/XBRdcEO4aAQAAooJfV8z27NlT5Z6y+Ph4TZo0SZMmTfK63v79+71+pyYAAADc+XXFbP/+/XrttdcCanjfvn169dVXgyoKAAAgGvkVzHr16iW73a4JEyZo79691S6bn5+vv/3tb3rooYd01113haVIAACAaOD3zf9//OMf1bx5c/Xq1UspKSk688wz1bx5c8XHx6uwsFC5ubn6/vvv9cMPP2jGjBmaP3++YmKCuoUNAAAgKgX0qcwrr7xSO3bs0Kuvvqrly5drzZo1ys3NVWJiotq3b68BAwZo6dKlfC0TAABAEAJ+XEb9+vV1yy236JZbbqmJegAAAKIW7zUCAAAYgmAGAABgCIIZAACAIQhmAAAAhgh7MHv99dfD3SQAAEBUCOpLzLds2aL7779fO3bsUElJiWu63W7XwYMHNXr06LAVCAAAEC2CCmbDhw9Xo0aNdPXVVys+Pt413W63a+XKlWErDgAAIJoEFcyOHDmi7777TnFxcVXmDR8+POSiAAAAolFQ95gNHTpUWVlZHuc1bNgwpIIAAACilV9XzN59913Z7XbX7xdddJGmTJmia665xm250tJSLVmyRKtXrw5vlQAAAFHAr2D2t7/9TWvXrq0yfdWqVVWm2Wy20KsCAACIQn69lTlo0CDt379f5eXl1f6UlJTokUceqemaAQAAfpP8Cma33nqrWrRo4fr9008/9bjcli1b1Ldv3/BUBgAAEGX8CmZ16tRx+33RokUelzv99NM1ceLE0KsCAACIQn4/LqOgoEBTp07V7t27tWPHDl166aVu8y3L0k8//STLssJepD927dqldu3a1UrfAAAA4eD34zIaNGigJ598UikpKUpMTFRGRobbz+9+9zsNGzZM77//fkgFbdiwwa/levToIZvN5vp58cUXQ+oXAACgtgX0gNkGDRrozTff1Nq1az3eS1ZSUqK6dYN6Zq3Wr1+ve+65R/v27dOePXuqXfarr75S586dddttt0lyfBK0X79+QfULAABgioBTVExMjNcb/D/77DOdOHFCAwcODKjN48ePKz09XWeccYb27dvnc/kFCxbo0UcfVWpqakD9VNb50t8rrn5iSG0AAAD4UlJ4XJ8v8b2cX8GsS5cu2rZtm18djxgxIuBglpiYqMTERDVv3tznstnZ2Vq4cKGWLFmiXr16acqUKRo0aFBA/Tld0qO16vNNBQAAoIYVHjum+X4s51cwGzx4sG644QY1bdpUlmXpvffeU+PGjXXeeee5fWJz7dq16tChQ7A1+yU3N1fTp0/XF198obVr12rNmjW666679Pjjj3tdp7i4WMXFxa7f8/PzJUmXJ61WUsOEGq0XAAAg33bCr+X8CmbXXXedW+D6/vvvPQah66+/PuCrZYHq0KGD6yG233//va6//nrNnTtXl19+eZVPijrNmTNHDz30UJXpx2YvlC0uuHviAAAA/HWspNSv5fxKJZWvgsXEeP4w57Fjx/z+VGU4dOrUSWvWrFG7du20aNEir8Fs5syZmjZtmuv3/Px8paen6/sP/6sGMXU8rgMAABAuBeVlfi3n9+My3BovKNDbb7/tNu2nn37S6NGj1bZt22CaDFrjxo119dVXKysry+sy8fHxSkpKcvsBAAAwTVDv482ZM0fDhw/XlClT1KZNG+Xm5mrnzp1KSUnRypUrw12jTykpKSor8y+JAgAAmCqoYJaYmKhVq1Zp9erVWrt2rY4dO6ZJkyZpzJgxSk5ODneNPm3atEnTp0+PeL8AAADhFNKd7/369avyYNft27erY8eOQbVXVlbm8Sudxo8fr/3792vlypWaNGmSmjdvrrvvvlv16tXT8uXL1bZtWw0YMCCoPgEAAEzhVzDbtGmTUlNT1apVK5WVlWnx4sUeA5TdbteSJUu0Zs2agIooKSnR6tWr9d577+ngwYN69dVX1bt3b2VkZEhyPCIjJydHkuNq3RNPPKHFixdr4MCB6tSpk+bP9+fJIAAAAGazWX5863hqaqr69eunRYsWSZJ69uypL7/80nODNpvx93vl5+crOTlZH7Rqx6cyAQBAjSsoL1P/fbuUl5dX7YcQ/bpi9u2336phhSfk33TTTXr33XfVpEkTt+Xsdrvmzp0bZMkAAADRza8rZpUVFRUpNjbW7an/TkeOHFHjxo3DUlxN4YoZAACIJH+vmAX1HLOpU6e67vmqzPRQBgAAYKqgPpWZnZ2tRx99VMePH9eQIUM0cOBAr98GAAAAAP8EFcwWLlyoBg0ayG63a+nSpRozZowyMjJ00003RfzJ/wAAAL8VQV3mSkhIkCTFxsZq6NChGjp0qFatWqX27dvr1ltvDWuBAAAA0SKoK2YPP/ywLrnkEi1evFj//Oc/VVJSohEjRujpp59Wz549w10jAABAVAjqU5kxMTGy2WxKTk5W165d9dZbb/1P3fTPpzIBAEAk1einMi+66CJ9++23OnLkiKZOnapbbrlFc+fOVX5+ftAFAwAARLuggtmiRYt0xhlnSJIGDx6sxx57TB999JHS09M1c+bMsBYIAAAQLYIKZnv37tXx48f12muvqW/fvmrXrp327t2rWbNmafr06eGuEQAAICoEdfP/ZZddpvLycsXGxmrEiBFat24dN/0DAACEKKhg1rJlS9199936wx/+oMTExHDXBAAAEJWCCmZr165VRkZGuGsBAACIakHdY0YoAwAACD++4BIAAMAQBDMAAABDEMwAAAAMEfZgtnHjxnA3CQAAEBX8+lTm3LlzdeLECZ/LlZeX6/333yecAQAABMGvYPbZZ59p3bp1SklJqXa5srIyHTx4MCyFAQAARBu/gtmQIUP06quv+gxmkvT888+HXBQAAEA08iuYjR49WvXq1fO53OHDh3XBBReEXBQAAEA08iuY1a9fv8q0nJwc7d69W3a73TUtOztb9913n3bs2BG+CgEAAKJEUF/J9Ne//lV33nmn63fLsiRJNptN3bt3D09lAAAAUSaoYPb5559r8+bNqlevnpYtW6aRI0fKsiytWLFCgwYNCneNAAAAUSGo55j16tVLnTt31qmnnqo6deqoadOmatOmja6//npNmTIl3DUCAABEhaCumG3dulUzZ85Uv379NHz4cF111VWaMmWKVq1apXXr1oW7RgAAgKgQVDCbMWOGrr/+erVs2VKXXHKJRowYoWHDhqmsrExjxowJd40AAABRwWY579wP0YEDB5STk6OuXbuGo7kalZ+fr+TkZH3Qqp0axNSp7XIAAMBvXEF5mfrv26W8vDwlJSV5XS6oK2aStHfvXi1ZskR79+5VixYtdNlll+m8884LtjkAAICoF1QwW7FihUaNGqWYmBi1a9dOcXFx+stf/qKePXtq0aJFSk5ODnedAAAAv3lBfSpz+vTpuuSSS7R37159++23+vrrr5WTk6P+/ftr0qRJ4a4RAAAgKgQVzHJzc/X888+rSZMmrml169bVlClTVF5eHrbiAAAAoklQwWzcuHHKz8/3OK9Zs2Zuv7/zzjvBdAEAABB1grrHrHHjxrrxxhs1aNAg2Ww21/Rdu3bp0KFDmj17tiTJbrdr6dKluvrqq8NTLQAAwG9YUMFs27Zt2rVrl1555RWP87dv3y5JKisr04EDB4IuDgAAIJoEFcxGjx6tJ554QqmpqT6Xfe6554LpAgAAIOoEdY/Z5ZdfrtTUVBUVFUlyvGX59ddfe1x2woQJwVcHAAAQRYIKZllZWTrvvPPUv39/SVJsbKwKCgp0++23y263uy1bpw5P1gcAAPBHUMFsypQpiomJUVpammta79691b59ez3wwANhKw4AACCaBBXM8vLytGHDBrVt29Ztevv27bVw4cKwFAYAABBtggpmnTp1cntMhtObb76pY8eOhVwUAABANAoqmHXo0EHLli2TZVkqKSnR5s2bNWnSJP3973/X8OHDw10jAABAVAj6HrN9+/bp//7v/5SQkKBu3brp5Zdf1uTJk/X000+Hu0YAAICoYLMsywp25dLSUu3atUtFRUU69dRT1aBBg3DWVmPy8/OVnJysD1q1U4MYPjUKAABqVkF5mfrv26W8vDwlJSV5XS6oK2ZbtmzRli1blJubqw4dOqiwsFBjxozR7bffrry8vKCLBgAAiGZBBbMLLrhA27ZtU2JiorZs2aI+ffrol19+Udu2bfXQQw+Fu0YAAICoENRXMt11110aPXq0JGnSpElq0qSJ3n//fSUmJurvf/97WAsEAACIFkFdMatb15Hnnn/+eX3++ed68sknlZiYKEnasGFD+KoDAACIIkEFs9NOO00ZGRmaPHmyZsyYoWHDhmnTpk2aOHGiXnvttXDXCAAAEBWC/lRmSUmJioqKXJ8syM7Odn2peUZGRvgqrAF8KhMAAESSv5/KDOoeM0mKi4tTXFyc6/eK35sJAACAwAX1ViYAAADCj2AGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGMC6Ybdiwwecyy5cv13XXXafZs2dr1KhR2rFjR43VU1xeroP2Eh0sLVFxeXnY2z5SVlqlXW/TA10mmP59tRuOfsOtNmqKdJ813Z+z/fyyUr/7qY1jML+sVLtLipRfVup3uyYcs5VrCOQ17u8+KS4v18HSEh20h36u8rVf/N0P3toLZJ/4GodwnS9NOk7yy0q97stQxtKfvsO5/YG2GeltCeQ4juTfxrohtxAm69ev1z333KN9+/Zpz5491S43btw47dy5U40aNdKGDRvUp08fbdu2TUlJSWGr59uiQv396CFtLjkhS5Lt5M+ZcQm6OaWpusTXD6ntN4/l6rMTx1UmqY6kCxMS1S2+vr4pLqwyfWRSiqs/b+tWXCaY/jvHJchmk7YUn/DYbjj6DbfaqCnSfdZ0f8721504LuvkNEuOf7Fd5KWfmjoGu8QlSJK2lFQ9BncWF2th/mEdKi9zvR6bxtTRmOQmGtowxWu7ztdteZB1hqpyPTGSmsTU0eHyMq81Vdwn5Sfr18n/eton3xYV6sWjh7Tl5LnKOT5d4xN0c6PAzlX+nBuc9VS3H0LZ/srrejs2w3W+NOHc5m2fV9yXlyQ0dNveQMbSn77Duf2BthmuGvxt5+38XL/OJ/60WRPjZ7Msy/K9WM06fvy4Dh8+rDlz5mjVqlXVBrM+ffqodevWeumll1zT2rdvrwkTJmj69Ol+9Zefn6/k5GR90KqdGsTUqTJ/6bFczcvN0amSbpHUVtJuSfMl/XBymTtT0nRVw0b+baCHtjtIGu+h7eaSZlSYvuDk9OkpabJkeVy34jK+avLU/2uS3pLUvtL2Otu9rH5DrS08FlK/4eZtHGuypkj3WdP9OdtPk5QteTwmd1TqJxw1eWvjeUk/ShouaUyFdrefXO80SRMrLP/Cyfr61G+oB5u2rPa1tUPSMEnbAqgzVN7qeeHkdk6U4zXn6TV+mqQJqjq+zn3lrH/psVz9JTdHkmP/VVzneUk7A9hWX+eGLpLelqrUVnk/hLL9lY8zb8ems89Qz5fhOKeGyrmtlce14nG7RY4xc27vjpPz28v9NRFo3TVxjgm0zXDV4G87sw7t19rC4z7PJ/60GejfxoLyMvXft0t5eXnVXkgyIpg5Pfjgg3rllVe8BrPc3Fw1a9ZM8+bN05QpU1zTR4wYoczMTG3cuNGvfqoLZt8WFWpyTpamSPqr3N/rLZc0VdIzcvxL5rm09IDTvD9tfyapV6XpT8uR6n2t+2w1NXnqf72ki3y0+7QcfyzfCLLfcPN3HMNZU6T7rOn+nO0Pk+OPrT/9WJZCrinQ18AzJ2u7rZrln5Y0qmEj/fPYUZ/tfirpTT/qDFUg23m+3M8r1W3rM5KulvSOpDtSmmlebk7I5wVv9VY8NwyT1NtHP09Lmp7STEMbpgS9/RWPM3+PzVDOl/6MtwnHScXj9hlJk1Uz+zyYdkJpM1w1+NvOyIaN9Maxoz7PJ9NTmun3sfHVtjlSjuMzkOPnNxnM1q1bp4svvlhLly7VVVdd5Zp+xx136Nlnn1VxcbFsNpvHdSuqLpjd98t+HTxxXN/L8w145ZI6S9onqVtCov5f6il+bp2j7ewTx/Wdj7Y7yfEirDg9RVILqdq6zpCUVk1NnvofLscVia3VtNvpZNtvBdlvuPkzjuGuKdJ91nR/zvY7yvEvu+r2v7MfSSHXFOhrIF1SA1V/3HeStEfS7yW/2n3DjzpDFeh2OrfjoKRcH+t0lGM8DsXUUXF5mVrJ9+u3hY9t9XVuGCn/zhNHYupoaat2QW1/5ePMn2MzlPOlP+Nt0nHyxsn/FkhKkn+v2UD3eTDthNJmuGrwt52fJWXI93FxJKaOTo9PqLZN5xX4QP4m+xvMjLv5vzrZ2dmSpAYNGrhNT0hIkN1u1+HDhz2uV1xcrPz8fLcfj8uVl+uzE8c1Ud4HJkbSzZKOS1p34nhANzV+duK4xvvR9lJJJyque7I/X3WNl/SZl5o89X9C0rKTfVbX7sSTy53wMr+6fsPN33EMZ02R7rOm+3O2P1bScvne/85+1oVYU6Cvgf0nf3wd9xMllUga62e7xT7qDFUwr3Xndhw/WV916yyTdK2kX8rLdFz+vX6r21Zf54Zi+X+eOFRepl9KS4La/vFynFMDOTZDOV/6M94mHSfFctS8X9INPtbxVXdNnGMCbTO/rDQsNfjb77X6dQz9OY4/rabNE3Icn6H8Ta7O/1Qws9vtkqQ6ddyvctWtW/1nGObMmaPk5GTXT3p6usflCqxylcnxPnF1fi9HGi4/uY4/Amm7TFLF6Jh/si9/1/VUk6f+808uH0xN/vYbboGOYzhqinSfNd2fs/3mCmz/18QxWF0bO+V4q8mf5S05tsefdvN91BmqYF/rzvOKt9dZxXWc/9YOdZ94q7fiuSGQ84QlKctuD2n7gzk2gz1f+jvephwngR7rgezzYNoJpc1fykrDUoO//SYpsPNJdcsG+rcz0OPnfyqYpaamSpIKCwvdphcVFSk2NlYpKVU/FSRJM2fOVF5enusnKyvL43INbDGqI8fNe9X5SY6Bizm5jj8CabuOfj3x6uT/xwSwrqeaPPWfdHL5YGryt99wC3Qcw1FTpPus6f6c7f9Xge3/mjgGq2vjVDnuE/JneZsc2+NPu0k+6gxVsK9153nF+xscv67jDBOh7hNv9VY8NwRynrBJSo+NDWn7gzk2gz1f+jvephwngR7rgezzYNoJpc3UOnXDUoO//eYrsPNJdcsG+rcz0OPnfyqYdenSRTabTQcOHHCbnpOTo27dulW5kuYUHx+vpKQktx+Py8XE6MKERL0gx7+kPCmX9KKkRDk+sh0f498QOtte4EfbQyUlVFz3ZH++6logx8d0PdXkqf8ESVed7LO6dl84uVyCl/nV9Rtu/o5jOGuKdJ813Z+z/VckDZHv/e/s56IQawr0NXDKyR9fx/0LkuIkveJnu/E+6gxVMK9153YknqyvunWukrRIUmpMHSXKv9dvddvq69wQL//PE01j6ii1blxQ279AjnNqIMdmKOdLf8bbpOMkXo6aT5H0qo91fNVdE+eYQNtMqlM3LDX42+8i/TqG/hzHF1fTZoIcx2cof5Or8z8VzNLS0nTBBRdo8+bNbtO3b9+uoUOHhqWPEQ1T9KOkO1R1wMvl+KTFdjnS98gkz1foqmv7Bz/anupher7ks64ffNTkqX9nn9W1u0O/PgsqmH7DzZ9xDHdNke6zpvtzth8j3/vf2U84agr0NeB8NICv4/Oqho38avc2P+sMVSDb6fz9Rzle577WkRz1X5/cRPnyvf9+lO9t9XVuuM2PfnZIGpPcxGt7nrZlqrwfZ/4cm6GcL/0Zb1OOE+dx+6OkmfL/NRtq34Fuf6BthqsGf9sZ0rCRX+eTMclNfLZpk+9zU7DHj1Gfyrz//vv12muvKTMz0236+PHjtX//fq1cuVKff/65RowYoe3btyspKUmZmZnq3bu3Nm3apEaNGvnVj6/nmC07dlR/yc12Pcfs93JckgzHc8ycbTufe1K5bedzapzTKz4PRZLHdQN53oun/v8h9+eYVW638rNaguk33LyNY03WFOk+a7o/Z/uVnxVV8Zis/ByzcNTkrY2KzzG7vkK7lZ9j5ly+8nOHqntt1cZzzLzV43yO14ST2+TpNe58plXl8a38HLNlx47qiVzHh6I6VFon0OeY+To3dJH7c8y87YdQtr/ycebt2HT2Ger50p9lInWcVB7XQJ5jFmzdNXGOCbTNcNXgbzsPHjqgDwuP+Tyf+NNmoH8b/6cel1FSUqLVq1fr/vvv17Zt2/Tiiy+qd+/eysjIkOR4TtmePXv09ddfS3J8JdPrr7+url27KjMzU7fddps6duzod3++gpkkbSl2PPl/U/GvT/6XpK5hePL/luJC/TO/6pOCz65XX/8pqv5J1t7WDeQpw57a6HLy6d7fennyfzj6DbfaqCnSfdZ0f872Kz5x3NeT/2vqGDwzPkGW5fnJ/7tLivVanu8ndVdut+IT1GvjmK1cT4x8P6090H2ypbhQL+Y6vqXEua02SWfFJ2hcgE/+9+fcEMiT/4PZ/srrehuHcJ0vTTi3edpWyX1fXlK/odv2BjKW/vQdzu0PtM1w1eBvO0uP5fp1PvGnzUBq/58KZpHmTzBzcn7/lWxS45i6Yb3XoLi8XAVWuRrYYtza9Tbdn3VD7d9Xu+HoN9xqo6ZI91nT/TnbryupVPKrn9o4BvPLSvVLWalS69RVUh3vn8au2IakWj9mK29TIK9xf/dJcXm5jpSXSpbUuE5o5ypf+6XYKvdrP3hrL5Bjx9c4hOt8acK5reK2FljlHvdlKGPpT9/h3P5A24z0tvh7PvGnTX/6JJhVI5BgBgAAEKrf5ANmAQAAfssIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGCI30ww27VrV22XAAAAEBIjgll+fr5uuukm/elPf9Itt9yiOXPm+FynR48estlsrp8XX3wxApUCAADUnLq1XYAkjRw5Uueff75mzZolSerbt6/q1aunO+64w+PyX331lTp37qzbbrtNkmSz2dSvX7+I1QsAAFATaj2YrV+/Xh988IEWLFjgmjZ27FhNmTJFkydPVmxsbJV1FixYoEcffVSpqamRLBUAAKBG1fpbmUuXLlXjxo3VunVr17SuXbsqNzdXa9asqbJ8dna2Fi5cqDZt2qhfv3569913I1kuAABAjan1YLZp0yalp6e7TWvVqpVrXmW5ubmaPn26evToobVr12rw4MG6++67q+2juLhY+fn5bj8AAACmqfVglp2drQYNGrhNS0hIkCQdPHiwyvIdOnTQI488oo8//lhbt25Vt27dNHfuXH300Ude+5gzZ46Sk5NdP5WDIAAAgAlqPZjZ7XbVqVPHbVrduv7d+tapUyetWbNGKSkpWrRokdflZs6cqby8PNdPVlZWSDUDAADUhFq/+T81NVWFhYVu04qKiiRJzZo187l+48aNdfXVV2vv3r1el4mPj1d8fHxohQIAANSwWr9i1rVrVx04cMBtWk5OjiTHs8r8kZKSolNOOSXstQEAAERSrQezYcOG6eDBg8rOznZN2759uxo3bqyLLrrIrzY2bdqkkSNH1lSJAAAAEVHrwezSSy/VgAED9PLLL7umvfPOO7r33ntVv359SdL48eN15ZVXSpImTZqk2bNnu97uXL58udq2basBAwZEvngAAIAwqvV7zCTp9ddf17Rp0zRjxgzVq1dPbdu21fTp013zc3NzXW9vJiYm6oknntDixYs1cOBAderUSfPnz6+t0gEAAMLGZlmWVdtFRFp+fr6Sk5P1Qat2ahBTx/cKAAAAISgoL1P/fbuUl5enpKQkr8vV+luZAAAAcCCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIaoW9sFSFJ+fr6mTp2qFi1a6MiRI2rdurVmzpzpdfnly5frzTffVPv27bVt2zbNnj1bp512WgQrBgAACD8jgtnIkSN1/vnna9asWZKkvn37ql69errjjjuqLLt+/XqNGzdOO3fuVKNGjbRhwwb16dNH27ZtU1JSUqRLBwAACJtafytz/fr1+uCDD3TjjTe6po0dO1Z//vOfZbfbqyz/4IMPatCgQWrUqJEkqVevXkpISNCLL74YqZIBAABqRK0Hs6VLl6px48Zq3bq1a1rXrl2Vm5urNWvWuC2bm5urTz/9VGeddZbb9DPPPFNvvPFGROoFAACoKbUezDZt2qT09HS3aa1atXLNq2jr1q0qLS31uPzmzZtlWVbNFgsAAFCDav0es+zsbNfbkk4JCQmSpIMHD1ZZVpIaNGhQZXm73a7Dhw+radOmVfooLi5WcXGx6/e8vDxJUkF5ecj1AwAA+OLMHL4uItV6MLPb7apTp47btLp1PZflvOfM3+Wd5syZo4ceeqjK9KsP/BRIqQAAACE5duyYkpOTvc6v9WCWmpqqwsJCt2lFRUWSpGbNmlVZVpLH5WNjY5WSkuKxj5kzZ2ratGmu38vLy3XkyBE1adJENpst5G0Il/z8fKWnpysrK4tPmHrA+PjGGFWP8fGNMaoe4+MbY+SZZVk6duyYWrZsWe1ytR7MunbtqqVLl7pNy8nJkST16NHDbXqXLl1ks9l04MCBKst369atypU0p/j4eMXHx7tNq/z2qUmSkpI4mKvB+PjGGFWP8fGNMaoe4+MbY1RVdVfKnGr95v9hw4bp4MGDrvvHJGn79u1q3LixLrroIrdl09LSdMEFF2jz5s1u07dv366hQ4dGolwAAIAaU+vB7NJLL9WAAQP08ssvu6a98847uvfee1W/fn1J0vjx43XllVdKkh599FEtX75c+fn5kqTMzEzl5ORowoQJkS8eAAAgjGr9rUxJev311zVt2jTNmDFD9erVU9u2bTV9+nTX/NzcXNfbmz179tRzzz2n8ePHq2vXrsrMzNT7779v9FuT/oqPj9esWbOqvO0KB8bHN8aoeoyPb4xR9Rgf3xij0NgsHv4FAABghFp/KxMAAAAOBDMAAABDEMwAAAAMYcTN/9Hso48+0sqVK9WuXTtdeumlOu200/T3v/9dGzZsUEZGhn788UfNmzdPzZs3r+1SI6qgoEB//vOf1ahRIxUUFOjnn3/W3Llz1aJFC0mK6jHasGGDevXq5TbN13h8//33mj17tk4//XT98MMPGj16tAYOHBjp0iPC0/gsXbpUDz74oH766SedeeaZevrpp3XWWWe55kfT+Eiex6iiqVOnqlGjRnrwwQdd0xijXx04cEDPPvuskpKS1LlzZ11xxRWSomuMPI3Pm2++qS+//FJNmjTR1q1bdf3117vGRoqu8QmJhVpRUFBgXXvttda4ceOs48ePu6YvXrzYat++vVVSUmJZlmUtXLjQ6tatm1VaWlpbpdaKG264wfrrX//q+v2VV16x+vfvb1lW9I7RZ599ZvXq1cvKyMhwm+5rPHJzc63mzZtbn3zyiWVZlnX06FGrefPm1saNGyNaf03zNj7Lli2z+vfvby1ZssSaN2+e1ahRI6tJkyZWTk6OZVnRMz6W5X2MKtqwYYNVt25da9asWa5pjNGvFi5caJ199tnW5s2b3aZHyxh5G5+PPvrI6tatm1VeXm5Z1q/b//PPP1uWFT3jEw68lVkLysrKNHLkSB0/flwLFixw+1L2Bx54QNdee61iY2MlSSNGjNCOHTu0bNmyWqq2dixdulRt2rRx/d69e3d9/vnnkqJzjI4fP6709HSdccYZVeb5Go9nnnlGCQkJuvjiiyU5njw9cOBAzZ49O2L117Tqxsd5VXrUqFG64447tGDBAh0+fFjLly+XFB3jI1U/Rk7FxcV69tlnde6557pNZ4wcFi9erMmTJ+utt97SmWee6TYvGsaouvFZtmyZ0tPTXV9zmJycrPT0dH3zzTeSomN8woVgVgueeeYZffjhh3rmmWfcvqtz69at2rVrl9tbLHFxcerYsaPeeOON2ii11qSlpWn+/PkqLy+XJH399de68MILo3aMEhMTlZGRUeXtWn/GY+nSpW7zJcdXoa1evVpHjx6t8dojwdv4lJSUaNSoUW5f19avXz9JjucjStExPpL3Maro4Ycf1owZM1S3rvtdLoyRtH//fo0fP15/+tOf3P7R6BQNY1Td+KSlpWn16tXavXu3JMf3ZWZmZuqcc86RFB3jEy4EswgrLy/XnDlz1K5dOz300EPq1KmT2rdvr1deeUWbNm2SJKWnp7ut06pVK9e8aHHfffdp1apV+sMf/qBPPvlEr732mubPn88YVeJrPMrLy/Xtt996nF9SUqJt27ZFrNbaEBcXp549e7pNc4b9rl27Rv34VPSf//xHsbGx6tKli9t0xsjhySefVEFBgfbt26fLLrtMzZs310033aRjx44xRpJuvvlmJSUl6dJLL9Wnn36qcePG6amnnlLr1q0ZnwARzCLs3//+t7Kzs9WnTx+9+OKL+v777zV69GjdeOONOnjwoCS5vbUpSQkJCa550WLMmDF64okn9M9//lOXX365nnzySbVq1cr1naqMkYOv8Th8+LDKyso8zpcUlWP20Ucf6cwzz1SfPn0Yn5PsdrvmzZunGTNmVJnHGDmsXLlSv//973X//fdr7dq1Wr16td58801NmDCBMZKUmpqqDz/8UKWlperdu7dOPfVUXXPNNZI4hgJFMIuwzMxMSdINN9zgmnbfffcpJSVF9913nyS5ve0iqcrbCtGgrKxMWVlZmj59uuLi4nTxxRdr06ZNstvtkhgjJ1/jwXi5Ky8v17x58/TSSy/JZrMxPic9/vjjuuOOOxQXF1dlHmPkkJmZqYEDB6pJkyaSpC5duuiGG27Q66+/zhidlJmZqf79++vcc8/VI4884vpUL+MTGIJZhDnfRql4AoyNjVX37t1d0woLC93WKSoqUrNmzSJXpAGmT5+uhg0bau7cudq4caMaNWqkwYMHq2HDhpIYI6fU1FRJ3sejcePGiomJ8ThfUtSN2cMPP6zJkyerW7duksT4SPruu+9UXFzsuheoMsbIoby8vEpwdT4u4qeffor6Mfr66681ffp0vfDCC1q3bp1uuOEGPfTQQ3rvvfc4hgJEMIuw9u3bS3LcSFpRo0aNXKHtwIEDbvNycnLUo0ePyBRogOLiYj333HMaNmyYJKlDhw5asWKF9u3b57pJNNrHyKlr166SvI9HvXr11KFDB4/z69at6woo0eBf//qXYmNjNXr0aNc0xkd6++239dhjj6levXqun3Xr1un//b//p3r16ik7Ozvqx0hynLs9nbclqWXLllE/Rk8//bQuv/xyxcXFKS4uTi+//LJ69Oihf/zjH7zOAkQwi7CuXbuqbdu2+uSTT9ymHzx4UNOmTVNGRoY2b97sNu+HH37Q0KFDI1dkLSsuLlZpaakrqErSGWecodTUVHXr1o0xquCcc87xOR7Dhg2rMn/79u3q06ePEhMTI1Rp7dq2bZs++eQT3XPPPa5pdrtdmZmZUT8+t956q7799ltt3rzZ9XPOOedo4sSJ2rx5s1q2bBn1YyRJw4cP16effirLslzTDh48qNatW6tNmzZRP0YFBQVu52ybzaaLL77YNV7RPj4Bqe0HqUWj1atXW23atLHy8/Mty7KszMxMq3nz5tbhw4etN954wzr99NMtu91uWZZlrV+/3uratetv/uGplfXv39+65557XL/v2bPHNWbRPEb33Xef1bp1a7dpvsbjyJEjVsuWLa0vvvjCsizLstvtVufOnV2//5Z4Gp8DBw5YF110kfXGG29Yb775pvXmm29ar7/+unXDDTdYeXl5UTU+luV5jCq7+OKL3R4wyxhZ1vHjx62OHTtaS5cudU3r37+/9eqrr1qWFV1j5Gl8Xn/9datNmzbWiRMnXNMuu+wy13hF0/iEijvvakHfvn01f/58TZgwQa1atdK+ffv0/vvvq3Hjxho5cqTy8vJ000036fTTT1dmZqZWrlxZ5abJ37rFixdr5syZuuOOO9SiRQvt2bNHq1atUsOGDaNyjEpKSrR69Wq99957OnjwoF599VX17t1bGRkZPscjJSVFH374oe6//3516dJFR44c0cMPP6zzzjuvlrcqfLyNT1pamq644gpt3rxZ69atc1vn2muvVVJSkiT95sdHqv4Y8iUajiGp+jFq0KCBPvnkE82cOVPr16/X0aNHdfnll2vMmDGSomOMqhufa665RoWFhbrxxhvVuXNnHTlyRGPGjNFVV10lKTrGJ1xsllXhuiwAAABqDfeYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgB+J9VUFCgv//97xo0aJAee+wxj8tkZWXpscce0/nnn6/PPvsswhU65Ofna86cOerZs6e++eaboNpYuXKlDh8+HObKpL179+qjjz4Ke7sAgkMwA/A/Kz4+XldffbXWrVunEydOeFwmLS1NPXr00JdffqmysrKI1nf8+HFJUsOGDdW7d2998cUXbl/07K8///nPio2NVZMmTcJdolq3bq0jR47olVdeCXvbAAJHMAPwP6tu3bpq3LixUlJSvC4TFxen3/3udxGsymH//v2aNWuWJMlms6lFixZBtfPCCy/Isiz169cvnOW5GT58uL744gt9/PHHNdYHAP/wJeYAEGaFhYUaMWKE2rdvH1I7+/bt04MPPqidO3eGqTLvpk2bpiuvvFLbtm1TfHx8jfcHwDOumAEIm7ffflsPP/ywZs+erYYNG2rz5s2ueX/72980adIk9e7dW0OGDNHevXslSf/61780ZswYPfbYY5oxY4aaNGmidu3aafny5a51d+zYoZtuukl//etfNXz4cM2bNy/kWn/88Ufdfvvtuvbaa9W1a1fNnz9fkvTLL7/o0Ucf1ZlnnqmdO3dqyJAhatCgga655hpZluVa/8SJE5o2bZoefvhh3Xzzzdq4caNr3rJly/Tzzz/r888/17hx47RlyxbXvJycHA0bNkzJyck666yzlJWV5bXGp59+Wuecc44aNmzoNn337t2aNm2aZs2apcsuu0x//vOfJUnZ2dl6/PHHde655+r777/XFVdcoQYNGmj48OGy2+2aPXu20tPTlZaW5ja+knTaaafJsiy9/vrrwQ8qgNBZABAGRUVFVnp6uuv3hQsXWps2bbIsy7L+9re/WW+//bZlWZZVVlZmnXfeeVb37t0ty7Ks7OxsKykpyTrrrLOszZs3Wz/99JN1+eWXW3FxcdbPP/9sWZZlde/e3br77rsty7Ksb7/91rLZbNbBgwddfWVkZFizZs3yWtvPP/9sSbI+/vhjy7Isq6CgwBoyZIhVUlJiWZZlvf/++5Yka+XKlVZxcbH17LPPWpKsqVOnWjt37rRWrFhhSbJWrFjhavO2226zZsyYYVmWZR04cMCKjY21LrnkEuupp56yLMuyLr74YuuGG26oUsO1115rHThwwDpy5IiVnp5uTZ482Wvd7du3t+699163aTk5OVaPHj2so0ePWpZlWR9//LElyVq1apV16NAha/LkyVZsbKz16KOPWsXFxdann35qSbKGDBlifffdd5bdbreuuuoqq1OnTlX6Gz58uHXFFVd4rQdAzeOKGYCwKCoq0r59+/Tcc89Jkq666iq1aNFCdrtdDz30kL788kvdc889uvfee3XKKacoPj5ehYWFatasmVJSUjRo0CCdeeaZ+t3vfqd//OMfqlOnjpYsWSJJuvbaa133WCUkJMiyLO3evTvoWufPn6+cnBzdf//9uueee7RmzRr16tVLP/30k+Li4tSpUydJ0u2336527dpp0KBBat68ub7//ntXG4sXL3bdu9aiRQt1795dvXv31m233VZt3+PGjVOLFi2UkpKiXr16aceOHR6XKyws1I8//qjmzZu7TX/88cfVt29fJScnS5J69+6thQsX6rzzzlOTJk109tlny263a8aMGYqLi9NFF12kxo0bq2vXrjr99NNVt25dXXHFFfrpp5+q9JmWlqZ///vf/g8kgLDjHjMAYZGcnKy77rpLt956qxYtWqTnnntOZ555prZv367Dhw/roYceUkJCgtf1bTab6/+bNm2qdu3aud7uvP3225WZmam//OUvrk9W2u32oGv98ssvdeGFF+rRRx/1e534+Hi3T35alqWff/7Z9Xt6erpyc3MDqiM+Pl4lJSUe5x09etS1TEVr167V+PHj3aZde+211faTmJjo9ntsbKyKioqqLJeUlKRDhw75KhtADeKKGYCweeyxx/TPf/5Te/bs0bnnnqv169e7AsD27durLO8tlEiOT1M6P8m4YMEC3XTTTRo/frxGjhwZcp1FRUUB11PZ2LFjtXDhQh05ckTl5eXasWOHrrvuupBrc3KGqcLCQrfpdrvdLRD6o2LorY5lWdz4D9QyghmAsPjvf/+rb775RiNGjND27dvVvXt3/eUvf9Gpp56q+Ph4LViwwG35jz/+WJs2bfLYlt1u1+7du3XppZdq//79mjRpkqZMmVLlJvhgde7cWWvWrHELOGVlZXryySf9bmPOnDk666yzNHPmTC1YsEAvvfSSzj77bNd8m83m9mGBQCUlJSktLa3KVbiOHTvqlVdeUV5enmva8ePH9eGHHwbdl9PRo0d16qmnhtwOgOARzACERVFRkeuTjUlJSRo+fLhatWqlxMRE3XzzzZo/f75uvvlmrV69Ws8884zeeecdnXvuua71d+3a5fr/F154Qeeff7569eqlnJwclZWVadWqVTpy5Ijr04TZ2dn65ZdfJDlCVXUPbnW+/elcZuLEiZKkiy66SK+++qpWrVql0aNHa8CAAW7LVwxWdrvdrY9bbrlFZ5xxhi655BI1b95chYWFbm91NmrUSD/88IPKysr0zTffuN56LS0tdS1TWlpa7Vuy/fv3d7uvTZLuuusuHTp0SBdeeKFeeeUVLVy4UGPHjtX555/vtfaioiK3fpzbUTk4/vjjj+rbt6/XegBEQC1+8ADAb4jzU4djxoyxFixYYN1+++3WkSNHLMtyfAryxhtvtBo2bGi1bNnSuvvuu62ioiLXuhkZGVb//v2tyZMnW6NGjbKuueYa17rl5eXW9ddfbzVo0MAaMGCAtWvXLqtHjx5Wu3btrM2bN1vPPvusFRMTY5199tnWF198UaWuvXv3WpMmTbIkWddff721a9cuy7Isa9WqVVbHjh2thIQE6/zzz7e+/PJLy7Is65dffrGuu+46S5I1a9YsKy8vz5o/f75ls9msHj16WD/++KNlWZZ17733Wi1btrTq1KljSbIkWc2bN3d9kvSDDz6wUlNTrYsvvtjas2ePdfvtt1uSrGuuucbav3+/9cknn1itWrWyEhMTrQ8++MDjmG7cuNFq0aKFVVZW5jb97bfftk499VQrPj7e6t+/v7Vnzx7Lsixr586dVv/+/S1J1vPPP2/l5+dbzz//vCXJOuOMM6x///vf1vbt261+/fpZkqx58+ZZx48ftyzLsgoLC63ExERr+/btQe1/AOFhs6wQrrUDQBi0adNGY8eO1YMPPljbpfilqKhI06ZN01NPPaXY2FhJUnFxsVatWqWdO3fqzjvvDFtf48aN06BBgzRkyJCwtenJ/PnztWPHjrA8Iw5A8HgrEwAC9Kc//UkJCQmuUCY5Pj3ZuXNnNWvWLKx9PfXUU3rzzTfd7ikLt71792rdunUBfUoVQM0gmAGodWVlZSE9/iLSCgoKtHz5cn399dcqLy+X3W7X+vXrNW/evLB8arSiBg0a6Omnn9aTTz6p/Pz8sLYtSVlZWXr55Zf10ksvKS4uLuztAwgMb2UCqDWlpaWaP3++pkyZog4dOmj27NkaPnx4bZfl07Fjx3THHXdoxYoVKi0tVceOHTV27FiNHTvW7SpaODlP1f4++sJf5eXlionh3+iAKQhmAAAAhuCfSQAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhvj/MBQKgkbkOeYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "# Plot the training points\n",
+    "for i, color in zip(range(2), plot_colors):\n",
+    "    idx = np.where(y == i)\n",
+    "    plt.scatter(\n",
+    "        X[idx, 0],\n",
+    "        X[idx, 1],\n",
+    "        c=color,\n",
+    "        label=labels,\n",
+    "        #cmap=plt.cm.RdYlBu,\n",
+    "        edgecolor=\"black\",\n",
+    "        s=50,\n",
+    "    )\n",
+    "\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "#plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Pro: Decision trees do not need feature preprocessing!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Con: Decision Trees are very prone to overfit "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import neighbors, datasets\n",
+    "# import some data to play with\n",
+    "iris = datasets.load_iris()\n",
+    "# we only take the first two features. We could avoid this ugly\n",
+    "# slicing by using a two-dim dataset\n",
+    "X = iris.data[:, :2] # Nx2 \n",
+    "y = iris.target      # Nx1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# The data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(*X.T, c=y, cmap='jet');\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "clf = tree.DecisionTreeClassifier(criterion='entropy', splitter='best')\n",
+    "clf = clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(clf);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "hide_input": false,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Parameters #########\n",
+    "n_classes = 3\n",
+    "plot_colors = \"ryb\"\n",
+    "plot_step = 0.02\n",
+    "######################\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(\n",
+    "    np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)\n",
+    ")\n",
+    "plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
+    "\n",
+    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
+    "\n",
+    "\n",
+    "score = clf.score(X, y)\n",
+    "# Plot the training points\n",
+    "for i, color in zip(range(n_classes), plot_colors):\n",
+    "    idx = np.where(y == i)\n",
+    "    plt.scatter(\n",
+    "        X[idx, 0],\n",
+    "        X[idx, 1],\n",
+    "        c=color,\n",
+    "        label=y,\n",
+    "        #cmap=plt.cm.RdYlBu,\n",
+    "        edgecolor=\"black\",\n",
+    "        s=50,\n",
+    "    )\n",
+    "plt.xlabel(iris.feature_names[0])\n",
+    "plt.ylabel(iris.feature_names[1]);\n",
+    "#plt.axis('scaled');\n",
+    "plt.axis('on');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Decision Tree Setup\n",
+    "\n",
+    "```python\n",
+    "        #########################################\n",
+    "        clf = tree.DecisionTreeClassifier(\n",
+    "            criterion='entropy', \n",
+    "            splitter='best',\n",
+    "            max_depth=depth,\n",
+    "            max_features=None,\n",
+    "            min_impurity_decrease=0.001)\n",
+    "       ################################\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### **min_impurity_decrease : float, default=0.0**\n",
+    "\n",
+    "A node will be split if this split induces a decrease of the impurity\n",
+    "greater than or equal to this value.\n",
+    "\n",
+    "The weighted impurity decrease equation is the following:\n",
+    "```\n",
+    "    N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
+    "                                - N_t_L / N_t * left_impurity)\n",
+    "```\n",
+    "where:\n",
+    "- ``N`` is the total number of samples\n",
+    "- ``N_t`` is the number of samples at the current node, \n",
+    "- ``N_t_L`` is the number of samples in the\n",
+    "left child, and \n",
+    "- ``N_t_R`` is the number of samples in the right child."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ex+A3AAAAAAAAAAAAAMDzGPwGAAAAAAAAAAAAAHgeg98AAAAAAAAAAAAAAM9j8BsAAAAAAAAAAAAA4HkMfgMAAAAAAAAAAAAAPI/BbwAAAAAAAAAAAACA5/1/NvTdouIJjOUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 2000x2000 with 26 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "import matplotlib.cbook\n",
+    "warnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)\n",
+    "###################\n",
+    "h = 0.02  # step size in the mesh\n",
+    "plot_train_points = True\n",
+    "idx_plot = 1  # plot points of attempt idx_plot\n",
+    "#################\n",
+    "\n",
+    "names = [\n",
+    "    \"DT\",\n",
+    "]\n",
+    "\n",
+    "Krange = list(range(1, 27))\n",
+    "accuracies = np.zeros_like(Krange, dtype=np.float32)\n",
+    "accuracies_valid = np.zeros_like(Krange, dtype=np.float32)\n",
+    "\n",
+    "datasets = [\n",
+    "    make_moons(n_samples=2000, noise=0.3, random_state=0),\n",
+    "]\n",
+    "\n",
+    "figure = plt.figure(figsize=(20, 20))\n",
+    "i = 1\n",
+    "# iterate over datasets\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # preprocess dataset, split into training and test part\n",
+    "    X, y = ds\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y, test_size=0.3, random_state=42\n",
+    "    )\n",
+    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
+    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    ax = plt.subplot(len(Krange) + 1, 1, i)\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "    ax.set_xlim(xx.min(), xx.max())\n",
+    "    ax.set_ylim(yy.min(), yy.max())\n",
+    "    ax.set_xticks(())\n",
+    "    ax.set_yticks(())\n",
+    "    #i += 1\n",
+    "    # iterate over classifiers\n",
+    "    mid = len(Krange)//2\n",
+    "    for depth in Krange:\n",
+    "        #########################################\n",
+    "        clf = tree.DecisionTreeClassifier(\n",
+    "            criterion='entropy', \n",
+    "            splitter='best',\n",
+    "            max_depth=depth,\n",
+    "            max_features=None,\n",
+    "            min_impurity_decrease=0.001)\n",
+    "       ################################\n",
+    "        ax = plt.subplot(mid, mid, i)\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        score = clf.score(X_train, y_train)\n",
+    "        score_test = clf.score(X_test, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        if i == idx_plot and plot_train_points:\n",
+    "            ax.scatter(\n",
+    "                X_train[:, 0], X_train[:, 1],\n",
+    "                c=y_train[:], cmap=cm_bright,  alpha=0.6, s=2\n",
+    "            )\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(names[0]+f' Depth={depth}')\n",
+    "        ax.text(\n",
+    "            xx.max() - 0.3,\n",
+    "            yy.min() + 0.3,\n",
+    "            (\"%.2f\" % score).lstrip(\"0\"),\n",
+    "            size=15,\n",
+    "            horizontalalignment=\"right\",\n",
+    "        )\n",
+    "        # recording the accuracies\n",
+    "        accuracies[i-1] = score\n",
+    "        accuracies_valid[i-1] = score_test\n",
+    "        # print(accuracies,i,score)\n",
+    "        i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "_ = plt.plot(Krange, 1 - accuracies, 'o--')\n",
+    "_ = plt.plot(Krange, 1 - accuracies_valid, 'o--')\n",
+    "_ = plt.title('Decision Trees Training/Valid Error')\n",
+    "plt.legend(['Train','Valid'])\n",
+    "_ = plt.xlabel('Depth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Wrap-up for classification\n",
+    "\n",
+    "Given training vectors $\\{\\mbf{x}_i, y_i\\}$ a decision tree recursively partitions the feature space such that the samples with the same labels are grouped\n",
+    "together **(decrease impurity)**\n",
+    "\n",
+    "Let the data at node $m$ be represented by $Q_m$ with $N_m$\n",
+    "samples. \n",
+    "\n",
+    "For each candidate split $\\boldsymbol{\\theta} = (j, t_m)$ consisting of a\n",
+    "**feature $j$** and **threshold $t_m$**, partition the data into\n",
+    "$Q_m^{L}(\\theta)$ and $Q_m^{R}(\\theta)$ subsets\n",
+    "\n",
+    "$$Q_m^{L}(\\theta) = \\{(x, y) | x_j \\le t_m\\} \\\\ Q_m^{R}(\\theta) = Q_m \\setminus Q_m^{L}(\\theta)\n",
+    "$$\n",
+    "\n",
+    "The set of all splits $\\{\\boldsymbol{\\theta}_m = (j, t_m)\\}_{m=1}^M$ can be interpreted as the \"parameters\" of the model.\n",
+    "\n",
+    "[From sklean](https://scikit-learn.org/stable/modules/tree.html#mathematical-formulation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Decision Tree at a glance\n",
+    "The **quality of a candidate** split of node $m$ is then computed using an\n",
+    "**impurity function or loss function $H()$**.\n",
+    "\n",
+    "$$G(Q, \\theta) = \\frac{N^{L}}{N} H(Q^{L}(\\theta))   + \\frac{N^{R}}{N} H(Q^{R}(\\theta))\n",
+    "$$\n",
+    "Select the parameters that minimises the impurity\n",
+    "\n",
+    "$$\n",
+    "\\boldsymbol{\\theta}^* = \\operatorname{argmin}_\\boldsymbol{\\theta}  G(Q_m, \\theta)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Decision tree for regression\n",
+    "\n",
+    "The idea is the same but changes the loss function from **impurity** to $\\ell_2^2$(MSE - Mean Square Error) or $\\ell_1$ (Mean Absolute Error)\n",
+    "\n",
+    "$$\\bar{y}_m = \\frac{1}{N_m} \\sum_{y \\in Q_m} y$$\n",
+    "\n",
+    "$$H(Q_m) = \\frac{1}{N_m} \\sum_{y \\in Q_m} (y - \\bar{y}_m)^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Create a random dataset\n",
+    "rng = np.random.RandomState(1)\n",
+    "X = np.sort(5 * rng.rand(80, 1), axis=0)\n",
+    "y = np.sin(X).ravel()\n",
+    "y[::5] += 3 * (0.5 - rng.rand(16))\n",
+    "\n",
+    "# Fit regression model\n",
+    "regr_1 = DecisionTreeRegressor(max_depth=1)\n",
+    "regr_2 = DecisionTreeRegressor(max_depth=2)\n",
+    "regr_1.fit(X, y)\n",
+    "regr_2.fit(X, y)\n",
+    "\n",
+    "# Predict\n",
+    "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n",
+    "y_1 = regr_1.predict(X_test)\n",
+    "y_2 = regr_2.predict(X_test)\n",
+    "\n",
+    "# Plot the results\n",
+    "plt.figure(figsize=(14,10))\n",
+    "plt.scatter(X, y, s=20, edgecolor=\"black\", c=\"darkorange\", label=\"data\")\n",
+    "plt.plot(X_test, y_1, color=\"cornflowerblue\", label=\"max_depth=1\", linewidth=2)\n",
+    "plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=2\", linewidth=2)\n",
+    "plt.xlabel(\"Data $x$\")\n",
+    "plt.ylabel(\"Target $y$\")\n",
+    "plt.title(\"Decision Tree Regression\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Decision Regression Trees\n",
+    "\n",
+    "Approximate the underlying function as a **piecewise constant function with discontinuities (non-smooth).**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(regr_1);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tree.plot_tree(regr_2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Remedies to Decision Trees Overfit and Intro to Random Forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# What makes a good tree\n",
+    "\n",
+    "- **Not too small (deep):** need to handle important but possibly subtle distinctions in data\n",
+    "\n",
+    "- **Not too big:**\n",
+    "    - Computational efficiency (avoid redundant, spurious attributes)\n",
+    "    - Avoid over-fitting training examples\n",
+    "\n",
+    "- **Occam’s Razor:** find the simplest hypothesis (smallest tree) that fits the observations\n",
+    "- **Inductive bias:** small trees with informative nodes near the root"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Quick Remedies\n",
+    "\n",
+    "\n",
+    "1. **Minimum Leaf Size:** We can setup a minimum number of leaf \"size\". The minimum number of samples required to be at a leaf node. \n",
+    "\n",
+    "2. **Maximum Depth:** We can also set the threshold value on the tree depth.\n",
+    "\n",
+    "3. **Relative Impurity Decrease:** A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', \n",
+    "splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1,\n",
+    "min_weight_fraction_leaf=0.0, max_features=None, \n",
+    "random_state=None, max_leaf_nodes=None, \n",
+    "min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Quick Remedies\n",
+    "\n",
+    "However, even if we have these **on-hand weapon to avoid overfitting**, it is **still hard to train a single decision tree to perform well generally**. Thus, we will use another useful training technique called **ensemble methods or bagging**, which leads to random-forest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Brief Intro to Random Forest\n",
+    "\n",
+    "Random Forest are an application of **Ensemble Methods** or **Bagging.**\n",
+    "\n",
+    "_[Tibshirani book, pag. 587]_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "## Ensemble Methods (Bagging)\n",
+    "\n",
+    "- Motivation: solve the **variance problem** (you fit too much the data)\n",
+    "- Train different classifiers $h(\\cdot)_{\\mbf{D}_j}$ on **$M$ different datasets** $\\{\\mbf{D}_1,\\ldots,\\mbf{D}_m\\}$ still sampled from the same generative process $\n",
+    " \\underbrace{\\{\\bx_i,y_i\\}_{i=1}^N}_{\\text{known}} \\sim \\underbrace{\\mathcal{\\mathcal{D}}}_{\\text{unknown}}\n",
+    "$\n",
+    "- Average the results of different classifiers:\n",
+    "$$\n",
+    "h(\\mbf{x}) = \\frac{1}{M}\\sum_{j=1}^M h(\\mbf{x})_{\\mbf{D}_j}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# How to create the ensemble\n",
+    "\n",
+    "<br>\n",
+    "<div align='center'>\n",
+    "<img width=\"80%\" src=\"figs/ensemble.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Random Forest\n",
+    "\n",
+    "- **Bagging** applied to **Decision Trees**\n",
+    "- Each tree is very specific to a dataset $\\mbf{D}_j$\n",
+    "- When splitting, each tree looks at <ins>most $K$ features randomly</ins> from the $D$ we have ($\\mbf{x}\\in \\mathbb{R}^D$)\n",
+    "- It follows that we do NOT split on all axis and $K \\ll D$ and $K=\\sqrt{D}$.\n",
+    "- **Idea:** Inject noise in each tree so they will make different errors for each $\\mbf{D}_j$ so that at the end each error \"clears out when averaging.\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "hide_input": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 26 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "###################\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "h = 0.02  # step size in the mesh\n",
+    "plot_train_points = True\n",
+    "idx_plot = 1  # plot points of attempt idx_plot\n",
+    "#################\n",
+    "names = [\n",
+    "    \"RF\",\n",
+    "]\n",
+    "\n",
+    "Krange = list(range(1, 27))\n",
+    "\n",
+    "accuracies_rf = np.zeros_like(Krange, dtype=np.float32)\n",
+    "accuracies_valid_rf = np.zeros_like(Krange, dtype=np.float32)\n",
+    "\n",
+    "datasets = [\n",
+    "    make_moons(n_samples=2000, noise=0.3, random_state=0),\n",
+    "]\n",
+    "\n",
+    "figure = plt.figure(figsize=(20, 20))\n",
+    "i = 1\n",
+    "# iterate over datasets\n",
+    "for ds_cnt, ds in enumerate(datasets):\n",
+    "    # preprocess dataset, split into training and test part\n",
+    "    X, y = ds\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(\n",
+    "        X, y, test_size=0.3, random_state=42\n",
+    "    )\n",
+    "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
+    "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                         np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    # just plot the dataset first\n",
+    "    cm = plt.cm.RdBu\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    ax = plt.subplot(len(Krange) + 1, 1, i)\n",
+    "    if ds_cnt == 0:\n",
+    "        ax.set_title(\"Input data\")\n",
+    "    ax.set_xlim(xx.min(), xx.max())\n",
+    "    ax.set_ylim(yy.min(), yy.max())\n",
+    "    ax.set_xticks(())\n",
+    "    ax.set_yticks(())\n",
+    "    #i += 1\n",
+    "    # iterate over classifiers\n",
+    "    mid = len(Krange)//2\n",
+    "    for depth in Krange:\n",
+    "        #######################\n",
+    "        clf = RandomForestClassifier(\n",
+    "            criterion='entropy',\n",
+    "            n_estimators=100,\n",
+    "            max_depth=depth,\n",
+    "            min_impurity_decrease=0.001)\n",
+    "        ###################################\n",
+    "        \n",
+    "        ax = plt.subplot(mid, mid, i)\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        score = clf.score(X_train, y_train)\n",
+    "        score_test = clf.score(X_test, y_test)\n",
+    "\n",
+    "        # Plot the decision boundary. For that, we will assign a color to each\n",
+    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "        if hasattr(clf, \"decision_function\"):\n",
+    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        else:\n",
+    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
+    "\n",
+    "        # Put the result into a color plot\n",
+    "        Z = Z.reshape(xx.shape)\n",
+    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=0.8)\n",
+    "\n",
+    "        # Plot the training points\n",
+    "        if i == idx_plot and plot_train_points:\n",
+    "            ax.scatter(\n",
+    "                X_train[:, 0], X_train[:, 1],\n",
+    "                c=y_train[:], cmap=cm_bright,  alpha=0.6, s=2\n",
+    "            )\n",
+    "\n",
+    "        ax.set_xlim(xx.min(), xx.max())\n",
+    "        ax.set_ylim(yy.min(), yy.max())\n",
+    "        ax.set_xticks(())\n",
+    "        ax.set_yticks(())\n",
+    "        if ds_cnt == 0:\n",
+    "            ax.set_title(names[0]+f' Depth={depth}')\n",
+    "        ax.text(\n",
+    "            xx.max() - 0.3,\n",
+    "            yy.min() + 0.3,\n",
+    "            (\"%.2f\" % score).lstrip(\"0\"),\n",
+    "            size=15,\n",
+    "            horizontalalignment=\"right\",\n",
+    "        )\n",
+    "        # recording the accuracies\n",
+    "        accuracies_rf[i-1] = score\n",
+    "        accuracies_valid_rf[i-1] = score_test\n",
+    "        # print(accuracies,i,score)\n",
+    "        i += 1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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48GCuxyckJBh6hX799ddcj0lMTMzzeqmpqQQGBvL777+zfPlyfH19GTRoEOvXr8/znKCgIABOnz6dY192T9bdvULWFBQUxMmTJ0lJScmxLy0tjbS0NIKCgvKcyJKUlERWVhZJSUk8+OCDHDp0iHnz5qHX63nqqac4dOiQpZ8CAN988w09evRg+vTpPPHEE/kmf9bg7e3N3r17SUlJYcSIEcybN49evXoxYcIEo+PuXmUgN+3ataNx48ZcuXKF559/ns6dO+eaUGZmZtKxY0cOHjzI119/nWsPZmFkr3CRncSaW3YCm9vPjqmCgoLIysri33//zbHPxsamRP3sCWEukrwKUUIlJiYyYMAAlFJ89dVX+Pn5YWtryyOPPMKyZctyJLDnz59nxowZeHh40KhRI7Zs2cLPP/9sdMzvv//Ojh078rzmn3/+adj/zDPPcPDgQTw8PNi2bVue53Tt2hU3NzeWLVtmNNMd4NSpU1SuXJlmzZoV9unn6156F9u3b09SUhKTJk0y2p6VlcXbb79NRkYG7du35/z583z99ddGx2TP6tbpdKxYsYIzZ85ga2vL4MGD2bNnD3q9np07dxbL85gwYQLBwcGGNVjvnulvbRkZGUycONGwssSQIUNyDKsIDw/nypUrJr0/3nrrLUC7TZ7XkIFt27bx+++/07ZtW6M4iiq7+lX2rP5s2e9zc/RyHz16lBkzZhT5/OzE/+71aRMTEwkPD6dPnz73FJ8QJZEMGxDCSpRSxMfHA8Z/YNPS0li9ejVTpkzh0qVLLFiwgP79+xv2f/DBB7Rs2ZIWLVrw4osvUq9ePS5evMjff//N2rVrAZg+fTodOnTgqaee4vnnn6dOnTocPXoUpRSLFy82tJVbiddp06YZxqf6+vpSvnx5WrduneOc7M++vr58/fXXDBgwgC+//JLRo0cD2nJemzdvZs2aNYbbmenp6QBGSW52D2hhkq87lwCKi4szLAZ/p+xr3T2coXfv3nz22Wd8+umn/PXXXzz++OMopVi9ejWTJk3Czc2NkSNHMmfOHF599VW2bt1Ku3btuHnzJmvXrmX+/PmGHuVPPvmE7777DoDKlSvj6elZqOpY2c8jvzLA6enpuQ7JsLe358iRI/zvf/+jXr16rFq1CkdHR86dO8eBAweoVauW4XW/+/t85/ch+7nk9n24+3t997nZcjt31apVLF26FL1eT2BgII6Ojnh4eFCjRg0effRR7O3tWb58OT179sz3NcrWo0cPqlevTuXKlalevXqux2Q/32+//ZYWLVoQFxfHunXrAPjnn39wd3enUaNGhueT/Vzufr7ZhgwZwrx581iwYAE1a9Zk4MCBxMXFGdZf/uGHHwz/LOYm+/ua1xCev/76iz59+jBt2jSjeHL7fue17+WXX2bdunXMmDGD/v37U61aNQBmzJjBAw88YLQMXV7vJSFKHWvNFBPiv+y3334zzDQHVPXq1VXbtm1Vw4YNVXBwsGrXrp2aPHmyunLlSq7nb9myRTVs2FA5OjqqypUrq1GjRqn4+HijY/bs2aMefvhh5ejoqAIDA9WkSZMMyzqFhYWpDz74wHD9Dz/8UEVERBhmYbds2VJNnTpVvfXWW2rWrFlKKaVSUlLUd999p/z8/BSg+vXrpw4fPmy43o4dO1Tbtm1Vz5491ciRI1Xfvn3V/v37Dfv/+usv1bhxYwWotm3bqgMHDqgLFy6oZ599VgGqVq1ahqWq8rNw4ULVqVMnQ+yPPfaY+vbbb42O2bx5s3rkkUcUoIKDg9V3331nNLP+6tWrqk+fPsrT01N5e3urTp065ZgBf+bMGfXYY48pV1dXFRAQoHr06GFYJis7juzrf/rpp2rkyJGGZaEK8vfff6s33nhDOTg4KEBVqFBBTZ061WjG+IULF9TYsWMVoGxsbNT777+vTp8+bdi/adMmVbFiReXt7a1eeuklde3aNfXCCy8oNzc39cYbb6hTp06p1157zTDbffbs2erGjRtq7ty5ytHRUQFqypQpKioqSm3evNkwA37IkCEqPDxcLVmyRFWrVs2w0sXu3btVaGio6ty5swJUvXr11K+//qqioqIMKxWUKVPGsOJCbGysat26tQoODlY2NjaG7xegQkJCVHp6unrooYdyvO75mTt3rlq5cmWe+/V6vRo2bJhyc3NTNWrUUHPmzFFXrlxRFSpUUJUrV1bbtm1T27ZtM6y20LRpU/Xzzz/n+fOglFLh4eGqV69eyt3dXXl6eqoBAwao+fPnqyeeeEL99NNPKikpKddYFi5cqFq0aGFos0aNGqp58+aqZcuWqlGjRsrf318Bys7OTt24cSPf9+zXX39t+P506tQpx6oW6enp6sMPP1RNmjRRr7zyiho6dKh6/fXXDStRREdHqw8//NDwfnvttdfUwYMHTX7dhShpdEqVkNH9Qggh7hurV68mKiqKoUOHGrbp9XoSEhKYMmUKvXv3pkmTJlaMUAhRWsmwASGEEGYVGhpK7969c1TFsrGxwcvLi5o1a8pEIiFEkUnyKoQQwqxSU1PR6/UMHTqUYcOGUaVKFZRSXLp0ifXr1/Pggw9SpkwZa4cphCilZNiAEEIIs/vll1/48MMPOXLkCHq9nqpVq/LEE0/w8ssvGxUpEEKIwpLkVQghhBBClBqyzqsQQgghhCg1JHkVQgghhBClRqEmbCUkJPD6669Trlw5YmNjqVixotECyHf7448/ePvttzl27BjBwcFMmzaN9u3bG/ZHRETwxhtvUKNGDS5dukSrVq148cUXjdr46KOPCAsLw8fHh2vXrvHVV1/h5uZmcsx6vZ6IiAjc3d1LXPlEIYQQQgihFe5JTEykfPnyhuIp+R1ssk6dOqlJkyYZHj/66KPq888/z/XYAwcOqBYtWqgffvhBzZs3TwUGBioHBwd18uRJpZS2qHLdunXV999/r5RSKiMjQ9WuXVutXr3a0MZHH32k2rZta3j8wQcfqCeffLIwIauwsDCjxbHlQz7kQz7kQz7kQz7ko2R+hIWFFZjbmTxha/fu3bRq1YpLly4Z1udbsmQJI0aMIDIyEnt7e6Pj33rrLSZNmoSLiwsAf//9N02bNuWdd97hgw8+4Mcff2Tw4MHExsbi5OQEwIcffsiyZcs4duwYqamp+Pv7M3v2bENpzPDwcIKCgjhw4ECe5fjuFh8fj5eXF2FhYXh4eJh0jhBCCCGEKD4JCQlUqFCBGzdu4Onpme+xJg8bWLNmDT4+PkYLS4eEhBAXF8e2bdt47LHHjI5/7LHHDIkrQJMmTfDy8jLUel6zZg21a9c2JK7Z7b377ruEhoby77//cvPmTRo0aGDYHxgYiJ+fHz/99JPJyWv2UAEPDw9JXoUQQgghSjBThniaPGHr0KFDVKhQwWhbUFCQYd/d2rRpk2ObUoqQkBCT2stuM7djcrtetrS0NBISEow+hBBCCCHE/cHk5DUyMhJXV1ejbc7OzgBcvXq1wPMPHjyIs7Mzffv2Nam97LKCuR2T3/WmTp2Kp6en4ePu5FcIIYQQQpReJievGRkZ2NraGm2zszN9sYIPP/yQ+fPnG4YSFNReRkYGQKGvOW7cOOLj4w0fYWFhJscohBBCCCFKNpOzT39/f5KTk422paamAhAQEJDvuYsXL6Zp06Y8/vjjJreXPTY2OTnZaGms1NTUfK/n6OiIo6OjCc9ICCGEEEKUNiYnryEhIaxZs8ZoW1RUFABNmzbN87xDhw5x8OBBvvrqqxztRURE5Nle9hCCiIgIatSoYXRMu3btTA1bCCGEEFaglCIrK4vMzExrhyKszN7ePsed9HthcvLao0cP5syZQ2RkJGXKlAHg5MmT+Pj40Lp161zPuXr1KvPnz2fGjBlG28+dO0ePHj0YNWoUGRkZhmW2Tp48Se3atalVqxbly5fHzc2Nw4cPG5LXmzdvEhYWxlNPPVWkJyuEEEIIy1JKcePGDaKjo8nKyrJ2OKKE8PLyomzZsmYpGGXyOq+gLX/VunVr3n77bQAGDRpErVq1GDNmDACDBw8mPDycn3/+mZs3b9K9e3f69+9vGOeq1+v5/fffef3116lcuTKNGzdm3Lhx9O7dG4AOHTowZMgQevbsCcCnn37Kzp072bJlC6CtK7ts2TI2bNhg8hNMSEjA09OT+Ph4WSpLCCGEsLCrV69y48YNwxKVdnZ2UuHyP0wpRXJyMlFRUXh5eVGuXLlcjytMvlao5DU+Pp7Ro0fj5+eHk5MTzs7OhkQW4JlnnuHixYv8/fffdOnSxZB03qlly5bs3r0b0IYEjBkzhuDgYNLS0qhZs6ZReVilFB999BEXL16katWqXLt2jcmTJxe4eO2dJHkVQgghikdWVhZnz57Fz88PPz8/a4cjSpDr168TFRVFjRo1ch1CYLHktTSS5FUIIYQoHqmpqVy4cIHKlSsb5q4IAZCSksLFixepUqWKUYGqbIXJ10xeKksIIYQQwhQyTEDczZzvCUlehRBCCCFEqSHJqxBCCCGEKDUkeRVCCCGEKED37t2xtbWlcePGdOrUCR8fH5ydnencuTMPPfQQzs7OhISEFLn9NWvWULFiRdLS0swS7+7duzl//jzLly+nfv366HQ6goOD6d27Nz179qR58+a8+eabHD9+3Og8U5/n5cuX2blzp1liLSxJXoUQQghR4mXpFfv+vc66w+Hs+/c6WfrinW9uY2PD3r17OXDgAFu3buXBBx+kTJkybNmyhf3793PixAnD0qBFUbFiRdq3b29Y+/5efPvtt5w7d46qVavSq1cvhg4dCsD48eNZtmwZK1euZPPmzSilaNCgAVOmTCn086xYsSKxsbF8//339xxvYZlcpEAIIYQQwhq2HL/K+xtCuRqfathWztOJiU/UoXPd3NcNNbeGDRvy0EMP5bm/atWqPPbYY0Vuv1GjRixcuLDI52f7+eef2b9/P/Pnzzdsy1754c5JU15eXnz22WfY2try3nvvUbZsWQYNGlSo59mzZ0+GDBlCpUqVaNu27T3HbirpeRVCCCFEibXl+FVe+fEfo8QV4Fp8Kq/8+A9bjl8tljjGjh1rlmPyo5RCr9cX+fykpCSGDBnC+PHjTT5n4sSJ+Pj48P7776PX6wv9PEePHs2gQYPMNtzBFJK8CiGEEKJYJKdn5vmRmpGV49jE1Awmrj9BbgMEsre9vyGUxNQMk9stKlNu59vb2xMeHs4nn3zCo48+yq5du6hQoYKhANOiRYsYOXIkn376KW3btuXo0aMAhIeH88EHH1C1alXCwsIA+Omnn3jqqaeYP38+U6dOJSAggCpVqnD69Ok8r7948WK8vLyoUqWKyc/LxcWFtm3bEh4ezj///GPy88xWs2ZNlFIsXbrU5GveKxk2IIQQQohiUee9rXnua1vTn4UvNDU8bjRlOykFJJ4KuBqfSouPd5KYmpnrMQ8GebJ++MNFircoUlJS2Lp1K4cOHeLQoUO89tprODo6cvz4cV544QVOnDhB7dq1uXbtGmPHjmXz5s34+/tTu3ZtLl68SHbtqGbNmjFo0CCuXr3Khx9+yP79+3nyyScZP348q1atyvXaa9eupX79+oWOuXLlygCEhYXRuHHjQp/fsGFDVqxYwcCBAwt9blFI8iqEEEKIUk1fgoqFVq9enZYtW3L8+HFGjhxp2H79+nWGDx9OUFAQoI1DPXfuHAAODg74+voatVOpUiV8fHzo3Lkz7du3B+Dxxx9n7dq1eV778OHD9OvXr9AxZ5drLeqQhTJlyrBixYoinVsUkrwKIYQQoliETu6U5z6buyowHZzwKH9diGXgwr8LbHf2cw1pWsXHpHaLg62tbY6VB3x9fZkxYwZbtmzh5MmThIaGkpGRUah2HR0dSUlJyXN/XFwcjo6OhY43PDwc0BLvovDw8CAmJqZI5xaFJK9mlqVX/HUhlqjEVALcnWhaxQdbGymTJ4QQQrg4mJ52uDjY0SrYn3KeTlyLT8113KsOKOvpRKtg/xL/tzYlJYUePXrQoUMHRo0aRXx8PIcOHTLrNdzc3EhOTi7UOVlZWezYsYMqVaoUacgBaBPNipI0F5Ukr2ZUEpbyEEIIIe4XtjY6Jj5Rh1d+/AcdGCWw2anqxCfqlPjEFWDGjBns27ePTZs2WewaNWrUIC4urlDnfPnll1y7du2eJlzduHGD4ODgIp9fWLLagJmUlKU8hBBCiPtJ57rl+LpfQ8p6OhltL+vpxNf9GlqtcyghIYGkpKRc9+n1+hxDAiIiIoiPj2fHjh2EhYWxZ88eEhMTiYyMJC0tzXD8nedlZWUZJnBl78tvXGqnTp0IDQ3NsT27N/bOc+Pj45k0aRJjx47lo48+onfv3oV+ntnOnDlDhw4d8j3GnCR5NYMsveL9DaEFLuVR3NVAhBBCiPtB57rl2D22HUsHNeOr3iEsHdSM3WPbWSVxDQsLY9KkSRw9epSYmBhGjx5tWPIK4LfffmPdunVcvXqV6dOnEx0dDcCwYcOoUaMGvXv3Zv78+UyePJmMjAxGjRrFxYsXWbBgAQCzZs0iMjKS//3vf0RERLB9+3ZOnjzJkSNHWLt2LREREaxevTrX2AYPHsypU6e4ceOGYduKFSuYPXs2AFOnTqVXr1506tSJtm3bcuXKFXbv3s24ceMK/TyzpaSk8Ndff/Hyyy8X+TUtLJ1SJWiKngUkJCTg6elJfHw8Hh4eFrnGvn+v02f+nwUet3RQM5pX8y3wOCGEEKI0Sk1N5cKFC1SpUgUnJ6eCTxBm98EHH+Du7s5rr71WLNebN28ep0+f5vPPP8/3uILeG4XJ16Tn1QyiElMLPqgQxwkhhBBCFMW4ceM4evQoly9ftvi1Ll++zB9//MHHH39s8WvdSZJXMwhwN+2/S1OPE0IIIYQoCltbW+bMmcPixYsN1bosISwsjIULF7JgwQIcHBwsdp3cyGoDZtC0io9JS3nktQadEEIIIYS5ODo68u677xa56IApAgMDmThxosXaz4/0vJpB9lIeuSltS3kIIYQQ4v5gY2O5NM+SbRd4batd+T6TvZRHgLvxIr3WXspDCCGEEOJ+IsMGzKhz3XK0q1WGmu9uRgFf92tIxzplpcdVCCGEEMJMJHk1MwcbRUeXMzimxlA71Q5bAgBba4clhBBCCHFfkOTVnELXw5axzNNHgAOwcRb8UR46fwJ1nrR2dEIIIYQQpZ6MeTWX0PWwvD8kRBhvT7iqbQ9db524hBBCCCHuI5K8moM+C7aMhfwKxG55WztOCCGEEEIUmSSv5nBpb84eVyMKEsK144QQQgghRJFJ8moONyPNe5wQQgghSpQdO3YQHByMTqejZcuW7Ny502j/1q1bqVOnDlWrVmX37t35tnX9+nVmzpxJ+/btWbJkiWH7V199RZMmTfI8b9WqVfTv35/Ro0ebFPOyZctITEzkyy+/xNvb2xB77969eeKJJ2jdujXTpk0jMtI4P/Hy8sLd3Z0WLVrQuXNndDodgYGBdOzYkfr166PT6Xj99dfZtWsX//77r0mxmJMkr+bgVsa8xwkhhBDCmD4LLuyCYyu1z8U8FK99+/Z88803AFSvXp127doZ7e/UqRONGzdmxowZPPzww/m25e7uTteuXdm5cycZGRmG7TVq1OCRRx7J87xu3bpx5MgRYmNj821fKcVrr71GzZo1cXd35/XXX6djx44A/PjjjyxbtowNGzbw/fffs337durUqcPGjRsN51eoUIFLly6xd+9etmzZAkCHDh345ZdfOHLkCEuXLgWgVatWLFiwgCNHjuQbj7lJ8moOlVqAR3lu19O6mw48ArXjhBBCCFE4oevhy7qw6HFY9ZL2+cu6xT4Zum3btjRs2JDVq1eTkJBgtC8rK4sTJ07w2GOPFdiOg4MDVatWzbG9S5cufPbZZ3meZ2dnh7e3d4Htjxs3jkaNGtGgQQPDNmdnZwB0utu5StWqVdm4cSN16tShV69eHD16FIBnn30WH5+8S9r36tWLgIAAACZNmsSoUaOIiooqMC5zkeTVHGxsteWwcnXrTdL5Y+04IYQQQpiuhK3mM2zYMG7evMnixYuNtu/YsYMOHTrcc9nUzMzMezr/4MGDrFy5kn79+pl0vL29PdOmTSMlJYWpU6cCMHbs2HzPsbGx4c033zSc37t3b0aNGnVPcReGJK/mUudJ6LUY3MoabVYe5bXtss6rEEKI/7r0pLw/MlJzHpuaAJvfosDVfFIT8mk3xaxPoU+fPvj4+DBnzhyj7UuXLqV///5G2/bv389LL73EF198QdeuXQ2323Nz9uxZxo4dS1BQkNH2P//8k/79+/Puu+8yYsSIAns4P/30Uzp37lyoJPqhhx7Cz8+PDRs2AFpCWpA7j+nSpQvLli0rtvGvUqTAnOo8CTUfQ30QgE5lMSx9BO+/PB4/DxdrRyaEEEJY30fl894X3BH6rrj9eFp1yEguoMFbq/l88QCkJeR+SPkGMPi3wkaaJ2dnZ1544QWmT5/Ozp07adeuHampqVy+fJk6dercjkwpnnzySSZNmsQrr7xCtWrV6NevHz179sw1OaxUqRIBAQFGk6fOnDnDgAED2L9/P15eXiQkJOSbAGdmZvLzzz/zxRdfFOo56XQ6KlWqxMGDB4mLizNpaMKdKlSogI+PD6tXrzb0yFqS9Lyam60dOg/thzNc+ROTfG/d/0IIIYQogNIX6+VeeeUVdDqdoff1559/5sknje+w6nQ6hgwZQvPmzQEt6U1MTMyz59TBwQFfX1+jbZMmTaJbt254eXkB4OHhQe3atfOM6+zZsyQlJVG2bNk8j8mLra02tFGvL9prWaZMGQ4cOFCkcwtLel4toWobrkSEM6zmA5Rxd7J2NEIIIUTJMD6fNdF1d80LefOctj76kp4Ft/vM93lPitaZv5+uWrVqdO7cmXXr1hEeHs7KlSv56quvchw3efJkTpw4wWeffUZ0dDSA0eoCBdm+fTuTJ0822padZOYmLi4OAEdHR5OvkS08PBxvb+98J2rlx8PDg5iYmCKdW1iSvFpCt1kEAUEFHiiEEEL8hzi4Fu7Yau201XwSrpL7uFedtr9au2KfFD1s2DA2b97MtGnTyMrKMsy+v9N7773H6dOn+fHHH9mzZw+ffvppoa6RmJhoSEhN4ebmBkByckHDLYyFhoYSHh7OwIEDjVYjKAylVJGS5qKQYQNCCCGEKJmMVvO5O6my7mo+Xbp0oWrVqsyYMYOePXP2Du/fv58pU6Ywbtw4kyZA5aZ27dps3rzZ5OOrVKmCnZ1doRJepRRvv/02Li4uTJgwoShhAnDjxg2Cg4OLfH5hSPJqIRFxyfweGsaxK/HWDkUIIYQovbJX8/EoZ7zdyqv52NjYMHToUDw8PHKMdwWIiNCGSGzcuJHY2Fg2bdoEwNWrV4mNjSUrSyuycOcY0+whBdmfR48eza5du5g3bx5KKcLCwjh58iSnT5/m7NmzOa6ZXRUrNDQ0x77s3tg7r3f+/HmeeeYZfvvtN9auXZvr2rPZ69kmJSXl+VpkZmZy4cIFOnTokOcx5iTDBizh+CoCVg3lVGYdvq83g+m96ls7IiGEEKL0qvMk1OqqjYG9GalVrKzUwurrp7/00ktcuXIFJ6ec81u6dOlCp06d+OSTTwgNDeXjjz9m+fLlDB8+nPXr1zNz5kwAli9fTpMmTcjIyOCnn34CYNq0aQwfPpx+/fpx+fJlJkyYwJQpU3juuedo164dlStXznPs7IgRI5g2bZrhcVpaGnPmzGHr1q0APP/885QtW5b4+Hji4+Np164dx48fp2LFijna2r9/P7NmzQLgl19+Ydq0aQwYMCDHEIk9e/ZQrlw5OnXqVIRXsfB0SqncBpHcNxISEvD09CQ+Ph4PD4/iuejZbbCkJyf0lfi08rcserFp8VxXCCGEsKLU1FQuXLhAlSpVck3ohOUppejUqRPTpk2jfv3i6Tx77rnnePbZZ+nWrVuexxT03ihMvibDBizBrQwAAbobxNxMs3IwQgghhPiv0Ol0LF68mM8//7xQKxsU1caNG6ldu3a+iau5SfJqCe7a+mq+JBCXWLgZf0IIIYQQ96Js2bJMnjyZjz/++J7Lzebn999/59q1a/c00asoZMyrJbj4oXS22JCFLikavV5hY1O0pSeEEEIIIQqrUqVKvPPOO4UqE1tYrVq14pFHHrFY+3mRnldLsLExDB3wVbHEp1i+214IIYQQ4k6WTFyLo/08r2uVq/4H6Nxvj3uNlnGvQgghhBBmIcMGLKVyK8KyfOhXrT4B7sVTcUIIIYQQ4n4nyauldJxCBaCCteMQQgghhLiPyLABIYQQQghRakjPqwWFxyVzLjwKHy9v6gV5WjscIYQQQohST3peLeXfXykzoxI+y7uxeN9Fa0cjhBBCCHFfkOTVUpy9sFPpUmVLCCGEEMKMJHm1FPdyAPgRT2xiipWDEUIIIYS4P0jyaimu/iidDbY6RdbNKGtHI4QQQoh70L17d2xtbWncuDGdOnXCx8cHZ2dnOnfuzEMPPYSzszMhISFFbn/NmjVUrFiRtDTz3K3dvXs358+fZ9GiRVSoUAGdTkeDBg14+umnadSoEe3atWP9+vVG5yxfvpz69euj0+moU6cOvXv3pnfv3nTv3p0aNWqg0+m4fPkyO3fuNEuMRSXJq6XY2KJ38QfANikKpZSVAxJCCCFEUdnY2LB3714OHDjA1q1befDBBylTpgxbtmxh//79nDhxAhcXlyK3X7FiRdq3b4+9vf09x/rtt99y7tw5qlatyoABA+jTpw8An3/+OatXr+bvv/+mWrVqdOvWjZUrVxrO69WrF0OHDgXgzTffZNmyZSxbtoy1a9dy5swZ2rdvj4eHB7GxsXz//ff3HGdRSfJqQTr3sgD4qDgpESuEEEIUwZzDc5h7ZG6u++Yemcucw3OKJY6GDRvy0EMP5bm/atWqPPbYY0Vuv1GjRixcuPCeS67+/PPP7N+/n4EDBxq2ZSfVOp0O0BLxCRMmADBz5kyj852dnY2OvVPv3r2xsbGhZ8+e7Nu3j19//fWeYi0qSV4tyMZDG/daRhcnk7aEEEKIIrDR2TD78OwcCezcI3OZfXg2NrriSWXGjh1rlmPyo5RCr9cX+fykpCSGDBnC+PHjCzy2TBmtjH1MTIzJ7b/88st4eHgAMHr0aAYNGmS2YQ6FIcmrJVVsRliZR3ni4cb4uUmJWCGEEP9tyRnJeX6kZaXlemz/Ov0ZXG8wsw/PZuY/M0nOSGbWoVnMPjybYSHDGFp/aL7tpmammiV2U27n29vbEx4ezieffMKjjz7Krl27qFChAi+++CIAixYtYuTIkXz66ae0bduWo0ePAhAeHs4HH3xA1apVCQsLA+Cnn37iqaeeYv78+UydOpWAgACqVKnC6dOn87z+4sWL8fLyokqVKgXGevz4cQDq1atX4LEAgwcPNnpcs2ZNlFIsXbrUpPPNSYoUWNLDo6jw8CgpESuEEEIAD/0v79vurQJbMefR20MA2ixvQ0qm8Wo93xz7hm+OfQNgSFwBOq/qTFxaXK7tPuD7AMseX3avoZssJSWFrVu3cujQIQ4dOsRrr72Go6Mjx48f54UXXuDEiRPUrl2ba9euMXbsWDZv3oy/vz+1a9fm4sWLhjkyzZo1Y9CgQVy9epUPP/yQ/fv38+STTzJ+/HhWrVqV67XXrl1L/fr1C4zx1KlTDBkyhMDAQKZMmZLrMTNmzGDt2rUopThx4gQxMTF88803Rsc0bNiQFStWGA1RKA6SvAohhBCiVNGhMySuJU316tVp2bIlx48fZ+TIkYbt169fZ/jw4QQFBQHa2NJz584B4ODggK+vr1E7lSpVwsfHh86dO9O+fXsAHn/8cdauXZvntQ8fPky/fv3y3P/ee+9x6dIlIiIi+Oqrr+jXr59hGMDdRo4caUhKU1JSDDHcqUyZMqxYsSLP61mKJK8WdiU2iX/Dr+Hj7SclYoUQQvyn7X9uf577bG1sjR7/1us3o8ffHfuOb459g72NPRn6DOYemWtIYLf02JJnu8U1JvZOtra2OVYe8PX1ZcaMGWzZsoWTJ08SGhpKRkbhJnM7OjqSkpL32vFxcXE4OuY9THHy5MlERETQt29fTp06lWfiejdnZ2e6dOmSY7uHh0ehxsyaS6kb86rX6zl//ry1wzDNtWOUm1mJ4JWPsmT/JWtHI4QQQliVi71Lnh+Oto55Hrs4dDHfHPuGYSHD+Of5fxgWMsxoEld+7TrZOVnjqeaQkpLCY489xsmTJxk1apRJt/cLy83NjeTk5HyPee655xg0aBAzZ85k4cKFJrf97rvv5timlMo3WbaUQvW8JiQk8Prrr1OuXDliY2OpWLEi48aNK/C86Ohobty4QXBwsGFbSkoKZcuWJSEhIcfxGzdupGvXroDWzR4UFERq6u0B15s2baJq1aqFCd06XHyxVRn4E891qbIlhBBCFFr2qgJ3jnHN/jz78GyjxyXZjBkz2LdvH5s2bbLYNWrUqEFcXO5jf++OZf/+/bzyyivUqVMn3yXAsuW2dNbduV1xKVTy2qtXL5o3b87EiRMB6NChA05OTowaNSrX4xMTE5k+fTrTp09n5syZRk9w1apVvPTSS7Rv3x5bW+1WQXJyMs8//zxt27Y1HPftt98yZcoUypbV1ky1t7enU6dOhXuW1uIagEKHvS6LtMTi71YXQgghSju90hslrtmyH+tV0ZeWuhcJCQkkJSXluk+v1+cYEhAREUF8fDw7duygRo0a7Nmzh8TERCIjI/Hy8jIcf+d5WVlZRkWOMjIy8l1Kq1OnTrkmxzdv3gQwdAQ6OTmxYsUKGjduzNNPP82BAwcoV05b3jMxMREgz+d2pzNnztChQ4cCjzM7ZaJdu3YpQF26dMmw7ccff1Te3t4qPT0913POnDmjLl68qAC1cOFCo30bNmzIcfyaNWtU165dDY8zMzPVU089ZWqIuYqPj1eAio+Pv6d2iip9alWlJnqo/h9+a5XrCyGEEMUlJSVFhYaGqpSUFGuHYjGXL19WEydOVLa2tgpQo0aNUkeOHDHs//XXX9WDDz6odDqd+uyzz1RUVJRSSqnTp0+rmjVrKj8/PzVhwgS1d+9e5e7urvr06aNOnTqlevfurQA1cuRIde3aNbVkyRJlY2OjWrRooUJDQ9Xhw4dVnTp1lK2trVq1alWusV25ckW5ubmpuLg4w7YFCxaooKAgBag2bdqo7du3G/YtXbpUAapmzZpq3bp1avny5apOnToKUHXr1lUrVqzI83VITk5Wbm5u6uTJkya9bgW9NwqTr+mUMq1u6ZgxY/j++++5fv26YduJEyeoW7cuP//8c75VJXQ6HQsXLixwKYUXXniBZs2aMWTIEECr8/v000/j5+dHp06deOutt3jwwQdNCdcgISEBT09P4uPjTR6YbE7ps1rgEHOCl7PGMn/yuFy73YUQQoj7QWpqKhcuXKBKlSo4OZWMsab/NR988AHu7u689tprFr3OvHnzOH36NJ9//rlJxxf03ihMvmbyhK1Dhw5RoYLxiqXZyz0cOnTI1GbypNfr2bRpE48//rhhm6urK6+//joBAQEsWbKEJk2asGbNmnu+VnGy9dS64b31cSSmZVo5GiGEEELcz8aNG8fRo0e5fPmyxa5x+fJl/vjjDz7++GOLXSM/JievkZGRuLq6Gm3Lrn979erVew5k7969VKhQgcDAQMO2jh078sUXX3DixAk2btyIu7s7AwcOzHcwclpaGgkJCUYf1mTroY3VLUMcMYlSIlYIIYQQlmNra8ucOXNYvHixoVqXOYWFhbFw4UIWLFiAg4OD2ds3hcnJa0ZGhmFiVTY7O/MtE7tu3TqeeOKJPPd37dqVFStWkJCQwMaNG/M8burUqXh6eho+7u4tLnZBTQgr8yitmzfDV0rECiGEEMLCHB0deffdd406BM0lMDCQiRMnWmWJrGwmJ6/+/v451g7LnrUWEBBwz4GsX78+3+QVoG3btlSrVi3f/yTGjRtHfHy84cMS/3UUSqOBVHhlFU0ffxlP54LrIgshhBBCmIONjfmX87dEm4WOwdQDQ0JCiIiIMNoWFRUFQNOmTe8piJMnT5KUlETDhg0LPNbb2zvf/yQcHR3x8PAw+hBCCCGEEPcHk5PXHj16cPXqVSIjIw3bTp48iY+PD61bt76nINavX280USsvycnJREREGAoYlBZh15P4/fBpjofHWzsUIYQQQohSzeTktV27dnTp0sWolNjq1asZP368oX7v4MGDcySWWVlZAOS3Ildu413j4uLo3r07S5YsMWybOHEiH3/8MX5+fqaGbX0JEZSbVYnma5qz5E8pESuEEEIIcS8KNeNq6dKljB49mrFjx+Lk5ES1atUYM2aMYX9cXJxhKAHAqVOnDEtbrVq1iqCgoByVGKKiojh27Bjt27c32u7i4kJSUhIvvvgiCxYsoFGjRnTt2pU2bdoU9jlal4sfdioDdJCaEG3taIQQQgghSjWTixSUVtYuUgCQ/lElHNJv8Jr3bL56rZ9VYhBCCCEsTYoUiLxYpUiBKLpM1zIA2CRFFXCkEEIIIYTIjySvxUDnrhUqcEqNzHfsrxBCCCGEyJ8kr8XAzrM8AN5ZcSSlZ1k5GiGEEEIU1o4dOwgODkan09GyZUt27txptH/r1q3UqVOHqlWrsnv37nzbun79OjNnzqR9+/ZGE9O/+uormjRpkud5q1aton///owePdqkmJctW0Z8fDwff/wx9vb26HQ6OnbsSLdu3ahTpw7PPvssJ06cMByfmprKl19+ibe3NzqdjjZt2tC7d2969+7NY489hr+/PyEhIezatYt///3XpBgsQZLXYmDvWQ4Af90NKRErhBBClELt27fnm2++AaB69eq0a9fOaH+nTp1o3LgxM2bM4OGHH863LXd3d7p27crOnTvJyMgwbK9RowaPPPJInud169aNI0eOEBsbm2/7Silee+01atasiaenJ2+//TYNGjQAtCR73bp17Nu3j3PnztGiRQvOnz8PgJOTE6+//jodO3YEYOHChSxbtoxly5axadMmzp07h4+PD61atWLBggUcOXIk3zgsRZLX4lA+hMtlO1C/8cN4u1qnDrAQQghRGkXPnEX0nDm575szh+iZs4otlrZt29KwYUNWr15NQkKC0b6srCxOnDjBY489VmA7Dg4OVK1aNcf2Ll268Nlnn+V5np2dHd7e3gW2P27cOBo1amRIWAHDsqY6nQ4AT09PXnvtNRISEli0aJHR+c7OzkbHZvP09DSsyz9p0iRGjRpltMpUcZHktTjU6UbFoStp2H2klIgVQgghCsPWhpgZM3MksNFz5hAzYybYFm8qM2zYMG7evMnixYuNtu/YsYMOHTrcc/nUzMzMezr/4MGDrFy5kn79Cl7dqEwZbUJ5TEyMye1nD1mwt7end+/ejBo1qmiB3gNJXoUQQghRLPTJyXl/pKXleqzvwIH4vjKUmBkzifrqK/TJyUTNmEHMjJn4jRyB/6uv5t9uaqpZn0OfPn3w8fFhzl3J9NKlS+nfv7/Rtv379/PSSy/xxRdf0LVrV5YuXZpnu2fPnmXs2LEEBQUZbf/zzz/p378/7777LiNGjCiwp/PTTz+lc+fOJiXRx48fB6BevXoFHpuSksJrr71mtK1Lly4sW7as2Me/FqpIgSi6sOs3OX8pDN+ActQN8rJ2OEIIIUSxO92wUZ77XB9pTcV58wyPz7R8GJWSYnTM9a/ncv3ruQCGxBXgXPtHyYqLy7Vdp7p1qbJyxb2GbuDs7MwLL7zA9OnT2blzJ+3atSM1NZXLly9Tp04dw3FKKZ588kkmTZrEK6+8QrVq1ejXrx89e/bE3j7nXdhKlSoREBBAZGSkYduZM2cYMGAA+/fvx8vLi4SEhHwT4MzMTH7++We++OKLfJ+DXq9nx44dfPTRR7Rq1YoXX3wx1+MGDx6Mi4sL6enp/P3337Rs2dJof4UKFfDx8WH16tW8+eab+V7TnKTntThkplNuVmUeWd+MdX+eKPh4IYQQQuRNpzMkrtbwyiuvoNPpDL2vP//8M08++aTRMTqdjiFDhtC8eXNAS3oTExPz7Dl1cHDA19fXaNukSZPo1q0bXl5eAHh4eFC7du084zp79ixJSUmULVs2z2OefPJJvL29GTRoEN9++y2//fYbDg65z8f55ptvWLt2LZs2bWLfvn25Jt1lypThwIEDeV7PEqTntTjYOZBl64xdZgYZ8VetHY0QQghhFTX/OZj3Tltbo4c19hgvNxUzfz7Xv56Lzt4elZFB9Jw5hgS2+o7tebd7j2NQc1OtWjU6d+7MunXrCA8PZ+XKlXz11Vc5jps8eTInTpzgs88+IzpaKxF/5+oCBdm+fTuTJ0822mZ71+t0p7hbvc+Ojo55HrN+/XoGDRrEd999h16vN3mMbvXq1XnwwQdzbPfw8CjUmFlzkJ7XYpLuHACALjGygCOFEEKI+5ONi0veH3clXHfuu/7991z/ei5+I0dQ69hR/EaOMJrElW+7FipTO2zYMDIzM5k2bRpZWVkEBATkOOa9995j8uTJvPbaa3Tp0qXQ10hMTDQkpKZwc3MDIDk5Od/jZs6cSb169RgwYIBh3Ksp3n333RzblFL5JsuWIMlrMdG7aTP67JMleRVCCCFMlb2qwJ1jXP1ffTVHAlvcunTpQtWqVZkxYwY9e/bMsX///v1MmTKFcePG5Xq73RS1a9dm8+bNJh9fpUoV7OzsCkx4nZycWLFiBTqdjm7duhW4bmy2u5fOArhx4wbBwcEmx2gOkrwWExuP7BKx0VaORAghhChFsvRGiWu27ASWLL1VwrKxsWHo0KF4eHjkGO8KEBERAcDGjRuJjY1l06ZNAFy9epXY2FiysrSKm3r97fizhxRkfx49ejS7du1i3rx5KKUICwvj5MmTnD59mrNnz+a4pru7Oy1atCA0NDTHvps3bwJaFS3QCiJ88803nD9/nt69exviAa3HFyApKSnf1yAzM5MLFy7QoUOHfI8zN0lei4mDl1Yi1ksfS3L6va3hJoQQQvxX+I8YnufkLP9XX8V/xPBijui2l156iQEDBuCUy9CELl260KlTJz755BOGDx/O8OHDqVSpEsOHDyclJYUPPvgAgOXLl3Ps2DH++ecffvrpJwCmTZtGQkIC/fr148MPP2TChAlUqFCBmTNn0q5dO9q0aZPn2NkRI0bwxx9/GB7Hx8fz0UcfGaphjRw5klOnTgHasl9Dhgxh27ZtPP744+zfv58vv/yS7du1McTvvvsuf/75Z57Pf8+ePZQrV45OnToV4dUrOp1SShXrFYtZQkICnp6exMfH4+HhYbU41L456LaOY2NWMx58fQ0VfV2sFosQQghhCampqVy4cIEqVarkmtAJy1NK0alTJ6ZNm0b9+vUteq3nnnuOZ599lm7duhV4bEHvjcLka9LzWkx0ZR7gctkOVAppi5erVNkSQgghhPnpdDoWL17M559/XqiVDQpr48aN1K5d26TE1dwkeS0uVR+h4tCV1OvxNh5OkrwKIYQQwjLKli3L5MmT+fjjj++53Gxufv/9d65du8aECRPM3rYpZJ1XIYQQQoj7TKVKlXjnnXdMXse1MFq1asUjjzxi9nZNJT2vxehyzE1++yeU42HXrR2KEEIIIe5zlkhcLdmuyde36tX/Y/zmPkCb9c3ZsSfvmXtCCCGEECJvkrwWo3RHbwD0CVIiVgghhBCiKCR5LUaZLlrpOJuka1aORAghhLCc+3wVTlEE5nxPSPJanG6ViHVIkSpbQggh7j/29vbodLoCKzOJ/57k5GSAIpfKvZOsNlCMbD21KluuaZK8CiGEuP/Y2tri6elJdHQ0aWlpeHh4YGdnh06ns3ZowkqUUiQnJxMVFYWXlxe2trb33KYkr8XIyft2idjUjCyc7O/9GyiEEEKUJGXLlsXZ2ZmoqCgSEhKsHY4oIby8vChbtqxZ2pLktRg5+QQCUEYXR8zNNIK8pUSsEEKI+4tOp8PLywtPT0+ysrIsski+KF3s7e3N0uOaTZLXYqTzq8Hlsh1x966Np7NU2RJCCHH/0ul02NnZYWcnqYYwL3lHFadyD1Jx6AprRyGEEEIIUWrJagNCCCGEEKLUkJ7XYnYpJpELly7j7+fHA5XKWDscIYQQQohSRXpei5nrdw/TZkML9u/6xdqhCCGEEEKUOpK8FrMMR1/ti5tSZUsIIYQQorAkeS1mWbeqbNkmRVo5EiGEEEKI0keS12Jm464t0OuUKlW2hBBCCCEKS5LXYmbvpVXZcsuIsXIkQgghhBCljySvxczlVpUt76xY0jKzrByNEEIIIUTpIslrMXP20XpeA3Q3uH4z3crRCCGEEEKULrLOazGz8anCpXKd0LtXxkNKxAohhBBCFIokr8XNuxKVhiy3dhRCCCGEEKWSDBsQQgghhBClhvS8WsHF6EQuXL5EgK8vD1QuZ+1whBBCCCFKDel5tQKbxU/QdkNLQn9fYe1QhBBCCCFKFUlerSDT2Q8AnVTZEkIIIYQoFElerUC5aVW2HJIleRVCCCGEKAxJXq3A1lMb5+qcJlW2hBBCCCEKQ5JXK3D01goVuEuJWCGEEEKIQpHk1QpcfbUSsT76WDKy9FaORgghhBCi9JDk1QrcfIMArURsbJKUiBVCCCGEMJWs82oFNl6BXCrXmSyXAMo4yP8PQgghhBCmkuTVGpw8qTTkJ2tHIYQQQghR6ki3nxBCCCGEKDWk59VKLkQlcDHsEmV9vahduYK1wxFCCCGEKBWk59VKUpcOoO2Gh7m0Y4G1QxFCCCGEKDUkebWSLFd/AGylypYQQgghhMkkebUSnbtWZcsxJcrKkQghhBBClB6SvFqJ3a0SsS7pUmVLCCGEEMJUkrxaibOPViLWM/O6lSMRQgghhCg9JHm1Ejc/bYUBHxVHppSIFUIIIYQwiSSvVuIZoCWvvrpEYhNvWjkaIYQQQojSQdZ5tRJbV18uluuC3jWAsnbS8yqEEEIIYYoSl7zGxsYC4OPjY+VILEyno/KQZdaOQgghhBCiVClU8pqQkMDrr79OuXLliI2NpWLFiowbN67A86Kjo7lx4wbBwcE59s2ePZvhw4cbHpcvX55Lly4ZHn/00UeEhYXh4+PDtWvX+Oqrr3BzcytM2EIIIYQQ4j5RqOS1V69eNG/enIkTJwLQoUMHnJycGDVqVK7HJyYmMn36dKZPn87MmTNzTV5/+eUXfvjhB8Pj4OBg7Oy0sKZOncr27dvZuXMnAB9++CF9+/Zl3bp1hQm7xDofGc+lyxcp7+tJzaqVrR2OEEIIIUSJp1NKKVMO3L17N61ateLSpUtUrFgRgCVLljBixAgiIyOxt7fPcc7Zs2dxcHCgcuXKLFy4kIEDBxrt3759OydPnmTEiBE5zk1NTcXf35/Zs2fTv39/AMLDwwkKCuLAgQM0atTIpCeYkJCAp6cn8fHxeHh4mHROcfl79os0iV7F7vIv8PDgL60djhBCCCGEVRQmXzN5tYE1a9bg4+NjSFwBQkJCiIuLY9u2bbmeExwcTKVKlfJsc+bMmbz22mvUrVuXKVOmkJiYaNi3bds2bt68SYMGDQzbAgMD8fPz46effjI17BJNuZUBwD5JSsQKIYQQQpjC5OT10KFDVKhQwWhbUFCQYV9RtGjRgr59+xIeHs57771Hs2bNiImJMWozt2sW9XoljY2HVmXLKS3aypEIIYQQQpQOJievkZGRuLq6Gm1zdnYG4OrVq0W6+NixY/nhhx8IDw9n9OjRhIaGMnr0aMP1gFyvmd/10tLSSEhIMPooqRy8tOTVLUNKxAohhBBCmMLk5DUjIwNbW1ujbdkTq+6Vi4sL06dPp3///qxYsYL09HQyMjIACn3NqVOn4unpafi4u+e2JHHx1XquvbJirRyJEEIIIUTpYHLy6u/vT3JystG21NRUAAICAswSzKBBg0hNTSUmJgZ/f3+AXK+Z3/XGjRtHfHy84SMsLMwssVmCR4CWvHqrBPQZ6VaORgghhBCi5DM5eQ0JCSEiIsJoW1RUFABNmzY1SzDe3t44Ojri6+tLSEgIQK7XzO96jo6OeHh4GH2UVN5+5chQttjoFPEx4dYORwghhBCixDM5ee3RowdXr141jEUFOHnyJD4+PrRu3doswRw6dIhu3brh6OhIp06dcHNz4/Dhw4b9N2/eJCwsjKeeesos17M2ezs7LlTqyfnqA3BydLR2OEIIIYQQJZ7JyWu7du3o0qULCxcuNGxbvXo148ePx8XFBYDBgwfTtWtXo/OysrIAuHs52XXr1vHcc89x5swZQKvC9cMPP/Dll18C4OHhwYQJE1iwYIHROY899hgPPfRQIZ5iyVbjxW+o2m8Gzj7lrR2KEEIIIUSJV6gZV0uXLmX06NGMHTsWJycnqlWrxpgxYwz74+LiDEMJAE6dOsWaNWsAWLVqFUFBQXTo0AHQhgjs3buXhg0b0qdPHwIDA/nhhx+MxrO++eabZGRkMGjQIKpWrcq1a9f48ccf7+kJCyGEEEKI0svkClulVUmusAXwb+QNLl26SJCPKzWq5yyfK4QQQghxv7NIhS1hGWFrp9Bu0yMkb51s7VCEEEIIIUo8SV6tTLlrJWIdUqTKlhBCCCFEQSR5tTI7T22ilkuaVNkSQgghhCiIJK9W5uQdCIB75nUrRyKEEEIIUfJJ8mplbn5a8uqlboA+y7rBCCGEEEKUcJK8WpmXf3mylA5b9OgTowo+QQghhBDiP0ySVyvzdXchBk8Abl6/YuVohBBCCCFKtkIVKRDm52BnQ3T1Z0iyyaS8q4+1wxFCCCGEKNEkeS0B6j7/mbVDEEIIIYQoFWTYgBBCCCGEKDWk57UEOHctjkuXL1HBy5EaNWpbOxwhhBBCiBJLel5LgJM/z6H9pkfQbX7T2qEIIYQQQpRokryWADoPrUSsY6qUiBVCCCGEyI8kryWAvadWqMAtXUrECiGEEELkR5LXEsDFpzwAnlmxoNdbORohhBBCiJJLktcSwN0/EP2tKlskS++rEEIIIUReJHktAfw8XLiOOwAq8aqVoxFCCCGEKLkkeS0B/NwciVLeACRfj7ByNEIIIYQQJZes81oCONnbklb7ac7r4wn0DrJ2OEIIIYQQJZYkr2YSPXMW2Nrg/+qrOffNmQNZevxHDM/z/IZ9JlkwOiGEEEKI+4MMGzAXWxtiZszUEtU7RM+ZQ8yMmWArL7UQQgghxL2Snlczye5xjZkx0/A4O3H1Gzki1x7ZO527FsflSxep6GlD9Vr1LR6vEEIIIURpJMmrGfm/+ir6m0nEzJhJzOw5kJVlUuIK8PfW/9Hnwngi3OpCrT3FEK0QQgghROkj97LNzKVxI+2LrCx09vYmJa4ANh5aoQLnNCkRK4QQQgiRF0lezSzl6FHD1yojI8cY2Lw4emvJq3vGdVDKIrEJIYQQQpR2kryaUfScOVyfOw+7cuUAcGvXLtdJXLlx9dXOsSMTkmMtGqcQQgghRGklyauZ3Dk5y+vppwHQOTrgN3KESQmsr6c715VWZYub1ywdrhBCCCFEqSTJq7lk6Q2Ts1xbtgAged+f+A0dit/IEZClz/d0/zuqbKkEKRErhBBCCJEbWW3ATO4sQOBcrx42rq5k3bhB6smTJk3a8nNz5LzyojaXSY2LwNmSwQohhBBClFLS82oBOnt7XJo2BXt70v/916RznB1scXqwOxdqvIhNQC0LRyiEEEIIUTrplLq/p7YnJCTg6elJfHw8Hh4exXbdjIgIbD09sXF1LbZrCiGEEEKURoXJ12TYgIXYly9v7RCEEEIIIe47krwWA6UUOp2uwOPOXI3j8qULVPHQU61O42KITAghhBCidJExrxaUsGUrF57uQfTnX5h0/K87t/Dolrb4retn4ciEEEIIIUonSV4tSGWkkxoays09u0063t5TK1Tgki5VtoQQQgghciPJqwW5NmsGQFroSTLj4go83ulWiVh7lQ6pNywZmhBCCCFEqSTJqwXZ+fvjWKMGAMn79hV4vI+nBzfUrdUJEqXKlhBCCCHE3SR5tTDX5s0BSDIhefV3dyDyVpUtSV6FEEIIIXKS5NXCskvFJu3ZS0FL6vq5ORKlvLQHkrwKIYQQQuQgyauFuTRuDPb2ZEREkHH5cr7H+rk5EoUXAOk3IoohOiGEEEKI0kXWebUwGxcX3Nu1Q2dri8rMzPdYV0c7yjTqxvnkmgQFyTqvQgghhBB3k/KwQgghhBDCqgqTr8mwATOZc3gOc4/MzXXf3CNzmXN4TjFHJIQQQghx/5Hk1UxsdDbMPjw7RwI798hcZh+ejQ060s6eJSMyKt92TkfcYNu+g1w4XvDqBEIIIYQQ/zUy5tVMhtYfCsDsw7MNj7MT12Ehw3jypzDOr34S/9dfw2/o0DzbWf/7n7x5+lkybBzhgUjQ6YolfiGEEEKI0kB6Xs1oaP2hDAsZxuzDs2nwQwND4jq0/lCc69UFtCWz8uPopZWItdenQVqCxWMWQgghhChNJHk1s6qeVQHI1Gdib2Nv6JF1baGt95p8+DD6pKQ8z/fy8iRBuWgPEiMtG6wQQgghRCkjyauZHY0+avg6Q59hGANrX7Ei9oGBkJFB8sGDeZ5vXKjgqiVDFUIIIYQodSR5NaO5R+ayKHQRPk4+AHSs3NEwiUun0+Ha4lap2HyGDvi5Od4uEXtTel6FEEIIIe4kyauZ3Dk56/GqjwPgaudqGAM798hcw9CBpL35Ja8OhipbUiJWCCGEEMKYrDZgJnqlN0zO2huxl8Whi9kTvoftz2w37Hdp1gx02pJZmdHR2Pn752jHz/12z2tmfIR8g4QQQggh7iC5kZm8GvKq4etGZRrhZOtEVEoUZ2+cNUzaAgh46y0cq1fHxtMz13bcHe2o0ewxzieUpULVNpYOWwghhBCiVJHk1QIcbR1pXLYxu8N3szd8LzW8axj2+b4wMN9zdTod7R7vC/S1bJBCCCGEEKWQJK8W8nzt5+latSstyrewdihCCCGEEPcNSV4tpEVg3klr0p/7ufnHH3j3egaHypVz7D919QZXLp4j2CWJSvUfsWCUQgghhBCliySvVnD9m29I2rsX+7Jl8cklef3frlNMDu2kPah1BRzdizdAIYQQQogSSpbKsqDwm+F8d+w7/nfyf0bbDeu95rFkloenNzeVk/ZAqmwJIYQQQhhI8mpBp2JP8eU/X7Lk5BKj7YZSsX/9hcrIyHGen5vD7UIFUmVLCCGEEMJAklcLeqjsQ9jp7LiceJmwhDDDdsdatbD19kafnEzK0aM5zvNzdyQ6u1CBVNkSQgghhDCQ5NWC3BzcCAkIAWBPxB7Ddp2NDa7NmwG5l4r1c3MkSnlpD6TKlhBCCCGEQalLXvV6PefPn7d2GCZrGdgSME5eAVya5z3u1c/NUYYNCCGEEELkolDJa0JCAi+++CLvvPMOr7zyClOnTjXpvOjoaM6ePZtje0xMDC+99BJ+fn5UqFCBd955h4y7xoBev34dZ2dndDodOp0OW1tbTp8+XZiwrapleS15/evqX2Rk3X5ubrfGvWZcu4ZKTzc6x/+OntesBOl5FUIIIYTIVqilsnr16kXz5s2ZOHEiAB06dMDJyYlRo0blenxiYiLTp09n+vTpzJw5k+DgYMO+rKwsnnnmGTp37sy8efP4/vvv+eijj7C1tWXy5MmG47799lumTJlC2bJlAbC3t6dTp06FfqLWUtOnJr5OvlxPvc7h6MM0KdsEAPvAQKpu+hmHKlXQ6XRG53g429G4VWfOx7lSoXYrbK0RuBBCCCFECaRTSilTDty9ezetWrXi0qVLVKxYEYAlS5YwYsQIIiMjsbe3z3HO2bNncXBwoHLlyixcuJCBAwca9m3cuNEoEc3KyqJBgwbExsZy5coVw7ZnnnmG1atXF/kJJiQk4OnpSXx8PB4eHkVu516M3zWe7Ze3M67pOJ4KfsoqMQghhBBClFSFyddMHjawZs0afHx8DIkrQEhICHFxcWzbti3Xc4KDg6lUqVKu+9zc3Ix6UG1tbWnfvj1xcXGGbevXr2fNmjX4+/vTr18/juYyM780GNN4DLt7784zcTXx/wchhBBCiP88k5PXQ4cOUaFCBaNtQUFBhn2F1aZNmxzb9Ho9ISEhhseurq68/vrrBAQEsGTJEpo0acKaNWsKfS1r83X2xcHWIcd2pdcTMXYsZx9uRcY147GtJyPi2bbvby4f+Q0y03OcK4QQQgjxX2Ry8hoZGYmrq6vRNmdnZwCuXjXPjPidO3fyxhtvGB537NiRL774ghMnTrBx40bc3d0ZOHCgUe/s3dLS0khISDD6KEnunLSls7Eh7eJFsq5fJ2nvPqPjFuy+QMstXam4phskXCnuMIUQQgghSiSTk9eMjAxsbY2nDtnZFWq+V77Wrl1L7dq1eeqp3G+td+3alRUrVpCQkMDGjRvzbGfq1Kl4enoaPu7uLbaWP678Qbe13XhnzztG213zWDLLz8NJ1noVQgghhLiLycmrv78/ycnJRttSU1MBCAgIuKcgoqKimD9/PgsXLsz3uLZt21KtWjXCwsLyPGbcuHHEx8cbPvI7tji52rtyPv48+yL2kaXPur391pJZSfv2ofR6w3Y/N0eisqtsSfIqhBBCCAEUInkNCQkhIiLCaFtUVBQATZs2LXIAGRkZjBkzhvnz5+cYlpAbb29vAgMD89zv6OiIh4eH0UdJ8KD/g7jZu3Ej7Qah10MN251DQtA5O5N1/Tppd6yF6+fmQJShUIEkr0IIIYQQUIjktUePHly9epXIyEjDtpMnT+Lj40Pr1q2LHMA777zDuHHjKF++vGHbuXPncj02OTmZiIgIunbtWuTrWYu9jT3NymklYe+stmXj4IBLk8aAcanYOwsVcFOSVyGEEEIIKETy2q5dO7p06WJ0a3/16tWMHz8eFxcXAAYPHpwjsczK0m6R57Yc1NSpU8nKyiI0NJSVK1eycuVK5syZw4YNG4iLi6N79+4sWbLEcPzEiRP5+OOP8fPzK9yzLCFaBGpDBPaEG5eKvXPoQDY/9ztLxEryKoQQQggBhaywtXTpUkaPHs3YsWNxcnKiWrVqjBkzxrA/Li7OMJQA4NSpU4alrVatWkVQUBAdOnQAYNmyZYwfPz7X65w9exYXFxeSkpJ48cUXWbBgAY0aNaJr1665LrFVWmSXij0ac5T4tHg8HT0BLXl1qlMH5/r1Dcf63dHzqk+4Vrg6vkIIIYQQ9ymTK2yVViWhwtaduq3txvn480x/ZDodK3fM8zi9XrF556/UidlEhVqNsQvpXYxRCiGEEEIUn8Lka+Zb60qY5IlqTxCWGEZ5t/L5Hmdjo6Pro+2AdsUTmBBCCCFEKSDJazF7ud7Lee7TJyeTevIkLo0aFWNEQgghhBClhySvJUTWjRucbdUalZlJjX17sfXyIjQigSsXT1PLNYmKtRqDo5u1wxRCCCGEsCqZB2QFWfosjkUf43DUYcM2Wy8v7CtVBKVI2v8XAN/tvkCtLX20ErGRx60UrRBC3Cf0WXBhFxxbqX2+o2CMEKL0kOTVCn46/RPPbXqOOYfnGG13bX5ryaxbpWL93B2IQpbLEkKIexa6Hr6sC4seh1UvaZ+/rKttF0KUKpK8WkF2sYKDkQdJyUwxbHdt0Ry4nbwaFyqIRAghRBGErofl/SHBuEokCVe17ZLAClGqSPJqBVU8q1DOtRzp+nQOXDtg2O7SpCnY2ZERFkZ6WBh+bncWKrhqpWiFEKIU02fBlrFAbqtC3tq25W0ZQiBEKSLJqxXodDpaBmoFC+4sFWvr5mooVJC0dx9+bo5EZ/e8JkrPqxBCFNqlvTl7XI0oSAjXjhNClAqSvFpJdrWtnKVibw0d2Lfv1phXL23HTRnzKoQoQUrL5CdTh1zJ0CwhSg1ZKstKHir3ELY6Wy4mXCT8ZjiBboEAeHTqhK2HJ64tW+J0x7ABlXgNnTUDFkKIbKHrtVvxd/ZoepSHzp9AnSetF1du3MqY9zghhNVJz6uVuDu4U99fGyJwZ++rY/Xq+DzfD8eqVfBxcaBnp/ZcqDWErKZDrRWqEELcVtomP1VqAY75lZrUgUegdpwQolSQnlcrGt5gOLY6W+r518t1v42Njm6PNAWaFm9gQgiRmwInP+m0yU+1uoKNbTEHl4ejP0FaQh47dYCCBs+XnHiFEAWSnlcralK2CQ3LNMText5oe9aNG8QtX070nDl5nCmEEFZQGic/lXkAXP2h1uPa0IY7Obprnw98p/UcCyFKBel5LYEyY2O59t5EdPb2RHbqQXhkGLVdE6kYXB9cfKwdnhDiv+rKgYKPgZI1+alcfRi6WxvTqvRaYn0zUntcLgQWdIKoE1rhgv7rwVb+LApR0knPq5WduH6CD/78gB9CfzBsc6hSBbuyZVEZGWxZtpWALUOouKY7XNqTd0NCCGEJej2c3gyLnoAdk0w7x9qTnyJPQNjftx+7lwWdThsaUKUV1OupfXZyh16LwcFN+/3664fWi1kIYTJJXq3s/I3z/HT6Jzb8u8GwTafT4dpcWzIrOCz0jkIFslyWEKIYXdgFsxrB0t5w4Q/ABuyc8j/HzhnKNyyW8HIVdwl+eFpLtk0ZvuBXHZ6cqX29+3M484tl4xNC3DNJXq2seXktST0Ze5KYlBjDdtcW2szX8v8eu10iVpJXIYSl6fW3v3YLgNjz4OQJLUbC60fh6floE53yWLwvMwWW9IDk2OKI1tjNaPjhKW1dbJ8qEFDbtPPqPg1NB2tfrxkMN8IsF6MQ4p5J8mplfs5+1PbRfsHui9hn2O7avBkAbmHniUt10zZKoQIhhCUoBZf3a0tdrX3l9nb/mtD7fzAqFDpOAa8K2jquvRaDRznjNjwC4ZG3tWWpLu+D7zpC3MXiew5pibCkJ8T+C54Vod9qcPY2/fyOH2g9xpnpEH3KcnEKIe6ZjEwvAVoGtuRk7En2ROzhiWpPAGDn54djzZqknT6NfVQ6uCE9r0KIwtNnGU9SqtTi9rJQWRkQug72zYaIf7RtNnbQ6UNw9dMe1+qas806T2rbc2u3Tjctibx+FhZ3g+EHwNY+ZxvmlJkGP/WDq4fBxReeX5MzuS6InSM88z1kpmpJuxDmkt/PoCgSSV5LgJblW/LtsW/ZG74XvdJjo9M6xF1btCD17FnsEm+VXUwsQTN4hRAlX16VsNpNgMSr8Ne3kHhrn60jPNgLmr1yO3HNT/bkp7uVqQMvb9fGybYZb/nEVa+HNUPh/G9g7wp9V2jjWIvCu1LOtm3kBqW4B6WpGl0pIj+VJUD9gPq42rsSlxbHydiThu2+L72Izy+/sqbWIwCoRFmHUAhhovwqYa19BXZM1hJX1wAtyRx1ArrN0tZFvVce5WHQr1Cz8+1tSdfvvd3c6DMgKx1s7KH3jxDYyDztXtwDcx6C6/+apz3x31PaqtGVItLzWgLY29jzUNmHOBN3hrjUOMN2Oz8//LL0vPx4ay6EDaFi5WrIjQYhRIFMqYRlaw+PfwH1ntFumZvbnbdFYy/Adx2gYX+t11eXx2SvorBzhGcWQcQhqNDEPG0qBb9NhZgzsGIAvLQN7J3N07b4byiN1ehKEUleS4iPW3+Ms13OX452tjb0aFkXpT5BZ85f+EKI+5cplbCy0sGrkmUS17ud2w5J0bBrujaTv9tssHO4tzavHNB6WXU6rbCAuRJX0Np8+huY+zBcO6YlGU98Zb72xf0t7Sb8s8i0anTn/4DqbYsttPuFJK8lRG6JK0DywYNETf8cu4AAgr78opijKsFkALwQeTO1wlVxVcJqOkhbH3bDa3BsuTbe9tkfwdmraO2Frtd6REOegydmWOZn36M89PhWWzP24PdQsQXUf9b817kfldbfz0WJW6+H+DDj8dI/dIcrf+d5ipFNo0FnC0FNIKix9jmgjumV3iz1Wpfw76EkryVMhj6D5IxkPB09AdA5OJDyzz/onV1Q/2yjQvW6hZ9Fm4fombPA1gb/V1/NuW/OHMjS4z9ieIlp10AGwAuRP1MrXBVnJayGz2u/u5YPgIu7YEFn6LcSPIMK186FP7RSrkqvrYygs+DUjWrt4JGx8PvHsPF1rdRsQC3LXe9+UFp/P5sad8oNCD+oJadX/tbuAKQlwrgwcHDVjglspA2VSY6hQDcugz5TW53jyP+0bfauENgQgjtAy9fuPebCKgXfQ5mwVYKsPrua1sta8+U/Xxq2OdWpQ6qTKzYpyfgt6AOnN5nvgrY2xMyYqSWUd4ieM4eYGTPBtohvD0u1CzIAXghTVGoBTl75HKDT1mWt1KK4ItJUfxRe2AxuZSH6JHz7KMScNf38q0dg6XPakIfaT0DXz807fjY3j7wFVdtARrL2OyY9ybLXK81K6+9nU+I+sBBmNYVPKsGPT2tjos9th9Qb2vjx6+dun9fxA3jjjJbw5VXMI/tncNRJeG4FtL71PnNwh4wk7R+8O3tvlYL1I+DPuXDlIBxfbZnXupR8D6XntQTxd/bnZsZN9oTvQSmFTqdDZ2tLTHA9go79yc1rjjibca3X7J7RmBkzUekZuDRqRPzGjSSsW4dHt24413uQm7t2A2AfWB7HqlUB0Kemkvz3gTzb9ejQwdAuWXo8u3cjfsMGYmbMxG/kiFx7ZE0iA+CFME1StJbg5erWH9POH1vn56Tcg9pSWkue0SZBeZQ37bzY8/BjT0hPhMqt4Olviyd+G1vtWnMf1pYBU/qCz/kvKq2/n02Nu/lwiDmtbfaucus2fxNtrHWZusZLwmV/3fkTLeFDd1f7d/wMugeAe0eo0fF2PDFntMT1zp+NuIvwz2ITntCtmDe+ro1nz+219q4CvtW0r9NuQtift6+98fWCX4sS8D2U5LUEaVy2MQ42DlxNusqF+AtU9dKSxaR6jeDYnyRHOt5zlS2VmUnauXOkHD6CY/VqRgnsnQvZJKxbR8K6dYbHPgMGUGbc2wBkxcURNmhQntfwevZZyr0/ydBuzOzZAEVPXPV67Yf58BLTBsBf2pv7+pNC/Ff8MU3rKfSqrCWxiXff/vvYurf/vCrAi1u0IgnZt1rzkxiplX1NioIy9aD3ErB3snyc2dz8YfCv4F7O8j29pZUpkwQTwuHURq2QRUkQfwX2zDQtbrcy0OcnbVyqKesgw+1qdLnegs/jZ9DGVitrfHdpY3tnaPuultRe2qv9E5dfzMnX4X+9ct/9yFhoO177Ov4K/NjDtOdTgv7GSvJagjjbOdOoTCP2Xd3Hnog9huRV16gJ/A+Srzugv3gImwu7TB48nXn9OilHjpBy+Ij2+dgxVHIyAF69euHSuDH+Q4cQM2cOZGrFEBxr185xp8OuzO2xcTo7Oxzr5F0z3L5cWYAc7To/WM+0FyIzTRvXZhhTdBDS4k07F4pvEsp/QQkftJ+r0hizuXWYrBUdCOmjTf4oia/H3ZO1fvsEUNofVqU3jjk1Ufsj610Z+q0CJ8/ij/fOXjClIClGS2qFxtTfu5EnbyevEYe1MZ8VmoJ7WdPOL+rPd0KE9vfEr8btxPDacfhrrmnXRRmvW2yq/KrRFYZ7WXjkTe3roytg9csFn+NZIfcSyXeOdbdzhLIPal+nxGmTzwpSAv7GSvJawrQMbKklr+F7eL7O80TPnEW1i8ewddWTlWRD0vEzuMc+Dh7liU7oAL41DJOfVHo6mTduYB8QAEDWjRucbflwjmvYuLriXP9BnGrXgtD1RH80FjJBZ6NQeh3urifxH5/3wGw7f3+qrl6d/xO5o93s2w1hgwdTeepwnLsNu31cViZEhWq9RBWbadv0mfC/Z0Fl3T7O3gV8qkLk8YJfxOKchHI/KwWD9nMojTFbgoMrdP7o9uOSfici4jD8divei3u08YN39xY/PFpLxt2t/POdGg/rhmu/iwb/Zp1EuqTIyoRTG7TZ8qb+3q3Q+PbXh36Ev+drX3tWuD3bPqipNrzk7mXcTP35zkjVxkcbOkD+1noMAVq/Ce3e1b4OagzlG0HEwYLjvpe/K3lVoysqUxP97l8XfF2fKjB0l/b1hV2w6PGC2y0Bf2MleS1hWpZvyWd8xoHIA6RmpsL1M9j+/AcOvhnYeumxtdfGokTvTSTm+DbcW0YS+UkyKUeOkHriBM4NG1Bp4UIAbL28cKhcGZ29HU716+MSEoJz/fo4VK2KztZWSzDfe5WY4+741U3Av+5Noo+7EXPAA957Ff/JFO0P/l3t+ta+yflNAWQk2XFp/AyqJJ7DsawnhP2t1VPPSIbAxjBoh3a+gyvUegwc3O5YOuQB7Xbdl3W1geO5jslBm6RS3JNQ7kfZg/bvfp2zB+33WlzyksHSGLO5/bsTqrQpfSVNy4dok69+HgMX/8i5P+GqNhSibD3tn1hr0mdpyXb8ZS2J7bX4vzeUIOWGNv7yr29uLRNVBYbdGqOZ5+9nnba/6h1rmnqU18aLRoVq7cSHwYk12j5bBxhzGlx8tMdHl8PqwTnbvvvnO+4izGysVV4zuryNVj3O5Y5b/q5+8PK2Av6u3Iq7JP1dqdTCtNe6sDFbql0L0Cml8sgC7g8JCQl4enoSHx+Ph4eHtcMpkFKKDis7EJkcydz2c2i5fDDRexOIOe5hSDDPb/UjLS73Bb7tA7ypNudNdI5uENwBlZ6OzsEB/v1V6zEwXEhP9IdvEXPI3tButujjbtr1GmTg/86nxkvR2NhB7Tv+M7u0F25GFdhuVoaO85v8yUyxw8Y+i6pdorF3uTXxwdEDKjaH534q+I+AIUGBPBPYFiPh0fdL3x/wkkKfdeuXeV7jwG79Anv9WMm4/QylM2ZzO7sdlvSASi2h/zrjCSSlgT4LplWHlNg8DihB38MrB2FBJy1B6vwJNBtq3XiKy/V/Yf9cOLREmxEPWjLY5CV4eBSc3ZbH7+dbv9fz+gcyLVGrkBb2l7b01JW/tbtto45p+/VZ8GE5yErLOzaPQO29gQ4+rawlv0FNb3eAlG8Ajm65n5vn35UC4rYmS8VsxdeiMPmaJK8l0MLjC0nLSuNx+zIELR8I3E4os2/tA6BTOHpl4OybgbNvOi5+6di7ZWn5n1cleP3o7UbntdZuo9wh+pg76JRR4mrYd9wNlA7/encNCndwg/Hhtx//2ENbLsSEdjNTbTi/yZ+sdFtcq7lRccoI7ZeKX83CJZq53joK1BLg4yu1xw88DT2+kwS2KEy9dTRgY8m5HV0aYzantESY01zruWr2KnSeau2ICq+0fQ/3z4PNb4GNvTb5LKhxweeUZrs+hx2TMSQ0AQ9As1e08sJ3Tp7L6/dzYSYJqlsTjrInRl34AxY9UfB52e+Nm9HauYXpETdH3MXNUjFb6bUoTL4mwwZKoBfqvqB9cWylYZt/3ZtcD3XXEledomLb6zj7ZGBjp7QB6K53TRy4e0xKufpa4pktKRr/emfyjMGQeN7dtt1dM3wD6kBGiknt2jnpqdwxhqt/eVFuzBitznlR5DcAPrgDrBum3R6SxLVoTB2Mf+OyZeMwVUqcVgHJFCVgooFFbH9fS1y9Kt0ez1falLSqYAVpOlj7HRS6FlYMhEG/QvSp0lXpKL+2M1IhM/X2xLqKzQAFNTprSWuVR3JPDs0xQUmnM57Rf+fdvfxkvzeKMpHOXBOripOlYi4Fr4UkryXZHQlo9HE3lF5n6HlNjnLANeDWOo5dPy+4J+LJmcaPTe3lKKjtjlMK1a6DWxaV2l2HKjULvnZ+8hoAX7+3dnvIr8a9tf9fZupg/IPfQ4O+Fg2lQKHrYM1Qbdy0KUrARAOzu7T39qSXJ2eYtvRUSVQSq4LlR6fTfq9eO6qtQftlXS3Zy1bSKx3l1Xab8drKDge+g7o9ocvH2r6KzWHkYW2CT0HMPUGpuN4b5o67OFgq5hL+WkjXVAkVnxbPVm7yt095oo+7G8a81up1Fb+62hjY6OPuRa+Skz0wu6DqH0Ud8G1iuwm//MLViZMw6+gV/5q3ewTSbmqLoUccMl/797sCv4dos4tbjLz9OOm6Ng7Q0pSC1ITbj8s+qPX8Bzxwq6KUmd/PJV1GijZpCLQ7GVXbWDWce2Kp30mW5OQBjV/Svr4zcYWSXekoz7YjYP1wrRxuUjSc/01bZxu036mmJK6WUBrfG8KipOe1hFp6aimzD8/mzbNlcDuO0eQn7bOOmOPuUPdR/IvSlW9ja1r1j8K2XYh2M65dI2LMG6iMDGzd3Qh4443CP4+C7PwAzv6iLb/zzPe3q5iIvJ3dps0eTrhKnt/Dngu020rZ/v5WW+qowkPaLcVaT4CtGX+9ZKTCseXw59fabPPeS7TtPlVg6G5tmMjJDXm8726xVkUpS/r9E4j9V1s8v8OUgo8vySz1O8mS9Fnw5+w8dt6Kf8NIrYRo9sTXB5663Tt+5YA21CDHqXr4ZQK5T0rNo92aj92emX/tWI45DkZt7/wwj7ZvsXXQllmq071kDL8qje8NYVEyYauEOh5znD4/96HvHlteKNcKJ4cteGbcMe7HI5DohEeN1nktEisP+L6xajVX33kHgICxY/F9YWDRr5mb1ARY/rzWg6Czhcc/h0Zmvsb9JO6SNrkv9YZ2y/DyXtPeG1vf0SawZC9P41lBGxPYsH/OxegLI/Ea/P0dHFgAyTHaNkcPGHU89/U1c3vfAVTvAP1W5jy+tIs4rPWUtRmvLS93PyhNE2dMHX51p9Enbxc82Pw27P/aPLEM2aWtjQrasmI7P7i39krKxLg7lab3hig0WW3gDqU1edUrPW1+akNcWhzfd/6e/Sfc+GP7eh6vYkO/R5sW36D9Ymg3Zv58oqd/DkD5Tz7Gs5uZSwdmpsOG1+DI/7THrd7QJrX819ZmLEhmGizorK29G9gIXtiifb9MfW8kRmo9sAe+02YKA9i7QpMXoeNdf0gLem9EnoC9M7VJi3cmxA8NgQbP558Q39l24jX45R2tJ2nkIfAMKvLLU2Lps+6/HqfSUiXt2EpY9VLBx5V9UOsdB3hq7u0e0gML4fTmnMcnXtXG0ham3S6f3L6tf3wVHPkp93NMbbvHd1CvZ8HHFbfS8t4QhSarDdwHbHQ2NCvfjM0XNrMnfA8Bbt35U1+H+CR3qunr0BQbzPXjmoUNf+nrEJVVlQC9k/naNnHAt+/LL5N1PZbY778n4p13sfXywu2RR8wRgcbOAbrP0eqp//4J7PpMm5n95Cxtn9D8MkFLXJ28tCEW2a+Nqb0v7mWg3TvQasztW/xRodqC5ncKXQdb3s5/EsrF3XBkqfZ1hWa3hiI8btpQhDvfd0ppycGl3dpSP49/btpzKelS4m6Xfbwf/3CX8MkiBqZOEOr0Ue7Pp/EL2sfdTO3Rzavduj20j9yUoipKuSot7w1hUdLzWoKt/3c97+x+hyCXYK6dHEpc8u2KIeU8nZj4RB061y13T9fYcvwq728I5Wr87ckG5mq7MJReT8Tbb5OwfgM6JyeqrF6FY1ULVNL5ZzFseF1bhmXQr+AZaP5rlEYn1mjL/QA8txxqdLr3NpWCC79rPaa+1bRte2bAtgl5n9PrBy2BTbsJm8dC4xchqNG9xXFxN3zfVVuPc+Q/4FXx3tqztqhT8G17aD4cHnnr/kxeSwtDcYwCKhIVtrCCpdq1dNtC3IPC5GslYCS2yEuL8trMySvJZ7mRFme071p8Kq/8+A9bjl8tcvtbjl/llR//MUpczdV2YelsbCj/4Ye4tm6F1zPP4FC5smUu1LC/lpw9t1wS12zX/4V1I7SvHx5lnsQVtGEZVdvcTlz1WfBbAYvnb3lbO87RDbrPvvfEFaDyw1CltTb8YNf0e2/PmvRZ2jrG6Tfh6mHj6nei+GVPJAJyzoQ3w8RXc7dr6baFKCbym68E83b0xSZDS7BsnS8Z7cv+f/n9DaGkZWSRmaU3+tDrb/9HrZTKsT8tI4tJ60Pzm8vK+xtCydIXX8e8zt6eCrNmUWb8OHSWnOEa/KhWSz3bma3a7Nz/qqRorUJOxRbQ1oIL3F/aW/B6rAnh2nHm1ma89vnQj1rt89Jq/zwIP6BNWuv6uYzbLgnqPKmVzPS4606VR/l7K6VpqXYt3bYQxUDGvJZgf12IJfFKd1SmOyrTK8d+BVyNT6XmhC059k16og4DW1YxtPPsN38W6trZbf91IZbm1XyLEH3R6Bxuj0FV6elEz5yJz4svYuftbZkLhv+jLb9iYw/PLtaqxvzXJgNUbKbNVNbpzLu81d2sWUGpUnOo/xwENrw9waW0ib0AO28th9Vhstw5KElKY6WjUlBFSYi8SPJagkUlpqJPrWDVGCITUgs+yEKuTnqf+NWrSfrrLyotXIiNi4v5L+JTBQIbaxN6fuyh9Wil3ri931zVbEqijNTbNcnv7oGxBGtXUHrKTEsSWYNS2rqeGclQuRU0HGDtiMTdSmOlI5n8JEopmbBVgu379zp95hfcY/pt/8Y0qexjtM3R3gYne+0/6IwsPclpWUb7/74Yy8uLDxTYtp+rA881q0TPhkFU9LVA8piPtH//5dJzfcmKj8e1VSsqzJmNzt7e/BfKTINF3SBsXy47b92Wvd9upUWGwg/docun8ED34rlmSZooolTpuuV+cJGWvNo5wyt7bo8jFkKI+4RM2LpPNK3iQzlPJ+zcTuFcYSH23ruN9uvQVgZoWysATxd7o4/sxBXA3tYmx/62tQLwCfoVB78duV7bwW8Hjn7biElKZ8aOs7Se9ivPzttHzM00Sz5lI47VqlFh3lx0zs4k7dpFxPh3UNmlCs3Jxg7iL+Wx81aSlT2R6H6QdhNWDNBuFf6zSEvkikNJmCiiFBxdDrMf0iaqlRb6TC1xbfeOJK5CiP88SV5LMFsbHROfqIPOPg47t9PYuZ8w7Mv+0z/xiTrY2hS+B8nWRkfbmmVw9N+WI4F18NuBo/82utQrz1e9Q2gV7IdOB+E3UvBxuT0mNSw2GUt33DuHhBD01ZdgZ0fChg1EffKp+a95aW/OikxGlOUmEhU3pWDj6xBzBtzLw9Pzi7cH0toTRXQ6bWH5mNPw+6eWvZY5NXkJhu2HZq9aOxIhhLA6GfNawnWuW45JaU/z8fF12LpcAptU0DtR1gxrsU7v8CZjtsEvLAYgPaa9IXHtWL4/0zu8CUC3kEDCb6RwJTYZm1uJcnqmnidn7cbdyZ6ejYJ4umEgQd7Gwwqy9Iq/LsQSlZhKgLsTTav4FCnRdmvdmvIffUjEW2OJXbQIuwB/fF8yoaqNqaw5kai4HVwIx1ZopXJ7LtDWuy1u1p4o0uZtOLtVK6TQ+g3wCy6e694r70rWjkAIIUoESV5LuDmH52BjZ0NF90pcTrzEkE562gQ1o2kVH+Yfm8ecw3peDTGtN0av9MSlxhGTEsP1lOvEpMbwQDlfrqXX5yjbcPbfiZ4sXq0/jFdChhqdG+jlTKCXs+HxmchEMrIUl2OT+XzbGT7fdoYW1Xx5pnEQnR8ox+9nosxa/MDzySfJjI0lavrnpP17PtdjoufMgSw9/iOGF65xa08kKi4Rh7WF/wEenaTNwLcWa04UCWwINR+D05u0ims9vrVOHAVJug4/9YX272nJvRBCCECS1xLPRmfD7MOzqedXDxIhw+Ekzas9w9wjc5l9eDbDQoaRlJFETEqM0cf1lOvEpMTwbM1necDvAUCr2DVhT97VjfRkYW9jzyshQzkYeZCZh2ZSy6cWNb1rUsunFtW8quFgqw0bqBvoyV/vtGfriWusOHCFvf9eN3yMsztGambOsanZxQ++7tewSAms78CBZEZHE/vdAuyDAvF/9XbSHj1nDjEzZuI3ckSh26VSC+22dUETiUpzApEar41zzUrXErcWRXid7idt3taS12MrodUbEFDL2hHltOVtuLwPNr2pLWVmybWPhRCiFJHktYQbWl/rAZ19eDYAK86sQIeO5WeWMyxkGIFugTT7X7M8z29YpqEhefV39gfAx8kHX2df/Jz88HP240riFQ5FH8Lexp4MfQZzj8zF2c6Zg5EHORh50NCWnc6Oql5VqeVTiwEPDKCGdw2eahDEUw2CuBKXzOp/wllxIIxI2/U4YEN6THujWBTaeNrxv/1KhzrTijSEoMybb2Lj6krMjJlkxlzH8/HHSfpznyFxvTOhNVn2RKLl/dFGE9+dwKrSX3HGzllLWk/9DN3nlK6Z9pZQrj7UehxObYTfP4Znvrd2RMbO3BrWoLOBJ2dI4iqEEHeQpbJKiZn/zOSbY98YHg8LGcbQ+kPZG76XIduH4GLngp+zloz6Ovsavn4k6BFq+tQEIFOfiUJhb3N7uak7e3CH1h9qePx8neep7VObU7GnDB8J6QmG85Z2XUpdv7oAbLmwhc0XNlPLpxYqrRxf7tmKo+9u0qI7GCWw2eNp06I70KFcXx6tU4Y65TwJDnAzjKU1VcT7k4lfuhSFlm76jhhBwLB7nMwSuh62jM198tbza6Bau3trvyRITQCn0vtzYFbXjsPcloAORhy07ix+fdbtMcAObrDhdbh5Vesh7/iB9eISQohiUph8TZLXUqTB4gZkqkzsbOw49PwhADKyMsjQZ+BiX/g1WO9OXPPbrpTiWtI1LZGNO8ULD7yAk522wP2UfVNYfma5UdtKb4fOJpPM5IqkXnkee++/DInrnQmtva2OE+93xsFO61nacy4GBzsbapfzwM0x9xsDW45f5dNVB5m19E10QKbOhsH9Z9zzBDbAOIlwKwMnN8Bf8yCgjnbr1pIVqCwh9gJ4BoGtBdbHvR/89olWYaxKa+v1Ruf1T5NbGRh5GByKd31lIYSwhsLka6XsL/F/19wjc8lUmUa39ofWH4q9rT32RUxM9EqfI3GF20MV9Or2uFWdTkc5t3KUcytH24ptjY7vUaMHVTyrcDL2JP9cO0HYzQvobDIBsHO5jGv1j9HZZBklrp3qlCE2OR0dOkPiCjB180mOh2s9vJV9XahT3oM65TxuffbkcFgcr/z4D71PbTcsF2an9LTdv4FX4lOLPJ7W4O6JRGUe0G7fRoVqa6I2MeMqB5aWHAvfPw5eFbRlqNwCrB1RydNmrHWvH7r+1nCVXPoQbkbCue33V3EMIYQwA0leS4G8bu0DORLPwshvlYLCtFvHtw51fOsA2vJYLT/5hejUy+icInAqtwqdTRZKb0t6THt0QFlPJ+b0a5RjzKtSigreLsQkpnMtIZWL15O5eD2ZTceuAVDRx5mMLEXvU9vof2orK6o/Qvd/d2Ovsnj+1FYA3t/gRIc6ZYs0njZXLj7QZhxsfgt+/RDq9QQnT/O0bUl6PaweDAlXtBKw9s4Fn/Nfl3YTHN2K73r6LK3HNddJggA6bdJWra6le7y1EEKYmSSvJVxut/DvnsR1Lwmsudna6Jj0xIO88mMm9u4n0ekUSm+LziYLx7JryIwPYeITPXJNLnU6HV/3awTA9ZtpnLyayImIeEKvJhAakYCvmwNVNv1E/1NbWVyrE0trdcAzPYmOlw9wyT2A509tRQF/XQiheTVf8z2pxi/C399qC/v/Ma10jEHc/Tmc2wZ2Tlqvq6O7tSMquZSC3z6GP+fAgA1QPqR4rluY4hhSf14IIQwkeS3hCnNrv6ToXLccT7cP5ZeI22NcHcuuxsF7Py4+h6ge2LnANnzdHHk42JGHg28vor/uUDh//aw3JK4Aq6q3oePlA1RIjGZN1VbYKD1Rial5NVs0tvbQ6SNY0hPC/tJ6zEpST9jd43T1mVovMUDX6drQB5E3nQ5iz0NagpbEPreseK77XyqOIYQQZiTJawlnrlv7xWnukbn8ErGYV+sPI8T9GaISU/FyacAHRwZwLfkaz29+nrXd1lLGtXCL/gd4OLGkdiejbZc9yvJXmVo0iD7LWe8K/FqhIY+7O5nz6WiCO0DfVdqKAyVp2aLcJvvobEDpIaQvNOhnvdhKk0fGwvGVcGYzhB+EwEaWv+Z/pTiGEKLUiJ45C2xtcl12ssiFgCygBP0VFveL7N7iV0KG0ryaL91CAnmkRnlWPLECL0cvbmbc5NUdr5KYnliodptW8aGcpxN3DziYV68bAzq+w28VGlLOUytDaxHBj5a8xHV5/5y3nrN746u2zXmOyJ1fdXjwWe3rX6cWzzX9a8Gtoh+504FHYOkujiGEKF1sbYiZMVNLVO+QXQgI25LxN7BkRCHuK6+GvJprr7CXkxdLuy7F18mXM3FnGPXbKDKyMkxu19ZGx8QntIlhdyawEW7+xN1au3TiE3XMN1krL+lJsH+edrveWgqc7ANsn2jdGEub1m+CzlYbKxz2t2WvFXcRFnbRKp7l6tZ7uLQXxxBClCr+r76K38gRRgnsnRUsi1QIyAIkeRXFKsg9iDmPzsHZzpn9V/fz3t73KMxSw53rluPrfg0p65lzaMCzTSvQxiYOfaqZx7zeSa+Hbx/VVh84vMRy1ylIgZN9uD3ZR5jGtxrU76N9/dtHlrtO+D/ae+j6WfAIgk5TtfLDd/Ior020k2WyhBDF7M4E9lS9B0tc4gqFHPOakJDA66+/Trly5YiNjaVixYqMGzeuwPOio6O5ceMGwcHBRtsjIiJ44403qFGjBpcuXaJVq1a8+OKLRsd89NFHhIWF4ePjw7Vr1/jqq69wcyvG5WyE2dXxrcPnbT5n+I7h3Ei7Qbo+HUdbR5PP71y3HB3qlOWvC7FEJaZyOOwGC/dcpOWqr7lw8DfKTpqId+/elgnexgYaPA9bx8GOKVCnu3UqVslkH8to/QYcXQYX/tAKPPhUMW/7/+6EZX0hIxnK1IO+K8CjHDw0xHjSXaUW0uMqhLAa14ceIoaZqIwMdPb2JSpxhUImr7169aJ58+ZMnDgRgA4dOuDk5MSoUaNyPT4xMZHp06czffp0Zs6caZS8ZmRk0KlTJ9544w0GDBhAZmYmDz74IN7e3jz11FMATJ06le3bt7Nz504APvzwQ/r27cu6deuK9GRFyfFw4MN81+k7HvR/0KhcralsbXSG5bC61C3HE/XLU/m360Qe/I3rCxfi9cwz6Gwt9Me/yctw4Du4fk5bkurRSZa5Tn5kso9l+FSBJ2dpyaN3JfO371VJW76sYjN4ZtHtf3zuLo4hhBBWkhkbS9jgIdoDGxtURgbRc+aUqATW5GEDu3fvZuvWrbzwwguGbQMHDmTKlClkZOQ+bvHatWu88MIL3Lx5M8e+n376iX///Zdnn9UmSdjZ2dG3b1/ee+89AFJTU/noo48YOHCg0fXWr1/PwYMHTQ1blGCNyjQyJK5KKS7EXyhSOw52NjSs6I1Xjx7YeHiQcekyibf+4bEIO4fba73um62NXyxulVqAW9l8DpDJPkUW0scyiStoQxNe+gWeW26dHnshhChAxNvj0CclYevtTc2//8oxBrYkMDl5XbNmDT4+PlSsWNGwLSQkhLi4OLZt25brOcHBwVSqlPsfgTVr1lC7dm2cnG6PXQwJCeH48eOEhoaybds2bt68SYMGDQz7AwMD8fPz46effjI1bFEKZOozmfznZJ7Z8AyHow4XuR0bV1ccnuoJQOx3C8wUXR5qdIaqbbQJN9ves+y1cpOWCHn2WMtkH7O5/u+9nZ+ZBmtegbPbb2/zC9bWDhZCiBImes4ckv74A7dHH6XS4kXYuLrmOonL2kxOXg8dOkSFChWMtgUFBRn2FVZB7WW3mdsx+V0vLS2NhIQEow9R8kUlR5GWlcaInSO4GH+xSG2cj75Jn5iKZNjYknL4MMn/FP59aTKdTitcoLOB0HVwcY/lrnW3jBRY2gcSwrRStXcPDZDJPvdOKVg1CGY21Ma/FkXKDfixBxz5H6x6CVLjzRqiEEKYXZYev5EjqDBrJo53DPXMTmDJKhmFkUwe8xoZGYmXl5fRNmdnrV761atXC33hyMhIXF1d82wvMlKbaJLbMfldb+rUqbz//vuFjkdYj52NHdNaT+OlrS9x/Ppxhm4fyo+P/Yifs1/BJ9+hip8rFYMrsuN4Izpf+ovrC77DpeEsC0WNVrmq0UBIiQOvCgUebhZZmbDyJbi8Fxw9YODPEFBHJvuYm053+7b+r1Ohcittm6luhMGSZyD6JDi4Qc8F2j8aQghRAmVERhH12WeUGT8OO2/vXI8plWNeMzIysL1rAoydXdELdBXUXvY42sJec9y4ccTHxxs+wsLCihyjKD4u9i7Maj+LILcgwm+GM2zHMJIzkgvVhk6n463ONVld/REAbh49hj4pyRLh3vbYZ/DM9+BVscBDzUKnA7cAsHWEPsugbL3bk33q9dQ+S+JqHg+P1l7ny3vhwu+mn3f1qLYUVvRJcC8HL2yG6u0tF6cQ4p5Ez5yV5+3w6DlztKpT9zGVmUn4mNEkbNhAxNtvWzsck5icvPr7+5OcbJxMpN5aTzMgIKDQFy6oPX9/f4Bcj8nveo6Ojnh4eBh9iNLB19mXuR3m4u3oTej1UMb8PoYMvelFDAAaV/ahZtN6jG8xmDkvT8Pmrp57s7s7USzEmrVFvt7jX8CQP6ByS8te67/OM1DrWQf49SPTvrfntmvFB25eA//a8PJ2KPegRcMUQtyjUlJVylKiv5pByoGD2Li6UuZ+S15DQkKIiDBeFD0qKgqApk2bFvrCBbUXEhICkOsxRbmeKB0qeVRiVvtZONk6sS9iX5EmcL3RqSaHy9RgQ2g0R6/cMHuMuYq/Aqtehl8/tEz757ZDdjUynQ4CalnmOsLYw6O0pa3C9mtrtBbk5EZIv6kNM3hxC3gGWT5GIcQ9KS1VpSwh8ddfuT5/PgDlPvwAxypmXtvaQkxOXnv06GE0FhXg5MmT+Pj40Lp160JfuEePHpw4ccJoma2TJ09Su3ZtatWqRadOnXBzc+Pw4cOG/Tdv3iQsLMywDqy4Pz3o/yDTHpnGjHYzaFK2SaHPr1XWg6dCAgH4bHMoaefOmTvEnCIOw7EVsHcm3Lhs3raPLtcm/izrC5l5lRMVFuFRDhq/pH1tSu/rY9O0ZdT6rQZnL4uHJ4QwD/9XX8Wze3diZszk5AN1/xOJa0Z4OBFva4WmvPv1w6NzZytHZDqTk9d27drRpUsXFi5caNi2evVqxo8fj4uLCwCDBw+ma9euRudlZWm11e8uAfr8889TuXJlVq1aZdTe5MmTAfDw8GDChAksWHB7yaN169bx2GOP8dBDD5katiil2lRoQ+ug2/8UZeozC3X+qA41qJZxg5ELx3Oxbz/0KSnmDtFYra5ab1tmKmyfZL52z26Hta9oX/tUlSWWrOHh18HOGW5cgrhLcGEXHFupfc5Ihf3ztIl0oH1/WozQ1gIWQpQKWYmJXJ0wgfi1a29t0PIWrx49rReUhan0dK6MGo0+Ph6nevUIeOtNa4dUKIWacbV06VJGjx7N2LFjcXJyolq1aowZM8awPy4uznDrH+DUqVOsWbMGgFWrVhEUFESHDh0AsLe3Z/PmzYwZM4bQ0FDS0tLo06cPPXvefrO8+eabZGRkMGjQIKpWrcq1a9f48ccf7+kJi9InLCGM4TuHM6bxGKOENj8VfFxYN6Un1578nowrkdxYvRqfvn0tF2T20lnzWsPxVdB0CFS8x3+yrhyA5c+DPhPqPXNraa5CzHgX5uEWoJVxTbwG33eBhDuGMtk6QlYaxJyBrtOtF6O470TPnAW2Nrn2/EXPmQNZevxHDLdCZHkrjTHf/P13rr43kczInKW0b6xaaXguGVevYl+uXHGHZzEZUdFkxcVh4+lJ4BdfYONQuv7h1qm7u0TvMwkJCXh6ehIfHy+Tt0qpT/76hB9P/oiznTMLOy3kAb8HTD439sclRH7wAfYVKlBty2bLlYzNtn4E/LMYyjeEl3eATREH+kefhgWdtGW4qrXXVhaQ3jzrCV0Py/sDefy6bDUG2luhWIW4b+U15rIkj8UsbTGnX7rEv527gFLYeHqij4/Hb+QI/IYMIWr658QuWIDfyBH49O3L2TZtcapVC58B/XF/9FF097DaUkmRlZBA+vnzON+aY2RthcnX7u8pdOK+MLrxaJqXa05KZgqv7niVsETTlz/zevoplLsHGWFh3Nj6iwWjvKXdBHBwh4h/tDGwRRF/BX54WktcAxtpBQckcbUefRZsGUueiSvAkWXacUKYSWmcRFTaYnaoVAmfgQNxbtDAkLj6v/oqOltbyrz1puG5XPvgQ1RmJimHDhH++ijOdezI9e8WkFUKiyCprNu/p2w9PEpM4lpY0vMqSoWkjCQGbhnIqdhTeDp6sqH7BrydjBdSnntkLnql59WQ278gUzOymN5nDE8f30py1Zo0/HkNOkvfet/1Oex4H8o3gEG/Fv5W/8U92gL3HuXhxa3g6muZOIVpLuyCRY8XfNyAjdo6u0KYkWG5Jp0OlMJ32KsEjBhh7bDyFTn9c2Lnz0dnb4/KyCgxiWtmbCxRn3yC79Chhln1SiliZs0ucLiD17O9uLFsGXFLl5EVFweAzsUFr6eewnfIYOyLsGRocdOnpnJpwAA8u3XDu08fy/8tLCTpeRX/b+++w5sq3z+Ov5N075a2UMouICBCQQEZoiIoDly4xYkyBb8q/hQX7r21WEVFGSqyBDfgQBkKCkX2HqUtlA66R5rk90dpILSFdKTz87quXpJznnOfO01T7z55RoPj6+7L1Ium4ufuR0ZBBtd/ez15RccnYcVuiCUmLgajwfFH2svdRIu7bqPQ6IbPnu1k/L3W9cmeO674I+Q7vq3cGNU2/eHO7+C2hSpc64Ls0mPhqtROxEk2mw2KikoeAHD0qzkceT+GorS0WsysbAW7d5M05WnSZ84Ekwmb2YzB3R3/iwZz+LXXMJ+09GVNsdlsZP7wA3suv4KMRYs5NOVp+yRyg8FA2IT7yi2uw8aNI2zCfbiHhxM2cSLtf/+NiOefw7NDB2y5uaTPmeP69b2ryeEXXiR/w3+kvB+D5ejR2k6nStTzKvXKnqN7uOG7GyiwFHBW6Fl8cfkX9sJ1fPR4xnQfU+qafLOF6cNHc8GOlRy8ZDhD3nm+FjI/DYu5eCJQcOvazkROpp5XB66clOOq2PVxIpHNYiHpscfJWLSo+IDRCNbj+8q3mTsX77O61lJ2x9lsNnJWrCRtxgxy/vzT4VxJz6tn584UbN0KJhP+Fw+hyR131NjH1UVHjnDo2WfJWroMAM+OHYl44YUqf+9sNhu5f/1F/rbtNLnrTvvxQy+8iFenTgRccTmpH02rMz93GYsWkfjIo2Aw0OqTj/Ht169G7lsR6nmVBqtdUDs+GvIRzX2bszFlIz1n9jxl4QrFva/NRo/iwfPu4/+aDCQzv2K7dlWJ1Qr7V5++zaL74KML4OC/NZKWVEDrfsVDOCivF90AAZHF7RoDV+5G5KrY9XEHJaORgj27AfC7aBCdt2wmdPx4ALzOOsuh+EqbMZOsX3/DdkJxWxPS585lzxXDiL/33uLC1WDAI6odAKETJtBp43+ETpxAwdatuLdoARYLWT/+xL6bbmbvjTeS8f332Myu+X1ss9nIWLSI3VcMKy5c3dwIHTeOtvOqp+g3GAz49u3rULgW7NpF+syZJD3+OLsGXUTu2rV14ueuYOdOkp5+BoDQ8ePrZOFaUfV/upw0Oj2b9uSn4T9x9qyzMVvNuBvdyy1cSwwbeg7vb8sl/UgOH/+xhwcvPsP1iRYVwmeXw8E1MHIZtCxnw4WlT8J/X4HBBLkprs9LKsZogqGvHFttwIDjxK1jBe3Ql0tvFdxAlfQipbz7nv1xdU3KcVVsV+bsKikffED+xk0E3XADEc8WFx5hE+5zKMTDxo2jKD2d5DfewFZQgHvrVoTcdjtB11zt+q2xgaxlyyjcvRujry9B1w3HBqR/PsPhe3ri9z7o5puxFRSQ+e235G/4j8SHJpF21lm0+XpOtY+/zPzu++KeRsCzS2eav1jcI+pKbuHhhD88ibRZsylKSiI3NRWMxa9X0ZEUIqY8VeM/d9acHA7+7wFseXn49utH6NhT/7+yvtCwAamXSoYKuBvdMVvNp+x5LfHTpiTGzFpHuC2fbycOpGlkmOsT/WYcxM2GFr1g5NLSY2BXvgNLjy2xdHUsRN/s+pykcrYsLl514MR1XgMiiwvXLlfWXl61JPmdd0n94AP7Y1NwMKaQkOOPg4JoM/v4utyJjzxC3qbNZcYyennRdv48++O9w68jf/PxtifHjvr+O/u/D734IjkrV5WbZ9sF8zF6egKw79YR5P37r33yU10rXPO3byfts8+JePYZUmI/dOoj56L0dNI++YT0r+diPTb73ejvT9B11xEy4lbcIyOrJbe8jZtImzmD8AcesK93mrtuHfkbNxI4fDgmPz+nh2cUpaSQ/tUc0r/8kuBbb7G3t1ksFB44UC1blNrMZvaNGIH/hYNoMvJuDO41t8GLzWwma9ky0j6fQd4Ju4Ti5gZFRTX2c2ez2Uj8v0fI/PZb3MLDabtwAW5N6u48iorUaypepd4pKVxHdB7B3sy9bEvdRmp+6mkLWJvNxgf3v8zA5V8TesftNHvwAdcnm5kE750N5hy4dhr4RxRP7PFrCun7YPGx8U5DnoP+E12fj1SN1QL7Vx1/DVv3azQ9riezWSxs63pWuZNVTE2a0HHlCvvj/SNuI/eff8psa/DxodO640NmDtw7qtT4yRN13rbV/u+DE+8na0n5y+CdEbceo5cXAImPPHp8DCkQfNtthD/wP4zHdomsTTmrVnFw4v1Ys7MJHTeWsIkV+31gzc3l6DffkD5jJoX79hUfNBpp/tqrBJ6086WzbEVFZP3yK2mff07eunUANLlnJOGTJlUqXqmcCwqgqMjeS5y1bBkH75uA7/kDCbn9dnz79XO6R9ackEDqJ5/Q9NFHMRxbcN9msbh+be/TyNuwgbTPZ5D5ww9A8TjgThv/I3/7DtwjIzH5ua6H3GazkfrRNFJiYmg1/VN8zj7bZfeqDhWp1zRsQOqVEydn3dL5Fi76+iLyLflc2+FaYuJiAMotYA0GAyOuOJuEJTPImDOH8DGjXf8/rYAIOO8B+PV5WDgGbGWsBdpvggrX+sJoahSTsspjOXoUg7c3Rk9PUj78sLhwNZnAYiHw2msJvOoqe1uDu+P/Xpo+NhlLVnaZcQ0njf1zb9mi+B/HeqpOjn2isAn3EXyK3fNO7HEz+vsd+0fx5Kf0mTPJ/v13Ip57Dt9za2/b8aMLvyHpySehqAifXr0IueOOCscw+vgQcsstBN90Ezl//kna55+Tu/YffHv3trcxH07GLST4tL261rw83IJDSJ816/gKAe7uBF52KQGXXVbp51kqZ09PONYrDpC/ZQsYDOQs/4Oc5X/g2aE9wbffTuCwYaRO+7jMnG1WKwfHjSd7xQooKsIU0oSw+4rHBtd24Qrg3b07Hu2jgOMT2I7ExJC5+FuKUlMJuu46gkeMwKNF9fSQn8hgMBA6ehSBV1+Ne9O6v5RXRah4lXrFarM69LBe2vZSFu5aSIGlgPHR47HaTj1hwf+ii3Bv3Qrz/gOkzZtP6O23uT7p4GMfgZVVuAJEljMWVqQOyVq2jKRnniHo6qsxeHs7jNsrGcfn3iKy3I9Dvbp0ceo+R6ZO5egXXzod27NDBzzLiFNW3PRZs+1xEx55lMxFizDHx3PgzjtpNuUpgm+u2WE7NpuNlA8+sI/FDbjsMiJefqlKW3UajEb8zj8fv/PPLy5Ww44Pj0p89BEKd+/BIyqK3NXFE0nL2gnL6OODNTcXKB6yEXTTjQTfdLPLC6CwiRMJvPJK0mbN5uiCBRTs3MWhJ5/iyJtv4dG+PXlr1zrkXHjgAAfuuhtzQgIA3mefTcDl1VdcV4eTx7iWPDYFBWHNzibts89ImzED/8GDCbnjdrx79qzy+F9LdjYGo9HeOdPQClfQsAGp5zalbOLm72/G3ejOsuuXEeIVctpr0r/8kkPPPMsRvyaEfbOYzi1Of02lWS3wdlfHcZIODMUz2f+3sdF+/Cx1W1FaGoeff57MH34EwBQSgiUtzSVbgLpqe9Hyrk9++21SYz/E4OFB1E8/4t68eaXyrgyb2cyhZ5/l6Nzisb5N7r2HsAcewFDZLaVPw5KRwZ4rhlF05EjxgWO92sEjbiXwyivJ/nMFKe8Vf48saenk/v2XvdezZNhFTbJkZnJ03nx7769X1674XXhhcY733YfJ34/Dr71evA6umxtNH3mE4Ftvcdn3rzJO9/MccOUwLCmp5Kw6Pmbbq2tXwh9+GN8+vcsKeVo2m42EifdTuG8vke+8g2e7dlV+HjVFwwak0ega2pUuTbqwJXULi3Yt4q6ud532msCrr2bva28Rlp3Kt+99QedXXLjO3v5VpyhcAWyQmVDcrhF/HC11j81mI+vHHzn03PPFOwqZTDS5+24wGTF4eJQqIu2PLVVYrsliLbNArXLscuKG/+9/GDw8sGZlOxSuRxd+g/9FgzC5sMOj8ODB4j8IjEaaPvE4Ibfc4rJ7AZgCA4n6ZRlZP/5I6uefU7CleNxw+qzZpM+aDWD/Hlnz8jB4edXqDkymgACa3H0XIbffRtYvv2L088Wvf38wHF81AsC9RQtaTf8Uj5Ytay3Xcjnx8xz26qvk79hB+syZZCxaTP6mTVW6ZfqMGWQtXQru7lizyx6m0xCo51XqvQU7FzBl1RRa+rfku2u+K7XLVll2vPImlunT2BHUguZffkWvti6agblxHswfefp2wz+Bs65zTQ4iFVSUksKhZ545vrB7hw5EvPhinVgU39WyV6wk/p57cAsPp9kzT+N/4YUuvZetsAD/QYNcdo+y2Gw28v75h7QZM+yvMSYTnTdXrXCqKVvP7AoWC5hMdNr4X53qba2KorQ0sn7+maCbbrL/4ZD89ttYUtMIueN2PNu3P+X1eXFx7BtxGxQV0fSJJwgZUf5Y8LpImxRIozK0zVD83f2Jz4pndeJpNgQ4pt29d1Lk5k67jEQ++XI5Lvsbzq9p9bYTqQG2wsLi5adKFnafP69RFK4ARl8fPFq3pig5mYNjx5Hwf/9H0bG97KuqYNcuh6WT/Ab0r/HCFYon8vj06oVn587Fj93dwWIptZh+XVS85JbFnnNKbGxtp1Rt3EJCCL75Znvhas3JIX3WbI4e2wziwMh7yP7zT2xWK0fee9/h9SpKT+fgAw9CUREe7dtTlF73tg+uTipepd7zcfdhbPRYnun3DD3Cezh1jVtICIHPv8ToS5/gp0xPftue7JrktDuT1BOWrCz7v92bNyfi5ZdoO28uYRMn2Jceagx8evSg7TcLCbn7bjAayVz8LXuGXUnmKZbjckbOmjXsu+VW4kePoWDv3mrKtvJOHI9ZshNWWbtB1SX1MeeqMPj40DL2A/yHDC5ehWHlSuLvHcWeK4aRv2WL/bnbrFYSH3mEoqQkTIGBFO7aVSdWWnAlDRuQRu2lH7fy4fI9dGrmzw8Tz8NodMEYry2Lj+3OBGXuznTDjEa5yL3UDTabjYz58zn8yqtEvv1W8bhCAYrX6Ex87HEKdxdv0xp8y800e+qpCsfJ+O57kiZPxmY2492jBy2mxuAWHFzd6TrNVRPjXKk+5lydCuPji3th583DmpMDFK+ukPfvv/gOGEDOihX2Zevq6/dCwwZEnDT2/Cj8vdw4vC+B1XtSXXOTLlcWF6gBEY7HA5qrcJVaZU5IIP6ee0l64kmsWVn2me9SzLt7d9ouXECT0aPBZMKnd8XWgrXZbKRMm0bipEnYzGb8L76YVtM/rdXCFTjlRKLQiROqNunOVepjztXIo2VLmk5+lPbLf6fpY5PxaN2a5q+8QujECcWFq8FQrwvXilLPqzQYBZYC5u+Yz2/xvzF18FTcjaffDtCam8u6u8bgs2UDHX5Zinu4C9fD0+5MUkfYrFaOzplD8muvY83NxeDpSdjEiYTceUeD/7ixsgoPHMCjVSv749y1a3Fv3brc3xm2oiIOvfACR7/8CoCQO24n/P/+T99fqRY2m80+NnbbWd2wmc323bvqKy2VJY2SESMf/vchaflpLI9fzuDWg09/jY8PoSYLeeZC0mfOIvyhB6s9L4f9vk9aDuvE/b6lapzdV72xKO/7UdbC7hHPP1ct+8k3ZCcWrkVpaRyceD82qxWfc87Bq0sXwsY7fp/TPp9hL1ybTn60UrtmiZSnpHA9MnWqvXC1mc0cmTq1UfS8atiANBjuJneuaX8NAF9v/9rp65qMvBuA9DlzSEhI4fftySyKS2D17lQs1mr4YMJkLHNSQclYLUx6G1YLfZ8dlfP9SH7r7eLC1c2Npo89RuuZM1S4VpA1Kwv35s2xZmSQ/csvpLz3HodffdWhjSWneI1N/8suU+EqLtHYJrCdSD2v0qBcf8b1fLrpU1YnrWZ/5n5aB7Q+7TV+F16IR5s2FO7bx2v3v8Y37Qfaz0UEejFlWBeGdo04RYRTK/kruGRh7RO3CGws45Nqgr7Pjk78ftgsFsInTODI1Klk/fgjPueeS8Rzz9bNhd3rAY/WrWkz5ytSP/mUlPffx2Y2k/bpdPI3b6HV9E9JiY0ldeoHhE6YUKpHVqQ6lPW7razfgQ2VxrxKgzNu2Tj+TPiTO8+8k4fOecipa1a8NY0mH77JYe8g7h4yGeuxsaglaw98MKJnpQrYko9zAA6/8SZp06YVD6y32RplQeVqluwcEh58gJw//rQf8+3fj8i338bk71+LmdUs86FD5MVtIG/DBjJ/+omipCT7x4r6uateBbt3k/TY4+Rt2FB8wGgEa9mTi0SqS0McJlWRek3FqzQ4v8f/zoRfJxDkGcSy65fhafI8ZXuL1cYFL/7MK3OfJLggm5fPuZXlLY6vF2sAmgV6seKRQZhOsZSWraiIgh07yNuwobhwiIvDo107Wn5w/COcrZ062//d9KknCb7ppgazO0xtKjx4kPSZszg6f37pLRGNRjquWYPJzxeAzCVLsOXl4R0djXurVrW6BWZ1ylmzhvTZX5AXF0fR4cNltqnvEzrqKpvFQtqMmSS/8gqg77NIZWjCljRq50WeR4RvBEk5SSzZt4RhUcNO2X7N3jQGrv2Bff7NCC7YxZADax2K15u2LcVos7JmbzR9o0pvI3skJobcv/4mb9MmbHl5DucsWVn2WaH2cUjHel4PP/scWT/+RMTzz+HR+vTDG6R8WUuXkfb55wCYgoOxpKeDmxsUFeHdrZu9cAVIm/4ZeevXF7cNCsK7e3e8o7vj3b07Xt26YfLzKxXfVb0cFY1rs9kwHzxo71UNvHIY3t26AWBJSyfr55+LG5pMeJ7REe/u3SlKSSF76bJGN6GjJhlMJqx5ucX/1vdZxOXU5SMNjslo4sYzbmRQy0FOjXlNzsrHajDSI2UXq5t14Zk+d9nP3bxtKbdv+xkDkLluPWkzZnD4pZcdrs9d/Re5a9diy8vD6O+Pb//+hI4bR8uPPqTd99/ZC1f7wPrNm/A9//zia9euZc9VV5P62WfYLJZq/T40VLbCQjIWLyb7z+NDA4KuG47fRRcRcNWVWNLTCZ04gc6bNhI6cQJ5cXEOExh8evXCOzoag7s7lqNHyV6+nCPvvMuBu0ey5/IrHO5lPnQIm9Xquslgp4lrs1nJ+etvUmI/JH7sOHb2H8DuIReT+PDDpM+aRc6qVcef1zlnE/bQg7Sa8TlnrF1DuwULcAsLI3vpskY5oaMmNeaJMyK1QT2v0iCNPGuk023D/b34stMQAG7f9jPX7fydpa17cc+mxZyf8B9HvAK5YeevuD+5lJIPY0PHj8N07GON4NtuI/Caq/Hu3h2Pdu1KDQMoa2B9qw9jOfTyy6R/9jm2/HwyFiwk5JZbindIkTIVpadzdM4c0md/QdGRI3h16YLvgAEYDAZM/v54ndnFqQkM4Q8+AIC1sJCCbdvsvZh5cXF4dTk+rMNms7H3uuux5efj3a0bPr17kfLue1jz8ggbP57UadNIiZlK6MQJhI4dizU/v9zcDUajfYtVm82GraAAgCZ3342tqKi4UDUXETjsCtK//JL0mbMInTgB/wsvZO811zoGc3fHq0tnvLt3x+fss+2H3UJDCb33Xvvjxj6ho6bo+yxS81S8SqPXu20IEYFefHVCATti2xKMx7ZyDcvPAIo/ji75iBnr8d1cAi65+NQ3KGdnmGaPPorJP4C8jRsJu+++48WN2QwGAwY3vT0BCnbuJG3GTDIWL7YXfW5hYfhffDEUFcGxCXGn2oGn5PyJjB4eeHfrduxj99uA4oK2hCU1FWtODra8PIcezrRpH5M27WMA+/0sWVns6NW73OcQcPnlRL7x+rHAFrZH9yjVJvWDD0j94AOHuLaiIjzatbMPAfCJjsazc2eMnqcex12Z74dUkr7PIjVOE7akQYvPjGf+zvnc2+1efN19y23306Ykxs5aB8CixY/gbrVgBb5v24+tIW249a5Lueiis2tkck/KBx+Q9cuvRLz4Al4dO7r8fnVZ8htvkHqsUATwOvNMQu68g4BLLrEX+65kKyqiYOdOh0l4hfv2FZ80Gum8ZTNAhYpXW1ER27qeVf5NT4grItJYaMKWCMUfz973633sydhDc7/m3HDGDeW2Hdo1gg9G9GTd82/gbrVgNppwt1ooCgzmxsn3MLgK67xWhDU3l7RZs7GkprJ3+HWEjh1D6L332pfbqu9ON0HJll9Ak1Gj7BOsvHv0AKMR/8GDCbnzDrx79KjR1QEMbm54de6MV+fOBN90k/0j4pMn5Rj9/Djj33/KD3RiL7rJVKptyrSPSY2N1WQfEREnaMKWNFgGg4HrOl4HwJztczjdhwxn/7GQa+K+J2/EPez94mfyRtzDNXHf0+3X+TWRLlC8XW3bBQvwu/BCMJtJefc99l5/A/lbttRYDi5VzgSlw6+8Ssq775H2+eekffaZ/bjf+ecTtWQJLd59B5+ePWt1WatTTcoxGAwYfX3L/zrhY/6T26Z+/jmpsbGa7CMi4iT1vEqDdmXUlby77l12pO9gw5ENRIdHl9nu5EkXPYEDLcfy3Z5ULpwag5vJSHgN7ZTj3jScFlNjyPzuew4//zwF27ax9/obaHLvPYSOG4exBj4ud5WTJ7L49e9P0hNPUrBzJwC2ggJyVq4k7L7xQPESRB4tImsn2RO4alKOJvuIiFScildp0AI9Axnadijf7PqGr7d/XW7xWtaki1B/Dz5oeT7xnXK5PiWb8JpJGSjunQscdgW+fc/l0HPPk/Xzz6RN/4yga66p92vCho0bhzUnl5R337MXaAA+ffoQcsft+B1bRqxOcdWkHE32ERGpME3YkgZv45GN3PLDLXgYPfjl+l8I8gpy+trHF25k9t8HGNa9Oe/dXHqGeE3J/OlnLEePEnzTjfZjNosFQz1dWivhoUlkfv998QOjkbbz5+HVufOpLxIRkQarIvWaxrxKg9c1tCudQzpTaC3km13fVOjam3q1AuDnTYdIzyk8TWvXCRh6iUPhmrtuPTsGnEfiE0+W2f7I1KnFk6PqKLemx/qx3dzAaiXrt99qNyEREak3VLxKg2cwGLjhjBvwd/fHSsU+hj2rRSBnNg+g0GJlwfoEF2VYcUfefRdrejoZ8+ax96absObmHj9X1V2fqpnNZuPo/PkcevFFoDi/tE+nO+yCpQlKIiLiLA0bkEahwFKA1WbF2827wtfO/Gs/T36ziQ7hfix5YGCtzngvYcnM5PDLr5CxYAEAxoAAWrz7Lrnr/i01Aag2mRMSSHpqCjkrVwIQOPxaMuYvKJVfWROXRESk8ahIvabiVeQ0MvPN9HnhF/LMFuaP7cvZrUNqOyW77D//JOGBB7FmZ9uPBV5/Hc2fe64WswKb1crROXNIfu11rLm5GDw9CZs4EUtONgY3t3LXecViJWzCfbWQsYiI1CZtUiBSDpvNxj+H/6FtYFtCvUOduibAy53/De5AmL8nZzYPdHGGFeN33nm0//03dvTuY9+y1n/QoFrNqfDAAZKeeJLcNWsA8D77bCKefw7Ptm1PeZ16XEVExBl1Y1CcSA15YuUT3P3z3czdPrdC140+P4pre7bAy73uze5PmzGjuHA9tgvXiRsapE7/jOTXX8eclFQjudgsFg6MvIfcNWsweHvT9PHHaT1zxmkLVxEREWepeJVGpV/zfgDM2zmPImtRLWdTdSeOFe1csjvTe+9zZOpUrAUFpE6bRurHn7Br8BASHnyQvLg4l+ZjMJkIf3gSPn360G7xIkJuG4HBqF8zIiJSfTTmVRqVQkshQ+YNIS0/jbcvfJuLWl3k9LVZ+Wa+XHOADQcziLmlpwuzdE55k5zsxyfch1fnzqR9PoPcv/+2n/fq3o0md9yB/5AhGI711laWraiItM8/x715cwIuvfT4cZutTkxsExGR+kHrvIqUw8PkwdXtrwbg6+1fV+has8XG6z/v4Pv/ktiUkOGC7CroFLszhU6cAFYb/oMG0frzz2i7cAGB11yDwd2d/A3/kfDgQyS/8WaVbl+wcyf7br6F5Nde59Azz1KUnm4/p8JVRERcRT2v0ujEZ8Vz+YLLsWHj+2u+p1VAK6evnfDler7dkMitfVrxwjVnuTBL1yg6coT0r+aQ/tVXtP5sOp4dOgDFk6xsRRY8251+bKrNbCb14485MvUDMJsx+vvT9NFHCbz2GhWtIiJSKep5FTmFlv4t6Rd5bOzrjnkVuvbmXi0BWBSXSG5h/Rsz6xYWRtiE++jw+2/2whXgyPvvs+eyyzgwejQ5q1aR/F7Zmwbkb93KzkGDOPLOu2A243fBBbT77luChl+rwlVERGqElsqSRunGjjeyMmEl/yb/W6Hxmee2a0KrEB8OpOXy/X9JXH9OSxdn6honjnW1Wa3Y8gvAYCBn+R/kLP8DU0gIlrQ0bEVFhE+cCIA5MZG9w68DqxWDpycRzz9HwBVXqGgVEZEapZ5XaZQGthjIxxd/zMxLZ1ao+DIaDdx4rPf1q7XxrkqvRhmMRlq8+w5RP/5A8K23YvDxwZKWBkDq1A84MGo0AEe/+QasVjyiomj/yzIChw1T4SoiIjVOY15FKig5M5++L/+KxWpjyQMD6djUv7ZTqlaWzEyOzp1H2uxZFCUWrw9rcHfHZjYTOn68dsASEZFqpzGvIhVQYCkgo8D51QPCA7y4tGszhnVvjrEB9jyaAgJoMvJu2i9ZQuTbb9sLV4O7uwpXERGpdSpepVH7dve3DJ47mPfWv1eh6967uQfv3dyD9uF+Lsqs9hnc3CjYs9teuNrM5jIncYmIiNQkFa/SqIX5hHG04Cjf7fmOXHOu09c1hrGeJ26C0Klk9653y16FQEREpKaoeJVGrU+zPrQJaEOOOYfv935f4et3Hs5i5up91Z9YLStr966SzQ9UwIqISG1S8SqNmsFg4LqO1wHFO25VZP5iclY+F7/9B08u2sz+1BxXpVg7Trd7l8VaS4mJiEhjp+JVGr2r21+Nh9GDbWnb+C/lP6evC/f34rwOYQDMaSDLZpUIm3BfqcLVfm7cOE3cEhGRWqPiVRq9QM9AhrYdChT3vlZEyY5bc/89iFm9kSIiIi6n4lUEuOGMGwBYsm8JOWbnhwBc1LkpoX4eHMkq4Ndtya5KT0RERI5R8SoCdAvtxv0972fOsDn4uvs6fZ2Hm5HhZ7cA4Ks1B1yVnoiIiByj4lUE+GDDBxRZi2gX2K7UudgNsUyNK392/U29WgGwfMcREo/muSxHERERUfEqAoDRYCQmLobYDbEA9lUHYjfEEhMXg9FQ/lulbagvfdqGEOjtzq7k7BrJV0REpLFyq+0EROqCMd3HABATF8PP+36mQ3AH2gW2IyYuhvHR4+3ny/PmjdGE+nng6WaqiXRFREQaLRWvIseM6T6G1LxUvtr+FbuO7gJwqnAFiAzydnV6IiIiQj0cNmC1WtmzZ09tpyEN1OPnPo6B4q1fjQajU4XriaxWG1uTMl2RmoiIiFDB4jUzM5O7776bxx9/nLFjx/LSSy+dsv2iRYsYMWIEzz77LDfeeCPbt2+3n8vLyyMwMBCDwVDq6/vvj2/TmZqaire3t/2cyWRyiCNSnWI3xGKjeLyr1WbllTWvOH1tRq6ZC17/nWHvreBIVoGrUhQREWnUKjRs4IYbbqBv375MmTIFgCFDhuDl5cUDDzxQqu2KFSu455572LlzJ0FBQaxcuZLBgwezefNmAgICmD9/PiNHjuSiiy7CZCoeJ5ibm8ttt93GhRdeaI/z8ccf89xzz9GsWTMA3N3dueSSSyr9hEXKUzI5a3z0eNYnr2dV4ipmbZ1FoGegUz2wgT7uhPh6cCAtl3n/HmTsBVE1kLWIiEjj4nTxumLFCn7++Wc++ugj+7E777yTCRMmcN999+Hu7u7Q/umnn2bYsGEEBQUB0L9/f7y9vZk2bRoPPfQQQUFBvPnmmw7XfPPNN1x44YX4+PgAYLFY+Pvvv1mwYEFln5+IU04sXMd0H8Pm1M2sSlwFFE/iApwqYG/q1ZK4+KPMWXuAMee3w2AwuDRvERGRxsbpYQMLFy4kJCSEVq1a2Y9FR0eTnp7O0qVLHdqmp6ezfPlyevTo4XC8e/fuzJkzB4Arrrii1D0WLVrEsGHD7I8XL17MwoULCQsLY8SIEfz3n/P7zotUhNVmdZicdWaTM7mkTXEP/xnBZ2C1Obf167DuzfH1MLEvNZe/9qS5LF8REZHGyunidf369bRs2dLhWIsWLeznTrRx40aKiorKbB8XF2dfQ/NEVquVH374waGo9fX15X//+x/h4eHMnj2bXr16sXDhQmdTFnHauOhxpXpW74u+j//1/B8zL5vJuOhxTsXx9XTjyujmAHy1VjtuiYiIVDeni9fDhw/j6+u4baa3d/HyQElJSaXaAmW2N5vNpKamloq/atUqWrZsSWRkpP3YxRdfzFtvvcXmzZv57rvv8Pf358477yQ9Pb3cPAsKCsjMzHT4EqmMNoFtGHnWSLzdKrYMVsmOWz9uOsTR3EJXpCYiItJoOV28ms1m+8SqEm5uZQ+ZNZvNAE63h9JDBk52+eWXM3fuXDIzM/nuu+/KbffSSy8RGBho/zq591ekMqw2K4dzDjvVtluLQDpHBFBYZOXnzYdcnJmIiEjj4nTxGhYWRm5ursOx/Px8AMLDw0u1Bcps7+7uTnBwcKn4ixcvPmXxCnDhhRcSFRVFfHx8uW0mT55MRkaG/etUbUWcsSt9F9d/ez1jfxnr1NhXg8HA45d15uvRfbnhHP3xJCIiUp2cLl6jo6NJTEx0OJacnAxA7969HY5369YNg8FQZvuePXuW6pHdunUrOTk59OzZ87R5BAcHOwwtOJmnpycBAQEOXyJVEeYTRlJOEjvTd/LD3h+cumZAh1B6tw3RagMiIiLVzOnidfjw4SQlJdnHs0Jx0RkSEsLAgQMd2jZt2pQBAwYQFxfncHzr1q1cc801pWIvXry4zNUHTpabm0tiYiKXX365s2mLVFmgZyB3d70bgPfXv4/ZYq7Q9RZr6QmKIiIiUjlOF6+DBg3i0ksvZfr06fZjCxYs4LHHHrOvyzpq1Ch7Yfnyyy+zaNEi+4Sp/fv3k5yczOjRo0vFLmu8a3p6OldffTWzZ8+2H5syZQovv/wyoaGhFXiKIlV3a+dbCfMOIyE7gXk75zl1TV6hhacWbWLAK7+SXVDk4gxFREQaB4OtrHWrypGRkcGDDz5IaGgoXl5eeHt78+ijj9rPX3/99ezbt4+1a9cCxUXpl19+SXR0NPv372fixIl07tzZIWZycjJRUVEcOXIELy8v+/GCggKuuOIK/vjjDwYMGMDZZ5/NZZddxgUXXFChJ5iZmUlgYCAZGRkaQiBV8vX2r3nur+do4tWEH679AR93n1O2t9lsDH5zObuP5PDiNWdxS59Wp2wvIiLSWFWkXqtQ8VofqXiV6mK2mrnqm6uIz4pnQo8JjOo26rTXTPtjDy/8sJVuLQJZfN+AGshSRESk/qlIveb0sAGRxs7d6M6EHhMA+Pfwv2VutnGya3tG4m4y8N/BDDYnZrg6RRERkQav/IVXRaSUS9pcQoBHAP2a93NqJYEmfp5c3KUZ329MYs7aeJ69KrAGshQREWm41PMqUgFGg5H+kf0rtATWTb2L13pdsO4gy7cnsygugdW7U7UKgYiISCWo51WkkrIKs1hzaA0XtbrolO36R4XSxM+D1OxC7pi+1n48ItCLKcO6MLRrhKtTFRERaTDU8ypSCWn5aVy24DIe/P1B9mTsOWXbJVsOkZpdWOr4oYx8xs5ax0+bklyVpoiISIOj4lWkEkK8QugR3gOrzcr7698vt53FauOZb7eUea5k0MAz327REAIREREnqXgVqaSJPSZiwMDS/UvZlLKpzDZr9qaRlJFfbgwbkJSRz5q9aS7KUkREpGFR8SpSSe2D2zMsqnhnuHfWvVNmm+Ss8gvXyrQTERFp7FS8ilTBuOhxuBvd+SvpL1Ynri51Ptzfq4yrSnO2nYiISGOn4lWkCiL9IrnxjBuB4t7Xkzcu6N02hIhAL061sFawjzu924a4MEsREZGGQ8WrSBXdc9Y9+Lv7ExUURV5RnsM5k9HAlGFdAMotYNNzzTz33RYKiiwuzlRERKT+M9ic2eOyHqvIXrkilZVRkEGgZ/m7Z/20KYlnvt3iMHkrItCLrpGBLN1yGIDJl3Zi9PlRLs9VRESkrqlIvaZNCkSqwakKV4ChXSMY0qUZa/amkZyVT7i/F73bhmAyGvhl62E+W7WPO/u3qZlkRURE6jH1vIpUo30Z+5ixZQb/1+v/8HKr3CSsIouV2X8f4OberfBw08geERFp+CpSr+n/jCLVxGqzMu6XcczdMZevtn1V6Tjv/LKTKYs3c/2Hq4lPy63GDEVEROo/Fa8i1cRoMDKq2ygApm2cRmZhZqXidGsRRICXGxvij3L5u3/ax8SKiIiIileRajWs3TCiAqPILMzks02fVSrGkC5N+X7ieXRvGURmfhH3zviHF77fgtlird5kRURE6iEVryLVyGQ0MaHnBABmbZ1FSl5KpeK0DPFh7ui+3HVsEte0P/dyw4erSTiad+oLRUREGjgVryLVbFDLQXQL7UZeUR4fbviw0nE83IxMGXYmsSPOxt/LjW1JWeQUFFVjpiIiIvWPVhsQcYG1h9Zy989342ZwY/E1i2np37JK8eLTctl+KIvBXZraj9lsNgwGAxarrcwluEREROoLrfMqUst6NevFpW0upUNwB5p4NalyvJYhPrQM8bE//mdfGq8v2c5V0c1595ddpTY/mDKsC0O7RlT5viIiInWNel5F6hmr1cbQd/5gx+HsMs+X9Ll+MKKnClgREakXtM6rSB1TnX8jGo0GYkecjVs5QwNK7vTMt1uwWBv036YiItIIqXgVcbGVCSu5/tvr+ffwv9UW83BmAUWnKExtQFJGPmv2plXbPUVEROoCFa8iLvbLgV/Ynr6dt/99u9p6YJOz8k/fqALtRERE6gsVryIu5u3mjclgIu5IHMsPLnc4F7shlqlxUyscM9zfq1rbiYiI1BcqXkVczN/DH4vNAsA7697BYi3+d+yGWGLiYjAaKv427N02hIhAL8pbEMtA8aoDvduGVDJrERGRuknFq4iLjek+hnu63gPArqO7+GHvD/bCdXz0eMZ0H1PhmCajgSnDugCUKmBLHk8Z1kXrvYqISIOjpbJEasioJaNYnbTa/riyheuJftqUxDPfbnFY57VpgCcjB7Rl1MCoKsUWERGpKRWp11S8itSQ/KJ8es3uBVA8Bvb2uGqJe+IOW0eyCnhjyXaaB3mz9IHzMarnVURE6gGt8ypSB322+TOguHC12CzEboitlrgmo4G+UU24KjqSG3u1xM1oZPeRHH7Zllwt8UVEROoSFa8iNeDEMa5xt8cxPno8MXEx1VbAlvD3cufWc1sD8OHy3dUaW0REpC5Q8SriYmVNzhrTfQwju44kJi6mUktlncrd/dvgYTLyz/50/tmnTQpERKRhUfEq4mJWm7XU5CyrzWpf83VN0ppqvV94gBfX9owEIHb5nmqNLSIiUttUvIq42LjocaVWFTAajIzuNhqA9UfWsz55fbXe896B7TAYYNnWw+xKzqrW2CIiIrVJxatILRnadijD2g3DarMy+c/JZBVWX5EZFebHxV2a4mY0sG7/0WqLKyIiUtu0VJZILcouzOa6b68jITuBYe2G8eJ5L1Zb7P2pOXi4GYkI9K62mCIiIq6gpbJE6gk/Dz9eOu8ljAYj3+75lh/3/lhtsVs38VXhKiIiDY6KV5Fa1iO8B6O6jQLg440fY7VZq/0e2w9lkZlvrva4IiIiNc2tthMQERjdbTQGDIzoMgKjoXr/pnzm281MX7mPR4Z2YuwF2jJWRETqN/W8itQBbkY3xkWPI8Cj+sdld4kojvnpyr0UFFmqPb6IiEhNUvEqUsfYbDbm7ZjH5tTN1RLvquhImgV4cSSrgG/WJ1RLTBERkdqi4lWkjpmxZQbPrH6GR/94lLyivCrH83AzMnJAWwA+/GMPVmuDXmBEREQaOBWvInXMVVFXEe4dzr7Mfby+9vVqiXlT75b4e7mx50gOy7YerpaYIiIitUHFq0gdE+QVxAvnvQDA1zu+5rcDv1U5pr+XO7ed2xqA2OW7aeDLO4uISAOm4lWkDjo34lzu6HIHAFNWTSElL6XKMe/s3wYPk5EDabkkZxVUOZ6IiEhtUPEqUkdN7DmRM4LPIL0gnSdWPlHl3tJwfy9mjuzNikcG0TTAq5qyFBERqVkqXkXqKA+TB68MfAVPkyerE1fzX8p/VY7Zp10TvNxN1ZCdiIhI7dAmBSJ1WFRQFFP6TiHSL5LuYd2rLa7VamNLUiZdIwOrLaaIiEhNUPEqUscNixpWrfEycs1c88FK4tNy+eP/LiQi0Lta44uIiLiShg2I1CN7MvYwe+vsKsUI9HEnzM8Ts8XG9JX7qicxERGRGqLiVaSeOJRziJu+u4mX17zM6sTVVYo15vwoAL74+wAZeebqSE9ERKRGqHgVqSea+TbjyqgrAXhixRMczT9a6VgXnBHGGU39yS4oYvbf+6spQxEREddT8SpSjzx0zkO0DWxLcl4yT69+utLLZxkMBkaf3w6A6Sv3kW+2VGeaIiIiLqPiVaQe8Xbz5uXzXsbN6MYvB35h4a6FlY41rHtzmgd6cSSrgIXrE6oxSxEREddR8SpSz3Rp0oUJPSYA8PKal9mfWbmP/d1NRu4e0BaAlbuqvoOXiIhITVDxKlIP3dHlDpr7NievKI/PN39e6nzshlimxk09bZybe7fii3v68N7NPVyRpoiISLVT8SpSD5mMJoa0HgJAqHeow7nYDbHExMVgNJz+7e3r6Ua/9qEYDAaX5CkiIlLdtEmBSD01qdck/Dz87IXqmO5j7IXr+OjxjOk+pkLxMvPNZOSaaRni46KMRUREqk7Fq0g9VlKgxsTF8MGGD7DarJUqXH/adIiH526gV9sQPr2zlytSFRERqRZ1bthAWloaaWlptZ2GSL0xpvsYDBiw2qwYMVa4cAXo1Myf7MIift2WzPZDWS7IUkREpHpUqHjNzMzk7rvv5vHHH2fs2LG89NJLp2y/aNEiRowYwbPPPsuNN97I9u3bS7WJiYnBYDDYv8466ywCAgLs51988UXGjh3L448/zsiRI8nOzq5IyiINXuyGWGwUr/dqxcpDvz9U4RhtQn25tGszAD78Y3e15iciIlKdKjRs4IYbbqBv375MmTIFgCFDhuDl5cUDDzxQqu2KFSu455572LlzJ0FBQaxcuZLBgwezefNmh+J0yZIlzJw50/64Q4cOuLkVp/XSSy+xbNkyfv31VwBeeOEFbr31VhYtWlTxZyrSAJ04xtVisxC7IZYl+5fw2trXeLjXwxWKNXpgFD9sPMTiuEQmXXwGzYO8XZS1iIhI5RlsTm7Rs2LFCs477zz2799Pq1atAJg9ezYTJkzg8OHDuLu7O7QfPHgwrVq14tNPP7Uf69ixI6NHj+ahh4p7hpYtW8bWrVuZMGFCqfvl5+cTFhZGTEwMt99+OwAJCQm0aNGCf/75h7PPPtupJ5iZmUlgYCAZGRkORbNIfXfy5KwiaxF3/HQH/x35D4Bx3ccxNnpshWLe/NFfrN6TysgBbXnyii6uSFtERKSUitRrTg8bWLhwISEhIfbCFSA6Opr09HSWLl3q0DY9PZ3ly5fTo4fj2pHdu3dnzpw59sfvvfce999/P127duW5554jK+v4WLulS5eSnZ3tECMyMpLQ0FCHGCKN1cmTs9yMbrw84GV83IpXC/j38L8VjlmyZeyXaw6QkWuuvmRFRESqidPF6/r162nZsqXDsRYtWtjPnWjjxo0UFRWV2T4uLs6+H3u/fv249dZbSUhI4KmnnuLcc88lJSXFIWZZMU6+n0hjNC56XKnJWS0DWjK5z2QAtqdvJ7uwYmPEz+8YRqdm/hQUWVm9J7XachUREakuTo95PXz4MEFBQQ7HvL2Lx8QlJSWVagvg6+tbqr3ZbCY1NZXQ0FAeeeQRAHJzc3nyySd58803efDBB5kxY8YpY5x8vxMVFBRQUFBgf5yZmensUxRpEK6KuoqknCSGtRuGn4dfha41GAy8PLwbTXw9tN6riIjUSU73vJrNZkwmk8OxkolVZbUFnG7v4+PDG2+8we23387cuXMpLCyscIwSL730EoGBgfavk3tuRRo6g8HA2O5jaeHfolLXR7cMUuEqIiJ1ltPFa1hYGLm5uQ7H8vPzAQgPDy/VFiizvbu7O8HBwWXe49577yU/P5+UlJRTxjj5fieaPHkyGRkZ9q/4+Hgnnp1Iw7U6cTV/HvyzUtfuPJzFyl0pLIpLYPXuVCxWp+Z3ioiIuIzTwwaio6NZuHChw7Hk5GQAevfu7XC8W7duGAwGEhMTS7Xv2bNnqd7UEsHBwXh6etKkSROio6MBSExMpGPHjg4xBg0aVG6enp6eeHp6Ovu0RBq05fHLue/X+wjyDGLBlQsI8wlz+tpbp/3Fyt2O414jAr2YMqwLQ7tGVHeqIiIiTnG653X48OEkJSXZx6ICbN26lZCQEAYOHOjQtmnTpgwYMIC4uDiH41u3buWaa64p9x7r16/nqquuwtPTk0suuQQ/Pz+HGNnZ2cTHx58yhogc17d5XzqFdOJowVGeWPkEVpvVqet+2pRUqnAFOJSRz9hZ6/hpU/njzkVERFzJ6eJ10KBBXHrppUyfPt1+bMGCBTz22GP4+BSPjxs1ahSXX345AC+//DKLFi2yT5jav38/ycnJjB49GijefeuWW25hx44dABw5coSZM2fy9ttvAxAQEMCTTz7psE7sokWLuOyyy+jTp08VnrJI4+Fh8uCV817B0+TJqsRVfLH1i9NeY7HaeObbLWWeKxk08My3WzSEQEREakWFdtj68ssvefDBB3nkkUfw8vIiKirKvuEAFK/vWjKUoF+/fkydOpVRo0YRHR3N/v37+eGHH+wrFgQHB7Nq1Sp69uzJzTffTGRkJDNnznQYz/rwww9jNpu59957adeuHYcOHWLWrFnV8LRFGo92Qe2YdM4kXvj7Bd769y16R/SmY3DHctuv2ZtGUkZ+uedtQFJGPmv2ptE3qokLMhYRESmf0zts1VfaYUsEbDYb9/16H38c/IP2Qe356oqv8DSVPTZ8UVwC938Vd9qY79wUzVXRkdWcqYiINEYu2WFLROovg8HAM/2eIcQrhF1Hd7Fk35Jy24b7ezkV09l2IiIi1alCwwZEpP4K9Q7l+f7Pk1GYwRXtrii3Xe+2IUQEenEoI5+yPpYxAM0CvejdNsRluYqIiJRHPa8ijch5Lc47ZeEKYDIamDKsC1BcqJ6o5PGUYV0wGU8+KyIi4noqXkUaqfT8dKZvmk5Zw96Hdo3ggxE9aRboODSgWaAXH4zoSftwP4oszi27JSIiUp00bECkESqwFHDTdzeRmJNIgEcAwzsOL9VmaNcIhnRpxpq9aSRn5RPuXzxU4KdNh7jivRXc1KsVT195Zi1kLyIijZl6XkUaIU+TJzd1ugmAV9a+wr6MfWW2MxkN9I1qwlXRkfSNaoLJaMBkNJBvtvLZqn3MWXugBrMWERFR8SrSaN1x5h30adaHvKI8Hv3zUcxWs1PXDe3ajAcGF68T+8Q3m/hnX5or0xQREXGg4lWkkTIajDw/4HkCPALYnLqZD+I+cPraCYPac2nXZpgtNsbM+pfEo3kuzFREROQ4Fa8ijVgz32Y81fcpAD7e+DH/HPrHqeuMRgOvX9+dTs38SckuZNTMf8grtLgyVREREUDFq0ijd0mbS7gq6ips2IiJi3H6Ol9PN6bdfg4hvh5sSshk5l/7XJekiIjIMVptQESY3GcyQZ5BjO4+ukLXtQzxYeqtPfl9+xFGDmjnouxERESOM9jKWuSxAanIXrkiIiIiUvMqUq9p2ICIOLDZbMzdMZfE7MQKX1tQZOGpRZvYeTjLBZmJiIioeBWRk7y3/j2eXf0sk/+cjMVasUlYr/y4nRmr93PvjH84mlvoogxFRKQxU/EqIg6u6XANbgY31iWv49NNn5Y6H7shlqlxU8u8dvyFUUQGebMvNZf7vlivLWRFRKTaqXgVEQct/VtyfsvzAXh//ftsStlkPxe7IZaYuBiMhrJ/dTTx82Ta7efg7W5ixa4UXvxhW43kLCIijYeKVxEp5a0L3iIqMAorVsYsG0OuOddeuI6PHs+Y7mPKvbZL8wDevKE7AJ+u3MvX/8TXVNoiItIIaLUBESlTRkEGl8y/hBxzDkaDEavNetrC9URvLd3BO7/sxMNk5MtR53J262AXZywiIvWVVhsQkSoL9Azk3QvfBcBqs+JmdHO6cAW4/6IOXHJmU7w9TBSYtfuWiIhUD21SICLlWpe8DgA3gxtF1iJiN8Q6XcAajQbevCGalOwCWjfxdWWaIiLSiKjnVUTKdOIY1/W3r2d89Hhi4mKI3RDrdAxfTzeHwvVwZj4NfKSSiIi4mIpXESmlrMlZY7qP4eYzbq5wAVvijx1HGPLmcmKX76nudEVEpBHRsAERKaWsyVlH84+yeM9iADanbK5wzP2pOWTmF/Hqz9voEO6Lr6c7yVn5hPt70bttCCajodryFxGRhkurDYiI0z7e+DHvrHsHT5Mnsy+bzRkhZzh9rc1m4/FvNvHF3wcwACf+4okI9GLKsC4M7RpR7TmLiEjdp9UGRMQl7u56N+dFnkeBpYCHlj9EdmG209caDAb6tmsCOBauAIcy8hk7ax0/bUqqxmxFRKQhUvEqIk4zGoy8OOBFInwj2J+5nymrpjg9ActitfHiD1vLPFcS4Zlvt2CxNugPg0REpIpUvIpIhQR5BfH6+a/jZnRjyf4lfLntS6euW7M3jaSM/HLP24CkjHzW7E2rpkxFRKQhUvEqIhXWLawbk86ZBMDyg8ud6n1Nziq/cK1MOxERaZy02oCIVMotnW4hyDOIS9pcgsFw+pUCwv29nIrrbDsREWmc1PMqIpViMBi4vN3luBmP/w18qh7Y3m1DiAj04lRlrrvJQMemftWYpYiINDQqXkWkygothbzw1wtM3zy93DYmo4Epw7oAlFvAmi02bv34bw0dEBGRcql4FZEq+y3+N77a/hXvrnuXfw79U267oV0j+GBET5oFOg4NiAj04skruhDm78m2Q1ncELuag+m5rk5bRETqIW1SICJVZrPZeGzFY3y35zvCvMOYO2wuTbyblNveYrWxZm9aqR229qfmcOvHf3MwPY/mgV7MvKcPUWEaRiAi0tBVpF5T8Soi1SLXnMst39/C7ozd9Inow4eDP8RkNFU4TlJGHiM+/pvdR3Jo4uvBjJG9ObN5oAsyFhGRukI7bIlIjfNx9+GNC97A282bv5P+5sP/PqxUnIhAb74e3ZczmweQkWfmcKbGv4qIyHEqXkWk2kQFRfHkuU8CELshllWJqyoVp4mfJ1+OOpdP7+zFoE5NqzNFERGp51S8iki1GhY1jOs6Xoefux9mi7nScQK83BnYMcz+eH9qDsu2HK6OFEVEpB7TmFcRqXYFlgJS8lKI9Iuslngp2QVcHbOSpIx8XruuG9f2bFEtcUVEpG7QmFcRqVWeJk+HwjXHnFOleEHe7vRt1wSL1caDX29g5up9VcxQRETqKxWvIuJSKxNWctmCy/jtwG+VjuFmMvLK8G7c1b8NAE8u2kzMb7uqKUMREalPVLyKiEutSFhBWn4aj698nITshErHMRoNPHVFFyZe1AGA137ezss/bjvllrQiItLwqHgVEZd68OwH6RbajazCLCb9PolCS2GlYxkMBh4c0pEnLu8MQOzy3Xz0x57qSlVEROoBFa8i4lLuJndeP/91Aj0D2ZS6idf/eb3KMe85rx0vX3sWnZr5c8M5LashSxERqS+02oCI1Ig/Dv7B+F/GA/Da+a8xtM3QKscsLLLi4Xb8b3CLtfjXWVlbz4qISN1VkXrNrYZyEpFGbmCLgfQI68H6I+t5etXTdA7pTOuA1vbzsRtisdqsjIse53TMEwvXWX/tZ8bqfcd25SqwH48I9GLKsC4M7RpRPU9ERERqlYYNiEiNObf5uQC08m9FuE+4/Xjshlhi4mIwGir3Kyk9p5Dnv9/CjsPZDoUrwKGMfMbOWsdPm5Iqn7iIiNQZKl5FpMaMix7H6G6j2Zq2lc83fw4cL1zHR49nTPcxlYob4O2Or0fZHySVjIt65tst9mEFIiJSf2nYgIjUqPt63Ieb0Y2YuBg++u8jzFZzlQpXKB7jmppT/ioGNiApI581e9PoG9Wk0vcREZHap55XEalxY7qPwd3ojtlqBqCpT9MqxUvOyq/WdiIiUnepeBWRGhe7IRaz1Wwf4/rUqqf4fs/3lY4X7u9Vre1ERKTuUvEqIjXqxDGucbfFcWaTMwGY/Odklu5fWqmYvduGEBHoRXkLYhkoXnWgd9uQyiUtIiJ1hopXEakxJ0/OMhgMfHH5F3QK7oQNG5N+n8Ty+OUVjmsyGpgyrAtAqQK25PHkSzuRb7ZU7QmIiEitU/EqIjXGarOWmpxlNBj56oqv6BDUAStWHvj9AVYlrKpw7KFdI/hgRE+aBToODWgW6MXbN0Xz1dp47v5sLXmFKmBFROozrTYgIjWmvA0ITEYTc4bN4eHlD7MqcRVGY+X+rh7aNYIhXZqV2mFrx+Es/juYQXZBEaNm/sO028/By91UlaciIiK1RNvDikidYbaY2ZOxhzNCzqj22P/sS+P2T9eQW2jhok7hfDDibIcdukREpPZUpF7Tb24RqTPcTe4Oheueo3vYnLq5WmKf0yaEj+84B083I79sS+b+r9ZTZLFWS2wREak5Kl5FpE7ac3QPd/18F6OWjGJ72vZqidkvKpQPbzsbD5ORHzcd4qG5G7TrlohIPaPiVUTqpKa+TWnh34LMwkxGLR3F7qO7qyXuBWeE8/4tPXAzGvhtWzLxabnVEldERGqGxryKSJ2VWZjJPT/fw9a0rYR5h/HZ0M9oFdCqWmL/tCmJliE+nNk8sFriiYhI5WnMq4g0CAEeAXw05CPaB7XnSN4RRi4ZSUJ2QrXEHto1wqFwTTiaRwP/W15EpEGod8Wr1Wplz549tZ2GiNSQIK8gpl08jTYBbTiUc4iRP4/kcM7har3HP/vSuOStP3hjyY5qjSsiItWvQsVrZmYmd999N48//jhjx47lpZdeOmX7RYsWMWLECJ599lluvPFGtm93nHSRkpLCyJEjCQ0NpWXLljz++OOYzWaHNqmpqXh7e2MwGDAYDJhMplJxRKRhC/UO5eOLP6aFXwvCvMPwcfep1vhbD2WRXVDE+7/t4v1fd1ZrbBERqV4VGvM6dOhQ+vbty5QpUwAYMmQIl112GQ888ECptitWrOCaa65h586dBAUFsXLlSm666SY2b95MQEAAFouFwYMHM3ToUNq3b89nn33Gd999x5NPPsmzzz5rj/PKK69gMplo1qwZAO7u7lx//fVOL2KuMa8iDcehnEMEeARUe/EK8NEfu3nxh20APH5ZZ+4d2K7a7yEiImWrSL3mdPG6YsUKzjvvPPbv30+rVsUTJmbPns2ECRM4fPgw7u7uDu0HDx5Mq1at+PTTT+3HOnbsyOjRo3nooYf47rvvcHd355JLLgHAYrHQo0cP0tLSOHjwoP3Y9ddfz4IFC5x/9idR8SrScC3evZjzW5xPoGf1TLp695edvLm0eOjAs1edye1921RLXBEROTWXTNhauHAhISEh9sIVIDo6mvT0dJYuXerQNj09neXLl9OjRw+H4927d2fOnDkA+Pn52QtXAJPJxEUXXUR6err92OLFi1m4cCFhYWGMGDGC//77z9l0RaSBm7llJo+veJyxy8aSXZhdLTEnDGrP+AujAHhq0WbmrD1QLXFFRKT6OF28rl+/npYtWzoca9Gihf3ciTZu3EhRUVGZ7ePi4rDZbFxwwQWl7mG1WomOjrY/9vX15X//+x/h4eHMnj2bXr16sXDhQmdTFpEGrE9EHwI9A9mYspHxv4wn11z19VoNBgOTLj6DkQPaAvDTpkNagUBEpI5xung9fPgwvr6+Dse8vb0BSEpKKtUWKLO92WwmNTW1zHv8+uuvTJo0yf744osv5q233mLz5s189913+Pv7c+eddzr0zp6soKCAzMxMhy8RaXg6BnfkwyEf4u/uz7rkdUz8dSL5RflVjmswGHji8s68eM1ZxN52NgaDwX7OYrWxencqi+ISWL07VbtziYjUAqeLV7PZjMlkcjjm5uZWblvA6fYA33zzDZ07d+aaa64p8/zll1/O3LlzyczM5Lvvvis3zksvvURgYKD96+TeXxFpOM5sciZTB0/Fx82Hvw/9zfDFwym0FJZqF7shlqlxU52OazAYuKVPKzzdin+H2Ww2pq/cy4BXfuXmaX9x/1dx3DztLwa88is/bUo6TTQREalOThevYWFh5OY6fiyXn1/cyxEeHl6qLVBme3d3d4KDgx2OJycnM23aNKZPn37KHC688EKioqKIj48vt83kyZPJyMiwf52qrYjUf9Hh0cRcFIObwY0DWQe4bvF1WG1W+/nYDbHExMVgNFRuWWubzcbIz//hmW+3kJTh2LN7KCOfsbPWqYAVEalBTv82j46OJjEx0eFYcnIyAL1793Y43q1bNwwGQ5nte/bs6dAjazabeeihh5g2bVqpYQZlCQ4OJjIystzznp6eBAQEOHyJSMN2TrNziBkcg8lgYm/mXj767yPgeOE6Pno8Y7qPqVTswiIrq3allHmuZNDAM99u0RACEZEa4nTxOnz4cJKSkuzjWQG2bt1KSEgIAwcOdGjbtGlTBgwYQFxcnMPxrVu3lhoW8PjjjzN58mSaN29uP7Zr164yc8jNzSUxMZHLL7/c2bRFpJHo17wfP177I+OjxxMTF0PPmT2rXLgCrDtwlPwia7nnbUBSRj5r9qZV+h4iIuI8p4vXQYMGcemllzp8tL9gwQIee+wxfHyKFwwfNWqUvbB8+eWXWbRokX3C1P79+0lOTmb06NH261966SUsFgtbtmxh3rx5zJs3j6lTp/Ltt9+Snp7O1VdfzezZs+3tp0yZwssvv0xoaGjVnrWINEgRfhGM6T4Gd6M7ZqsZN4NblQpXgOQs5yaBOdtORESqpvwZVGX48ssvefDBB3nkkUfw8vIiKiqKhx56yH4+PT3dPpSgX79+TJ06lVGjRhEdHc3+/fv54YcfCAoKAuCrr77iscceK/M+O3fuxMfHh5ycHO6++24+/fRTzj77bC6//PIyl9gSESkRuyEWs7V40miRrYhX1rzCI70fqXS8cH+vam0nIiJVU6HtYesj7bAl0niUjHEd130caw6t4Z/D/wAwtvtYxkWPq1RMi9XGgFd+5VBGPmX9sjQAzQK9WPHIIExGQxktRETkdFyyw5aISF124uSssdFjeXHAi/i7+wPwwYYPiN0QW6m4JqOBKcO6AMWF6olKHk8Z1oX4tFysmrQlIuJyKl5FpEGw2qwOk7Mi/CJ4qu9TABgwkJCdUOnYQ7tG8MGInjQLdBwa0CzQiw9G9KR7yyCu/WAVd0xfQ2p2QeWfhIiInJaGDYhIg/bYn4/x7Z5vifSLZN6wefh5+FU6lsVqY83eNJKz8gn396J32xBMRgO/bUtm7Ox/yTdbaRrgyXs396R325BqfBYiIg2bhg2IiBzzWJ/HiPSLJCE7gbfXvV2lWCajgb5RTbgqOpK+UU3sY1wv7BTOovEDiArz5XBmATdP+4uY33ZpGIGIiAuoeBWRBs3Pw4+XznuJ/pH9GdVtlMvuc0YzfxbfN4BrekRisdp47eft3PXZWtJySm9XKyIiladhAyIi1chms/H1P/E8tWgzBUVW7j2vLY9f3qW20xIRqdM0bEBE5BTWHlqLxWpxSWyDwcCNvVqx6L7+DD2zGQ8OOcMl9xERaaxUvIpIo/Lq2le5++e7mb55+ukbV0GnZgHE3nY23h4mAKxWG+/+spN0DSMQEakSFa8i0qh0COoAQMz6GDanbq6x+374xx7eXLqDy9/9k3/3p9fYfUVEGhoVryLSqFzd/mqGtB5Cka2IR/94lFxzbo3cd2DHUNqG+pKYkc+NH65m2h97KJlyYLHaWL07lUVxCazenYpFqxSIiJRLE7ZEpNE5mn+U4YuHk5yXzPUdr7dvZuBqWflmJi/YyHf/JQEwuHM4l3ZtxutLdpCUkW9vFxHoxZRhXRjaNaJG8hIRqW0VqddUvIpIo/RX0l/cu+ReAN698F0ubHVhjdzXZrMx++8DPPvdFgqLrGW2Kdl29oMRPVXAikijoNUGRERO49yIc7mjyx0APL36aXLMOTVyX4PBwIhzWzN3dF/7JgcnK+lReObbLRpCICJyErfaTkBEpLZM7DmR7enbubnTzfi6+9bovXMLLacsTG1AUkY+a/am0TeqSc0lJiJSx6l4FZFGy8PkwbSLp9XKvZOz8k/fqALtREQaCw0bEBE55nDOYfZl7KuRe4X7eznV7v1fd/LF3wfIzDe7OCMRkfpBxauICLDu8DqGfzuch5Y/RIGlwOX36902hIhAL8oe9XrczuQcHlu4kV7PL2PH4SyX5yUiUtepeBURAVoFtMJkMLEjfQfvrHvH5fczGQ1MGdYFoFQBazj29cq1Z/HYZZ3oEO5HqJ8n7cP87G1+25bM3pSamWQmIlKXaKksEZFjlscv575f7wPgwyEf0q95P5ff86dNSTzz7ZZTrvNqs9k4kl1gH2pQWGTl3Jd+IS2nkHNaB3P9OS247KwI/L3cS8W3WG2s2ZtGclY+4f5e9G4bUu4qByIitUXrvJ5AxauIVMTzfz3PnO1zCPMOY/6V8wn2Cnb5PStaYCZn5vN/8//jjx1HKFmwwMvdyKVdI7j+7Bac264JRqPBqcJYRKQuUPF6AhWvIlIReUV53PjdjezN2MtFrS7irQvewmComz2VhzPzWbg+gbn/xLP7yPEhBBMv6kCXCH/GzlrHyb/gtQGCiNRF2qRARKSSvN28eeW8V3AzuvHLgV/4Ye8PtZ1SuZoGeDHm/CiWPXg+C8f149Y+rfD3cuOSM5vyzLdbShWuoA0QRKT+0zqvIiIn6dykMxN7TCQhO4FBrQbVdjqnZTAY6NEqmB6tgnlqWBfW7T/qMFTgZNoAQUTqMxWvIiJluPPMO+vscIFT8XQzOb2xwRtLtvHlqL64m/QhnIjUH/qNJSJShhMLV6vNyvrk9bWYTcU4uwHC4cwCh8J17j/x/LMvjSKL9bTXWqw2Vu9OZVFcAqt3p2oIgojUGPW8ioicQoGlgKsWXkViTiKfDf2Mnk17OpyP3RCL1WZlXPS4WsqwtJINEA5l5Jc57tUAhPh68MyVZ9qP5RVaePybTRQWWQnwcuO8DmGc3zGMgR3DaBboWAxrFQMRqU3qeRUROQVPkyd+Hn7YsHHfL/eRVXh8l6vYDbHExMVgNNStX6Wn2wAB4IVrujKoc1P78cx8M5ec2YwgH3cy84v4fmMS/zf/P8596ReGvv0HX6+NB4oL17Gz1pUaU3soI5+xs9bx06YkVz0tERFAxauIyGl9NvQz/D38yTJncedPdwLHC9fx0eMZ031M7SZYhqFdI/hgRM9SvabNAr3KXCaraYAX793cg3+fGMKCcf24/6IORLcMwmCAbYeyyMw3Y7HatIqBiNQ6rfMqIuKEuOQ4bv/xdmzYMGDAho27z7ybB855oLZTO6Wq7rCVnlPIn7tSOLt1MAdSc7l52l+nvebLe8/VKgYiUiFa51VEpJpFh0dzX4/irWNtx/oZzwo7y34+uzAbs8VcK7mdislooG9UE66KjqRvVJMKbw0b7OvBld2bExnk7fQqBpsSMiqTqoiIU1S8iog4yWornoVfMsZ1S+oW+7kZW2Yw4KsBTPx1Il9v/5qE7IRaydGVnF3FoFWIt/3ff+w4wrIth8kpKHJVWiLSyGi1ARERJ5w8xrXksYfJgzHdx7AxZSO5Rbn8Fv8bv8X/BkCbgDYMiBzAgMgB9Inog5uxfv/KPd0qBgBB3u4M7tLM/jjmt138vTcNd5OBXm1CuOCMMM7vGE7Hpn7lrqNb1aEO5XFVXBGpWfX7N6mISA0oa3JWyX9j4mKK/3tRDNvStrEyYSUrElaw4cgG9mXuY1/mPhbtXsQfN/5hj5een06QZxAfbPgAo8FY5oSvurgEV8kqBmNnrcMADgVsSQn48vCz7AWhzWbjzOaBJGbkEZ+Wx6rdqazancqLP2yjWYAXl57VjCnDznS4h6uW4dLyXiINhyZsiYicxtS4qRUuMjMLM/k76W9WJKzA0+TJY30eA4oLuqHzh2IwGAj2DGZT6iZGnTWKCT0nOMSsyysZVLQQtNls7EvNZfn2ZH7fcYTVu1MpKLIyuHM4H9/Ry97ukXn/Meef+FLXlxTGZa2S4Gy+Y2etK9VbXNW4J6qPvcXKuWZiK2fnVKReU/EqIlKDDuUc4tIFl1JkdRwD2sKvBdd2uJaknCTm7phbZwvXkkL+3rNGl/qf27SNHzrVW5xvtrBmbxreHiZ6tQkBYF9KDhe8/vspr4sI9GLFI4M4nJnPjNX7y203oH0oAzqEAsXrz170xu/kFFpOG7ey/3Ouj73FyrlmYitn56l4PYGKVxGpa3LNuaw5tIYVCStYkbCi1OSuksI1oyCD6Zum0ymkE2eEnEEr/1aYjKZayrpYeb3CVe0tnvdPPJPm/Xfadl/eey7eHiaujllZbpv/De7A/wZ3BIq3vH3YybhR4b7cELuaMH/P4i8/T/u/w/29iArzo1UTH4frXNWr68reYuVcM7GVc8VUpF7TmFcRkRrm4+7DBS0v4IKWF2Cz2TiQdYCrvrkKi82Cm8HNXvxtS9vGJ5s+sV/n7eZNh+AOdAouLmb7RvSlZUDLMu9RmaEOp2Oz2bi5082k5aURExfD9vTt3Bd9H0v3L63yMAd3N+cWv0nOyuecNiHcM6BtuW16tgq2/zu/qPwe15Pj+nu5sS81l32puWW2uWdAW564oou9/W0f/83e1NxyN20wULxpQ++2Tfh+Y/k7j3Vq5m/vgc4tLGLuvwd54+ftp407sGMY89eVv6pFmyY+nNchDCj+GPiLNQew2mxOxT6SXUDp/dmKRQR4MbjL8d3Z5qw9QH6R1am4WflF5BdZy4wb7OPOFd2a2x8viksgM7/IqZwnL9jI4awCjGVMAvR2N3Hd2S3sj3/adOjY8+O0sQEemV92bKMBbu3T2v749+3JxKfnVTkuwA3ntMDTrfgP1dW7U9l1JNt+riqxr+zenEBvdwDWH0hnU2Km03FLXsMhXZrV+kRHFa8iIrXIYDDw494fsdgsuBvdMVvNxG6IZUz3MQR5BnFDxxvYlr6NHWk7yCvK478j//HfkeKexCfPfdJevO7P3M8vB36xF7ZGg9E+may8HtISeUV5pOSlkJqXSkpeCv2a98PHvbiHce6OuSzYsYCU/BRS8lIchjss27+M3w/8TpGtiPHR4xncajDrk9dzVuhZFV5ZwdlluML9vYgM8rYXkafTPszf6bhtQ335enRfjmQVcCQrnyPZBcf+XcCR7ALahPra2x/OKGD74exTRCz+H35SRj7Ltyfz5Debym13Z7829uI1K7+IKYs2OxV39e7UU8a9sntze/FqtdlO2fbk2E8v3oyl7BqTAe1DHYrXF77fSmb+qZdCK4n74g9bSc8tez3kTs38HYrXt5ftZG9KjlM5p+eay/2+hft7OhSv0/7cw7/7008bt0RGXtmx3YwGh+L1i78PsGTL4SrHBbiyW3N78bp4QwJfrik9FrwysftHNbEXr8u2Hibmt91Oxyx5DdfsTav1TUhUvIqI1KLyluCC4qLzyb5PAmCxWtiftZ/tadvZlraN7WnbOSv0+CYJaw6t4a1/37I/DvcOp7V/a2LiYtiRvoPn+z/PjC0ziImLYVCrQaxOXM13e74jJS+FHLNjgTD/yvl0DC7+2P1o/lE2pToWPYGegYR6hbInYw9FtiLcje6M6T6GZ1Y/w7wd8/B39+fc5udyXuR59Gvej6a+TTmd0y3DZaB4a9vebUOc+bZWKq7JaHA6fptQH8aeH8UHy0//P//sgiIu7dqs3PNdIo5/ROrpZqR7i0A2HDz9Rg9pOYWnjNu9ZZD93wbg0q7NSDya51TsbpFBpbYWLtGpmeNHuoO7NGV3crZTcTtF+BPk7VHmucggb4fHAzuE0qmZv9M5d28RSPOTYgD2Yq3Eue1CCPf3BKhSbONJvY89WwfbeySrmrOb6XjsM5sHcmnX4wV/VWL7eh4v+zo29Xf4+XE2rrOblbiSxryKiNSS6hw/ujJhJQt3LWR72nb2Z+637wJWws3oRpG1uIfUzejGO+vecTjvafIk1DuUUO9Qnjz3Sc4IOQOAvRl72Z+5334uxCsED5OHPceS3uLx0ePJKMhg8e7FZBZmOsTuENyBAc0HMKHnBNyNjoXEiUrG20HZy3BVdRxfdcddvTvVJdvluiquK2PXx5xdGVs5V5zGvIqI1ANWm7XMArXkccmOXs7oH9mf/pH9geIJYTvSd7AtbRvb0raxYOcCiqzHe0h3H91NC/8WhHqF2otSX3ffMjcNaBvYlraBjuNLy+stHh89nj9u/IONKRtZmbiSlQkr2ZSyiZ3pOzFbzDx4zoP2GH8e/JN2Qe2I9Iu0H9tTtJBrL8ph1T89HWY6Nwv0ot8569hTtB+o+Lq3Q7tG8MGInqVmUDer4gzqutBbrJzrZmzl7FoqXkVEasmpJkxVZZksH3cfosOjiQ6PJnZDLDZspcbTRgVFVSq2Mxs2jOk+hujwaMZHjyc9P53ViauxcrwQN1vMTFo+idyiXIddyKw2K0sSZzDuUn+i/a+3L8MVlzWXqRtmMD58fOmEnDS0awRDujSr1rUrndm0YcqwLhW+h6viKueai62cXcu56Z0iIlLvnFhorrttHeOjxxMTF0PshthKxzxVb/H46PGleouDvYK5rN1lXNHuCvux1PxUOoV0wmQwsS9zH7O2zmLMsjF8tvkzWvm3YuqGGDZkz+Wq6Eg2ZM9l6obq2bDBZDTQN6oJV0VH0jeqSbX8T7ikV/fk8aHNAr2qtKyQq+K6MnZxz/mWMuNee9EW9hQtrHTO9TF2fXwNXZlzddKYVxGRBshV67FWp6zCLPsuZCsSVnA4t3im9rkR5/JX0l8O42lrO9fTqY87HVV37JKfrXHdx5fRc161n7n6Ghvq12vo6rinojGvIiKNXHWOp3UVfw9/BrcezODWg7HZbOw+upuViSs5r8V5XLf4OsxWM24GN+Kz4jmcc9ipVQtq2onr6Z48iaWy6+meqKS32BWqO/aJw0fGR8OY6OLx0NVRANbX2OCa17A+/9xVB/W8iohInXLySgZQvEHDXWfexR1n3mFfg7YuqA893DUhvyifXUd3sS1tG/N3zHdYXq3ke/D9nu95ac1L5cZ4rt9zXNjqQgB+2f8LU1ZPKfM+BZYCTAYTFpuF8dHjOSv0LB7585Fy4z509kNc0+EaAP49/C/3/3Z/uc+hwFJQL3r8G+LPnXpeRUSkXjr5f77PrHqGeTvnkVeUx9QNU5m3cx7397yfK9pdgdFQ+9M2ypqsVtcLiKruvmaz2ewrU/x24DfeXf8uezP2YrGV3s3sxB3jCi2FZBSUv45oyR8qJf8+VduSTT3GdB/D8vjlp2xbaCk8fp3Vcsq2JoMJs9WMu9GdG864gR3pO+xrHtcl9fHnrjqp51VEROqE0/Um+Xv4k1WYBUDnkM481ucxosOjaynb47ILs3lsxWP8Fv8bBgzYsHHXmXc5LA1Wlzjba2e1WUnISmBb+jb7xhhb07byaO9HGdJ6CFC85Nm4X4oL3WDPYDqFdCK/KJ/1R9Y7rC08pvsYMgszSclLKTevcO9w/Dz8gOLvaXJecqk2c7bN4YttXzj0jt7e5XYO5R4qN24TryYEegYCxcvIldf25Nhnh5/Nv8n/MrDFQO456x56hPc4zXe2ZuWac3ng9wdYlbgKo8GI1Wbl3rPuZWLPibWdWqWo51VEROqd043TLbQU4ufhx7T/prE1bSvJuaWLm5pktph5atVTLNu/jHxL8fqxJZtDTN88ndT8VF4Y8EJtplim0/XaXdDyAu748Q62p28vtfsawLa0bfbitXt4d2IuiuGM4DMI9wnnw/8+POWOcQEeznUi+Xn42QvZErEbYvli2xflxnaGj7sP7QLblTp+qth/HPyDPw7+Qc/wnow8ayTnRZ5X5prINeHEXm+jwWjfKrpkDPv0TdNZl7yOAZEDOL/F+XQI7lArebqailcREakTnF339qqoq1i8e7G9gAKIS46jXVA7p4ujykrPTyfYKxgAd5M7+zP3k2/JJ8gziKMFR+09YADN/Zrbr8suzOapVU/Rv3nxZhLNfMvf1tXVMgoy6Bnek/7N+xMTF8PUuKnYsNkLt/2Z+1mXXLwjmYfRg/bB7ekU0sn+deLH6AEeAQxsMRBwfg3gyqjN2F1CurDj6A7WJa9j3S/r6BjckdHdRnNxm4srdb+KstlsbEndwje7vmFb2jZmXDoDg8GAl5sX3cO6szJxpf3nrshWxL+H/+Xfw/+yN2Ov/Y8nq81KVmGWvQe6vlPxKiIi9UoT7ybc1fUu++Oswiwm/joRGzbGRY/juo7XnXIb2orKNeeyZP8Svtn1DZtSNvHrDb/ai+QHzn6AH/b8wLyd80r12hUWHR9r+XfS3yzdv5Sl+5cC0D6oPQMiB9A/sj89w3viYfKotnzLkpKXwrOrn2V72nYScxIdztmwYcBgL9ha+rfkxQEv0imkE20C2zj9vXTlChe1HfvdQe8yc8tMvt7xNTvSd7Dm0BqXF68peSl8v+d7vtn1DbuO7rIf35iykW5h3YjdEMvKxJWlfu4GthiIAQMXtrzQfs32tO3c9P1NdA3tyoDmxZuCdGnSBZPR5NLn4Coa8yoiIvXa7qO7eej3h9idsRso3tJ20jmTqvTxrtVm5d/D//LNrm9Yun8peUV5ABgw8M6F79hnxTs7fjQ+M57v9n7HyoSVbEzZ6FBsebt589rA1zi/5fn2YxWdVGW2mNmTsce+JfD29O10DunMw70eBoqHXPSZ3YciWxEAkX6ReJo82ZOxx2HmfkOf6FNVGQUZfLntSy5vdzkt/VsCsCV1C6sTV3PjGTeWGupQGZtSNvHhhg/5M+FP+yQ4T5Mng1oN4uqoq+kT0YdpG6dVaLWBr7d/zXN/PedwnyDPIPo272sfYhDoGVjlyXxVoTGvIiLSaEQFRTHvynnM2zGPqXFT2Zuxl/G/jKdvRF8m9ZpU4dnicclxPPrnoyRkJ9iPtQ5ozdXtr+aKdlc4fOTvbI9gy4CWjO0+lrHdx5JRkMHqxNWsSFjBysSVpOSl0C7o+DjMn/f9zMqElfyX8h9F1iLu63Gf/dyJxYnVZuXpVU+zLW0bu47ucpitD8U9xiU8TB482/9ZInwj6BjSkS+2fnHKsalStkDPwFLfn2n/TWPZgWV8svETbux0I7d2vpVQ79AKxTVbzLibinu4s83Z/H7wdwC6hXXjqqirGNp2qMOQmIr2RN9wxg0MbDGQlQkrWZm4ktWJqzlacJQf9/7Ij3t/5JOLP6F3RG+MBiMxcTGlitQTf+7qAvW8iohIg5FZmMm0/6Yxa+ssiqxFuBncuLnTzQR4BpTbm1RQVMC1Ha+196QdyT3CkHlD8HLzYmiboVzd/mq6h3V3ySQdm83GrqO7HCbWTPh1Ar/H/25/3NK/JRe3vpjf4n9jT8Yeh6Ll0vmXcjD7IAD+7v6cEXKGfWxq5yadyyzcG+IaobXp293f8vHGj9mTsQco7iW9uv3VuBncCPIKKvfnLsecQ7hPOIt2LaJPRB97L7nVZuWj/z7i4tYXO/xRU53MVjMbj2xkRcIK/j38Lx9f/LG9eL71+1v5L+U/ogKjeHngy/we/3uN/FxUpF5T8SoiIg1OfGY8b617Cx83H1r4t7D/z3dUt1EYDUZsNhtTVk1h4a6FuBvd6dm0Jx9f/LH9+r+T/qZbWDe83bxrPPc/Dv7Bb/G/sSJhBYdyHJd18jB58M+t/9gL6Z/2/oS7yZ1OIZ1o7tvcqQK7Nj8abqisNiu/x//OJxs/4b+U4hUASpZNO7HoM1vNPLHiCX7Y+wNGjFgp7iFt5tuMn4f/XCfWLr71h1vtqxicvNyZK6l4PYGKVxGRxqvIWoSb0c3eqxjkGcTV7a9mwc4FZBZm2tu1DmjN3GFza6VYLY/NZmNPxh6GLx6OxWbBZDDx0ZCPOKfZOXWiyJHSbDYb/xz+h082fsLKxJUMiBzAioQVjI8ejwEDn2z6xD5+GuDMJmdydfurubTtpXVmJQCL1cLm1M3c/uPt9s0g1t22zuX31ZhXERERinuOoHgs4K8HfmVr2lY+2/wZAO5Gd4ZFDeOqqKvoEd6j1tbuLI/BYGDp/qX2AsJsNbMueR29I3rXdmpSDoPBQK9mvejVrBdbUrfQJqANM7bMICYuxr6clbebNzeecSNXRl1ZJ9dhNRlNrEpc5fBzF7shtk4NJVHxKiIijcLnl37OuV+ci9VmxWQwseKmFfi4+9R2WuU6eQyqJlXVL12adAGKX6uP/vsIs9WMm8GNlTevrNal3Kpbffi5U/EqIiKNwuebP8dqs9p7k2ZsmVFn/md8Mlcuyi81K3ZDLGar2f5z98nGT+rsa1dffu5UvIqISINXH3qTTuTKRfml5ujnzjU0YUtERBo0LQ0ltUE/dxWjCVsiIiLH1JfeJGlY9HPnOhXqec3MzOR///sfERERpKWl0apVKyZPnlxu+0WLFjF37lw6duzI5s2befbZZznjjDPs5xMTE5k0aRIdO3Zk//79nHfeedx9990OMV588UXi4+MJCQnh0KFDvPPOO/j5Ob/9mnpeRUREROo2l/W83nDDDfTt25cpU6YAMGTIELy8vHjggQdKtV2xYgX33HMPO3fuJCgoiJUrVzJ48GA2b95MQEAAZrOZSy65hEmTJnHHHXdQVFREt27dCA4O5pprrgHgpZdeYtmyZfz6668AvPDCC9x6660sWrSoImmLiIiISAPh9CrHK1as4Oeff+auu+6yH7vzzjt57rnnMJvNpdo//fTTDBs2jKCgIAD69++Pt7c306ZNA2DOnDns3r2bG2+8EQA3NzduvfVWnnrqKQDy8/N58cUXufPOOx3ut3jxYv79998KP1ERERERqf+cLl4XLlxISEgIrVq1sh+Ljo4mPT2dpUuXOrRNT09n+fLl9OjRw+F49+7dmTNnjj1e586d8fLycoi3adMmtmzZwtKlS8nOznaIERkZSWhoqD2GiIiIiDQuThev69evp2XLlg7HWrRoYT93oo0bN1JUVFRm+7i4OGw222njlcQsq83J9ztRQUEBmZmZDl8iIiIi0jA4XbwePnwYX19fh2Pe3sV7QCclJZVqC5TZ3mw2k5qaetp4p4px8v1O9NJLLxEYGGj/Orn4FREREZH6y+ni1Ww2YzKZHI65uZU936tkDOyp2p8unjMxyjJ58mQyMjLsX/Hx8adsLyIiIiL1h9OrDYSFhZGbm+twLD8/H4Dw8PBSbYEy27u7uxMcHHzaeOnp6fYYJy6NlZ+fX+p+J/L09MTT09PZpyUiIiIi9YjTPa/R0dEkJiY6HEtOTgagd+/eDse7deuGwWAos33Pnj0xmUynjRcdHQ1QZpuT7yciIiIijYPTxevw4cMdxqICbN26lZCQEAYOHOjQtmnTpgwYMIC4uDiH41u3brWv4Tp8+HA2b97ssMzW1q1b6dy5M506deKSSy7Bz8/PIUZ2djbx8fH2GCIiIiLSuDhdvA4aNIhLL72U6dOn248tWLCAxx57DB8fHwBGjRrF5ZdfDsDLL7/MokWL7LP99+/fT3JyMqNHjwbgtttuo02bNsyfP98h3rPPPgtAQEAATz75JJ9++qn9/KJFi7jsssvo06dPZZ+viIiIiNRjFdoeNiMjgwcffJDQ0FC8vLzw9vbm0UcftZ+//vrr2bdvH2vXrgWKi80vv/yS6Oho9u/fz8SJE+ncubO9fWJiIg899BAdOnSgoKCAM844w2F7WJvNxosvvsi+ffto164dhw4d4tlnnyUwMNDpJ6jtYUVERETqtorUaxUqXusjFa8iIiIidVtF6jWnhw2IiIiIiNQ2Fa8iIiIiUm+oeBURERGRekPFq4iIiIjUGypeRURERKTeUPEqIiIiIvWGilcRERERqTdUvIqIiIhIvaHiVURERETqDbfaTsDVSjYQy8zMrOVMRERERKQsJXWaMxu/NvjiNSsrC4CWLVvWciYiIiIicipZWVkEBgaeso3B5kyJW49ZrVYSExPx9/fHYDDYj2dmZtKyZUvi4+NPu4eu1E16Des3vX71n17D+k+vYf3XUF5Dm81GVlYWzZs3x2g89ajWBt/zajQaadGiRbnnAwIC6vWLLXoN6zu9fvWfXsP6T69h/dcQXsPT9biW0IQtEREREak3VLyKiIiISL3RaItXT09PpkyZgqenZ22nIpWk17B+0+tX/+k1rP/0GtZ/jfE1bPATtkRERESk4Wi0Pa8iIiIiUv+oeBURERGRekPFqzQIeXl5JCQk1HYaInKCXbt21XYKIo1aQ30PNrriNTMzk7vvvpvHH3+csWPH8tJLL9V2SlIJ33//PQaDwf4VEhKCt7d3baclTli5cmWpYx9//DF33XUXTz/9NLfccguHDh2qhczEWWW9hkVFRURGRjq8L5cuXVoL2UlZcnNzmTRpEhERETRt2pQxY8aQk5NjP6/3YN13utewMb0HG/wmBSe74YYb6Nu3L1OmTAFgyJAheHl58cADD9RyZlIRs2fPZubMmfbHERERhISE1GJGcjorVqzg0Ucf5eDBg+zbt89+/Msvv+S1115j06ZNuLu7M3v2bC6//HLWrFmDyWSqvYSllPJeQ4D58+dz22230bVrV6B4g5irr7665pOUMt111120b9+eqVOnsnjxYj788ENyc3OZMWOG3oP1xKleQ2hk70FbI/Lnn3/aANv+/fvtx2bNmmULDg62FRYW1mJmUhE7duywPfTQQ7WdhlRAVlaWbd++fbbRo0fbWrdu7XCuffv2tmeeecb+uKCgwObr62ubN29eDWcpp3Kq19Bms9mGDx9uKygoqPnE5LQ2btxoi42NdTh22WWX2Uwmky0/P1/vwXrgdK+hzda43oONatjAwoULCQkJoVWrVvZj0dHRpKenN9iu9Ybo/fff54033qB9+/Y8/PDDJCcn13ZKchp+fn60bt2aZs2aORzfuHEju3btokePHvZjHh4edO7cmTlz5tR0mnIK5b2GAOvXr2f+/PmEh4dzzTXXsGLFilrIUMpTMlzuRBdffDEWi4U1a9boPVgPnOo1zMzMbHTvwUZVvK5fv56WLVs6HGvRooX9nNQPXbp04Z577iEnJ4fXX3+dnj17snPnztpOSyqh5H1X1vtS78n6o7CwkIcffpiOHTvyzTffMHDgQGJiYmo7LTmmX79+uLu7OxyzWq00b96cvXv3AnoP1nWneg3DwsIa3XuwURWvhw8fxtfX1+FYySSfpKSk2khJKmH06NFMmzaN+Ph4Xn31VZKSkhg5cmRtpyWVcPjwYYAy35d6T9Yfffr04dVXX2XNmjWsXLmSNm3acP/997Njx47aTk3K8euvvzJp0iS9B+uxktcQGt97sFEVr2azudTgcze3RjdnrcFwc3Pj4Ycf5oknnuDPP//kwIEDtZ2SVJDZbAbQ+7IB6devHz///DMGg0EfO9dR69at4+jRo9x33316D9ZTJ76GJ2sM78FGVbyGhYWRm5vrcCw/Px+A8PDw2khJqsG9994LwMGDB2s5E6mosLAwgDLfl3pP1l8dOnTg/PPPJz4+vrZTkZPk5eXx5JNPMm/ePNzd3fUerIdOfg3L0tDfg42qeI2OjiYxMdHhWMlkn969e9dGSlINgoODAYiMjKzlTKSioqOjAcp8X+o9Wb8FBwfrPVkHPfTQQ7z66qs0bdoU0HuwPjr5NSxPQ34PNqridfjw4SQlJdnH+ABs3bqVkJAQBg4cWIuZSVWsX7+e3r1707p169pORSronHPOoXXr1sTFxTkc37ZtG9dcc03tJCVVZrPZ2LhxI9ddd11tpyIneO2117jxxhs588wz7ceCgoL0HqxHynoNy9pFq6G/BxtV8Tpo0CAuvfRSpk+fbj+2YMECHnvsMXx8fGoxM3HW6tWrGT58OGvXrgWKP+p67bXX+OSTT2o5M3GGxWLBZrPZHxsMBl599VVmzJhBUVERULx7U0RERIP9pVvfnfwaWq1WbrzxRt577z2sVisA7733HrfffrvD/2Cldn3xxRds3LiRI0eOMG/ePObNm8cnn3zCzJkz9R6sJ8p7DWfMmNHo3oMG24m/hRqBjIwMHnzwQUJDQ/Hy8sLb25tHH320ttMSJ23dupXhw4eze/durr/+elq3bs3IkSNp165dbacmp1BYWMiSJUt48skn2bx5M9OmTeOCCy6w95ZPmzaNP//8kzPPPJP9+/fzxBNP0Lx581rOWk50qtfwlltu4ZtvvuHMM8/kwgsv5Nxzz+Xaa6+t7ZTlmD/++IMhQ4ZQWFhY6tzSpUsZPHiw3oN13Olew08//bRRvQcbXfEqIiIiIvVXoxo2ICIiIiL1m4pXEREREak3VLyKiIiISL2h4lVERERE6g0VryIiIiJSb6h4FREREZF6Q8WriIiIiNQbKl5FREREpN5Q8SoiUgN++uknLrzwQgwGA5GRkdx0003ccMMN9OvXj3HjxvHXX3+57N67du1i0qRJ9OzZ02X3EBGpKSpeRURqwNChQ/m///s/AO655x6++uorvv76a3755Rdat27NwIEDGTt2LBaLpdrumZ2dDUBUVBShoaHs2LGj2mKLiNQWt9pOQESksfD29gbAYDA4HHvkkUcICAhg3LhxhISE8MILL1T5Xj/88ANHjhzhjjvuwGAw0KxZsyrHFBGpC9TzKiJSB4wZM4bOnTvz5ptvkpGRUaVYO3fu5I477sBms1VTdiIidYeKVxGROsBgMHDZZZeRn5/P0qVLycrK4vHHH2fkyJGcc8453H///RQUFJCXl8fnn3/OJZdcwrx587juuuvw9/enb9++bN++HYBZs2aRkpLC9OnTueeeeygoKLDfZ+3atfTv3x8fHx+uu+46ioqKauspi4hUioYNiIjUEW3atAEgPj6esWPH8sorrxAZGUlqaiotW7bE09OTF198kZYtW7JkyRLCwsJ4++23OXjwILfeeitXX301mzZt4plnnuHZZ5/lrrvu4s4777THLygoYNmyZfzyyy9s2rSJXr16sWjRIoYPH147T1hEpBJUvIqI1BEmkwkAq9XK77//znvvvWc/179/f1JTU3Fzc6Ndu3YAjBw5khYtWtCiRQveffddrrjiCtauXcu5555bZvyioiImT54MwDnnnEOzZs3svbUiIvWFilcRkToiISEBADc3NyIjI3n55ZdP2f7EiV99+/YF4MCBA+UWryfz9PSksLCwktmKiNQOjXkVEakjlixZgp+fH5mZmezcubPUslkWiwWr1VrmtR4eHgBERES4PE8Rkdqk4lVEpA6YP38+a9eu5YknnqBv376kp6czd+5chzYxMTHk5eWVef3WrVsJDg6mR48e9mNabUBEGiINGxARqSG5ubkADr2neXl5fPzxxzz88MOMHTvWvpFBt27duPPOO9m3bx/nnHMOP//8M507d8bX15cjR44AxTtnXXDBBdhsNl555RUeffRR/Pz8AAgKCmLz5s1kZWWRlJSE2WwGise9urm52f9dclxEpL5Q8SoiUgOWLFnCiy++CMDHH3/M9u3bycjI4MiRI3Tq1IlFixZxySWX2NsvWLCAe++9l2eeeYbWrVszefJk7rjjDoeYq1evZsOGDezevZuzzz6bhx9+2H5u0qRJvPHGG6SmpvLoo48yZ84cAF599VUeeughZs6cSWJiIt999x233XYbnTp1qoHvgohI1Rls+lxJRKRe2bdvH23btuW3337jggsuqO10RERqlMa8ioiIiEi9oeJVRKSeKVmFQONVRaQxUvEqIlKPJCUl8eabbwLw/vvvs3r16lrOSESkZmnMq4iIiIjUG+p5FREREZF6Q8WriIiIiNQbKl5FREREpN5Q8SoiIiIi9YaKVxERERGpN1S8ioiIiEi9oeJVREREROoNFa8iIiIiUm+oeBURERGReuP/ARNIq864KUsCAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 8))\n",
+    "_ = plt.plot(Krange, 1 - accuracies, 'o--')\n",
+    "_ = plt.plot(Krange, 1 - accuracies_valid, 'o--')\n",
+    "_ = plt.plot(Krange, 1 - accuracies_rf, 'x--')\n",
+    "_ = plt.plot(Krange, 1 - accuracies_valid_rf, 'x--')\n",
+    "_ = plt.title('Decision Trees Training/Valid Error')\n",
+    "plt.legend(['Train (DT)','Valid (DT)','Train (RF)','Valid (RF)'])\n",
+    "_ = plt.xlabel('Depth')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Advantages of Random Forest\n",
+    "\n",
+    "- Bagging removes hyper-param related to Depth of the Tree.\n",
+    "- $K=\\sqrt{D}$ so it is a fixed hyper-param.\n",
+    "- You have to tune $M$ but in general it needs to be large.\n",
+    "- DT are **very interpretable**; DT/RF could be used for **feature selection**\n",
+    "    - To answer the question: __which feature contribute more to the label?__\n",
+    "- You can evaluate them **without a validation split** (Out of Bag Generalization - OOB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Feature Importance Evaluation and Interpretability\n",
+    "\n",
+    "- The **relative rank (i.e. depth)** of a feature used as a decision node in a tree can be used to assess the relative importance of that feature with respect to the predictability of the target variable. \n",
+    "- **Features used at the top of the tree contribute to the final prediction decision of a larger fraction** of the input samples. \n",
+    "- The expected fraction of the samples they contribute to can thus be used as an estimate of the relative importance of the features. \n",
+    "\n",
+    "- In scikit-learn, the **1) fraction of samples a feature contributes** to is combined with the **2) decrease in impurity** from splitting them to create a normalized estimate of the predictive power of that feature.\n",
+    "\n",
+    "**By averaging the estimates of predictive ability over several randomized trees one can reduce the variance of such an estimate and use it for feature selection. This is known as the mean decrease in impurity, or MDI.**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "```python\n",
+    "importances = forest.feature_importances_\n",
+    "std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n",
+    "```\n",
+    "<div align='center'>\n",
+    "<img width=\"40%\" src=\"figs/mdi.png\" />\n",
+    "<div/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Out-of-Bag (OOB) Evaluation\n",
+    "\n",
+    "- When training a random forest, each tree is constructed using a **bootstrap sample** from the original dataset\n",
+    "- This means that each tree is built using a subset of original data, and some data points are left out\n",
+    "- The key idea is that the data points that were not used in the construction of a particular tree can be used as a validation set for that tree\n",
+    "- We can obtain an unbiased estimate of the tree's performance without the need for a separate validation set\n",
+    "OOB Evaluation:\n",
+    "- Removes **the need of a validation set**\n",
+    "- It gives you a **quick way to estimate the number of ensembles**\n",
+    "- Quick good hint of how the forest **generalization error** could be (without doing K-fold cross-validation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "cell_style": "center",
+    "hide_input": true,
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "RANDOM_STATE = 123\n",
+    "\n",
+    "# Generate a binary classification dataset.\n",
+    "X, y = make_classification(\n",
+    "    n_samples=500,\n",
+    "    n_features=25,\n",
+    "    n_clusters_per_class=1,\n",
+    "    n_informative=15,\n",
+    "    random_state=RANDOM_STATE,\n",
+    ")\n",
+    "\n",
+    "# NOTE: Setting the `warm_start` construction parameter to `True` disables\n",
+    "# support for parallelized ensembles but is necessary for tracking the OOB\n",
+    "# error trajectory during training.\n",
+    "ensemble_clfs = [\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features='sqrt'\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            oob_score=True,\n",
+    "            max_features=\"sqrt\",\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features='log2'\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            max_features=\"log2\",\n",
+    "            oob_score=True,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features=None\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            max_features=None,\n",
+    "            oob_score=True,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n",
+    "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n",
+    "\n",
+    "# Range of `n_estimators` values to explore.\n",
+    "min_estimators = 15\n",
+    "max_estimators = 150\n",
+    "\n",
+    "for label, clf in ensemble_clfs:\n",
+    "    for i in range(min_estimators, max_estimators + 1, 5):\n",
+    "        clf.set_params(n_estimators=i)\n",
+    "        clf.fit(X, y)\n",
+    "\n",
+    "        # Record the OOB error for each `n_estimators=i` setting.\n",
+    "        oob_error = 1 - clf.oob_score_\n",
+    "        error_rate[label].append((i, oob_error))\n",
+    "        \n",
+    "plt.figure(figsize=(12,7))\n",
+    "\n",
+    "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n",
+    "for label, clf_err in error_rate.items():\n",
+    "    xs, ys = zip(*clf_err)\n",
+    "    plt.plot(xs, ys, label=label)\n",
+    "\n",
+    "plt.xlim(min_estimators, max_estimators)\n",
+    "plt.xlabel(\"n_estimators\")\n",
+    "plt.ylabel(\"OOB error rate\")\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "cell_style": "center",
+    "hide_input": false,
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "RANDOM_STATE = 123\n",
+    "\n",
+    "# Generate a binary classification dataset.\n",
+    "X, y = make_classification(\n",
+    "    n_samples=500,\n",
+    "    n_features=25,\n",
+    "    n_clusters_per_class=1,\n",
+    "    n_informative=15,\n",
+    "    random_state=RANDOM_STATE,\n",
+    ")\n",
+    "\n",
+    "# NOTE: Setting the `warm_start` construction parameter to `True` disables\n",
+    "# support for parallelized ensembles but is necessary for tracking the OOB\n",
+    "# error trajectory during training.\n",
+    "ensemble_clfs = [\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features='sqrt'\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            oob_score=True,\n",
+    "            max_features=\"sqrt\",\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features='log2'\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            max_features=\"log2\",\n",
+    "            oob_score=True,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "    (\n",
+    "        \"RandomForestClassifier, max_features=None\",\n",
+    "        RandomForestClassifier(\n",
+    "            warm_start=True,\n",
+    "            max_features=None,\n",
+    "            oob_score=True,\n",
+    "            random_state=RANDOM_STATE,\n",
+    "        ),\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n",
+    "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n",
+    "\n",
+    "# Range of `n_estimators` values to explore.\n",
+    "min_estimators = 15\n",
+    "max_estimators = 150\n",
+    "\n",
+    "for label, clf in ensemble_clfs:\n",
+    "    for i in range(min_estimators, max_estimators + 1, 5):\n",
+    "        clf.set_params(n_estimators=i)\n",
+    "        clf.fit(X, y)\n",
+    "\n",
+    "        # Record the OOB error for each `n_estimators=i` setting.\n",
+    "        oob_error = 1 - clf.oob_score_\n",
+    "        error_rate[label].append((i, oob_error))\n",
+    "        \n",
+    "plt.figure(figsize=(12,7))\n",
+    "\n",
+    "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n",
+    "for label, clf_err in error_rate.items():\n",
+    "    xs, ys = zip(*clf_err)\n",
+    "    plt.plot(xs, ys, label=label)\n",
+    "\n",
+    "plt.xlim(min_estimators, max_estimators)\n",
+    "plt.xlabel(\"n_estimators\")\n",
+    "plt.ylabel(\"OOB error rate\")\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Application: Human Pose Prediction in Xbox Kinect\n",
+    "\n",
+    "<center>\n",
+    "<img width=\"60%\" src=\"figs/xbox4.png\" />\n",
+    "<center/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "\n",
+    "<center>\n",
+    "<img width=\"90%\" src=\"figs/xbox3.png\" />\n",
+    "<center/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Decision trees are in XBox: Classifying body parts\n",
+    "### Trained on Millions of synthetic images\n",
+    "\n",
+    "<center>\n",
+    "<img width=\"60%\" src=\"figs/xbox.png\" />\n",
+    "<center/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "<center>\n",
+    "<img width=\"90%\" src=\"figs/xbox2.png\" />\n",
+    "<center/>\n",
+    "\n",
+    "[Link to the Microsoft paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/BodyPartRecognition.pdf)\n",
+    "\n",
+    "_To keep the training times down we employ a distributed implementation. Training 3 trees to depth 20 from 1 million images takes about a day on a 1000 core cluster._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "# Microsoft Kinect Research Presentation\n",
+    "\n",
+    "\n",
+    "[Link to video at the right time](https://www.youtube.com/watch?v=QPYf6pXe_4Q&t=24m39s)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "hide_input": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+    "from IPython.display import Audio,Image, YouTubeVideo\n",
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+   "source": [
+    "# Advantages of Decision Trees\n",
+    "\n",
+    "- The decision is **highly interpretable** (you can explain the decision to a doctor).\n",
+    "- Learn which **feature correlates better w/ $y$** \n",
+    "- **Do not need pre-processing** of the data\n",
+    "- **Complex** decision boundary that adapt to data density\n",
+    "- **Axis-aligned yet complex/segmented** decision boundaries\n",
+    "- Inference is **fast**: test attribute and split"
+   ]
+  },
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+   "metadata": {
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+   "source": [
+    "# Disadvantages of Decision Trees\n",
+    "\n",
+    "\n",
+    "- Suffer for **Variance problem** (overfit)\n",
+    "- Sensitive to the training set _(variance)_\n",
+    "- **Greedy approach** does not yield the global optimal tree"
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+  }
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diff --git a/AA2324/course/09_decision_trees/09_decision_trees.pdf b/AA2324/course/09_decision_trees/09_decision_trees.pdf
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