class_notebooks/lecture_10.ipynb

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    "# Data Mining & Machine Learning: Intro to Scikit-Learn\n",
    "\n",
    "*N. Hernitschek, 2022*\n",
    "\n",
    "This is the tenth Jupyter notebook for **ASTR 3890 - Selected Topics: Data Science for Large Astronomical Surveys (Spring 2022)**. \n",
    "This Jupyter notebook gives an introduction to the usage of scikit-learn.\n",
    "\n",
    "It is based on code from the textbook\n",
    "\n",
    "Statistics, Data Mining and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of\n",
    "Survey Data - Z. Ivezic, A. J. Connolly, J. T. VanderPlas, A. Gray\n",
    "\n",
    "as well as https://github.com/jakevdp/ESAC-stats-2014/blob/master/notebooks/03.1-Scikit-Learn-Intro.ipynb.\n",
    "\n",
    "***Exercises required for class participation are in <font color='red'>red</font>.***\n"
   ]
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    "---\n",
    "## Contents\n",
    "* [The scikit-learn Package](#first-bullet)\n",
    "* [Basic Principles of Machine Learning](#second-bullet)\n",
    "* [Supervised Learning](#third-bullet)\n",
    "\n",
    "\n",
    "\n"
   ]
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    "## 1. The scikit-learn Package <a class=\"anchor\" id=\"first-bullet\"></a>\n",
    "\n",
    "scikit-learn is a popular Python package containing a collection of tools for machine learning.\n",
    "\n",
    "It comes with an extensive list of tutorials, see http://scikit-learn.org/stable/tutorial/basic/tutorial.html#.\n",
    "\n",
    "In general, we are interested in taking a set of samples of data and then predicting properties of unknown data. In the simplest case the data will be one dimensional, but generally we will be dealing with multi-dimensional data.\n",
    "\n",
    "As we saw in the lecture, we can break machine learning into a few distinct categories. Common to all of these is the concept of **training sets and test sets**: training data will be used to make predictions about the test data.\n"
   ]
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    "### Representation of Data in Scikit-learn\n",
    "\n",
    "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in scikit-learn.\n",
    "\n",
    "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a two-dimensional array or matrix. The arrays can be either numpy arrays, or in some cases `scipy.sparse matrices`. The size of the array is expected to be `[n_samples, n_features]`\n",
    "\n",
    "`n_samples`: The number of samples: each sample is an item to process (e.g. classify). A sample can be a document, a picture, a sound, a video, an astronomical object, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.\n",
    "    \n",
    "`n_features`: The number of features or distinct traits that can be used to describe each item in a quantitative manner. Features are generally real-valued, but may be boolean or discrete-valued in some cases.\n",
    "\n",
    "The number of features must be fixed in advance. However it can be very high dimensional (e.g. millions of features) with most of them being zeros for a given sample. This is a case where `scipy.sparse` matrices can be useful, in that they are much more memory-efficient than numpy arrays.\n"
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    "### A Simple Example: the Iris Dataset\n",
    "\n",
    "As an example of a simple dataset, we take a look at the iris data stored by scikit-learn. It is a commonly used example data set for machine learning algorithms. The data consists of measurements of three different species of irises. There are three species of iris in the dataset, which we can picture here:\n",
    "\n",
    "The data come from measurements made in Quebec by botanist Dr. Edgar Anderson and first used for Sir Ronald Fisher's 1936 [classification paper](http://rcs.chemometrics.ru/Tutorials/classification/Fisher.pdf), see [https://en.wikipedia.org/wiki/Iris_flower_data_set](https://en.wikipedia.org/wiki/Iris_flower_data_set).\n",
    "\n",
    "\n",
    "### Loading the Iris Data with Scikit-Learn\n",
    "\n",
    "Scikit-learn has a very straightforward set of data on these iris species.  The data consist of\n",
    "the following:\n",
    "\n",
    "- **Features/attributes in the Iris dataset:**\n",
    "  1. sepal length in cm\n",
    "  2. sepal width in cm\n",
    "  3. petal length in cm\n",
    "  4. petal width in cm\n",
    "  \n",
    "  (The petal is the colorful \"leaf\" of a flower. The sepal is the green \"leaf\" at the bottom of a flower.)\n",
    "\n",
    "Scikit-learn refers to the \"labels\" as \"targets\".  So, every time you see \"target\", just think \"label\" and it will make more sense.  \n",
    "- **The target classes are:**\n",
    "  1. Iris Setosa\n",
    "  2. Iris Versicolour\n",
    "  3. Iris Virginica\n",
    "  \n",
    "``Scikit-Learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:"
   ]
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   "execution_count": 1,
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   "source": [
    "# Execute this cell\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iris` is a dictionary, so we can look at the \"keys\" of the dictionary as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `target_names` were defined above\n",
    "* `data` is the `[n_samples, n_features]` data array\n",
    "* `target` is the list of labels for all of the entries in `data`\n",
    "* `DESCR` is a README file with all of the information about the data set\n",
    "* `feature_names` were defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can query the database to determine the shape of the data and find that there are 150 objects with 4 measurements each:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "150 4\n"
     ]
    }
   ],
   "source": [
    "print(iris.data.shape)\n",
    "n_samples, n_features = iris.data.shape\n",
    "print(n_samples, n_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at both the first entry in `data` and the full $N\\times M$ data array. The first entry shows the values of the 4 features for the first object.\n",
    "\n",
    "Note the structure of the full data array: It is an `n_samples` array of arrays with `n_features`.  Scikit-learn requires this exact format which isn't always what you would generate naturally (particulary in the case where we have only one feature)."
   ]
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      "[5.1 3.5 1.4 0.2]\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.2]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.6 1.4 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n"
     ]
    }
   ],
   "source": [
    "print(iris.data[0])\n",
    "print(iris.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also inpect the shape of the target (labels) array, which is an `n_samples`-dimensional array, print the values of those labels, and also learn how those numerical values relate to the names of the iris species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150,)\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "['setosa' 'versicolor' 'virginica']\n",
      "setosa\n"
     ]
    }
   ],
   "source": [
    "# The shape of the target (labels) array is just an n_samples X 1 array\n",
    "print(iris.target.shape)\n",
    "\n",
    "# Here we see that the labels are given numerical values\n",
    "print(iris.target)\n",
    "\n",
    "# Use target_names to translate those numerical values to names\n",
    "print(iris.target_names)\n",
    "print(iris.target_names[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i.e., $0 = $ setosa, $1 = $ versicolor, and $2 = $ virginica.\n",
    "\n",
    "This data is four-dimensional, so it's not easy to visualize. But we can visualize two of the dimensions\n",
    "at a time using a simple scatter-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E5Gs8i+Ib4xnJ+QO40Nv8WTyFxxwR+RRIB3p5230OnFpZeZdU46dsici5xbsDeH9ArMQeKSKfek+0zPOexHm/iERWVL5KqYr1yf99w6PnPHdYMVJs9+a9PHP5K7xx54eVnJlSSqlK9g6eYqT4v4PCO91pKDAbOA64Du+6DGC9t1mGr9iKYoy5x5vLPDyjJTfjKTxWAe+WaPc9nh3HVuI5fPESIB9P7t9VZs4l1egREhFpgefQmywgxmJsXzy7DITiqQi3AMfiOVhmuIgMD+RkTKVU9TF/6iJev/39gNp+/NhXtOycxMjzqmzKrFJKqQpkjHkIeKiM52fjWXtx6OPHBHDtP/GMiBxERJ4EXMC/h7T31c9kYHIp199oNTdjzHRgell5e9tNA6b5eOo3PFPdKl2NHSERzyEMbwH7gFcsxjq9sVHAqcaYs40xtwF9gS+AgcCk4GaslKpoHz/+taX2nzz+Nb43UVFKKaV8E5EoEUnw8fiFeBa1zzLGZFd2XjVZjS1I8AyPHQtcBFj9nz4UzwKn34wx3xY/6D3Q5lbvlxNFT55Tqsb4b+lGVs5bYylm08qtLPttZQVlpJRSqpZqCewQkW9F5GkReV5EfsfzZncacFOVZlcD1cgpWyLSGXgMeM4Y85uIHGvxEsXtDzvR0hizXkTW4tkloS2ehUpl5bK4lKc6WcxJKVUO/8y1VoyUjOsxtEuQs1FKKVWL7cKzY9dQPGsvwvEcovgW8LAxpsx7R3W4GleQiEgI8B6wGbjT5mU6ej+vLeX5f/EUJB3wU5AopaqH/Bx7S77ysvOCnIlSSqnazBiTimdLYRUkNa4gwbPo/GhgUACnUZYm3vs5vZTnix9P8HchY0xPX497R06SLWemlLIlpl60rbjYxNggZ6KUUkopK2rUGhLvzlh3Ak8ZY+ZXdT5Kqeqj95ijcDit/0nrN17fN1BKKaWqUo0pSLxTtd7FM83qnnJerngEJL6U54sfTytnP0qpStIgqT4DT+pjKSZ5RDdadEyqoIyUUkopFYgaU5DgOWekA57dsfJKHIZogPu8bV7zPvasn2sVr37tUMrz7b2fS1tjopSqhs6751QiosMDahsSFsIF959RwRkppZRSyp+aVJDkA2+U8vGXt80c79f+pnP97P085tAnRKQtnkJlEwdO21RK1QBturXi/q9uJTImosx2YRGh3P3xJI7s37HMdkoppZSqeDVmUbt3AbvPHQ1EZDKehe7vGGNeL/F4FJ69onOMMZtLhPwKrAKGiMjxxWeRiIgDeNzb5hWjJ6YpVeMkj+jOiwse49P/+4ZfPp5DQV7h/udCwkIYcmo/Tr/lBI7o0brqklRKKaXUfjWmILGpD/ALngLkmOIHjTEuEbkIz0jJ5yLyOZ5thIcDvYC5wDOVnq1SKihadkri5jev4vInz2PFnNVkp+cQFRvJkQM6Uq9RaUvHlFJKKVUVantBUipjzJ8i0hu4HxgFxOKZpvUA8Jgxxt6hBkqpaiMuMZYBx/eu6jSUUkopVYZaUZAYYyYDk308PhuQMuJWAqdVVF5K1RRut5tNK7eStjudRq0akHRE06pOSSmllFJ1RK0oSJRS9uzevIeXb3ibP6cvoaigaP/jEdHhDDtzEJc/cS4xCTFVmKFSSimlaruatMuWUiqIfvt8Pue1vZq5Xy84qBgByMvOZ8YbP3FGs8tZu2hdFWWolFJKqbpACxKl6qC/fl7OQ2c8jdtd9kZyBXmFXD/oHnZs2FVJmSmllFKqrtGCRKk66LHznifQTa2LCop49JznKjYhpZRSStVZWpAoVcesnL+GlB1plmJW//kvWWlZFZOQUkoppeo0LUiUqmM+fORLyzHGwGdPTauAbJRSSilV12lBolQds297qq24nbqORCmllFIVQAsSpeoYZ4i9X/uQMN0lXCmllFLBpwWJUnVMm26tbMV1GdAxyJkopZRSSmlBolSdc/EjZ1mOCY0IZczFx1ZANkoppZSq67QgUaqOqdcogY69j7AUc8zpA3A49M+FUkoppYJP7zCUqoMem3k3UXGRAbVt0rYRN795VQVnpJRSSqm6SgsSpeqgmIQY3l77PI1aNSizXYdeR/DGymd1dEQppZRSFUa3zVGqjqrXKIEPNkxh0Q9LefPOD9myehtFhS5Cw0Lo1Kc9E58+n7bdW1d1mkoppZSq5bQgUaqO6zWyB71G9qjqNJRSSilVR+k8DKWUUkoppVSV0RESpSrYu/d/yrcvzyQvKw9xCPWb1eP6KZdx9LHdqzq1GmPbuh3Mens2O7ynxTdt05iRFxxD8/ZNqzgzpVR1ZUwR5P+Eyf8dTCZINBLWByLGIhIe5L5ckP8rJv8XMBkgkUhoT4gcj0hgG4goVZdpQaJUBXn3/k95/8HPMW5z0OPb/t3JrSMeJCI6nBfmP0Lrri2rKMPqb9emPTx31WssnPHXYc99+MiX9Brdg+unXE6T1o2qIDulVHVlcj7DZD0H7t0HP577OWQ8AjGXQ9QliEj5+8qdisl8GtzbDnn8S8h8HKIvgOirEdFJKUqVRn87lKoAz1/zOu/d/9lhxUhJedn5XNbjJtb9vaESM6s5tq3bwXUD7vJZjBRbNHMp1/W/k61rt1diZkqp6sxkvYDJuOuwYuRAgzRM5v9hMu7FmNL/RgfUV/abmPSbDitGDjTI8OSTfgvGuMvVl1K1mRYkSgXZ0l//YerLMwNrbODafndWbEI1kKvIxT0THiNlR6rftqm70rl7wmMUFRZVQmZKqerM5M3CZL0QWOPcTyD3Q/t95c/FZD4WWOO8qZD9mu2+lKrttCBRKsieuniKpfZFBUV8+dy0CsqmZpr37SK2rAl81GPbvzuY983CCsxIKVUTmOzXLbZ/w7P+w1Zfb1hrn/M2xhTY6kup2k4LEqWCKDc3f//Cayvef/CLCsim5pr2SoAjTCVMnWI9RilVe5jClVD4t7Ug11YomGO9r6LN1uPc+yDvB8t9KVUXaEGiVBD9/dMKW3FZadlBzqRmW/3nOusxC6zHKKVqkcJltsJMwdLK66vQRl9K1QFakCgVROl7M2zFlXdhZW2Tn2t9WkNBboF+H5Wqy0y+zcC8yuvL2OhLqTpACxKlgqhNtxa24hwO/VUsKa5+jI2Y2KBs4amUqqEcCbbCROpVWl84bPSlVB2gd0FKBVHHnu1wOK3/WrU6snkFZFNzDTyxT6XEKKVqkfDBQJj1uIgR1mPC+oFEWw6TiFHW+1KqDtCCRKkg6znS+gnst79/bQVkUnMdf9VoyzETbMQopWoPcSRCxDhrQWEDkZA2NvqKhsiTrAWF9kBCu1juS6m6QAsSpYLsnk9vtDRK0rxjM9p2a11xCdVAbbq1YsxFwwJuP/KCoRzRo3XFJaSUqhEk5mqQuABbhyOxk+z3FX05OBoE2DoEib3Fdl9K1XZakCgVZJExkTz/x8OIw/96hvrN6vH6iqcrIaua57oplzH09P5+2w0+tR+T/ndFJWSklKruJKQVUu8NkAQ/DSORei8iodZHtPdfwtnE05ejoZ+WYUjCM0iYTitVqjRakChVATr2bMfHW/9Hm24tfT4fEupk+LlD+HjrqzidzkrOrmYIDQvlzg9v4LZ3r6VT3/aHPd+pTztuffsa7v54EqFhoVWQoVKqOpKwHkiDryHqwsNHSyQSIs9E6n+FhA8tf1+hnZH6X0H0ZXDY4vhwiDwFqf8FEqFTSpUqS0hVJ6BUbZXYpB6vLn0Kl8vFBw99waaVWwmLCKXf+J4MPW1AVadXIzgcDkacO4QR5w5h08ot7NywG4AmbRrR6kh7O5oppWo/cTZD4u7ExE6CwuXgzgCJgtBuiCM2yH01QmJvwcRc5zmfxJ3uKXxCuyB2d+NSqo7RgkSpCuZ0Ojn/vtOrOo0ar9WRLbQIUUpZIhIJlTRVSiQcwnpXSl9K1TZakChVCVJ3p5OyI5WQUCeNWjUkMjqiwvpK35vB3m0pOEOcNGrZgKjYSL8xriIXOzbsJi8rj5h60TRu1VDP9FBKKaVUpdCCRKkK4na7mffNQqZOmcmSH5fvfzw8KpwR5wzmhGvG0KZbq6D0ZYxhwfQlfDtlJgtn/L3/xPKwiFCOOXMgJ1w9hg49jzgsLnV3Ot/97we+e/UH9m5L2f94y85JTJg4mlEXHhNQQaOUUkopZZcU37io4BKRxcnJycmLFy+u6lRUFcjPzefRc55j7tcLS20jDuHaFy5hwpXlW+xYWFDIU5dO4af3fy+z3WWPn8vpt5yw/+tVf/7LPRMeJX1vZqkxzTs05ZHpd9G0beNy5aiUUio4evbsyZIlS5YYY3pWVQ4isji5W3jywlm+N26xq/eozSxZnl+lr01VDd1lS6kgM8bw2HkvlFmMABi34fmrX+eHd38tV1/PXPE/v8UIwGu3vc83L30PwKZVW7ljzENlFiMAW9fu4NaRD5C2J912jkoppZRSZdGCRKkgWzDjL+Z8+WfA7V++4S3ycvJt9fXP3NX88E7gBc1rt75HZmoWr97yLtnpOQHF7Nywm48f+9pWfkoppZRS/mhBolSQTZ0y01L7rLRsZn8811Zf31rsKz+3gM+fnsbCGX9bipv59i+2iyallFJKqbJoQaJUEGWnZ7Ng+l+W4375eI7lmKLCIuZ8EfhITLEf3pmN1bVjWanZLJ611HJfSimllFL+aEGiVBCl7cmwfLMPkLIzzXJMZmo2hQVFluOyApyqdSg7OSqllFJK+aMFiVJBFBpmbyft0PDQSuvLGWLv195OjkoppZRS/mhBolQQJTatR1z9WMtxbbpZ3zoxOj6KRi0bWI5rdkQTyzFgL0ellFJKKX+CUpCISAcRuUpEPhaReSKyVkSWisgPIvKEiIwTkYo7mlqpaiIkNIQxFx9rOW78FaMsx4gI4y4bYTnu/PtOIzYxxlJM+55t6dCzreW+lFJKKaX8KVdBIiJnishvwCrgBeB0oB/QDugGDAduAqYC20TkKRFpXa6MlarmJlw5ytL0ps792tOpTztbfY27dDiRMYHX+q27tqD32KM53uJhjCdffxwiYjU9pZRSSim/bBUkIjJMRP4CPgS6Au8AlwNHAU2AMCAeaAuMAx4E1gCTgFUi8riIxJU7e6WqoSatG3HbO9cgDv838A2SErn740m2b/brNU7gro8nERLq9Ns2vmEc931+Mw6Hg3PvPZVeo3sE1MeEiaMYfs5gW/kppZRSSvljd4TkJ8AFnAE0NcZcbIx5wxizzBiz2xhTZIzJNMZsNMZ8b4yZbIwZAHQEXgauAa4PzktQqvoZevoAHvzmNhokJZbaptuQzjw372EatWxYrr76jkvmkRl30bhV6dfp1Kcdz897mOYdmgGeqWX3f30bx10+EmeI72ImPCqcC+4/g2tfulRHR5RSSilVYcTOFqUicpIx5ivbnYo0AVobY/6we43qTkQWJycnJy9evLiqU1FVqKiwiPlTF/Pzh7+TsiMVZ6iTVp2bc9zlI2l3dJug9uVyuVg4429+fP9X9mxNwel00LxDM8ZdNpyOvduVWlTs3Z7CjNd/Yvnvq8jNyiMmIYo+Y5MZef5QYhKig5qjUkqp8unZsydLlixZYozpWVU5iMji5G7hyQtnBXezk96jNrNkeX6VvjZVNWztG1qeYsQbvxPYWZ5rKFUThISGMPjkvgw+uW+F9+V0Ouk3vif9xlv7O96gWSLn3XtaBWWllFJKKVU23fZXKaWUUkopVWXsnaxWBvHMC2kC+NxmyBizOdh9qupl16Y9zHjjJzYs30xhfiH1miQw7MxBJI/ohsNR9TWw2+1m8aylzP50Hqk70wgND6Vt91aMveTYMtdzpO5KY+Zbv7B64Tryc/KJbxDHwJP6MuD4XqWuw1BKqcpijIHCvzC5X4N7B+AEZ2sk6lQkxN5OfkopVRmCVpCIyGnA7Xh23SrtuiaYfarqJTM1i2cnvsqcL/7A7T54bdKst2fTrF0Trn3xUnqNCmx3p4qwcObfvHjN62z/b9dBj8/7ZiEfPvwFg0/txw2vXHHQ2on83Hxevv4tZr0zm6JC10FxP33wOw2SErn8ifMZdubASnkNSil1KFO4ApN+FxStOvy5nDcxYQOR+EcQZ9MqyE7VRitSG9Du0yuCes1tqc8A24J6TVUzBKU4EJGrgeeBImAunp+momBcW9UMGSmZ3DT0Pjb+s6XUNtvX7eSu4x7h7o8nMfiUfpWYncdvn8/n4bOexe1y+3ze7Tb8+ul8Nq/axlOz7ye2Xgz5ufncMfZhlv92+D/yxfZuS+GRs58lY18mJ1w9pqLSV0opn0zBIkzKxUBe6Y0K5mL2nQ6JHyEhzSstN6WUCkSwRismAbuBAcaYDUG6pqpBnrz45TKLkWJul5tHz32eI45qTbMjmlRCZh7b1u3gsfNeKLUYKWnD8s08dcnLTP7yVv5383tlFiMlvXTdm7RPbsOR/TuWN12llAqIcWdgUq+izGKkmHsXJu1qqP+1buWtlKpWgjWhPwn4TIuRumnLmm3M/3ZRwO0L8wv59uWZFZjR4b558XsK8wsDbj/364WsWbiOmW/9HHCMMYYvn/vOTnpKKWVP7ldg0gJvX7QKCuZXWDpKKWVHsAqSLUB4kK6lapjvXv3RcszMt36hIK+gArI5XH5uPrPemW057rXb3qcgL/AiBmDOlwtI3ZVmuS+llLLKGIPJ+dB6XM5HFZCNUkrZF6yC5B1grIjEBul6qgZZ95f1gbGstGx2btxTAdkcbsf63WSn51iO27xqq+UYV5GLDct1IzmlVGXIA5eNiQlF/wQ/FaWUKodgFSSPAQuBH0VkqBYmdUt+rr2RjsoaIbHbj6vI5b+Rz/6sjaoopZQtJt9mXOX87VVKqUAFpSAxxriAl4B2wM9Amoi4fHzozlu1UEKjOHtxDe3FWe6nUbytuMjYSFtx8ZX0upRSdZzEUsqRX2Vz1At6KkopVR5BKUhE5ARgJlAP2AjMA37z8fF7MPpT1cuQU/pbjjmyfwcaJNWvgGwO16hFAzr1sX4o2LCzBlmOadyqIR16tbUcp5RSVok4IWKk9biIsRWQjVJK2ResKVuTgRxgiDHmCGPMYGPMMF8fQepPVSNDT+9PXH1rs/TGTxxVQdn4NuHK0ZbaxzeI5dx7TqVFpyRLccddPhKnU09tV0pVDok6x2JECESeViG5KKWUXcEqSDoCHxlj5gTpeqoGCYsI44onzw+4fddBnSr9VPNhZw2ky8DAzwe54qkLCI8I46pnL8LhDOzXpGXnJI6/qnILLaVUHRfaCyyMeEjM1YizYQUmpJRS1gWrINkL6Cq5OmzUBcdw1bMX+T1sq8vAjjzwzW2EhAbrTM7AhIaF8sA3t3HkgLKLEhHh6ucvZuR5QwHoNaoHd7x/HSFhZefbuksLHv3+bqLjo4OWs1JK+SMiSPz/QfgI/42jL4Xoqyo+KaWUsihYd4VfAGNEJNQYo1sM1VEnXTeOzv3a8/ULM/j1s/kUFRzYw6Btj1ZMmDiaURceQ1i4jUWYQRCXGMsTP93HzLd+Ydors1i/bNP+50LCQhh6en9OunYcHXsfvN7kmDMG0rprS75+fjo/fvA7+TkHdrZp1q4Jx185mrGXDifK5iJ4pZQqD5FwSHgB8mZgcj6AwsUlnnVA+DAk6lwkvHJHppVSKlBijCn/RUSigR+A3cANxpiN5b5oDScii5OTk5MXL17sv3EtlLEvk61rt1OYX0S9Jgm06NjM7+hJZTLGsGXNdlJ3phEaHkLzDs0CWgeTnZHD5lXbKMgtIK5+DK26tMDhCNZAo1JKlZ8p2gzuXYADnC0QZ6OqTkkFUc+ePVmyZMkSY0zPqspBRBaHNU9KTrplUlCvu+2JZyjYuq1KX5uqGsEaIVmOZ+/BvsAEEUkD0n20M8aYI4LUp6rG4urHcmT/wNdsVDYRoWWnJFpaXLQeHRdF577tKygrpZQqPwlpCbSs6jSUUipgwSpIHEARUPKIal9vh1eft8iVsiFlZyqLZv5NVloOjVs3ov+EngGNkPz183I2r9xKaEQoPYZ2Ial9U78xeTn5bFq5lbzsPOISq+dozO4te9m9aQ/icNC0bSMSm+j5BkoppZSyJigFiTGmdTCuo1R1NX/aYt64/X02rdx60OMhYSH0HZfMdS9fetjNeEFeAS9e+ya/fDyHvOyDT1Ru1LIBZ991MsdddvgZAtvW7eDrF2bww7u/kp2es//xpm0bM/6KkRx3+YgqXTxvjGHOVwv49uXv+fvnFfsfFxH6HpfMCdeMpdeoHlWWn1JKKaVqlur1dmsARORxEflJRLaISK6IpIjIXyJyn4gEfNKeiGwUEVPKx86KfA2qZply4zvce/xjhxUjAEUFRcz9egHntrmatYvW7X987/YUzmx+BTPe+OmwYgRg9+a9PHvFq9x53CMHPf7nd4u54qhb+PqFGQcVIwA71u/itdve5+o+d7Bj/a4gvTprigqLeOy853ng1CcPKkbAU6j8MW0xd4x5iJdveAu3210lOSqllFKqZqlxBQkwCSheRP8c8AGe6WKTgWUi0sLCtdKB+318PBnEfFUN9vFjX/Hls9P8tivML+SGwfewZ+s+CgoKmXjUzWSmZPmNWzjjLx4+6xkA/pm3hsmnPHnQLl6+bPt3B7ePfpCMfZmBvYggem7iq/z8of/jhr56fjrv3PtJJWSklFJKqZouKAWJiNwtIoUi0qyU55NEpEBEbgtCd3HGmH7GmIuNMbcbY641xvQGHgGaAXdYuFaaMWayjw8tSBRut5t3Jn8acPvC/CKevmwK7973Kel7Ay8WZn8yj73bU3jlxrcP2iq5LNv/28XnT08NuI9gWLPoP75/65eA23/82Ffs3rynAjNSSimlVG0QrBGSCcBsY8x2X08aY7YBvwAnlrcjY0xeKU8V3znqFkgqKL589ruAC4Rif/20nGn/m2W5rycvfonVC9b5b1jCjDd+piC/8o79mfryTEvt3W7Dd6/+WEHZKKWUUqq2CFZB0g5Y6afNSm+7ijLB+3mZhZhwETlXRO4UketFZJiIOCsiOVXzfP/mz5ZjXEVustNy/Dc8xLJfV1mOSdudzvLf/P3aBYcxht8+n2857tfPrMcopZRSqm4J1ra/kYC/u7A8wP/JcwESkZuBGCAe6AUMwlOMPGbhMk2A9w55bIOIXGSM+TXAPEo7+bCThTxUNXToovKKVFRobSSmWPqejCBn4lthfiG5WaUNTpausvJTSimlVM0VrIJkK9DPT5t+wLYg9QdwM9C4xNffAxcaYwKdtP4W8DvwD5AJtAWuAS4HZohIf2PM0iDmq2qYkNDKGywTh2DcxnJcaERYBWRzOKfN70VYRGiQM1FKKaVUbROsKVvfA0NE5AxfT4rImcBQYEaQ+sMY08QYI3hGOU7GU1D8JSLJAcbfb4z52RizyxiTY4xZYYyZCDyNZ8RncoDX6enrA1ht64WpaqNNt1aV1ld8fXuDh+2Oah3cRErhdDo5wkZfRxzdJvjJKKWUUqpWCVZB8jiQBnwoIl+KyOUicpz381d4tuZNwdp0qoB4C4qvgFFAfeDdcl7yFe/nIeW8jqrhrnjyfMsxDZIS6TrQ+my9ix4+i6i4SEsxvUb3oGnbxv4bBsn4K0ZZjpkw0XqMUkoppeqWoBQk3l20RgOb8eykNQX41vv5BGATMNoYc/jJckFijNmEZ+F8FxFpUI5LFU/5qrqjsFW1kNS+Ka2ObG4p5py7T+WKpy+wFBOdEMXYi4dbvuE/ZdIE/42CaPg5g6jXOD7g9i06NqPPuKMrMCOllFJK1QZBOxjRGLMI6ACcCjwFvOH9fCrQ0RhT2uLvYCo+B8VVjmsUr4VZX85cVC3w9G8PBDxyMfiUfoy/YiSderfjvHtPDSjGGeLk6dn3A3Dhg2fQa3SPgOIufPBMeo0KrG2wRMZEcv/XtxEZE+G3bXzDOB745jacTt20TimllFJlC+pJ7caYQmPMl8aYW40xl3s/f2mMCcphCSLSQUQOe4tWRBwi8jDQCJhnjEn1Ph4qIp1E5IhD2ncWkcNGQESkNfCi98v3g5GzqtniEmN5Z92LNO/QtNQ24hAmTBzFvZ/dtP+x8yefwcSnLyhzYXxCo3he+esJ2nZvDUBoWCj3f30bx181mpAw3/tNxDeMY9L/ruCcu06x94LKqXPf9jz92wO0K2NtSLfBnXlh/iM07+DznFSllFJKqYOIMdZ39qkqInID8CgwB9gA7MOz09ZQPIvadwLDjTErve1be9ttMsa0LnGdycBNwG94ppNlAkcAxwERwHTgJGNMQTlyXZycnJy8eHFlDAypyrB+2UZeueldNv2zhaLCIiJiIhh8Sj8ufOAMIqJ8jxoUFRXxxdPTmP76T2SlZuNwOmjcuiEXP3wWycO7l9pX2p50Zr41m1V/rCE3O5+4xBj6T+jFoFP6ERZe9TtXGWNY9ee/zHp7Nrs370EcQrMjmjDm4mM5okfrqk5PKaVqrZ49e7JkyZIl3g10qoSILA5rnpScdMukoF532xPPULB1W5W+NlU1bG37KyKnGmM+t9upiDQFWhtjrJ6a9iOewxUHAUcDCUA2sBbPeSLPG2NSArjOL0BH7zUG4lkvkoan0HkPeM/UpEpNVYq23Vvzfz/caykmJCSEM249kTNuPdFSXELDeM649QRLMZVJRDiyXweO7NehqlNRSimlVA1n9xyST0XkL+D/gK+NMfmBBIlIR2AinrM+HgMsFSTGmBV4zgoJtP1GQHw8/isQ0MGHSimllFJKqYpjtyAZDjwDfASki8g3wFxgEbADSMUz9ak+nhPL++HZhasXUAg8BzxbnsRV9bVp5Ra+e/VHNizfREF+EYlNEjjmjIEMPLE3IaHBOovTY9EPS3nrro/Y/t9OXIUuImMjGHRSXy5+5Gyi46KC2tey3/7h9Ts+ZMvqbbgKXUREh9P3uJ5c9n/nEpfo+xwRV5GLP6Yt5ueP5rBvewohoSG0OrI54y4bUebUpj1b9zH9tR9ZOX8Nedn5xHqnbB179iAiY6xtD+zP+mUbeeXGd1n39waKCooIiwyj+9Ajmfjk+TRq2TCofdVWmfn5fLV6JbM3bSAjL4+o0DB6JyVxRpduNIqOCWpfxp0DeVMx+T+DOw0kEkKPRqJOR5ylr3UyhasxuZ9C0b9gCsHRCIkcD+HHIhLc30ullFLKCttrSEREgLOAq4H+QFkXEjxTot7GM61qo61Oa5C6uIYkdVca/3fhiyya6fuA+8Sm9bj2xUsYdFLfcve17b8d3Dj4XlJ2pvl8XkQYf8VIrnv5snL3tXd7Ctf1v5M9W/b5biAw4pwh3PbutQc9vGDGXzw78X+lxvU4pgu3v3ctDZLq738sPzef569+nR/f+w23y31YTFRcJBdMPoOTrh+H51fQvpysXK7rdyebVpa+G3fyiO48+v1dOBxB3f+i1jDG8MriBby08E9yCg/fuyPE4eC0I7ty75BhhIeU76bfGAM572GyngWT5aOFAyKOR+LvR+RA0WpcuzDpt0DBH74v7GiCxD+AhB9TrvyUUpVD15Co2sj2XYbx+NAYMxDoDFwHfAr8AfwLLMOz5uNpYAKQZIy5sS4UI3VR6q40bhh0d6nFCEDKjlQeOPUpfnivfLPldmzYxWVdbyy1GAHPzdvUV2bx0JnPlKuvlJ2pXNjhutKLEQADP77/G3eMe3j/Q79/8Qf3HP9YmXFLZ//DdQPuYs9WT5uCvALuGPsws96e7bMYAcjJyGXKjW/z1t0f2XtBXnk5eZx/xDVlFiMAS35cxsSjb8Ht9p1PXWaM4cHffuGJeXN8FiMARW43H61YxhXTvqHQVZ7dyMFkPY/JfKiUYgTADXlfY1Iuxpg8T4xrJ2bfGaUXIwDunZjUiZi8GeXKTymllLIrWAcjrjHGvGSMOcsYM9AY08kYc7QxZrQx5hZjzHfGmNxg9KWqp8cveJHt/+3y284Yw1OXTGHz6m22+7pxyL0U5hcF1PbXT+fx4/v2C6Abh95Hfk5AS6RY9P3ffPHsNHZt2sNj5z1falFR0p4t+3jozGcwxvDGHR+y/LdVAfX10aNf8cc0+6Nvd459hPQ9GQG13bB8My9d96btvmqr7/5dw9tL/wqo7W+bN/LCgjKKAj9M/q+Q/VJgjQsXYzKf9MSl3Qju7QEEuTFpt2CKttjOUSmllLJL52GoctuwYjOLZ5U+MnIoV5GLb1/63lZfy+esYu+2QDZSO+DdyZ/Z6mvjP1vY9u8OSzGfPvENU6fMpCAv8KN3Vs5bw9+zVzDjjZ8s9fXFs9MstS+WlZbFijmBFT7FZr4921Zftdkbf1krCN9f/jd5RfaOZDLZb1kLyP0Md/4fULjIQlABJrd8I29KKaWUHVqQqHKb/uqPlmN+ePdX8gIceSjpjds/sByzY/0udmzwP3pzqFdvfc9yTMqONL579QfLcW/f/TG5WXmWYv7+eQVb1wby7vfB3rr7Y6wuHcvPyS/XSFNts3LPbpbu2mkpJi0vjxn//mu5L1O0GQrmWQzKhSwb0xVzPqccxy8ppZRStmhBospt/fJNlmNyMnPZtWmP5bht66zdBBb7+5d/LMds9rO+ojRZaTmWY6yOxBTbsHyz5Zg1i/6z1Zed72FttWqv9Z9dgNX7bMQVrbbVFy7rv5eYNHDtttefUkopZZMWJKrcCvPtTUOxE+e2uTA4L9va6AN4ppZVFleRvUXjdr6HrgJ7r6sgV985L2Z3gXqBnThj7/cLE9g6q8Pp/2ellFKVSwsSVW6JTRJsxdVrbD0uKt7e2SItOjSzHBNTL9pWX4cfxelfdIK911XPxvc+oXGcrb4atdIzSYo1iLL3/6tBlI2fKUcDW30h9v4/46jvv41SSikVRFqQqHIbevpAyzHdhnSmftN6luNGnjfUckxYZBjJI7tbjpswcZTlGGeIk95jjrIcZ+d1JTatR7fBnS3HnX7LCZZjAE69aYKtuNpoUMtWxIeHW447rn0H652F9QRHI+txkSfZ6Gsw4oi3HqeUUkqVgxYkqtwGndyHeo2t3cRMmDjaVl9n3nESzhBrP7aDT+5r62C/8RNHERoRaimm1+genHDVGEsx0fFRnH7rCXQZ2NFS3LhLhxMSav2wvaOP7UZ8Q2vvnrc7qg0JDWy+414LRYSEctqRXS3FDG7ZitYJ1otwkRAk6kxrQaFHITGXglj7vZSos631o5RSSgWBFiSq3ELDQrn6+UsCbt9zVA+GnNrPVl9hYaGcc89pAbePio3k2pcutdWXw+HgssfPDbh9eGQYN71xJb3HHs3AE3sHHHflMxcSGR3Blc9cRFhkWEAxLTolccqk8QH3cahJr04MuK0zxMFt713rv2Edc0XPPrSIC+yGPzo0jDsHH2O/s6gLIKRdgI0jkNi7EIlE4u4KvI/w4RA+zFZ6SimlVHkEtSAREaeIHCkiA0VkiK+PYPanqo+hp/XnpjeuwuEs+0eq56ge3PvZTThDnLb7Ou+eUzn5+nF+20XFRfLK308QHWdvvj/ASdeO49x7TvXbLjwqnBf+fJR6jRJwOBzc/v719D++V5kxIsI1L1zC6As9N4Edex3BQ1NvJyoussy41l1b8NjMu4lJsLnGBRh4Qm9u+N/liJ/1Ls5QJ49+fzetu7Sw3VdtVT8qivdOOpU2fkY9EsIjeOuEk+lY3+ZaEEAcsUi9NyHEzyiaRCP1piBhPTxfRp6IxE3G75/68GFIwtOI6HtUSimlKp8YqwcSlHYhkXuASUCZbxkaY+zfidYgIrI4OTk5efFi+6dp10Qb/9nCty99z4/v/3bQuRrdBndm/MRRDD2tf7mKkZIWzfyb125/n/VLD97eNDImgmFnDeKKp84nKqbsm/tALfvtH/5383usXfwflPiVCY8MY8hp/bnq2QuJSYg5KMbtdjP3qwV8O2Umf/+84qCYY88ezAnXjOGIHq0P62vP1n1MnTKT6a//dNBp6q27tuD4K0cz4vyhREZHBOV1rV+2kZevf5tlv6/EuA+8sJBQJ32P68nVz19Mw+a6yLksWQUFfL5yBe8v/5v1qan7H28UHc2ZXbpzdrfuNIqOKeMKgTMmF3K/xuR8CEVrDjwhiRB1GhJ1NuJsenhc4WpMzvuQOxXIPfBEWD8k6iwIH4VInfjTrFSN17NnT5YsWbLEGNOzqnIQkcVhzZOSk26ZFNTrbnviGQq2bqvS16aqRlAKEhG5FXgMSAe+AbYAPvecNMbcX+4Oa4C6WpAUy83OY9fGPRTmF1KvSQINmiVWWF8ZKZn8M3c1OZl5NDuiMZ372lg4HKDsjByW/baKnIwcmrRqSOf+HQJan7JvRyqpO9Nwhjpp3KohUbH+C6XCgkJ2rN9NXnYesYkxNGndCPE3pGFTQV4Bf89eQcbeTOo3S6THMV1srbupy4wxbE5PJ6Mgn6iQEFrGJxDqrJibfGMMuLaCSQeJAGdLRPxP9zPubHBtA4rA0QBx2lgsr5SqUlqQqNrI+opY3y4DtgHJxhh7J4apWiUyOqLSpvnEJcbSf0LgazbKIzouiv7jrf+drN+0nuVdxULDQmnZKclyX3aERYTRZ0xypfRVW4kIrRISKq0vQloA1n7HxBENjoor2JVSSik7glWQtABe02JE1XbpezP4b+kmCnILiGsQS8deR/idgmaM4b+/N7JvRyrOECctOyfRqIX99QRK2bUzfS170pfhMoXERCTRtuGgWjES5i78F/J/AZMLziSIOB6HI7ANIpRSSlW9YBUku4J4LaWqnbWL/+OLZ6bx22fzKSo8cNp2g6REjrt8JCdcM4bYegevEyjIK2D6az8x9ZWZbF61bf/jIkLvsUdx0nXH0WtUj0p7DaruWrblC9zZ79A1fjWNiu/TDWxeX5+d7vEc3eYGwkPtb5JQVdw5n0PWC+DecfATGffgDusHcQ/jCLF+KKpSSqnKFay3xj4FRoqI9ZPClKrmvn/rF67tdyc/fzjnoGIEYO+2FN657xOu6XsHO9bv2v94ZmoWt4x4gJeuf/OgYgQ8IyYLpv/FHWMe4q27PyJYG0sodSi328381TfTNfQOuies5tAVSC1j9tEn7h3Wr59AWs7OKsnRLnfaJMi48/BiBAAXFMyFvSNxF/xV6bkppZSyJlgFyX3ADuBzEWkTpGsqVeXmfbuQpy+dgtvlLrPd9nU7uX30g2SkZOIqcnHfSf/HynlryowB+PCRL/n8qanBSlepg/y57mH6JnxLcc3ra08EY6Bj/Fa2bzmXwqK8wxtUQ+70hyHvuwBaFkLKubiLalaxpZRSdY2tgkRE1pf8AFYArYFxwDoRSTm0jffjvyDmrlSFcrvd/O/mdwMewdj+3y6+fWkmc75awPLfVgXczzuTPyUrLdtumkr5lJm3j25RHwG+C5FiIp6ipFP8ZpZtfq+SsrPP7c6DXCt5FkLmfRWWj1JKqfKzO0LiAOSQjyJgs/cj3cfzUo7+lKp0i39YxvZ11t5Z/e7VH/jmpRmWYvJz8vnh3V8txSjlzz+bXyUqxOfu64cpLlgiCj+twIyCJOtFoOwRy8Pk/47bHdj3QimlVOWztRDdGNM6yHkoVe3M/WqB5Zi921LYuy3Fctycr/7kpOv8nz6vVKCi3daKXGOgc/wmUrO3US+6crabtiXPWsHvUQT530Pk+KCno5RSqvx0xEKpUmTsy/DfKGh9ZVZaX6puiHJmWWpfPEqSmbe7ArIJJpvTG126jkQppaqroBQkIvKziJzvp825IvJzMPpTqjKERVbeOQbhldiXqhsKTaituNCQ6r79r73XhVT316WUUnVXsEZIjsGzqL0srYChQepPqQrXqXd7yzEhoU4SmyRYjuvQq53lGKXKkuayfiL7nrwYGsVU840SQzraiwvXf36UUqq6qswpW5F4Fr4rVSOMOG8I4VHWjtYZclp/Jlw52nJfEyaOtByjVFmaNLwMACvH3KzPG4HTaXMEorLE3mY9xtlGD0hUSqlqLJgFic9/9sSjFZ4tgbcEsT+lKlRMQjTjLx8RcHuH08HJN4znuMtHEB0fFXBc7zFH0aZbKzspKlWq1g16sTS1c5lb/paUVRhGu6QrKzapIHCEtgfnEdaCYm6qmGSUUkoFhe2CRETcIuISkeKjqycXf13yA8+oyHrgKODj8qesVOW55LFz6Dmqh992IsKkVyfSsdcR1GucwOQvbwloDUrrLi24/f3rgpGqUodp0+p/bMhsBPgeKSl+LM/lZJM8SMPYaj5dq1j9T0BiA2sbeS6OyFEVm49SSqlyKc8IyW8lPgye80d+8/HxC/A5MBG4tzzJKlXZQsNCefDb2zjlhuNKnb7VrF0TJn91C2MuGrb/saOGdeWpXybTPtn3DZ4zxMmxZw/imd8fJC4xwBsrpSxKiGpCw+Zfsji1J0Xm8KESEfgvswlbnC/SrflJVZChPQ5HHDT8BULKWicTCjHX4YjXf3aUUqq6s3UOCYAx5pji/xYRN/CWMeaBYCSlVHUSGhbKxKcv5Nx7T+PH935jzaJ15OcWEJcYy6CT+5I8ohsOx+G1fac+7Xlp4eOsWbiOnz74nZSdaYSEOmnZuTmjLxpGg2aJVfBqVF0TF9mI3p0/Yk/mBtZte50Q8x8OXBSSSL16p9P+iGE+f36rO4cjDhpMw120CTIfh8LVQCFIHESeBFEX18jXpZRSdZHtguQQbYC0IF1LqWopJiGaE68daylGROjUpz2d+ljfsUupYGoY24aGnR6u6jSCzhHSCuq9XNVpKKWUKoegFCTGmE3BuI5SSimllFKqbrFVkIiI3Um5xhjzoM1YZZMxhhVzVvPdaz+weeVWXEVu6jerx7CzBjH0tP6ERQTvUD5jDKv+/Jfpr/7I+uWbKCoool6TBI45fQDDzhpERCnrMPZs3cf/bn6XxT8sJT+nAIfTQeNWDTj37lMZdtagoOVXHhuWb2La/35gzULPlK34BnEMPLEPI88fSkyCHrpWG7iNYc7mTXz6z3I2pqVigGaxsZzcuQsj2hxBqNNZ1SlWqoy8vazc/Cox5leinFkUukNJLWpHo4aX0rZhv1LjTOEyTM7HB6ZRORKRiHEQMQFxBL4DXUUxJhdyv8PkfQfuvUAohHZAIs+E0B5IKVuTmcJ/MbkfQeFSMPkgCUjECIg8CXHE+4zJLypixrq1fLNmFbuyswkR4YjE+pzVtTu9myWV2pe911UAeT9i8r4G1w7ACSGtkcjTIGxAUPtSSqlgEmNlk/riIM+akUOVvJD4eFzwFCR14l90EVmcnJycvHjx4irNY9u6HTx85jP8u2SDz+fjG8Ry/ZTLGXxK6TcXgdq9ZS8Pn/kMK+ev9fl8TEI0Vz17ESPPP/iAsofOfIZfP51X6nVj6kXz9K8P0KZry3LnaEdGSiaPnfcCC2f85fP58KhwLrj/DE69cbz+g1+Drdyzm+u+n8b61FSfzzeJjuGJUWMY2KL2b9Hsdrv5c93DdI/6iMgQ38dHLU9rT6sWr5MQ3XT/Y8a1E5M2CQpL+bsnsUjsnUjUKRWRdkBM7jeYjIfApPtuENoDSXgOcR44t8S40zHpt0L+L6VcNQKJuQaiLzvob8D36/7l7p9/ICUv12fUkQ0a8sK4CbRJqGf35RzIMf83TPqd4N7tu0FIOyT+GSTU5sGSqtro2bMnS5YsWWKM6VlVOYjI4rDmSclJt0wK6nW3PfEMBVu3Velrqywi0hrYALxjjLmwkvq8EHgLuMgY83Zl9Bkouyv+hvn4mAq4gHeBi4Cx3s/veR//Bji2nPkqC7at28H1A+8utRgBSN+byYOnP80P7/1arr52b9nL9QPvLrUYAchKy+b/LnyRqVNm7n/sjrEPlVmMAGSlZnNlz1vZtGpruXK0Iystm5uOua/UYgQgPyefV295l7fv0V2ta6rlu3dxxueflFqMAOzMzuKir79k9sbSf59qiz/X3krfuPdKLUaMgW4J/5K64xTSczw3wMa1C7PvzNKLEQCTicm4A5P9bkWk7ZfJ+RiTfkvpxQhA4VLMvjMwru2eGHcmJuW8MooRgDxM1pOYrCf3P/LNmlVcPf3bUosRgJV793Dqpx+xIa30n7tAmLwfMalXlF6MABStw6ScjSlcXa6+lFKqItgqSIwxv5b8AFoBI4F+xpiLjDHvGGNmej9fCAwARgNV8xZ3HWSM4aEzniF9T0ZAbZ++dArb/9tpu7/Hz3+BvVv3BdT2xWvfYMPyTXw7ZSaLZi4NKMZV6OKW4ffbzs+uF699g40rAjvP88NHvmTRrMBej6o+Cl0urvzuG7ILC/y2LTJurp0xjdTc0m8ya7rlW7+ib8K3ZZ7wXjwI0CpmL/9uuhbAM4Lg3h5QHybzYUzhyvKmaokp/BeTMTmwxu5dmDTPYYom4yEoCvAmPvs1TP5stqSnc+sP3/s+LfgQqXm5XD19KnZmKwAY115vri6/bTGZmLRrMCaAtkqpirYN6AzcUdWJVAfB2hNxEvCJMWaJryeNMYuAT73tVCVY/vsq1v0V+Du5RYUupr0yy1Zf6/7awLJfA7+5cLsN37z4PR8/9pWlflJ3pvHXz8utpmfb3u0pzP6k7NGbQ331/HcVlI2qKD+sX8f2zMyA22cXFvDFqn8qMKOqVZT5NkBAJ7wbA93j/2Zf2o9QMN9CLwaT876t/Ozy9OdrtnEpChfjzpsDeVOt9ZP9Lh+sWEqhO/C+Vu/dw5/bbI4A534GWCiQXZshf7a9vpRSQWOMKTTGrDbG7KjqXKqDYBUkHQF/39Dt3naqEkx/7UfLMd+/9QtFhb6naAS7rx/e+5U9WwIbUSnprbs/shxj18y3fsFVZO2dxIUz/mb35j0VlJGqCB+uWFYpMTXBrvR1dEtYVeboSEkiEOIw5Kc/Zb2z3KkYd5b1OBuMyYW8r60HZj0LWPybWDCHeRvmWO7qwxXWR1eNMZicT6zH5er0UlU7iUg/ETEiUuo7niKySkTyRSSxxGOjRWS6iOz1PvefiDwhIgk+4jd6P+JE5GnvfxeKyGTv87Eico+IrBCRDBHJ9F7vExHpWeI6rb25vu2jjygRuU1EFnnjs7x5Py8ijQ9p21REXvLmUSAie0Tky5J9Bfi96ykiX4jIbu/3YJOIvCwiTX20fdube1sRuVZElolIrojMttJnScE6hyQDGOinzSCgcv71UWz8J7BpRiVlpmSRuiudhs3rW+trpfW+CvIKLccA7NxYeTf7m2y8LmMMm1dvp1HLhhWQkaoI/+6zXhhvTEul0OWqdbtu7c5YTsNQ63FRjl02essH1xZwdLYRa5FrOxgb0+xc9kYt6oftwuoM5XUpKdY7MjkBT5M7SNF/1mOUqjydRMTnYjR/i92NMX+IyBpgnIjUN8Yc9AdeRPoAnYAvjDEp3sfuAyYDKcA0YDfQHbjZe53+xphD57+HAT8DicAsPPfBG8Szq8X3eJYqzAdex/OuRnM8661/B8rc7UhE6gG/AD2ANcCbQAFwBJ612V8Cu7xt2wBzgGbefD4CWgCnAceJyCnGmGll9ee9znjgCzwbUH0ObAJ6AlcCJ4jIIGOMr2k3zwGDge+A6QQ0d9S3YBUk3wEXisiTwP3GmP3zH0QkFs//6IF4VvarSuAqtPczYWeExG5fdrhdFqZclJOryF5fLhvfQ1V1XBam1pRU5HbXuoLEGHtvFDisTIU6SCX9rtheM2HvdYU6rPdX6LKTo83XZfyvl1KqBnsHeAQ4C3jxkOcuKNEGERmG5x51PjDOGJNW3LDEjlT3c/iSg6bASmCoMSa7REw3PMXI18aYk0oGiIgD8L0/+MFewlOMvAJcbYzZ/4dIRGKAkv/wvIKnGLnbGPNwiXYvA78B74hIK2NMqQMC3mu+g6cmOMYY83uJ524DHgP+B4zyEZ4MHF1KsWJJsKZs3QFsxPM/bIuIzPYOTc0Gtngf3wDcGaT+lB+JzaxvI+lwOkhoGGc5rr6NvuyqzPM+Epsk2ItrWnnfD1V+DaOt/0zFhoUTERKs93Oqj5iIJFtx+cbm76WjkkYSnQ3sxTms/z0E2JcfYzmmUbT1GCQGiLQe52hkPUapyrPaGNPT10eA8e/heTfhgpIPikgYcCaeEZAZ3oev836+rGQxAuDdFvdv4JxS+rmpZDFyiMOGZI0xbmNMmVvqiUgj4Aw8yyBuLlmMeK+RZYxnm0ARaY6nSNgM/N8h7ebhGS1JBE4uq0/gBG+7T0oWI15P4bm/HykivoZ9/y8YxQgEqSAxxuwG+gBv4KmwhuAZLhri/fo1oK+3naoEx9o4THDACb2JjLH+j9uwswZbjkke0Y3wUg5JLMtxl4+wHGPX8HOsv64WnZJod3SbCshGVZQTOlqfMnRCx0618syZ1g36szEr8Jv34rUm+aH+/r3zIawv4mxiPc4GcSRCmI0DViPPsB7jbE3Tev0th53QsZPlGBEHRB5nPS7yeMsxStUUxpitwE9ALxE5ssRTE/DceH9gjCkenu0PFAKnicjkQz/wTM1qKCKHzmXPA3wtJlyJp4g5S0TmisitIjLAWwwFojeee/Pfyih2ih3t/fy78T28/fMh7UqTfEj7/bzfp9/KuM4CP9cOWLBGSDDG7DPGXA4k4Jl7N9j7OcEYc8Wh8/hUxTrmjAHEJlp7x23ClaNt9TXg+F40SEr03/CQvo45Y4ClmJCwEE6ZNN5STHl07N2O9j3bWoo5/srRtfJGtTY77ciuhFmcenVO96MqJpkq5nA42G1OAAhoYbsI/JfZhGaNrgNHM/8BJWOjzraTom2W+3M0QqIvhNAeFvs5i3O6+fv3/2Bx4eFM6GC9IPH0Z/X7GAGRJ/lvplTN9rb3c8lRkoOma3nVB0KB+0r5KC5oDr2h2m187NVtPHtqHws8i2ch2ePAXGCviLzgnR5VlgTv521+2sGB6V+lbSpV/HhCKc8H4zr2z4s4RNAKkmLGmCJjzApjzFzvZ51QXwXCI8O57qVLA24//NzBHH1sV1t9OUOc3PDK5YgjsBvxgSf2ZsAJvbnmhYuJqRf4VI/LHj8XhyPoP7KlEhGue+lSwiIDe2PjyAEdGXfZ8ArOSgVb/ago7hg0JOD2lx7dk471bU4BqgGObn0tq9NbIuK/KMlzOXHH3ofDGYrEP8DBU5vLED4Mwn1NR65A4cda6NOBxD2ASCgSdx8QEVhYaA+IOot+zVtwSucuAaf2wDHDiQy1sZsAIKFdIeq8wNvH3Yk4ApnGrlSN9hWehebniojTOxVqLLDUGFNyS7t0INUYI34+Nh1y/VL/OhpjUo0xk4wxLYD2wKXAauAaYIqfvNO8nwOZP1t8wmtpQ81ND2lXEdexd4CSD5V3d6cq3TFnDOTmN6/CGVL2TcLwcwdz0+tXluud/b7H9eSujyYRGl72P6oDT+rDHR9cj8PhICIqgteWP01cff8jOefddxonX299akJ5derTnoen3UF0fFSZ7boN6cxDU28nLCLQUVlVnVzQI5k7Bg3B32/ARUclc/ugoZWSU1UJD42iecsPWZneZv9ZJL4Kk6zCMP4zj9CxiacIl/AhSMKzgJ+pmOHDkYRnEancDQFEHEjCUxDubyQ4FIl/Eok41hMX2hVJfAPEz3qS0GSk3quIRCAiPHLsSL9FiVOER4eP4ngb0wZLktg7we9IiSCxdyNRZ5arL6VqAmNMLp7z75oBI4Cz8SwheOeQpn8A9UQk8HcQrOWxzhjzBjAUz06zJ/gJWYBn/csQEfH3ju1f3s+DRMTXosZh3s8+zwj0cZ1jDn3Ce93i+ev+rlMuYud0WBH5GU9VdIExZqv360AYY0ydeAtZRBYnJycnL15c5u5ulWLrvzuYNmUmM9+eTVaaZ0qiwyH0P6E3x185mqOHdwvaNKOdG3cz7ZVZzHjjZzL2eTZbExH6jDuaCRNH0Xvs0YeNchQUFPLOvZ8w4/WfyEw5sBGEOIQuAzpy5dMX0KFXu6DkZ1fKzlSmv/YT3736A3u3Hdies8cxXZhw5WgGndTHb+Gnqr9Ve/fw/rK/+WbNKnIKPVNyQx1OxrXvwLnde9Czqb1F3zVRkauApZvfJ7zgE46MP7BmcU9eDOvzRtIuaSINYw9fL2Vc2zA5H0POZ2CKf1cEwgZ7pheFH+NZ+1BFjDFQ8Bsm50PvAYHefwMlAaJORSLPQkJaHB7n2gu5n3lem7vEzIbQ3kjUWRAxGpGD35AxxjBv62beX7aUH9evw+X99zY2LJxTj+zCOd160LaetemuZb62goWe15U3k/07mEkURJ6IRJ6DhLYPWl+q6vTs2ZMlS5YssbDIO+hEZHFY86TkpFuCe971tieeoWDrtqC8NhEZiGdL3A/xnIjeDUgquZ5ZRIYDP+LZZetUY8z2Q64RDXQzxvxR4rGNAMaY1j76bIPn3nr9IY83w7M4PNUY09j7WGs8Gz69Y4y5sETbD/AUUKXuslViYfssYCRwizHmyRLt+uKZKpYBtCre/bbEzmEXeRftF19zE56pW4MOea03A08APxpjRpZ4/G08U+DaGGM2Hvp9sMNuQeLG81e8szFmrffrQBhjTJ24a6tOBUmxwoJCUnemUVToIqFRPFGxNnZnCVBRYREpO9MozC8koWEc0fGBTc3atWk329btJCouirbdWla7EQeXy0XqzjTycwuITYwhLjG2qlNSFSC/qIg9Odm4jaFBVDRRNqfT1BbpObvJzNtJqDOKBjGtcDr9fz+MKQL3Hs8Ws45ExFH9fleMOwvc+0DCwNHgsILCZ4xxg3s3mDxwJCCOhID6yiooYF9ODiFOBw2joi2vW7LCuHPAvRfECY6GBL6eVtUEWpAETkT+BVrhWScy1Rhz2I4O3q1tH8WzM9Z0PEVCjDduKDDHGDOmRPuNUGpBciKec0IWAqvwHAreEM/ISEM8O3M97W3bGt8FST1gNp512KuBmXjOIWkDjAaON8bM9rZti6fwaAL8ACziwDkkDuA0Y8w3Ja59IYcUJN7HTwA+w3Nv/xmenbt64tnFaycwsGSRVREFia19K40xjrK+VtVTaFhopR3YFxIaQqMW1ufZN27ViMatqu+WlE6nkwZJ1g6OVDVPeEgIzeN0nn2x+KhGxEdZ+70UCQHnYQf8ViviiAGHtc0/RBxgY3ewmLAwYsIqpzAQRxQ4rB3MqFQt9Q7wYIn/Powx5nERmYtnC+BBeIqHdDwLy1/FM8ISqEV4zu0YCowB6gF78ByG+LwxZkYZscX5pIrIAOAGPFsAX47n0KEteA5JXFmi7XoR6QXcDYzDM+0qA8/hjA8bYxYGkrQx5hvviNKdeIqeeDyFyCvAg4eOHFWE2reRvqoWtq7dzoblmyksKCKxSQJdB3UiJLRiftx2rN/Fur83UphXQEKjeLoO7kyYn7UseTl5TJ0yi50b9xAeEUryqB70GmltNx2l6gJj8qFgIbhTQcIhtAvirF5T1/7eupD/9szB7c4jPKwhw9qfTmyEvTNE/NmdOpfMzGlgchBHY5o2upTI8Or7JopSdZkx5iHgoQDazcEzvSuQa7Yu47mtBHjmnndkwed8ee+Wvw97P/xdZxueE9UD6fNtDuxAduhzC4GAtuDzjuhcGEjbQAXlDlFEWgdryEbVbPOnLuLLZ6fx9y//HPR4YtN6HHfZCE6ZdFzA07f8WTRrKZ8/PZXFs5Ye9HhCo3jGXHwsp900gbj6B08T2bVpN4+f/yIr5q7GuA9MV/zsqanE1IvmxGvGcsH9Ns4eUKqWMa69mJw3IedzOOi8MMGED0WiLkHC+1ZVegBMW/Em8a6P6d9oI91LDMak7HyeWbt7cnTbu2jbIDjrzzZse4EE11vUD82iQYmBDnfK22zLb0l4vcdoEN8rKH0ppVRdE6ypVutFZJ2IvCYiZ3q3V1N1iDGGN+74gHtPePywYgQgZUcq7z3wGdcPvJu928p/JM2Hj3zJHWMeOqwYAUjbnc7Hj33FNX3vYMf6XfsfX71wHRd2vJ7lv686qBgplpWazfsPfs71g+7C7Q50WZRStY8p+g+z7xTIfv2QYgTAQP5sTOp5mOy3qyA7j08XXMe4Bo8xsPHGw95iTIzI46SWcwlJP4OFm+eWu6/1Gy+kpeMF4sOyDnvOIdA0YjPxOeewdc/n5e5LKaXqomAVJJ8BscAlwAfADhFZLiLPicjxIv72S1Q13RfPTOPjx7/2227Tyq3cOe4R8nLybfc1/fWfeOvuj/y227F+F7ePeYjs9GzS9mZw45B7KSrwfyzOynlruf+UJ/22U6o2Mu4UTMolB+8kVVrbzEcwuVMrIauDfbrofk5t+f3+7YhL2ySweXQmDQuuZ2vaZtt9/bfpNlpHzNvfR2l9OcXQuOgu9qX/5buBUkqpUgWlIDHGnOHdxuwo4GZgBp4TKq/FczjNPhH5U0QeCUZ/qnrJyczlvfs/C7j9huWb+fG932z1VZBfyJt3Bb6+bPu6nXz36o+8dO0bFOYXBhw375uFpOxMtZOiUjWayX4f3IGvXzSZT+I5nLhyZOVnM6T+V0DpxUFJLWMymLf2/2z15XYV0iL0G/8NvZxiyE25w1ZfSilVlwV1dyxjzDJjzDPGmPFAIjAQuB/YB/QGbgtmf6p6+OmD38nJzLUUM3XKTOxsOT3niz9I35Nhra9XZjH36wWW+3r1lvcsxyhVkxlTCLmfWgty7/Ce51E5vv/nZRpF5liK6V9/HgWF1kdlN25/nBCHtembTSLWk1+QZrkvpZSqyypku14R6QhcAdyCZ9uy4jUl/1ZEf6pqzZ+6yHLM+mWb2L15b6X0tXPDbgrz/U/VOtTiHw5fn6JUrVb4j+eMDYtM/k8VkIxvMcb6mpCk6CzmbZxlOS7S9YPlGIfA9t1vW45TSqm6LFi7bCUBw70fxwLN8GxlthX4FvgZ+Mm7NZmqZUqerm41rnEra+eiZNjsy478nIJK60upauGwBewBctuMsyEqxNroSLHMvD2WY0Id1kZ+i7ndu/w3UkoptV+wDoYoXjG4D8/pksUFiI6I1AER0eGVFhcRZa8vO0LC9JgeVddE2guTqOCmUYZCt73DBcNDrZ8U7zL+T273RaT6nUqvlFLVWbCmbIn3YzPwH7AOz4mSqg44sl8HyzH1GsfTpI313aGP7N/RckxkbAQSyOrXQ7Q6srnlGKVqtNDOQITlMAk7Ovi5lGJvkfW/N9mFoRyVNMRyXJbpYqm9MZ6PegnHW+5LKaXqsmAVJMfiOU2yALgJmAmkisjPInKXiPQTkQpZr6Kq3nGXj8DhsHbDP+7SEbZObh9z8TBC/ZzCfqjRFw6jc//2lvu64qkLLMcoVZOJIw4ix1sMioKIEyomIR8GtLuOPJfTUswvO7vRKLax5b6aNn0AK3tviEBKQSL1Yrta7ksppeqyYG37O9sYc48xZgCe3bVOAF4B6gMPAHOBFBH5Nhj9qeqlUcuGDD838Hcfo+OjGD9xpK2+4hvEMe7S4QG3D48K58Rrx3LlMxdZ6qfpEY3p1Ds4JzwrVZNI1IWAhaI/6hzEEVNR6RymRb3WzNzuORE9kGIht8hJk0ZX2eorKrwJ2/I7BNyXMZAbfo2tvpRSqi4L+qiFMSbLGDPNGDMJGIVnxGQPEAccF+z+VPVw3cuX0XVQJ7/twqPCmfzlLTRIqm+7ryueOp9eo3v4bRcSFsI9n0wiqV1TOvVux+VPnBfQ9aPjo3h+vh6Zo+omCe2AJDwFBDAKET4CiZlU4TkdamT3V5izq3Wp55AUFw+Fbgcz91xJn1bWp2sVS2r+OakF8X77MgY2FoylZZNzbfellFJ1VVALEhGJFZHxIvKMiCwDtgNP4dn2NxXPIYmqFoqICuexmXdz4rVjS12s3mVgR57+9X6OGla+6QyhYaE88M1tnH7LCUTF+V6E26lPO5746T76Htdz/2On3XQ8t7x9DZGxpc+RP+Ko1ry/4SUSGsSVK0elajKJGIPUewtCjiylQTxEX40kPI9I5W/+EBMeTa9OX/Hl5kFkFBy+yF0EVqQ24ufUezj56OvK1ZczJIKEZr+zNbczbh+jJCKQ7wphU9FFHNHquXL1pZRSdZXYOZzusIuIPIhny99eeN5WEyAbmAP8hGfXrb9MMDqrIURkcXJycvLixYurOpVKl52ezc8fzmHD8s0U5hdSr0kCx5wxkLbdWwW9r9ysXH75eB7r/tpAYV4B8Q3jGHJafzr0PKLMuN8+n88Xz04jfU8mIaFO2nZvxWX/dx4Nm9sfuVGqtjHGQOEyTP5Mz9a+EoaEdIfIsYjY3JEryPZm7+OnVc8TadbglELy3HEkxJ/A8I4nBr2vvII9bNtxP1FmBU4ppMBEURR+Mi0bXY7DaW1di1J29ezZkyVLliwxxvT037piiMjisOZJyUm3BHeEdNsTz1CwdVuVvjZVNYJVkLjxLGj/kwMFyB/GGOun0dUSdbkgUUoppVTF0IJE1UbBGmsfA/xujLF3ipRSSimllFKqTgpKQWKMmRWM6yj/dm/Zy/RXf2T+tEVkpWYTHhVOlwEdmXDlqFKnKblcLhZ9/zffvfYjm1ZuxV3kIrFZIseeNYgR5w0hOq7yDjULJrfbzVfPT+fLZ78jZUcqbrchNDyU7kOP5MqnL6BFxySfcRkpmcx6eza/fjqP1F3phISF0LZHK8ZfPpKjju2Kw3H40ipjDCvnr2Xa/2axZsE68nMLiG8Qy8AT+zL20mNJbFKvol9uhVm1ZzfvL1/Kou3byC4sIC48gqGtWnN21x60iI8Pal8/b/iPJ+fPZX1qCkVuNyEOB+0T63PLgEEMadXGZ0x+UREz1v3L56tWsDU9HQO0iI/n1M5dGNuuA+EhwVvDUFiUx7ItHxCS/yX1w/YAhrTCeuQ4j6Nby4uIDPO9tsgUbcXkfgz5v4I7w7MVblgvJOosJNT3OowiVwHLt3yC5H1G/bCdODCkF8aT6RhD15aXEh2eELTXVZncLhdk3gt5U4E876MCzrYQ/384wrr5jEvJzeHzlf/w3b9rSMnNJczppEvDRpzVtTv9mrfweZaQ2+1m9Y4ZZGe8R6OwjYQ6CskqimGfezAdkiZSP8b3WULGnQ65X2HyvgPXXpBQCOmARJ0JYQOoDrvUm8J/MDkfQsESMLngiIPwYUjUGYizme8Ydw7kTcPkfg3unYADnK2RqNMgfHiVrPcJBmMKIG8WJvcLcG0GDDibI5EnQ8RYRHyvWzRFmzE5H0HBHHBngiMKwvogkWcjodbPs1FKVZygTNmqTCLyOJ61Kh2ABkAusAn4GnjRGLPPwrWa49mWeAyeLYp3eK9zvzEmtZx5BnXKlsvl4rVb3uOr56fj9rWyEkge2Z27PrqBuMQDpwRvWLGZB059kq1rd/iMiYyJ4OrnL2b0hcOCkmdlWTl/DbeOfID8nIJS2/QeezQPTb39oALj6xdn8Nqt71GQV+gzpk23ltz3xc0ktWu6/7GUnak8cNpT/DN3jc8YZ4iTs+44ifMnn27rAMaqkpmfz42zpvPThvU+nxfg7G49uHfIMELLOT8+JSeH4z9+n+1ZmaW2aRkXz7dnnktcxIFNB+Zu2cSkmdPZm5PjM6Z+ZBTPjB7HoJblX5+0esdM4vPuoHFkls/n0woi2Gxu5ahWB3ZRMqYIk/kw5HwIlPK3NPwYJP4pxHHg93Ldrt+JyL6RZlHpPkMyC8P4t/A6erW93PbrqQrunG8g41ZK/V4AODviaDh1/5fGGF7/axFPzZ9LgcvlM6Rrw0ZMOe4EkuIOFIS70teRuusyOsRt8xlT6HawJOMU+nZ48KC/ASbnQ0zGYxwolg4R0h5JeBEJ8V0gVzTjTsOk3QQFv5fSwgFR5yOxtyFy4PfS5H2PSb8bTEYpYUlIwrNImP8dCqsTk/8nJv0mcO/23UASkYT/Q8IP7KRmTAEm437I/az0C4ePQuIfRxzRQc644umULVUbVf3bQNZNAqKBH4DngA+AImAysExEWgRyERE5AlgMXAQsAJ4B1gPXA/NFpNqsbjbG8NQlU/ji2e9KLUYAlvywjJuHTSY7PRvwFCM3Drmn1GIEIDcrjycvfpmpr9ScQa7VC9cxaci9ZRYjAAtn/MVNQ+/b//XHj3/NS9e9WWoxArBh+WZuGHQPO9bvAiB1dzqThtxbajEC4Cpy8f6Dn/Py9W9RUwr8nMJCzvv681KLEfDcUn6wfCk3zJyOy+223Vdmfh7D3n2jzGIEYHNGOsPefZOcAs//1983beSib74stRgB2Jebw8XffMmvGzfYzg9g1fbvaem+odRiBCAhLI8uYQ+yZMNbABjj9two5XxAmTfg+bMxKRdg3J7fy3W7fqNx4cRSixGA2NACkqOeZOF/L9h6PVXBnTMNMm6hzO8FgGsN7j0j9n/57J/zeHTOb6UWIwAr9uzmtM8+Ynum52Z7T8YGivadWWoxAhAibvomfMafa27Y/5jJfgOTMZlSixGAon8x+87EFG0s+3VUAOPOxKScV0YxAuCGnLcx6bft/3tjcqdh0q4vvRgBcG/DpJyHKVga3KQrkMmfh0m9uPRiBMCkYFKvwOT94vnSFGHSriu7GAHIn4VJvQRjyvhZUEpVmppYkMQZY/oZYy42xtxujLnWGNMbeARoBtwR4HVexrMd8XXGmBO91zoWT2HSEc/J89XCj+//xg/v/hpQ2w3LN/PKje/gdrt5+MxnyEor/WaupBeveZ3Nq0v/x706uXPsw7hdgd0gr5i7mi+encaaRf/xxh0fBBSTtjudR8/1bN/5wtWvsX3dzoDivn5xBvOnLgqobVV7cv4clu0K7HXNWLeWj1Yss93XpVO/JrOg7OKxWGpeLldPn0pWQQHXfj+NogAKoSLj5rrvp5GZn28rv/zCHOoX3E6Es/Qb4mJOMXQKfYJ9WVsh91PImxFYJ0UrMFlP4XIVEpF9M9EhpRfFJXWPfIkdaasC66OqZdwceFvXZtwZz7Bg21ZeWPBHQCE7s7O45YeZAGzbfh1No8q4+cazHa8x0Lfe9yzf8hWmcCUm8/HA8jOpmLQbK/0NBpP5OBSV/ubHQfK+9Uw7c+3EpN+O30LQE4RJuw5jAvv5q0rGnYNJuwEIJFcXJv1Gz1S8nPch/+fAOilcgsl6vhxZKqWCpcYVJKb0tzM+9X5u7+8a3tGRUcBG4KVDnr4Pz5bF54lItRjL/fqFAG96vH76cA5zv17AppVbA45xuw1Tp8y0mlqlmz9tMZkppb+L7ctnT37LNy9a+x6u+uNf5k9bzNyvFliKs9pPVcgqKOCzf1ZYinl36V+2bs4y8/NYtN1aofv7lk18sXIFGRYKjMyCAr5avdJqegAs2/wODSICK9wBIkKKWLt1CibnPWsd5X7JP1vfo1lUWsAhoQ43G3f8z1o/VcCd9TJgcRQt5x3eWfqXpZD5Wzfzx8bf6Z6wJqCT04tnUBZlvYPJed9afkUroLDyRhOMOw1yv7EWk/MuJucTPJtcBsi9A/J/stRPlcibCiYt8PYmG5PzpfX/zzmf6SiJUtVAjStIyjDB+zmQt3KLF0zMMsYc9K+oMSYTmAtEAf2Cl5496/7ewNpF/1mKKcwv5IOHvrDc1w/v/kphQfV+5+y9yZ/6b3SIfdtT+fmjObb6KmuKnC9LflzOjg27LPdVmb77dw3ZhRZuYIB1qSks2mF9BO25P+cH9L5tSW5jeHmRtUIQ4ON/lluOAYgssn5e6xER06HoX2tBJof4orct99Uh6hdcrur9e0n2W5ZDDDls2BPY6EhJW3a/AVDqyemH9WOga8IqTO63lvsyuZ9YjrEtdypgcZSvaCXkfGS5K5Nj/e9oZTP+plz5kvOOd9G7lY7SIa/6vxmnVG1XM7fcAETkZiAGiMezyH0QnmLksQDCO3o/ry3l+X/xjKB0wHOuSll5lLZqvVMAefi1dc12W3F7tgS8tn+/7PQcUnel06hFA1t9VobdW/bainMV+p+OE6y+tq7dQdM2jW3FVob1qSk241Lp3cz3rkWlWbV3j62+9uUGPmJRzO7rahxexvz0UjSIyLbVV0KY9b0y6oXnkp63m3rRvneNqxaMtVFL8GyaMLjJJlanJ1qKaxAW2FTD/f0IODFYGkUoVlS+tUlWGFfp67nKDrTxc1+Jr8u2IhvfD3fp6yXLYoo2UHO2I1GqdqqxBQlwM1Dyru974EJjTCB3QMV7mZa2qrT48QR7qQWPq8jeYmK3zUXIriLrN+6VyVgcsaiKvtzV/Hvosvm67Cxst9uXnelhbmMwxlje6cwhlfczJZbHizxc7tp5xmxYAOt2DuWw+T20pxK/78b+xhHW1YSfJzvfD7s/GzXh+6FU7WarIBGRe232Z4wxD9qMPfRCTby5NAYG4BkZ+UtExhtjlgSjjwDz8Lk1nXfkJLm812/Ywt5mX3H1Y8lKtfYubkiok4RGwT13Itji6seQsa/s3Zqquq8GzavNBm0+NYmJsRkX67/RIZrHxbLAxiBfdFgYWQEuhC/WODrG1rbLqQVx1Au3dqZrdlFowAvTD46LJibU2uvKczmJj6y+I24eYXh2YLdmyZ5GlmPSChMstTfGu8AdrL8L7mhiNcI2cTaxeTsdQZm7hvnirLzXZZujMbgsjuRIvGcKlkVSif+flVK+2V1DMrkcH0FljNlljPkKzxSr+sC7AYQV/8Uq7e67+PG08mVXfl0GdqRxq4aWYkSECRNHWe5r0Cn9iIyO8N+wCk24crTlmKi4SHqNtr73/vFXj7Ec07Z7K9p2L/+ZGBVpQodOhPg4/LEsDaKibJ31cX3fAZZjAM7s0t1yzMmdfR9A6M8e9wj/jQ7xT+YgcFi9mXay03Wc5b5WZPQiNKR6/14SYf33EkJwhVr/+QiNOhEgoEXt4ClG1mYkIeHWz1qSyBMtx9gWMR7LJZOjGUSeYLkribAeU9lsfe8jTwVJsBgUCpHjrPellAoquwXJMJsfx5Yz31IZYzYBK4EuIuJvEUTxvoqlHdVavFNXaWtMKo3T6WS8xeKi15ijOP7qMZZHO463cbNf2U68diyh4aGWYkacN5Tjr7JWXDRp04jjrxrNkf2tneY74crR1f5wxMYxMYxq285SzFlduxNm43DEFvEJtEmwdop95wYNueTonpaKJqeIrSIG4MgWV5JbFNhgcfFNcLNGEyHydGsdhY+gU8sbyCjwfap0aX3VS7zMWj9VIfYh6zHhwzm3u7U3Cton1mdMp9NZn9k4oEXtxd/DTOepSNTZ1vJzNAMbRYxdEtICwodai4k6E4k613/Dg4OgMgstuyJPw9okDgcSdQ5EnWqtn4ixiMPaOialVPDZKkiMMb/a/Qj2CzhEM+9nfxOTf/F+HiUiB30PRCQWGAjkANa3gKkAJ103lk59/e5mDHimal317EWEhYdy42sTEUdgN8fjrxhJt8Gdy5NmpXA4HFz70qUBt09sksAVT51Pv/E9GXbWwIBiQkKd3PzGVTidTq6fcjmRsZEBxR09vBtjLq4ZJ97fMXgojaID29W6c4OGXJbc23ZfL42bgDPAIi3U4eDlcRNoHBPDHYMCvzm7beAQmsZan1IGEB/ViH/yrvDbrnjqz59pJ9Cy/tFI9CUQEuCojKMhEnc7kWFx/Oe6IfC+UkfSvvEQv+2rmsMZBpHnWYiIgLhnGde+I6OOCKw4Dnc6eXT4KBxOJ+7YB/wWkcXfw2VpnUhufTmEDYGARwacSPzDB52EXhkk9m6QAAv40O4QfQES2hGiLw+8j7gHEIe9aZuVSZwNkNg7A28fcxMS0hyJngghgf17iaMJEmvh/BylVIWpUdv+ikgHETnsbX8RcYjIw3gOOpxnjEn1Ph4qIp28547sZ4z5D5gFtAauPuRy9+M5Cf49Y4y9rXSCLDwynIe/u4NuQ8ouGBo0r88TP91H8/ZNAeg/oRd3fzyJsIiyRxQmXDmaa168JGj5VrSxFx/LxKcv8NuuQVIir614mrCwUESEW966mhHnlX1zFxkTwQPf3EaPY7oAnilYj8+6h/iGcWXG9R57NJO/vIWQ0JqxT0RSbBwfnnw6LeLKHkU7qnFT3jnxVGLCwmz31alBQz446XRC/Yx4hDudfHrqmbTyjqhcdFQydw4aWuYkFgFuHziES472uZQrYH3aXc/CzCsocpfem6cYOZ6+HTyH64kjGkl8E0KPLvvizuZI4juI07NLVs82l7A4exIFrtK/H8XFSN+ONefQNkf8PRBxcgAtI6HhbBxOJw4Rnht9HMe1L3skMjYsjDeOP5nkpp73nDo0GcYmeZJ0H6NNxaMiIvB3ahc6tn0fp9PzN0DiH4HIU8pOTyKRhJeR8MDewAgmCWmJJL4HjqZlNwzthdR7DRHPmyUSc1MARUkIEvcoEnl8cJKtBBJ9LhJ7F2XfqggSMwmiPW9UiSMOqfcWhHQt++LOVkjie0hNWE+jVB0glX0SbXmIyA3Ao8AcYAOwD89OW0OBtsBOYLgxZqW3fWtvu03GmNaHXOsIYB6eIuYbYBXQF8/UsrXAAGOM9b1zD1x/cXJycvLixaXtCmydy+Xiz++WMHXKTBbPWrZ/J6I23Voy4crRDD9nMFE+3s3fu20f01/7iemv/8i+7Z5tR8MiQhl6xgAmTBxN5wBHX6qbLWu28cpN77B41rKDdgdrkJTIyTccxymTxuM45CbYGMPy31cx9ZVZzPniD4q82wE3btWQ4y4fyZhLjqWej6luWWnZzHpnNtNemcUW71bMDofQd3xPJlw5mp4jux/WV02QW1jIt2tX88Gyv1mx58D2t/2bt+Tc7j0Y0eYIQm1M1fIlIy+Px+f+zjdrV5FTeGBBeExYGCd1OpJbBgz2WfisT03hg+VL+WLVP/sPS4wNC+eUzkdyTrceHJEYvE0EtqX+w+adr9Ax6lcSwj0LhbMLQ/knqx+NGlxG24aHH01kTBHk/4TJ+RAK5h94IuRIzzShiPGII+qwuB3pa9m44xXaRfxMfe/BjLlFIazI6E1i/UtqxMiIL+78hZBxJ7g2HfJMhGeOf8xdOA75mTLG8MfWLXywfCmz1q+jyLujW4u4eM7u1p3TjuxKYuTh38O0nJ2s2vI/kkKm0zza87etyC2sSO9CaMx5dEk6weffAAqXYHI+grwZ7D8J3NEUiToDIk9DnNbW7QWbcWdD3lRMzgclTm4XCBvo+ZkKH+Zz9MYUrvS+rqlgvFtnOxIh8lQk8kwkxNq23dWFKdroeV25Xx5YtC4xEHkSEnUWEnL4KJsxhZD3g+f3srDEuUYhXT3T3CLHIVLN12aVomfPnixZsmRJaZvqVAYRWRzWPCk56ZZJQb3utieeoWDrtip9bapqBK0gEZGmwN3AaCAJz7YrhzLGGNtvIYtIV2AinjNHmuPZljcbTwHxHfC8MQc2ZS+rIPE+3wJ4ABiDZ0H8DuAr4P7iUZZy5Br0gqSkgrwCstNziIgOJzImsClFxhiy03MoKiwitl4MzpDKnY5QUdxuN/u2p5KdnkOjVg2ICvD7UVRYRGZqNmHhIUTFRQW09sMYQ25WHvm5BUTHRxFmcT1LdZZTWEh2YQGxYWFEhFTs60rJyWF3TjaNoqJJjDr8RtMXl9u9vyCJCw/HWYEFoNvtIiNvD8a4iItohNMZ2PfDmHxwZ3reZXcENiXO7XaRmbcPl7uAuMhGhDjtj0ZVJ26XC1z/eW4gQ9rjcCYEFFfgcpGRn0+400lMWFhAv5dut5vsglQKinKJjahPWEigfxMLwZ0BEgZib5e2imbc2WBywRGLSKDrj4rAZABOkFgOmZlcYxnjApMJGJC4gKfUGZMH7iyQKJ9vDtQ0WpCo2igo80tEJAlYgGe04h8gHNiE59jZtt5+/qb0cz8CYoxZAVxjof1Gyti2xBizBbioPDlVlbCIMMIirN24iAgxCYHdJNUkDoeDhs3r09DidrshoSE+R0PKIiJExUb6HImq6aJCQ4kKrZwCKzEqKuBCpJjT4aBeZOV83x0OJwlR1qdyiISDM7CbxpJ9xUdZ3/62unM4neC0tikEQJjTSQOLPxsOh4PYCOsjZSKh4Kze23R7Cltrf7dFQkBq30JtEaeNXbTwjIQ4a+ZoiFJ1RbAmvN8LNAFGG2N+FBE38JYx5gERaQ68hme9xvAg9acsMsaw6s9/2bxyK64iF/WbJZI8opvlokap8tqemcGCbdvIKsgnNjycfkktaOznbBSX280f27awNT0dA7SIj6dfUosKHSWxal9ODvO2biY9L4/o0DB6NmtGy/iEMmOMMSzesZ3/UlNwG0Pj6BgGtWzld0eztLxc5m7eTFp+HpEhIRzVpClt65V9A+qZqrQMiv4FisDREMIH+X3XPSM/nzmbN5Gal0tESAjdGzWhff2KuYnPLijg982bSMnNIczppEvDRnRuWPuKterIuLZBwSLPVC+JhbB+iNPfhpVKKRUcwSpIRgPfG2N+PPQJY8xWETkNWIFnwfh1QepTBcAYw/dv/sxXz09nw/LNBz0X3yCWMRcfyxm3nUhsveq/64qq2Zbu3MGURQv4ccN/uEtMFQ1xOBjZth1X9epDl0YHHwBY4HLx9t9LeH/532zNyDjouaTYOM7p1oOLjkomPKTqNhNYl7KPlxb+yfR/11BY4jR7AYa0asOVvfrQJ+nguftuY/hoxTLe+XsJ61JTDnqufmQUZ3btxuXJvYkNP7hY2JSWxkuL/mDqmtXkuw7eTHBAi5Zc0bM3g1u2PuhxYwzkfoXJeRuKVh+cvCRgok5Foi9HHAkHPbUtM4OXF/7JN6tXkVN08CGQvZslcXlyb4a3PWi/ENt2Z2cxZdECvlj1z2EHYh7VuCmXJvdkXPuOQelLHcwULMZkvwr5szn4pPMQTMRoJPpKJNT6SJdSSlkRlDUkIpKHZ/3Grd6vC4EnjTF3lGjzGjDKGFO9T40LkopeQxIIl8vFU5dM4Yd3y95tuWXnJB6fdQ8Nkqr31AVVc323dg2TZk3fv1jZl3CnkxfGjmeE94yUnMJCLpv6FfO3binz2n2TmvP6hJOILsdOYHbN27KZy6d9fdAi/UM5RHh42AjO6Oo5J6XI7WbSzO/47t+yjznq1KAh75x4Cg2jPNN1/tqxnYu//ZJ07zqa0tw9+Bgu9u46Zowbk3E35H5e9gtxtkES30acnt2dVu7ZzQVff8G+3Jwywyb1G8C1ffqXfW0//kvZx3lff87OrKwy212W3IvbBw6plus8aiqT+xUm/U7K3ik/Aqn3EhI+uLLSUn7oGhJVGwVrvkMGBy9iT8WzsL2kdKBqty6pY96840O/xQjA5lXbuOu4RynIK/DbVimrFmzbyqSZZRcjAPkuF9fMmMayXTsxxjBp5nd+ixGAP7dt5YaZ3xGsDToCtS5ln99iBDyjIXf+/AO/bFwPwIO//eK3GAFYvXcPl039mkKXi60Z6Vzy7Vd+ixGAh36fzXdrPTszmaxn/BcjAK4NmNTLMCaPPdnZXPTNl36LEYBn/pjHJ/8s93/9UqTl5XLhN1/6LUYAXluyiDf/XmK7L3Uwkz8Xk34H/o/tysOkXo0pXO2nnVJK2ResgmQT0KLE10uBY0UkCjznhACjgK1B6k/5kbIzlS+e/S7g9uuXbWL2J/MqMCNVVz335zyKTNnFSLECl4sXFsxn6a6d/LD+v4D7+GnDev7aucNuirZMWbTAbzFSzABPzZvD1vR0Pli+NOA+lu3ayQ/r1/HakkWk5ecFHPfk/Dm4Xfsg+82AYyhaC7lTeXvpEvbkBH4E0zN/zKXQ5e+m1rcPly9jW2aG/4ZeLyyYT26A33NVNpP1LBDY7yXkYbKnVGA2Sqm6LlgFyU/AMBEp3qLnHTynps8TkSeAuUAX4JMg9af8mPH6zwedzRGIqa/MqqBsVF21LmVfQKMcJf28YT3/W7zQcl/vL/vbcoxdKbk5TFtr7R3jlXv38Oyf8w5aPxOId/7+iy9XrbQUsyk9jY07Xmf/GRsBcme/zyf/LLMUszs721LxWMzldvPRCmt9ZeTnM9Xi910dzhT+A4WBF8aA50wP127/7ZRSyoZgFSRvAI8DDQCMMe8DzwFdgZvwHDj4CfBwkPpTfiz4/i/LMav//JeMfZkVkI2qq2Zv3GA5xgC/b95oOe4XG33ZNX/LloMWsAfqt00bLccs3LGN7ELr0yml4DfrMa5VOEyK/4aHKJ6OZsW61BRLoyMH+qq8/8+1Vr71nw0oggIdRVdKVYygFCTGmH+NMY8bY3aUeGwS0BToDzQ1xpxtjAl8zoEql6xU/3OyfcalBT5VQyl/Mgv8r3nwJa+oyHJMRn5epa0jybD5unKLKm+6UajY+12OC7X+2jIDWNtyqAwLU9AOjrP3vVcHGLf1QhDwHCKplFIVoEL3yjTG7AH2VGQfyrfIGHuHQNmNU8oXuwcthjmdlouS6NDATvUOBvuvK4TsSloDUWTs/S7nFFl/bXa+H9Gh9nZFi66kwztrM3FEYat0l9p3sK5SqnoI+qliItJCRI4XkfO8n1v4j1LB1nVQZ8sxzTs0JcHiyeVKlaV3s+b+G/nQo7H1U9J7J9nry45eTZOwU/r0aGL9dbVPrE+IjQMgC51HW45xO5qTWZRgOe7Qc1YCcUS9ROpFWC+a7PSlDhHay15cmO7EqpSqGEErSESkvYj8AGwEvgLe9n7eKCI/iIierFSJxk8cZTlmwpWjdY9/FVRHN2nKkQ2s7fbdq1kSE3v2sdzXud17WI6xKykujmGt21qKaRkXzy0DrJ/lcOFRyYxrb+3PZ4OoKFo1nQgWyyZn9Dmc0KmLpZjYsHCO72j9DZDwkBBO69LNWozTyalHWstP+RDWD5xtLMYMREJaV0g6SikVlIJERNoB84DhwHrgXeD/vJ/Xex+f422nKkHz9k0ZdtbAgNvXb1aPURccU3EJqTpJRLi6Tz9LMVf16svgVq3pbmGUpGujxgxtZfEGq5wm9uqD00IBf2XvvnRu0JCRFk43bx4XxwkdO3N5cm9CLYySXNGzD2FhrSDi+IBjcDSEyFO4+KhkIkMCn8178dHJtqewnd/9KGItHGh5bvejSIiItNWXOkBEkJirLEQ4kOgrKiwfpZQK1gjJo0B94HqgozHmImPMHcaYi4COwCQ8O3A9EqT+VAAmvTqRboP9v3MZ3yCWh7+7k5gEnR+sgm9suw7c3H9QQG3vHTKMY1q3wSHCq+NPoE1CPb8xreITeG38iTgqeXSvV7MkHh0+KqB+L+/ZmzO8owFPjhzLUY2b+o1pGBXNm8efTFRoKEc2bMQzo48jRPz/yT67Ww8uPioZAIl/EML6+o1B4pF6ryKOBNrWS+TFcRMIdzr9hp3YsXO5TmpvFhvHq+NPJDLEf0Ezqm07bhs4xHZf6mASeQJEXx1ISyRuMhJu7Y0FpZSyIlgFyXBgujHmBWMOPgHNGOM2xjwHfA+MCFJ/KgCR0RE8NvNuTr/5eKLjow573uEQBp7Ym+fnP8IRPVpXfoKqzriqd1+eG3Mc7eol+ny+Y/0GTDnueC703kgDNIqO4fPTzuK0I7v6vDkOczo5pXMXvjj9LBrHxFRY7mU59ciuvHn8yXRt1Njn863iE3hs+ChuL3EjHRsezgcnn8ZFRyX7XNjtFGFsuw58efrZtEusv//xce078N5Jp9KzaTOffTWLjWXy0GN58Jjh+6deikQg9d6A6MtA4nxEOSD8WKT+Z0jogalQw1q35aNTzqBfku8lgI2jY7hj0BCeHDW23IVg3+Yt+Py0MxnUopXP5+tHRjGp3wBeGjfB1loaVTpH7PVI/OPgbO27QciRSMIrSNSZlZqXUqrukWBskykiWcCzxpi7y2jzMHCdMSa23B3WACKyODk5OXnx4sVVnQoAudl5/P75H2xauRVXYRH1myUy9IwBNGrRoKpTU3WIMYY/tm5h/tYtZBUWEBsWxqCWrTyLxMu4sU3Ly2Xq2jVsSU8DoHlcPBM6dKJeZPWZvrN0105+2bCejIJ8okJC6ZPUnEEtW5V5w55dUMB3/65hfWoKRW5Dk5gYxnfoSJOYsv9Mrtyzmx/X/0dafh6RISEkN2nGMa3b4Czjht2YXMj7HlO0Dkwh4mwEEWMRZ1KZfa3dt5dZ/60jNS+XcGcI3Rs3YXibtoQGMIJi1Ya0VGb8u5Z9uTmEO0M4smFDRrZtR7iFKWTKOmPcUDAfU7AATA5ILBI+BEJ76LrCaqhnz54sWbJkiTGmynYZEJHFYc2TkpNumRTU62574hkKtm6r0temqkawCpK5wBZjTKlvo4jIJ0BzY0zgCxtqsOpWkCillFKq5tOCRNVGwRr/fgQ4WUTG+npSRI4DTkJPaldKKaWUUkqVEKxx8PrADGCaiPwE/AbsAhoDQ4FjgalAAxE5v2SgMebdIOWglKrGjDFQuAyT8yEU/AEmCyQWwgcgUecctIahpF1ZWTz8+2x+2rCePO9J5xEhoRzbpi13DhpK01jf05tM4UpMzgeQPxdMpudQt7B+SNTZ1WIqSkZeHo/P/Z2p/64mu6AAgFCnk4HNW3LXkKG0rVffZ5wpWo/J+RjyfwJ3OkgEhB6NRJ0FYf19vi5jCiDvB0zuJ1D0L5gicDZEIiZA5GmI0/fUTXf+H5D1JBSuBIoABzgSIepsiJqIw3H4PyFFbjc/rv+Pj1csY+Xe3RS63NSPiuS49h05s0v3Uv9/Vabd2Vl8vGI509auZk9ODqFOBx3rN+Csrt0Z2bZdhUxHq22MOxNyv8bkfgWurYBASEsk8hSImIA4qn6TFFO4yvP3Jn+O929AFIT1QaLOgdCjqvxvgFLqgGBN2XIDBv+b3pfsTABjjKmVf/l1ypZSBxh3JiZtEhT8Vnqj8BFI/BMH3cg8Oe93Xl60oMxrT+zZm1tLLBo37hxM+q2QP6v0oLDBSMIziMPXQu+K9/6yv7lv9k9lnpZ9UsfOPDV63P6vjSnEZDwIuR+XHhTaDUl4GXEeWGRvCldgUq8G945SgkKQ2Fsg6sL9N2hudwGknAFF/5SRYRjUextH+IFD9tbu28sV075hk3etz6GcIlzXtz/X9O5XJTeDxhj+t3ghT/8xlyK322eb5nFx/O+4E+jcsFElZ1dzmLwZmPQ7POtNfJF4JOFJJHxo5SbmZUwuJv12yJtReqOwAUjCc4ij5h0GrFO2VG0UrBGSi4J0HaVULWPcOZjUi6BwWdkN83/EpF4OiW8hEsb/zf2NVxYv9Hv9VxYvxGUMdwwaijEFmLQroODPsoMKfsekXAiJ7yOOw3egq0gfLl/KvbN/8tvuqzWryHe5eHHcBIxxY9JvgbzpZQcVLseknA2JnyLO+p5RopTzwGSXEVSEyXwUMQUQcwVutxv2TQDXBj8ZFkDqubgTP8UR1p3/UvZx5uefkJafV2qEyxie+WMeeUVFtg6JLK/nF8znuT/nl9lma0YGZ37xCZ+edhYd6+umH4cyudMw6Tf6aZSOSZ0I9aYg4cdUSl77uzYFnr4Lyv7/TME8TMoF3r8BVbNLn1LqgKAUJMaYd4JxHaVU7WOyX/JfjBQrXAjZr7LVfV5AxUix15Ys4txuPWju/Mh/MVKsaAUm63kk7vaA+ymvnIKCgIqRYtPXreX3zRsZ1HCJ/2KkmGsLJvNBiH8ak3ajn2LkAJP1FIQPhtzvAihGirkh9TJMoz+4cdaMMouRkqYsWsDQVm3ok9Q8wH7Kb+nOHX6LkWKZBQVMmjmd7846T6f1lGBc+zwjIwFxYdJuhoazK/eGP/tt/8VIsaKVmKznkLi7KjQlpZR/uqm7UqrCGJMHOZ9ai8n5mEd+D/ymvdjDc37yrBmxIvdzz3a4leTZP+fhtjhN9om5v2Gy37PWUd4sTN50cK23FGZyPoDcT6z1ZVJZt2say3fvshT23rK/rPVTTu8u+9tS+9V797Bw+7aKSaamyv0cyA+8vcmAvGkVls5h3RmXZ82IFblfYNyBFe1KqYoT1IJERBqKyEQReU5EXj/k8T4iUn0ODVBKVby8H8GkW4tx78bk/2q5K3fe7+DebS3IZEBeGWtNguzLVSstxxQVrPazlsNnFGT/z3Jf5H7j+Z5YFJrzouWYmf+tIy2vcorBrIICpq1dYznuk3+WV0A2NZfJ/dx6jMU3JMqlYD64t1uLMVmQN7Ni8lFKBSxoBYmIXAJsBF4CruXgdSWNgfnA2cHqTylVA7g22gpLikqzHNM8KtVWX6Zok604OzIKLLy77NU61mJBV8xlbcTCo8BWV9HOfZZjitxutmVYL37s2JGZSaHbZTluU5q9n6nayBg3uDZbD3RV3u8XRRtthRmbf6eUUsETlIJEREYCrwJr8Zw3MqXk88aYFcA/wInB6E8pVVPY28VPxHqc/an+5d9psCJJNc8P7Ofoe5+r4DN286v+3/pKZLD3u1KZ30S7fen/aKWqWrBGSG4DdgBDjTHfAr7mTSwDjgxSf0qpmsCRZCtsV4717Xh32ogBEGczW3F2RIWGWo7ZlmPz3A5Hoo0ge7uw57isb50qQJOYylns3Dg6BqeNijUprurPTKkuRJzgaGo90Gnvb4AtNvuSysxRKeVTsAqSXsA0Y8qcfLwVaBKk/pRSNUHEKLC6dEwSyHYMtNxVjvQHSbAYFQkRYyz3Zde4dh0sx+SYjuBsazFKIOoCy30RMdb6/y8gJ/RiyzHHtG5Lw6jKOTwvPiKCEW3bWY47pXPXCsimBos8yXKI2IixLXwQOHwfKFq6CM/PvVKqSgWrIAkD/G1TkQBYn8SrlKqxxBEDERZvSKJO5ZaBIyz3ddugkRB1mrWgyBMQR+W9C377wCF+T4891A39BnlOlrYifCgSdRo4rI3+SNQ5EDHBWl8SQ8fm59CunrURmfO6H2Wtn3Ky2l+r+ASGtGpdIbnUVBJ1BtZG0SIg8uSKSucwImEQeYa1oMjxNfJwRKVqm2AVJBsBf6dq9gWsb3OilKrRJPYGcLYJrHFIRyT6Kjo1aMjJnQOf4XlCx850atgQib4SQjoHFuRs7cmtEsVFRHBtn/4Bt+/dLIlx7TtA1BkQNiCwIEcDJPYeREKQ+McJ+LipqAuQsJ4Qey84GgacI/FP8//t3Xd4HOW59/HvrS65F4yNKzbgbowNNrappoQWei85QEJCKqSQk/oGkpOQnCRAekLIgYQOMT30junYYNzA4IK7DW6SJavu/f4xIyPLK2lntdJqV7/Pde21MPM889wzGkt77zzFzPj1McdRlJdYW2eNGcfh7fxhf9qgwVwwfv+Eyhbk5vLrY44jR2uQ7MJyB2DdEl2HBKzHT7Gcnm0XULw2u1wGeQk+2codjHX7TtsGJCIJSVVC8iBwqJnF/XrSzC4BJgCzUtSeiGQIy+mJ9f4X5I1tvmD+AVjvf+5cRO03xxzPaSNbTi5O3m8U13/mhLCtrljvmyF/UvOV8sZgvf+FJTXOonWuPHg6X548pcVyUwcO4s7TzwaCb36t55+h8KjmK+UOxnrfiuUNDuoVTsV6/R2shadAJZ/f+UEzJ6cA+j6WwNOVHOhxHTlFRwCwf/8B/PPUM+hV1HyXrwvG78/PZx7T7gsOmhlXHz6Tiyc2f2/0KCzk5pNP58C9NK4gHuvyOazbj2j+SUk+1v2XWPGp7RTVpyynC9b7H5B/UPMF80ZivW9Ly+8AEdmdecRFuuIexKwXMBcYTJB09ACOAa4ADgVOB5YCk90TXDY4w5nZnEmTJk2aM2dOukMR6RDca6Hq+WDxverXgVogHwqnY8XnQ+FhwcDZRuatX8cvZr/AnHVrdy4qmGPGpP578f1DDuOAAbt/cHavg+qXgkXSql4GaoA8KJiClZwPhTMxS/DJQRv5cNMmfjH7eWavWkltLJhvyoAxe/TjqumHcNjQ3Z8quTvUvBVcw8pnCBapy4H8/YPzKjoOs8Ld68W2wY778Yq7Pl0s0XoE3VWKz8fy992tTiwWg8p/w/a/Qmx1gz3FQZ/77t8lJ86HubKqKh54fzF3zJ/H+5s+AaBrQQEn7TuSC8bvz9h+e0a9VCn33icfc/v8eTy85D1Kq4KpmPft3Yfzxk3g9NFj6F5YlOYIOz6vW4NX3A077vt0/Z+cAVjJmVB8Npab3p+zewyqZ4e/A14i+B2QCwUHBV0TC2diFn2SiY5g8uTJzJ07d667t9Qzpc2Y2ZyCQQMnDbzqmyk97ppfX0/16jVpPTdJj5QkJABmNgT4F3BYnN0vAee7e6dZ9lYJiUjTgt87VUBhpG/Kt1cH62R0LSho87baU2VtLTV1tXSL8EH40/MqwCzxh93utUAs6G+foFgsBmwHSsjJSTyRq4vFqI3FKEywK1c6VNXWkpeTQ25OStcJ7lTcqwHrsB/wM+F3QBQdJSHpRs9JUy36eL/mvO5PU8ZWJSSdUMr+Srj7SuAIM5sATAP6ANuA19xdn8pFZKfgQ0H0b6GjJCKtbas9FeXlJTz+ol6y55XMk6GcnBwg+rTKuRnwQb8jJ0uZIkpymw6Z8DtApLNL+W9id3+XYM0REWmFBRs3MHfdWnbU1tCjsIjDh+7NgG6Zvy7C0s2beHX1KrZXV9OtsJAZg4cwrGevNmlr/qoHqSy7gwIrp4YS8kpOZeLQ89ukrTWlpby4cgXbKispyc/noIGDGN03wuDwNhZ0YXkVaj8AaoOB64UzW5xlzOs+garnIbYFrBDyJwRdxLLgm2YREekY2vSrITPrQ9CFqwJ42t017a9IC55c+gF/eesN5m1Yv8v2XDOOHr4PV0ydxqgO9EE3Ua+uWskf33ydV1ev3G3foUOG8vUp01I2kPi1Jb9hv8J/MbawEnYZ5vAOW1f+gsU7zmLayJ+kpK35Gzfw+9df4bkVy3eOcak3ecBefOWgqRw5LOo6IqnjHoOK2/CKf0Ldql13WjFedArW9etY7q73lNcux7f/ASofJxjv00DeKOhyGVYccYpgERGROFLyLN3Mvmxmr5tZ7wbbJgPvAf8GHgVeMbP2WQVLJEP96c3XuPw/D+2WjADUufPE0g844547mb3yozREl7x7Fs7nogf+HTcZAXhp5UecP+seHnp/cavbenXRlzio2430LKyk8RA5d+heUM2U7rfzxqLWPyl5ZvlSzr73Tp5Zvmy3ZARgzrq1fP6h+/m/t9PTa9W9Ft/2Tbzsf3ZPRgB8B+y4C990Fl776T3l1e/gm86CykfYLRkBqH0P3/ZtYqW/IlXjEEVEpPNKVefecwB3980Ntv0a6AXcTJCQHARcnqL2RLLO/YsX8dtXX26x3I7aGi7/z4Ms27K5xbIdweyVH/GDZ5+K+4G9oVqP8Z0nH+ettcnPffH6B79lSq/nqO9N1LhXUcPtk3u9xavvJb6mQmOLP97I1x59mKq6lh/8/s9Lz/P4hx8k3VayvOxXUPlYywVja/EtX8Bj5XjdOnzLZeClLder+AdU3Nr6QEVEpFNLVUKyLw3GjZhZX+Bw4B/u/gV3/yzwJtA2nbdFMlxdLMYNr7+ScPmKmhpunPNmG0aUOr97/ZUWk5F6tR7jj2+8lnRbIwr+tVsS0hQzGNfloaTb+stbbySUjNS74bWX2/VpgtdthIrbE69Q9xFUPoiX/xN8W+LtbP9zOMuSiIhIclKVkPQBNjb4/xnh+/0Ntr0EDE1ReyJZ5cWVK1hVmviHQICHlrzHtsrKNoooNRZ/vJE569ZGqvPiyhUs37olclsLVz9Er4Idu3XTaoo7dMmv4c2lf4vc1sfl5Ty+NNoTjyWbN/HGmtUtF0yVHfcSt7tVM7z8Vqj4d7R2fHM4zkRERCQ5qUpINgN9G/z/4UAMaPiVr6N590TieimJMSGVtbWt6t7UHmavSm6sy8tJXI/y0jsx272bVlPqyxVUPxy5rdfWrNq5mGEUyfyck+VVs6NXqlsKJNBVKxVtiYiIhFKVkCwGPmtmfcysJ3Au8Kb7Lp2QhwG7j9QVEcrC1aIj16vu2F1lyqqSi297EueVb9uTais/Z0fkOsn+vLZXJ1cvKZ7c9ejwbYmISNZJVULyO2AAsBpYBewJ/LlRmYOBeSlqTySrdEtiwT+ArgUdc2XkesksZAjQJYl6tZ7cJH413j4LNEJy55W09pzUUBMoiohIK6QkIXH3hwhm0FoIvA98x91vq99vZkcAXYEnUtGeSLaZNmhI5DoFublMGrBXG0STOtMGRz8vgOmDBkeuU9D1DIBIY0gAKvOOjdzWlIGDyE1iYcBkfs5JK5gWvU7uMCB6cmHJtCUiIhJK1RMS3P1Gdz8wfF3faN/z7t7L3W9MVXsi2eTIvYczoGu0VdhP2nckvYtL2iii1Bjfb0/237N/pDrTBg1hRO8+kdvaf8hZbK0ujDSGpKI2j6n7XBG5rf5du3H08H0i1RneqxczkkzQkmEl5wC5EetcCCVnRGyoBxSfEK2OiIhIAylLSEQkeXk5OXx9ysEJly/Ky+OyyQe1YUSp8/UpiX97nmPG1w6amnRb7+84J+EnJADvlh2XdFtfPnAK+TmJ/wr9+pRpWBJPVZJluf2h+KzEK+TsBcWnYyX/BdY18Xa6fgkzzVciIiLJU0Ii0kGcO24Cl0+e0mK5gtxc/nD8SYzs07fFsh3BzL2Hc/XhM1ssl2PGL2Yek3Q3L4BpI3/EW1sO3pmUxFupvd7bW8YxffR1Sbc1Yc/+XP+ZE8hLICn51sEzOGXk6KTbSpZ1/xEUHtFywZw9sN43YTldsbzBWM+/gCXw9K34PCj5fKvjFBGRzk0JiUgH8t0Zh3LdscezXxNdlg4bMoy7zziHo/Ye0c6Rtc7n9j+Amz57GhP3HBB3/+QBe3HzKadz9tjxrW5r6ph/8UbpuWyvyY+7UntFbR6vbTmJyaPva3VbJ+w7kttPP6vJsSGj++7BH48/ia9FePqVSmYFWM8/Y12/CTn94pTIh6KTsT73YnmfdkGzwqlY77uh8Cji/pnIHYZ1/ynW/ep2feojIiLZydpz5eDOxMzmTJo0adKcOXPSHYpkIHfnzbVrmLtuLTtqa+hRWMTMvYczrGevdIfWavM3buDVVSspr6mma0EBhwweyug94n1Ybr23lv0fuVUPUpBTTnWsmNr8z3DQPl9rk7aWbt7EcyuWU1ZdRXFePlMGDuKA/gM6zAd29xqoeh5qP8S9BsvtB0XHYjm9m69XtwYqn8FjWzArhPzxUDANM32fJZIOkydPZu7cuXPdfXK6YjCzOd3oOWmqHZ3S477uT1PG1rSem6RHXroDEJHdmRlTBg5iysBB6Q4l5cb325Px/fZsl7YOHH4pcGm7tDWid5+kBuO3F7N8KDoGOIYoKZLlDoQun4tUR0REJAp9xSUiIiIiImmjJyQiEtnH5eXcvXA+sxYvZG1ZKYYxtGdPzhozjjPHjKVnUXHK2tq8o4J7Fy3g3kULWL2tFMcZ3KMHZ44ex1ljxtGnZPfB1+7OG2tWc/v8eby4cgVlVVV0KShg+qAhXDBhf2YMHkpOmrtSeWw77HgA33EP1C4DYpCzJxSfgpWcg+XGH28jIi1zd6h5C6+4E6peAi8LJmoomIqVnA8FM9TtUKQD0RiSNqIxJJKt7lrwLj95/hlqYrG4+0vy8vnNscdz3D77trqtB99fzPeefoKqurq4+wtzc7n2qGM5ddSYndtKqyr52qOPMHvVR00ed/KAvfjLiafQN04y0x68aja+9Urw0iZK5GLdvgMll3aYMSgimcJj2/GtV0D1S00Xyt8/mPAhd4/2CyxFNIZEspG+HhCRhN0xfx4/ePapJpMRgIraGr766EM8sfSDVrX14PuL+eYTjzaZjABU1dXxrScf4/7FiwDYUVPDxQ/c12wyAjBn3VouvO8eSqsqWxVjMrzqFXzLF5tJRgDq8LJfQfnf2y0ukWzgXolv+ULzyQhAzTx880V4bFv7BCYizVJCIiIJWVtWytUvPJtQWQeueupxtldXJ9XW5h0VfP+ZJxMu/4Nnn2RTRQV/nfMG72xYl1CdJZs3cd2rLycVX7Lcq/FtVwG1iZXf/lu89sO2DUokm5TfBDVzEytbtwwvS34tIhFJHSUkIpKQuxbMp7aZJyONba+u5oH3FiXV1r8XLaSyNrEP7RA8KblrwbvcMf/dSO3MWrwo6aQpKZVPQuzjCBUcr7ijzcIRySbuNXjFXdEqVT6Ax8raJiARSZgSEhFJyL8XLYhc594k6iTb1q3z32HTjopIdcprqnn8wyWR20qW75gVvdKOB3BPPDkT6bSqX4bYxmh1fAdUPtY28YhIwpSQiEiLquvqWF++PXK91aXJ9c9emUS9TyqiJSP1ViUZY1LqVkWv49tB/dxFWlabxL8vwJP5dykiKaWEREQ6HMvaZfiy9bxEOgL9+xLJVEpIRKRFBbm59O/SNXK9wT16JtXe4O7dI9fZI8kpfIckGWNScgdFr2NdIadH6mMRyTZ5Sfz7Aix3SIoDEZGolJCISELOGjsuep0x0esk29ZFEyZGXlekS34Bx41o/XopibLiM6JXKj4NM61hK9KigkMgp1+0OlYMRce3TTwikjAlJCKSkHPHTiAvJ/FfGd0KCjll5Oik2jpz9DiK8hL/EF6Ym8u54yZw3rgJ0doZM5YuBQVRw0te0bERPzAZVnJem4Ujkk3M8qL/eyk6DcuJ/vRXRFJLCYmIJGRAt2789IijEiqbY8ZvjjmOrkl+2O9VXMyvjv5MwuWvPeoz9C4u4csHTuGA/gMSqjOyT1++efCMpOJLllkB1uM3QGLJlnW7Csvbp22DEskmXb4A+ZMSK5u3D9btm20bj4gkRAmJiCTs3HETuPaoY8lv5klJSX4+fznhZI4Z0boP0p/dbxS/O+7EZp+UFOXlcf1nTuDUUaPD/8/n5lPO4LAhw5o99oF7DeT208+ie2Fhq2JMhhUejPX6O1hz42RysW7fx7p8od3iEskGZoVYr5ug8PDmC+YfgPW6FdP4LJEOQR2TRSSSc8aOZ+bew7l34QJmLV7ImrJSDGNYz56cPXY8Z4weQ/fCopS09dn9RnHI4KH8e/EC/r1oIR9t2wrAkO49OHPMOM4cM5bexbuOG+leWMjNp5zOW+vWcNu785i9cgWlVVV0LSjk4EGDuXDC/kwfNASz9M3IY4UzYI/nofJhvOIeqFsGXgu5/aHoFKzkbCy3f9riE8lkltMVet4INW8HC4tWvQheBtYFCqZgJRdAwTTM9J2sSEdh7p7uGLKSmc2ZNGnSpDlz5qQ7FBEREckSkydPZu7cuXPdfXK6YjCzOd3oOWmqHZ3S477uT1PG1rSem6SHvh4QEREREZG0UZct6ZS8bgNUPonHNmGWD3mjoPDwNple9ZOKCh7/cAkfV5STazns26cPR+09goLc3JS31Z5Kq6p4/MMlu3TZ+syIfSnOz2+23rwN63l11Uq2V1fTrbCAQwYPZWy/PZut47FyqHoCr10NOJY3GAqP1ew4EVTX7mDBqrupqloMXovl9WOfAefRt2tmr8HgXgfVs6FmIe5VWE5vKDoayx2Y7tBERCRBSkikU/Haj/Cy30LVU0BdsK1+Z86eUPI56HIpZq1PFtaUlfLrV17i0Q+WUBuL7bKvb0kJF46fyJcPnEJ+hiUmW3bs4DevzuaB9xaxo7Z2l33dC5/l7DHjuPLgGZQ0SkyeWbaU37/xKvM3bthl+694iQP6D+DKqdM5dOiwXfZ5rAzffgPsuA+8/NPtAPYzvPg0rOs3sZxuKTzD7FJTW8lbH17DiKLHmVhcDsUN9pXezJurJjJ4wDX07zkyfUEmwd2h4ja84maoW/3pdoCya/HCI7Cu38by22+dGRERSU5Gddkysz5m9gUzu9/MPjSzHWa2zcxmm9nnLcIINTNbYWbexGt9W56HpIfXLMQ3nQVVj1OfjOwitgHf/mt865W41+6+P4IPN2/i9Lvv4KH339stGYHgqckNr7/CFx95gKra1rXVntZvL+PMe+/kzgXv7paMQPDU5Ka353DerLsprarcuf2Wd+Zy2SMP7JaM1Ht7/ToufnAWdy94d+c2j23GN58PFbfukox8WqA8+EC6+Ty8blPrTy4LVdVUsPDDs5jacxZ9i3a/hnkWY3KvueRuPYcVn7yRhgiT4x7DS3+Al/1sl2TkUzGoehbffDZe/Wa7xyciItFkVEICnAX8HZgKvA7cAMwCxgE3AfdYtKlztgHXxHn9JnUhS0fgsc34li+Cb225cNUTeNn/Jt1WWVUVlzx4Hx9XxPkQ3cgLH63g/z3/TNJttafaWIzLHn6A5Vu3tFh2/sYNfOOx/+DuPLN8KT998bkW6zjww+ee5pVVK3F3fMtXofb9BAJbgm/9Ku67J36d3TsffIUJPd+nqblL6n9b9imqoKDsy5RWftJ+wbVG+Z9hx6yWy3k5vuXLeN3ato9JRESSlmkJyRLgZGCQu1/g7t9390uBUcAq4Azg9AjH2+ruV8d5KSHJNhV3Q+zjCOVvx2Obk2rqvveCqXATNWvxQtaUJl4+XZ5ZvpSFH29MuPyLK1cwb8N6/vDGawnXibnzpzdfg+pXoSbCDHU1c4M6stP6bUuY1OtV3D9NPJrTv7iMRSv/2vaBtZLHyvHyf0SoUIqX39p2AYmISKtlVELi7s+6+8Pe6KtQd18P1P8lPaLdA5MOzb0Or7grYq0aqPh3Em05t707L1KdmDt3Nuiq1FHd9u47kev84Y1XeXdDtB6Qr65exfatt0Ruyytuj1wnmy1f+zdyzRNKRuoNyH2EWJwuhh1K5cPxu/A1Z8cs3CtbLiciImmRTYPaa8L3KB3yC83sQmAIUA68C7zo7nEGGMRnZk19jTsqQhzSluo+gti6yNW8+jWML0aq83FFOUu3RH+y8urqlZHrtKeYO6+tXhW53ltr1yTVXl7dm9G/LqlO/ElMZ9Az553IdQZ32cwn25fTr/uI1AeUIp7MkzDfCjXvQ8H+KY9HRERaLysSEgvmav1c+L+PR6jaH2j8LH+5mV3i7i+kJDhJv6jfprai3vbq6qSaKk+yXnupqq2lLolFVKvqEs7td5FvO6JX8nLcPa0rsHckBTlVSdWrrNma2kBSLZbsv+ftqY1DRERSJisSEuCXBAPbH3X3JxKsczPwErAQKAOGA18Dvgg8ZmbT3L3FvjdNrSYaPjmZlGAs0pYsySlhLfoaF90KC5NqKtl67aUoL4/8nBxqInbnKcrLozqJpKTGSyi0iB8grZuSkQaqYkVJ1Ssu6J3iSFIs2SmeNTW0iEiHlVFjSOIxs28A3wbeAy5KtJ67XxOOSdng7hXuvsDdLweuI5ip/+o2CVjaX+4QyB0cuZoVHhK5Tt/iEkb33SNyvUOGDI1cpz2ZGYcMGRa53rRB0a87QG3e9OiVCmck1Va22uYHRa6zvKwffbp08Hux4NDolXL6BIufiohIh5TRCYmZfQ34HbAIONLdk5sWaVf1g+MPS8GxpAMwy8FKzo9YqwiKo0zYVt+WceGEiZHq5Jpx7tgJkdtqbxdOiN7//utTpnHgXtFWzD586DC69rw4cltWcmHkOtls34GXUxOzJqf8jecTTiEnp4P/WSg+AaxHxDpnY1bQNvGIiEirdfC/PE0zsyuBPwALCJKRVC1mWD83bJcUHU86guKzICfCB+Mul2A5ET/0hE4dOZrhvXolXP7CCRPZs2v07mHt7fChezN5wF4Jl//MiH0Zs0c/rpg6jZwEu1Ll5eTwtSkHQ/5kiPJNeMF0yD8w8fKdQN9uQ5m7bSZmJJSUrC7vxfih0SZxSAezIqzr5YlXyOmLlVzQdgGJiEirZWRCYmb/DVwPvEOQjCS+OELLDg7fl6XwmJJmltMd6/13yOnXcuHiM7CuVyTdVnF+PjeffAaDu7ec0Jywz3788NAjkm6rPeWY8beTTmFMAl3SDh44mN8eezwAMwYP5ZdHHUtuC0lJXk4O1x17PJMHDMTMsJ43QP7ElgPLn4D1/L3Gj8Rx0H6/Y+6WiS1O/buuogc5vf5BSUFySXi7K7kUSi5uuZz1wnrdiOUm8O9eRETSJuMSEjP7McEg9jnAUe7e5NLCZpZvZqPMbESj7aPNbLcnIGY2DPhj+L+3pS5q6Qgsbx+sz7+DpyUU714gdwTW/adY919g1rp/GoN79GDW2edz0YSJdMnfvavI0B49+cnhR/L7408ir6N3kWmgd3EJd515Ll+afBC9inYfNL1nl6586+AZ3HzK6ZTk5+/cfuaYcdx22lkcMnj38QlG8PTlrjPO4aT9Pu3nbzndsN7/gi5fhZy+uweT0we6fAXrfSuW0z0l55dt8nILmDTyTt4ovYTV5bs/tSuvyeeNLYdS2G8Wg3qPS0OEyTEzrNv3sR6/bmJsSAEUnYr1uRfLz5zzEhHprMyTmMozXczsv4BbgDqC7lrb4hRb4e63hOWHAcuBj9x9WIPjXE0wEP5F4COCWbZGACcCRcCjwGnunvRcrGY2Z9KkSZPmzImw2rS0G4+VQtVzEPsEKID8UZB/YJt8y769uppnly9lY3k5+bk57NO7D9MGDUm4G1NHVVVby3MrlrOmrBQDhvXsxWFDh7WYYC3bspnXVq9ie3U13QoLmTF4CEN69Gy2jns1VL0EdeF6LbmDofAwjQuIIBarY9HaR9hesRC8hrz8/owaeDZdCxPvXtgRuTvUzIOa+UA15PSCwiOxnMw+L5GmTJ48mblz585tapbP9mBmc7rRc9JUOzqlx33dn6aMrWk9N0mPTJv2d+/wPRe4sokyLxAkLc15DhgJHADMIBgvshWYTbAuya2eSZmaRGY53aH4lHZpq2tBASePHN0ubbWnwrw8jttn38j1hvfqzfBe0aaWNSuAoqMityWfysnJZdygU4D2ue/bi5lBwcTgJSIiGSmjEhJ3v5oI0/G6+wqCHiGNt79AkLhIJ+buVNbWkpeTQ35ubrrDyVillZWYQbfC5Na9EBERkc4toxISkVRYsHEDt737Dv/54H3Ka2oA2Ld3H84bN4HTR4+huz5Yt+i11Sv55ewXmb9xA/WPEnPMOHDAQH546OGM37N/WuMTERGRzKGERDqN2liMa154ltvnz9tt3webN/HTF5/jd6+/wl9PPIWpSS7ol+1isRhfeuRBnlmx+yR0MXfeWLuaU+6+nXPHjucXRx2bhghFREQk02TO9D4ireDu/OjZp+ImIw1tq6ri4gdnMXfd2naKLLN86T/xk5HG7lo4nx8/93Q7RCQiIiKZTgmJdAovrfyIexYtSKhsVV0dVz31ODHNa7CLN1av5pnliS/Pc/v8eawujTcRnoiIiMinlJBIp/CveW9HKr986xZeXvVRG0WTma59Ofo8EP/z0vMpj0NERESyixISyXqbKip4LoFuRo3NWrywDaLJXO9uWB+5zosrVqQ+EBEREckqSkgk620o304yna/WlpWlPJZMtb26OqlrWFVXm/JYREREJLsoIZGsl+x66DlJ18w++kUhIiIibUWfMyTrDezenbyc6Lf60J49Ux9MhiopKCDXoidoxfn5bRCNiIiIZBMlJJL1uhcWcdw++0aud87Y8W0QTeaaMnBQ5DrJXHcRERHpXJSQSKfwX/sfEKn8uD36cUD/AW0UTWb68aFHRipvwA9mHN42wYiIiEjWUEIincLkAQP5+pSDEyrbo7CQ6z9zApZEF6VsNmqPPbhw/P4Jl//O9EPoXVLShhGJiIhINlBCIp3GlVOnc9X0Q5odTzK0R0/uPvNcRvTu046RZY6fHnk0F7fwtMmA704/lC8fOLV9ghIREZGMlpfuAETai5nx5QOncsbosdy9cD6PLHmfjyvKKczNY1Tfvpw/bn+O3Ht4UgPgO5P/d/hMPn/AgVw7+wWeW7GMytpgat+S/AKO33c/vjf9UD0ZERERkYQpIZFOp1+Xrnx9yjS+PmVaukPJWAO7d+ePJ3w23WGIiIhIFtBXwSIiIiIiTTCzi83MzezidMeSrfSERJq1dN4KXnngTUo3lVFQXMCoKfsw7eQDycvvfLdOTV0dTy9fyrwN66mqraVXUTHHjNiH0X33SHdoafFxeTkPL3mPNWWlGMbQnj05eb9R9CgqSndoEkdFTQ2PfvA+SzZ9Qm0sxh5dunDSvqMY3KNHukMTEZFOrvN9qpSELHj5PW763m0sfPn93fb1HtCLM795Emd86yRyOsF4i5g7N819i/97Zw4by8t32XfD669w4F4D+e8ZhzJ5wMA0Rdi+NpZv5+cvvcBjHy6hNhbbZd+1s1/g1JGj+d4hh9G9UIlJR1BVW8v1r73MnQvmU1Zdtcu+37wymyOGDeeHhx7O8F690xShiIh0dtn/aVIie+XBN7nqqGviJiMAm9dt4cbv3sqvL/kTsUYfSLNNzJ3vPPkYv3z5xd2SkXpvrV3D+bPu4ZllS9s5uva3ats2Tr/nDh5e8t5uyQhAZW0tdy2cz9n33sWmioo0RCgN7aip4eIHZ3Hj3Ld2S0YAHHhuxTLOuOcO5m/c0P4BioikmJmdbGbPmNk6M6sys7Vm9oKZfaVRud5mdq2ZLTazHWa2Lax3bKNyzwM3h/97c9h1q/41rEG5HuHx3jezSjPbYmZPmNnRcWI0M/svM3vFzD4Oy68Ky5/TqOyRZnajmS0ys9Iw1gVm9hMzy5pv/pSQyC4+WrSKn593PbXVtS2WffrWF7nzF/e3Q1Tp88c3XuOB9xe3WK4mFuPrjz/Ch5s3tUNU6VFdV8fnH7qPtWVlLZZdsnkTX330Ydy9HSKTpvzw2ad4fc3qFsttq6ri8w/dx9bKHe0QlYhIs0aZ2Zx4r5YqmtkXgQeBMcDDwG+BR4Fi4JIG5YYCc4DvAR8DfwXuBkYDj5vZZQ0Oe0t4TML3axq8tobH6wm8Eh5vG3ADMAuYBjxpZl9qFOrPw+P2B+4BrgOeBgYCZzUq+9/AscA7wN+Am4Bq4GrgMTPLbem6ZAJ12ZJd/Pu6R6iurEm8/PUPc8a3TqKopLANo0qPipoa/vF2i7//dqqsreX/3p7DL446tuXCGeippR/y4ZbNCZd/Y+1q3lq3hoP2GtSGUUlTVm3bxoMJJNP1Pqmo4N5FC7hs0kFtGJWISJv6EsGH9f3dfWPDHWbWt8H//hMYCpzn7nc1KNMTeB74vZk95O4b3P2WcKHkU4AH3P2WOO3+iiAJuhG43MNv48zsV8Bb4fGecPcVDeJcA4xz9126EzSKE+ArwHJv9A2fmf0M+BFwJkEyldH0hER22r61nOfunB2tzpZynr/7lTaKKL0eXvJe3G4uzXnw/cWUVkWrkylum/9O5Dq3vhu9jqTGHQvmEfX51O3vziOmp1oikl7vufvkeK8E69cCu32z6u6fAJjZ/sDhwKyGyUhYZivwE6AIOCORxsysALgQ2A58v2Hi4O4fAL8HCoDPNapaA9Q1FWeD/1/WOBkJXR++fyaRODs6JSSy05I5y6jaUR253vyXFrVBNOn3ZgJdXRrbUVvLgizsix9z5821ayLXe3NN9DqSGsncvytLt7F+e8td8kREOqjbgRJgkZldb2anmlnjqTDrFyHrYWZXN34RdI+CoPtWIkaGbc5z93jdCJ4N3w9oFOewMM5rzew4M4s75aGZdTGzH5jZm+E4l5iZOVDfRzwrZtRRly3ZqXJ7ZXL1yrPziUB5TeJd1xrakWS9jqyqtjapb84rsvBaZArdvyLS2bj7dWb2CUE3p28AVwJuZi8AV7n7W0CfsPgx4aspXRNstj6RWNfE/vrtPRts+yawjGBcy/fCV62ZPQp8290/BDCzfIKEZgqwgKBr1sd8+gToJ0BW9JlXQiI7de3VJbl6PUpSHEnH0KMwuX/j3ZKs15EV5eVRkJNLdWy3p8vN6p6F1yJT9Ehy2mVN1ywimczd/wX8KxwPMh04DbgUeMLMRhEMOge4wt1/n4Im64/Xv4n9AxqVw93rCAa+32Bm/YBDgHMJBrSPNbOx7l5FMG5lCnCLu1/S8KBmNoAgIckK6rIlO408aATdeif6hcCnpp6YaLfOzHLEsOGR6/QuKmb/PZv6nZS5zIwjhu0dud6RSdSR1Ejm5zVuj370LcnOLxhEpHNx963u/qi7X0Ywo1Vv4DDgtbDIoREOV/9tXLwZrd4HKoD9wySosSPD97lNxLnR3e9z97MJnoaMAMaFu/cJ3++LU/XwBOLOGEpIZKfC4kKOv3RmpDp7DO7D1BMntVFE6XX08BH07xItQTtr7DgK87LzweOFEya2Sx1JjbPHjqMgN9pskBdOmEg4m4yISMYJ1+yI90usX/heEXbbegk43cwubeI448MnF/Xqx2sMaVzW3asJxoR0A37W6DgjCLqO1QC3htsKzWxGnDbzCZImCBIcgBXh+xGNyg4nmNkra2TnJydJ2mlXnshTt77Alg3bWi4MXPKz88jNy4opsHeTl5PDt6cfwlVPPZ5Q+T1KunDJxOxMzgBmDB7CjMFDeHnVyoTKnzpyNPv1aTx7obSX3sUlfOGAA/nzW68nVH5kn76cPHJUG0clItKm7ge2m9lrBB/mjeBJyEEE6448HZY7n+BpxD/M7BvA6wRrigwCJhA8oZgG1E8d/CpBknClmfUB1ofb/+Du2wjGgBwKfM3MDgKeA/oCZxMkKl9z9+VhnWJgtpl9GMb0EcGsXscQDKR/yN3r52x/GPgQ+JaZjQfeJkiKTgL+Q5wEKVPpCYnsou9evfnFoz+kxx7dWyz7+Wsv4JjPZdUTw92cMXosV00/pMVyfYpLuPmU0+kX8YlKJjEz/nTCyUzqP6DFskcOG861WboeSyb51rQZnDN2fIvlRvTqzf+dfDpFefntEJWISJv5HvAmMIlgYPslQD7B4oJHunsNgLuvBiYDPyTojnUBwZOM6cBKgnVC5tcf1N23EEwDvAi4mOBJyM+AXuH+zQQJzP8SDJr/FsF4kDeA49z9zw1iLA/j+TBs7wqCBKkU+DINFkZ093JgJnAHMDaMcULY9oWtuVAdjWkl5bZhZnMmTZo0ac6cxBfW60g2rvyYO6+9n6dve3G3WbQmzhzH2d85mYOOO6CJ2tnn+RXL+fvcN3l19apdtnfJz+fUUWO4/MApDOzWchKXDSpra/jH23O5Y/481jWaInbvnr24aMJELpwwkbwcfd/REbg797+3iJvfmcvCj3dZJ4zeRcWcNXYcl0+eQo8iDWYXyQSTJ09m7ty5cyOsy5FyZjanGz0nTbWjU3rc1/1pytia1nOT9FBC0kYyPSGpV15awdyn3qV0UxkFxQWMmrIPg0dmxZTXSVm6eRPvbthAZV0tvYqKmTF4SFbOqpWI2liM11avYk1ZKQYM69mLg/YaqDEIHZS7M3/jBj7YvImaujr6denKjMFDsnbMk0i2UkIi2Uh/iaRZXbqXcOgZB6c7jA5jRO8+jOjdp+WCnUBeTg6HDBma7jAkQWbGhD37MyELZ4ETEZHMpoRERCRFdlSXsr1yE0UF3elW1LaJa2VtDaVVVRTl5dOtoEBPpkREJGMpIRERaYWa2kreXXU7hdX3MKbH8mDJ3ApYWtaPTziVcUO+QJfCnilpqy4W49nly7ht/jvMXvkR9R1u9+nVmwsm7M9po8ZqMUoREck4SkhERJK0aftqPl57EQd0XwPF4A5mwfve3TayNzeybs1dfNz9zwzrO6VVbW2rrOTL/3mI19as2m3fh1s2c80Lz/GXt97gps+exrh+e7aqLRERkfakaXBERJKwvWoLW9adx37d1+zcVt9rqmHvqQElpXQt/yLrti4mWZW1NXz+ofviJiMNbSwv58L772Xp5k3NlhMREelIlJCIiCRh/vKfM7zbhoTK9i6sYO26HyXd1m3vzmPu+nUJlS2tquLqF55Nui0REZH2poRERCSimtpKRhQ9k3B5dxjfYwEbtn0Yua2YO7fPnxepzsurVrJsy+bIbYmIiKSDEhIRkYgWr32IvkXlCZc3g7wcZ9n6WyO39fb6tXy0bWvkeg+8l3wXMRERkfakhEREJKLKqtVJ1bPY+sh11pdtT6qtddvLkqonIiLS3pSQiIhEZblJVfMkJjbMyUlufZG8HP16FxGRzKC/WCIiEXXvMiapejl5wyPXGdGrd1JtDe/VK6l6IiIi7U0JiYhIRPvteRQrtveNVKe8Jp8xgy+O3lafvkzqPyBSnfycHE4fNTZyWyIiIumghEREJKKcnBw+8TOAYAatRCzcfgjdivok1d7FEydFKn/yyNH0KSlJqi0REZH2poRERCQJB464krlbDti5Mns89ds/KN2LCSP+N+m2Ttx3JOeOHZ9Q2f369OXHhx2RdFsiIiLtTQmJiEgScnJymbjfrbyx5UhixB94bgbztoxhryGzKCnokXRbZsb/zDyGyydPaXaw+iGDh3Ln6WfTvbAo6bZERETaW/QpX0REBIC83AIOHv031m1bwop1f2WP3Dcpzt1BVayAjTWj6df3Mg4YfXBK2sox47szDuW/9j+AuxfO5+llH7K1qpLivHwO6D+ACyZMZHy/PVPSloiISHtSQiIi0koDeuzHgB7X7bIt+nxaidmza1e+MXUa35g6rY1aEBERaV/qsiUiIiIiImmjhERERERERNJGCYmIiIiIiKSNEhIREREREUkbJSQiIiIiIpI2SkhERERERCRtlJCIiIiIiEjaKCEREREREZG0UUIiIiIiIiJpo4RERERERETSRgmJiIiIiIikjRISERERERFJGyUkIiIiIiKSNkpIREREREQkbZSQiIiIiIhI2ighERERERGRtFFCIiIiIiIiaZOX7gBEMkldLMb68u1U1dbSs6iI3sUl6Q5JREREJKMpIRFJwMby7dwx/13uWvguG8vLd24/aK+BXDhhIseN2Jf83Nw0RigiIiKSmTIqITGzPsBpwInAeGAgUA3MB24Gbnb3WITjDQJ+ChwH9AHWAQ8A17j7lpQGLxnr5VUf8eX/PMT26urd9r25dg1vrl3DgXsN5MaTTqFnUXEaIhQRERHJXJk2huQs4O/AVOB14AZgFjAOuAm4x8wskQOZ2QhgDnAJ8AZwPbAMuAJ4NUx+pJN7Z/06vvDQ/XGTkYbeWruGzz90P1W1te0UmYiIiEh2yLSEZAlwMjDI3S9w9++7+6XAKGAVcAZweoLH+jPQD/iGu5/q7t9z95kEiclI4OepD18yibvz/557mqq6uoTKv71+HXcueLeNoxIRERHJLhmVkLj7s+7+cONuWe6+Hvhr+L9HtHSc8OnIscAK4E+Ndv8EKAcuMrMurY1ZMte8DetZ8PHGSHVunz8Pd2+jiERERESyT0YlJC2oCd8T6TNzZPj+ZJzkpgx4GSgBDk5deJJpHnp/ceQ6S7dsZmHEJEZERESkM8uoQe1NMbM84HPh/z6eQJWR4fuSJvZ/QPAEZT/gmRbantPErlEJxCEd2IYGs2lFsTHJeiIiIiKdUbY8IfklwcD2R939iQTK9wjftzWxv357z1bGJRksLye5fx75SdYTERER6Ywy/gmJmX0D+DbwHnBRe7fv7pPjbQ+fnExq53Akhfbr0zdyHQOG9+6d+mBEREREslRGf5VrZl8DfgcsAo50980JVq1/AtKjif3127cmH51kurPGjI38lOSIYcMZ2K17G0UkIiIikn0yNiExsyuBPwALCJKR9RGqvx++79fE/n3D96bGmEgn0K9LV07ad2TLBRu4eOIBbRSNiIiISHbKyITEzP6bYL2QdwiSkajTGj0Xvh9rZrtcAzPrBswAKoDXWhmqZLirj5jJyAS7bn19ysEcOmRY2wYkIiIikmUyLiExsx8TDGKfAxzl7p80UzbfzEaF647s5O5LgSeBYcBXG1W7BugC3Orumi6pk+teWMRdZ5zDzGHDmyxTkpfPDw89giunTm/HyERERESyQ0YNajez/wJ+CtQBLwHfMLPGxVa4+y3hfw8EFgMfESQfDX0FeAX4vZkdFZabSrBGyRLgh6k/A8lEPYqKuOnk0/hg0ybuWDCPeevXU1lXS6+iYj4zYh9OGzWGboWF6Q5TREREJCNlVEIC7B2+5wJXNlHmBeCWlg7k7kvN7ECCBOc44ARgHcEg+WvcfUtrg5Xssm+fPvzk8JnpDkNEREQkq2RUQuLuVwNXRyi/gmAm1qb2rwIuaW1cIiIiIiKSnIwbQyIiIiIiItlDCYmIiIiIiKSNEhIREREREUkbJSQiIiIiIpI2SkhERERERCRtlJCIiIiIiEjaKCEREREREZG0UUIiIiIiIiJpo4RERERERETSRgmJiIiIiIikTV66A8hiwxYvXszkyZPTHYeIiIhkicWLFwMMS3MYlFPG6/50yo8pnZMSkrZTumPHDubOnbsi3YGkyKjw/b20RtEx6FrsStdjV7oen9K12JWux6d0LXYV5XoMA0rbLpSEvBejjjK2tsmx2+Kg0rGZu6c7BskAZjYHwN07/SMfXYtd6XrsStfjU7oWu9L1+JSuxa50PaSz0xgSERERERFJGyUkIiIiIiKSNkpIREREREQkbZSQiIiIiIhI2ighERERERGRtNEsWyIiIiIikjZ6QiIiIiIiImmjhERERERERNJGCYmIiIiIiKSNEhIREREREUkbJSQiIiIiIpI2SkhERERERCRtlJCIiIiIiEjaKCERAMzsQjPz8PWFCPW8mddrbRlzqpjZimbOYX3EYw0ys/8zs7VmVhUe+wYz69VW8adaqq5HKq9rupnZUWZ2v5mtD3+ua83sCTM7IcIxMv7eqNfa65EN94aZXdzC7z83s7oIx8vY+yOV1yIb7o16ZnaimT1pZqvNbIeZLTOze81sWsTjZOy9IZKovHQHIOlnZoOBPwLbga5JHOIj4JY421e3Iqz2tg24Ic727YkewMxGAK8A/YAHgfeAKcAVwHFmNsPdN7U+1HbR6uuR4uOkjZn9L3AVwf38EPAJsAcwGTgCeDSBY2TNvZGK6xHK9HvjHeCaJvYdCswEHkvkQFlwf7xDiq5FKNPvDczsV8B3gU3AAwT/TvYBTgHOMLPPufttCRwn0+8NkcS4u16d+AUY8DSwFPg14MAXItR34Pl0n0crr8EKYEUKjvNEeD2+3mj7deH2v6b7XNv5eqTkOGm+FpeFP7tbgII4+/M72b2RquuR8fdGC+f3anidTu5M90eKrkXG3xtAf6AOWA/0a7TvyPB6LOvs94ZeejV8mbsnkLZItjKzK4DrCb7ZnAn8BLjM3W9KsL4DL7j7EW0VY1szsxUA7j6sFccYAXxI8Md0hLvHGuzrBqwjSP76uXt5K8Jtc6m4Hqk8TrqYWSGwCtgB7Ovu1UkeJyvujVRdj/BYKyBz743mmNl44F1gDTDU3ZvtqpQt90c8Ua9FWGcFZPa9YWZTgdeAh9z9lDj7SwFz924tHCdr7w2RxtRlqxMzs9HAL4HfufuLZjYzyUP1NLNLCb4V2gbMcfeMGD/SQKGZXQgMAcoJ/oi+mMgf0NCR4fuTDf9oALh7mZm9DBwLHAw8k6KY21Jrr0eqj5MOxxB0RboBiJnZicA4oBJ4w91fTfA42XJvpOp61Mvke6M5Xwzf/5HguWTL/RFP1GtRL9PvjQ+AamCKmfV190/qd5jZYUA3gm5cLcnme0NkF0pIOikzywNuBVYCP2jl4fYH/tHo+POAi9x9fiuP3V76E1yPhpab2SXu/kIC9UeG70ua2P8BwR+O/ciMPxytvR6pPk46HBS+VwJvE3z43snMXgTOdPePWzhOttwbqboe9TL53ojLzIqBCwm66yT0lJnsuT92keS1qJfR94a7bzaz/yboVrXIzB4gGEsyAjgZeAr4UgKHysp7QyQezbLVef0/4ADgYnff0YrjXAfMIPjmtBvBh5Z/EyQpz5rZwNYG2g5uBo4i+CPYBRgP/A0YBjxmZvsncIwe4fu2JvbXb++ZdJTtJxXXI5XHSZd+4ftVBH21DyW4xycATwKHAfcmcJxsuTdSdT0g8++NppxN8HN83N1XJVgnW+6PxpK5FpAl94a73wCcTvDF72XA94CzCLo93uLuGxM4TLbeGyK7UULSCYX9W38A/DaJbha7cPdvu/sr7v6Ju29397fc/SxgFtAX+E4KQm5T7n6Nuz/r7hvcvcLdF7j75QTJVjFwdXojbF+puh5ZcF3rfz/WEgzInR3e4/OB0whmmTo86hSeGSxl1yML7o2m1HdR+ltao+gYkroW2XJvmNl3Cb6cu4XgyUgXgpnolgG3h7PViUhICUknE3bV+hfBI+Aft2FTfw3fD2vDNtpalHOo/6aqRxP767dvbU1AaZaqn2mm3Btbw/e33X1Fwx3uXkEw+w0EU3A2J1vuja3he2uvR3My5d7YjZmNBaYTJGaJTn0M2XN/7NSKa9GcjLk3zOwI4FcEg9q/5e7LwuRqLkHyvgb4tpkNb+FQWXdviDRFCUnn05Wgv+looLLholMEM2wB/D3cdkMr2qnvR96lFcdItyjn8H74vl8T+/cN35vqC5wJUvUzzZR7o/5nurWJ/VvC9+IEj5Pp90aqrkdzMuXeiCfZAdzZcn80lOy1aE4m3Rsnhe/PNd4RJu9vEHz+OqCF42TjvSESlwa1dz5VNBqA3sAkgl+Qswl+EbamO9fB4fuyVhwj3aKcQ/0fnmPNLCfO9IwzgAqCqSAzVap+pplybzxDMFZiTOOfaah+UPfyFo6TLfdGqq5HczLl3tiFmRUBFxEM4G7q92tTsuX+AFp9LZqTSfdGYfi+RxP767e3NHV2Vt0bIs1qaaESvTrPi6Bv7m4LIwIlwChgSKPtE4izEFq4/ZPwWOen+7xaOOfRQJc424cRzGDiwA8abM8Pr8WIOHUyfgGrVF2PqMfpqC+ClZEd+Gaj7ccCMYKnAj06w72RquuRLfdGo9gvCuN+uJkyWX9/tPZaZMu9QTCg3wkWRhzYaN/x4b+VHUCfznRv6KVXcy8tjCg7mdnVxFkYMewP+xyNFkA0s1uAzwIvEcwcUkXwS/U4IBf4O/Al78A3WXjO3wZeBD4CyggGIJ4IFBH0fz7Nw0XgzGwYwTfAH3mjhbvCRaxeIZiN6EFgMTCVYC75JcB0d9/U1ufUGqm6HlGP01GZ2SCCn+lggicEbwN7A6cSfBg4191nhWWHkcX3BqTmemTLvdGQmb0EHEIw2P/hJsoMI8vvD2jdtciWe8PMcggSiaMJzuF+guRkNEF3LgOudPffheWH0QnuDZFmpTsj0qvjvGj6CckR4fbnG20/FbiPYCXZUoLHz+uAhwn+GKX9nBI458OBO4H3CPrG1xD0VX4K+BzBaroNyw8Lr8WKJo43mGDaynXh9fiIYCG5Xuk+1/a8HlGP05FfBN0r/hD+LKsJnv7dD0zpTPdGqq5HNt0b4fmMDs9zFZDbTLmsvz9aey2y6d4geOpxJUF3qlKC2ek2Ao8Ax3a2e0MvvVp66QmJiIiIiIikjWbZEhERERGRtFFCIiIiIiIiaaOERERERERE0kYJiYiIiIiIpI0SEhERERERSRslJCIiIiIikjZKSEREREREJG2UkIiIiIiISNooIRERERERkbRRQiIiIiIiImmjhERERERERNJGCYmIdBpmdoSZuZldnWD5i8PyF7dtZG3PzIaF53JLio97jZlVmtngVB43WWb2ezPbYmZ90x2LiIgkRgmJiEiWCBOO59uxvcHAVcCN7r6qvdptwS+AQuDqNMchIiIJUkIiIiLJ+jHBh///TXcg9dx9PXAL8CUzG5LmcEREJAFKSEREJDIz6wFcADzj7qvTHU8j/wTygC+mOxAREWmZEhIRicTMTjazZ8xsnZlVmdlaM3vBzL4Sp2xvM7vWzBab2Q4z2xbWPTZO2Z3jNczsRDN7xczKw/EA/zazfePU2c/Mfmlmb5nZx2E8H5nZjWY2qK2uQdj2IDP7o5ktC9vdZGYPmdlBccpeHZ7bEWZ2ppm9YWYVZrbZzO4ys4FNtHGQmT1pZmVmVmpmT5vZtIbHC8tdbGYeVjs83Ff/ujrOcYeF7X4Sjv94y8xOingJzgNKgLubuUZTzOxuM1sTXqN14fmc3SgWN7NbzGxE+LPeFJ7zk2Y2Liy3R/hzXRfG/KaZHRmvXXd/HVgBXGpmFvG8RESkneWlOwARyRxm9kXgb8B64GHgE6AfMAG4BPhzg7JDgeeBYcBLwONAF+Ak4HEz+5K7/z1OM6cDxwP3h/UnAmcAR5rZdHd/v1HZy4HngFeAamAs8AXgs2Z2oLuvaf2Z78rMJgFPAr2BJ4D7gL7AqcBsMzvN3R+NU/UrwMnAQ8ALwFTgHGB/M5vo7lUN2jgsbCM3PP5SYHx4rs82Ou47wDXAT4CPCLos1Xu+UdmhwBvAMuDW8BzOAR40s6Pd/bnErgJHh++z4+00s8uAvwB14fl+QHCvHEhwHe5pVGUY8DqwOIx/GHAa8LyZTSO4f0oJEqDewLnAY2a2n7uvjBPCywRPcMYCCxI8JxERSQd310svvfRK6AXMAaqAfnH29W30/88DMeDcRtt7EnyA3gHs2WD7xYCHr5Ma1bki3P5Mo+0DgcI4sRxL8EH4L422HxEe5+oEz7c+posbbMsDPgQqgcMbld8LWAOsaxgXwQBrJ/hAPb5RnTvCfWc32JZD8AHegeMblb+8wXU6otE+B55v4lyGNaj3k0b7PhNufzTCvbAe2AZYnH1jgBpgMzA2zv5BTcT1w0blfhxu3wz8FchpsO+icN/1TcRXf898Jd3/bvTSSy+99Gr+pS5bIhJVLcGHzV24+yf1/21m+wOHA7Pc/a5G5bYSfJNfRPDko7Fn3f2RRtv+SPCEYGb45KX+WGu8wVOFBtufBBYSfNBOtROBEcAf3P2FRu2uJRjg3R84Kk7d37v7/Ebb6p8STWmwbTqwD/Ccuz/WqPyNwJIkY4fgCcr/NNzg7k8AKxvF0CQzKwD2BDa4u8cp8mWCxO1n7r6w8U6PP+ZkBfDLRtv+Gb4XAle5e6zBvjsI7sWJTYS5PnzXwHYRkQ5OXbZEJIrbgd8Ci8zsLoJuRy+7+8eNyk0L33vEG8MA7BG+j46z74XGG9y9zsxmEyQCBxB8qCYcH3ABwZOM/YFeBF2c6lW3fEqR1Z/b0CbOrX6sy2igcbett+KUr58ut1eDbQeE77t1h3L3mJm9AuyXULS7e8fd65qIY1qc7fH0Cd+3NLH/4PC9cTIVNa614fsSdy9ruCO8JzYATY0V2hy+az0SEZEOTgmJiCTM3a8zs08IxgB8A7gScDN7geAb7PoP3PUfWI8JX03pGmfbhibK1n/j3aPBtuvCGNYRjOVYQ9AVDIIkZSipV39uZ7VQLt65bY2zrTZ8b5hI1Z9jU9eiqe2JiBdDfRyJPjWvv8ZFTezvGb5HGb+zrfEGd68Nx6Tvti9UC+Q3sa84fN/RxH4REekglJCISCTu/i/gX2bWk6Br0WnApcATZjYqfFpS/wHyCnf/fcQm9mxie//wfRuAmfUjSIoWANMbf4NuZudFbDdR9ed2irs/1EZtlIbvTV2Lpra3C3ffambVfJqcNbY1fB8IvNcuQe2uPraNaWpfREQSpDEkIpIUd9/q7o+6+2UEsyL1Bg4Ld78Wvh+axKEPb7zBzHKBQ8L/fTt8H07wO+zJOMnIoHB/W2jNuSWq/hwPabzDzHIIEsF4Yuz6pKUtzQcGmFn3OPvqr9Hx7RRLPKPC93fSGIOIiCRACYmIJMzMjmxiXYd+4XsFQNh16yXgdDO7tIljjQ+fcjQ2M86aGF8jGD/ynLt/FG5bEb4fEiYs9cftSjBQvK2eAD9IMMD+q2Z2QrwC4VohJa1o4+WwjSPNrPGH+i/S9PiRTcDgVrQbxfMEf0PiDYT/C0F3qh+b2ZjGO9t6jZjQwQQzrb3YDm2JiEgrqMuWiERxP7DdzF4jSAiM4EnBQQRTAj/doOz5BOtl/MPMvkGwxsRWgkHIE4BxBIOoG3epeRi438zuJ5hedyLBN+2bCcauAODu68OB9ecC75jZkwRjL44hmJL3HZqegSlp7l5jZqcTjFn5TzjA/B2CZGwwwbUYDgwItyXTRszMvkCw9sZDZjaLIEGZQHB+jxFck1ijqs8A55rZw8BcgtnQXnT3tvhQPgv4NsFMZg1/7rj7IgsWyvwr8LaZPUgwjXEfgutTCsRd1DAVLFhFfgrBNNFNjT8REZEOQk9IRCSK7wFvApMIkoNLCAYV/zdwpLvvnA44nNp1MvBDgm+qLyAY8zGdYIrZLxF0+2nsPoJxKYMJ1pKYHm6b5u6NxyN8HvgFwQDmrxJ8OH4krNNmH0Td/V2CWb1+RZAEXUIw1e1kgu5WFxEsGtmaNp4n6L72PMFUw98gOM8jCRY1hE/HmtS7AriT4MP4j4CfATNbE0cz8b1KkIhd0PAJVYP9fyfocvYIwfovVxEsCvkx8Ke2iKmBcwgG3P+ljdsREZEUsPhTyIuItC8zuxi4GbjE3W9JbzQdm5m9TLDKew93L09jHOcRrAdyurvfn644GjOztwhmORvbxBTHIiLSgegJiYhIB2RmJeFMZo23X0zwBOjJdCYjobsIuuJd3cTYonZnZqcSPKn6jpIREZHMoDEkIiId0xCC8RdPEYylySNYMPEQgrE4305faAF3dzP7InA6sBfR1h1pK8XAN939kXQHIiIiiVFCIiLSMW0AbicYR3IkUEiwOOTNwM/dfWkaY9spHE/zbrrjqOfud6Y7BhERiUZjSEREREREJG00hkRERERERNJGCYmIiIiIiKSNEhIREREREUkbJSQiIiIiIpI2SkhERERERCRtlJCIiIiIiEjaKCEREREREZG0UUIiIiIiIiJpo4RERERERETSRgmJiIiIiIikjRISERERERFJGyUkIiIiIiKSNkpIREREREQkbf4/xrc1KkWsiRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 402
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "x_index = 0\n",
    "y_index = 1\n",
    "\n",
    "# this formatter will label the colorbar with the correct target names\n",
    "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n",
    "\n",
    "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], \n",
    "            c=iris.target, cmap=plt.cm.get_cmap('viridis', 3))\n",
    "\n",
    "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n",
    "plt.clim(-0.5, 2.5)\n",
    "plt.xlabel(iris.feature_names[x_index])\n",
    "plt.ylabel(iris.feature_names[y_index]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<font color='red'>Change `x_index` and `y_index` in the above script and find a combination of two parameters that maximally separate the three classes.</font>\n",
    "\n",
    "This exercise is a preview of **dimensionality reduction**, which we'll see later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other Available Test Data\n",
    "\n",
    "`scikit-learn` comes with a variety of test data:\n",
    "\n",
    "- **Packaged Data:** these small datasets are packaged with the scikit-learn installation,\n",
    "  and can be downloaded using the tools in ``sklearn.datasets.load_*``\n",
    "  \n",
    "\n",
    "- **Downloadable Data:** these larger datasets are available for download, and scikit-learn\n",
    "  includes tools which streamline this process.  These tools can be found in\n",
    "  ``sklearn.datasets.fetch_*``\n",
    "  \n",
    "  \n",
    "- **Generated Data:** there are several datasets which are generated from models based on a\n",
    "  random seed.  These are available in the ``sklearn.datasets.make_*``\n",
    "\n",
    "You can explore the available dataset loaders, fetchers, and generators using IPython's\n",
    "tab-completion functionality.  After importing the ``datasets`` submodule from ``sklearn`` (see cells below),\n",
    "type\n",
    "\n",
    "    datasets.load_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.fetch_ + TAB\n",
    "\n",
    "or\n",
    "\n",
    "    datasets.make_ + TAB\n",
    "\n",
    "to see a list of available functions.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sklearn.datasets' has no attribute 'load_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-43240de307be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Type datasets.load_<TAB>  to see all possibilities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# A dropdown menu should appear.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'sklearn.datasets' has no attribute 'load_'"
     ]
    }
   ],
   "source": [
    "# Type datasets.load_<TAB>  to see all possibilities\n",
    "# A dropdown menu should appear.\n",
    "datasets.load_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**in case this does not work:**\n",
    "    \n",
    " **Quick software update:** Try the following task. In the cell below, place your cursor after `ls` and press `TAB`.\n",
    " If you see a dropdown menu of files and options then you don't need to do the pip installation.   \n",
    " \n",
    "If nothing happens then execute the subsequent cell that will downgrade the `jedi` package. After you do the pip installation you will need to close down and re-open your Jupyter notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install jedi==0.17.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A similar interface to [datasets](http://www.astroml.org/examples/datasets/#) is available in ``astroML``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroML import datasets\n",
    "\n",
    "# Use tab completion to explore datasets\n",
    "datasets.fetch_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic Principles of Machine Learning <a class=\"anchor\" id=\"second-bullet\"></a>\n",
    "\n",
    "We can now continue with the basic principles of machine learning, and how to utilize them via Scikit-Learn.\n",
    "\n",
    "After briefly introducing scikit-learn's ***Estimator*** object, we continue with an introduction to **supervised learning**, including ***classification*** and ***regression*** problems, and **unsupervised learning**, including ***dimensionality reduction*** and ***clustering*** problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Scikit-Learn Estimator Object\n",
    "\n",
    "Every algorithm in scikit-learn generates an `Estimator` object. \n",
    "\n",
    "All the **parameters** of an estimator can be set when it is instantiated, and have suitable default values.  \n",
    "\n",
    "Let's take for example a look at all of the parameters, attributes and methods in the [LinearRegression module](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).\n",
    "\n",
    "The ''Estimator'' object is implemented as follows for linear regression:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "LinearRegression(normalize=True)\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a LinearRegression estimator object, call it `model`\n",
    "model = LinearRegression(normalize=True)\n",
    "\n",
    "# Check an individual parameters\n",
    "print(model.normalize)\n",
    "\n",
    "# Check all of the parameters\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our model instantiated, we are ready to input some data.  We make a simple test case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    }
   ],
   "source": [
    "x_train = np.arange(10)    # An array of 10 integers\n",
    "y_train = 2 * x_train + 1  # Some operation performed on that array\n",
    "print(x_train)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 378
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_train, y_train, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'd like to `fit` that data to determine the model parameters that we would need to `predict` the $y$ values of any new measurement of $x$.  \n",
    "\n",
    "The general code syntax for this sort of thing might be\n",
    "\n",
    "```\n",
    "lsqfit x_train y_train x_test y_pred\n",
    "```\n",
    "\n",
    "Scikit-Learn breaks this into two steps:\n",
    "1. **fitting**\n",
    "2. **predicting**\n",
    "    \n",
    "where the syntax of the fit looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[0 1 2 3 4 5 6 7 8 9].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-d3f696c689e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# fit the model on our data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# caution: this will not work, as the input data must be 2D!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    516\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m         X, y = self._validate_data(X, y, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    519\u001b[0m                                    y_numeric=True, multi_output=True)\n\u001b[1;32m    520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    812\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    815\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    638\u001b[0m                     \u001b[0;34m\"Expected 2D array, got 1D array instead:\\narray={}.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0 1 2 3 4 5 6 7 8 9].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "# fit the model on our data\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# caution: this will not work, as the input data must be 2D!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    }
   ],
   "source": [
    "# The input data for sklearn must be 2D: (samples == N x features == 1)\n",
    "print(x_train)\n",
    "\n",
    "# All of these give the same result.  I'll adopt the convention from the last one.\n",
    "print(x_train.reshape(-1,1))\n",
    "print(x_train[:, np.newaxis])\n",
    "print(np.atleast_2d(x_train).T)\n",
    "print(x_train[:, None])\n",
    "\n",
    "X = x_train[:, None]\n",
    "y = y_train\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can fit the model on our data\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Estimated Model parameters**\n",
    "\n",
    "All the estimated parameters are attributes of the estimator object ending with an *underscore*:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.]\n",
      "0.9999999999999982\n"
     ]
    }
   ],
   "source": [
    "# Let's look at the fit parameters, which are indicated by an underscore at the end\n",
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The model found a line with a slope 2 and intercept 1 (as expected)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 3. Supervised Learning <a class=\"anchor\" id=\"third-bullet\"></a>\n",
    "\n",
    "\n",
    "In **Supervised Learning**, we have a dataset consisting of both ***features*** and ***labels***. The task is to construct an estimator that is able to predict the label of an object given the set of features. A relatively simple example is predicting the species of iris given a set of measurements of its flower.\n",
    "\n",
    "Some more complicated examples are:\n",
    "\n",
    "- given a multicolor image of an object through a telescope, determine\n",
    "  whether that object is a star, a quasar, or a galaxy.\n",
    "  \n",
    "  \n",
    "- given a time series and its extracted features, determine which kind of variable star the object is.\n",
    "\n",
    "The commonality of these tasks is that there are one or more unknown properties of an object to be determined from other observed quantities.\n",
    "\n",
    "Supervised learning is further broken down into two categories, \n",
    "1. **classification** (discrete labels)\n",
    "2. **regression** (continuous labels)\n",
    "\n",
    "For example, the task of determining whether an object is a star, a galaxy, an asteroid or a quasar is a classification problem: the label is from three distinct categories. Estimating the age of an object based on such observations however would be a regression problem, because the label (age) is a continuous quantity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Example\n",
    "\n",
    "[K nearest neighbors (kNN)](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier) is one of the simplest learning strategies: given a new, unknown observation, look up in your reference database which ones have the closest features and assign the predominant class.\n",
    "\n",
    "We it out on our iris classification problem.  First see if you can do a kNN fit on the iris data using the 5 nearest neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor']\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "# create the model\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "# fit the model\n",
    "knn.fit(X, y)\n",
    "\n",
    "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
    "# call the \"predict\" method:\n",
    "result = knn.predict([[3, 5, 4, 2],])\n",
    "\n",
    "print(iris.target_names[result])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do probabilistic predictions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 0.8, 0.2]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[3, 5, 4, 2],])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Can you output the name of the iris type along with the probability?</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-36-7d77255be11a>:27: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  pl.pcolormesh(xx, yy, Z, cmap=cmap_light)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.2, 8.0, 1.9, 4.5)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Create color maps for 3-class classification problem, as with iris\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features. We could\n",
    "                        # avoid this ugly slicing by using a two-dim dataset\n",
    "y = iris.target\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X, y)\n",
    "\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(np.linspace(x_min, x_max, 100),\n",
    "                         np.linspace(y_min, y_max, 100))\n",
    "Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "pl.figure()\n",
    "pl.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# Plot also the training points\n",
    "pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "pl.xlabel('sepal length (cm)')\n",
    "pl.ylabel('sepal width (cm)')\n",
    "pl.axis('tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Validation\n",
    "\n",
    "An important aspect of ML is **model validation**:\n",
    "*determining how well your model will generalize from the training data to future unlabeled data.* \n",
    "\n",
    "Let's look at an example using the ***nearest neighbor classifier***. This is a very simple classifier. It stores all training data, and for any unknown quantity, simply returns the label of the closest training point.\n",
    "\n",
    "With the iris data, it very easily returns the correct prediction for each of the input points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element-wise check: [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True]\n",
      "All-at-once check: True\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "X, y = iris.data, iris.target\n",
    "clf = KNeighborsClassifier(n_neighbors=1)\n",
    "clf.fit(X, y)\n",
    "y_pred = clf.predict(X)\n",
    "print('Element-wise check: ' + str(y==y_pred))\n",
    "print('All-at-once check: ' + str(np.all(y == y_pred))) # To check all at once."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See what happens if we used more than the first nearest neighbor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more useful way to look at the results is to view the **confusion matrix**, or the matrix showing the frequency of inputs and outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  0  0]\n",
      " [ 0 50  0]\n",
      " [ 0  0 50]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read each element of the matrix as \"*the number of observations known to be in row-$i$ and predicted to be in column-$j$*\".\n",
    "\n",
    "For each of the three classes, all 50 training samples are correctly identified. But this **does not mean that our model is perfect!** In particular, such a model generalizes extremely poorly to new data. We can simulate this by splitting our data into a ***training set*** and a ***test set***. Scikit-learn contains some convenient routines to do this: here we will apply [`train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17  0  0]\n",
      " [ 0  8  0]\n",
      " [ 0  2 11]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)\n",
    "clf.fit(Xtrain, ytrain)\n",
    "ypred = clf.predict(Xtest)\n",
    "print(confusion_matrix(ytest, ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This paints a better picture of the true performance of our classifier: apparently there is some confusion between the second and third species, which we might anticipate given what we've seen of the data above.\n",
    "\n",
    "This is why it's **extremely important** to use a train/test split like 10-fold cross-validation when evaluating machine-learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Scikit-learn's estimator interface\n",
    "\n",
    "Scikit-learn provides a uniform interface (API) across all methods, and we'll see examples of these in later Jupyter notebooks. Given a scikit-learn ***estimator*** object named `model`, the following methods are available:\n",
    "\n",
    "- Available in **all Estimators**\n",
    "     - `model.fit()`: fit training data. For supervised learning applications, this accepts two arguments: the data `X` and the labels `y` [e.g., `model.fit(X, y)`].  For unsupervised learning applications, this accepts only a single argument, the data `X` [e.g., `model.fit(X)`].\n",
    "  \n",
    "  \n",
    "- Available in **supervised estimators**\n",
    "    - `model.predict()`: given a trained model, predict the label of a new set of data. This method accepts one argument, the new data `X_new` [e.g., `model.predict(X_new)`], and returns the learned label for each object in the array.\n",
    "    - `model.predict_proba()`: For classification problems, some estimators also provide this method, which returns the probability that a new observation has each categorical label. In this case, the label with the highest probability is returned by `model.predict()`.\n",
    "    - `model.score()`: For classification or regression problems, most (all?) estimators implement a score method.  Scores are between 0 and 1, with a larger score indicating a better fit.\n",
    "    \n",
    "    \n",
    "- Available in **unsupervised estimators**\n",
    "    - `model.transform()`: given an unsupervised model, transform new data into the new basis. This also accepts one argument `X_new`, and returns the new representation of the data based on the unsupervised model.\n",
    "    - `model.fit_transform()`: some estimators implement this method, which more efficiently performs a fit and a transform on the same input data.\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary <a class=\"anchor\" id=\"fourth-bullet\"></a>\n",
    "\n",
    "At this point, all of you should have:\n",
    "* seen examples on how to use machine learning methods in scikit-learn\n",
    "* seen why model validation is crucial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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