Deploying to gh-pages from @ db3460c 🚀

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 39366f9605333641156b8d78b6580d6b
+config: 45f46a47a2b1a98e61f5e88465e68f9a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/3988e2237cc0401749dcb522c104a647/05_common_transformations.ipynb

@@ -15,7 +15,25 @@
       },
       "outputs": [],
       "source": [
-        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'\n\nimport torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
+        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's start by importing the necessary packages.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
       ]
     },
     {

_downloads/4407a3dbbb1547f15d94cab7374d606b/03_construct_hmm.py

@@ -7,6 +7,9 @@
 
 # sphinx_gallery_thumbnail_path = 'imgs/juice.png'
 
+# %% 
+# Let's start by importing the necessary packages.
+
 import torch
 import pyjuice as juice
 import pyjuice.nodes.distributions as dists

_downloads/5871728fbee9fa77cb6f3d50f136f2e8/05_common_transformations.py

@@ -7,6 +7,9 @@
 
 # sphinx_gallery_thumbnail_path = 'imgs/juice.png'
 
+# %% 
+# Let's start by importing the necessary packages.
+
 import torch
 import pyjuice as juice
 import pyjuice.nodes.distributions as dists

_downloads/94056bd45c0afc58d516144c0928bf8b/02_construct_simple_pc.ipynb

@@ -15,7 +15,25 @@
       },
       "outputs": [],
       "source": [
-        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'\n\nimport torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
+        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's start by importing the necessary packages.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
       ]
     },
     {

_downloads/a6f4e70e566387d03c9a1773c5f52cdb/03_construct_hmm.ipynb

@@ -15,7 +15,25 @@
       },
       "outputs": [],
       "source": [
-        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'\n\nimport torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
+        "# sphinx_gallery_thumbnail_path = 'imgs/juice.png'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's start by importing the necessary packages.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport pyjuice as juice\nimport pyjuice.nodes.distributions as dists"
       ]
     },
     {

_downloads/ab853d12dcf8246e42ce1eb5d40a84fb/02_construct_simple_pc.py

@@ -7,6 +7,9 @@
 
 # sphinx_gallery_thumbnail_path = 'imgs/juice.png'
 
+# %% 
+# Let's start by importing the necessary packages.
+
 import torch
 import pyjuice as juice
 import pyjuice.nodes.distributions as dists

_sources/getting-started/tutorials/02_construct_simple_pc.rst.txt

@@ -23,41 +23,51 @@ Construct Simple PCs
 
 In this tutorial, you will learn about the basic APIs to construct PCs.
 
-.. GENERATED FROM PYTHON SOURCE LINES 7-14
+.. GENERATED FROM PYTHON SOURCE LINES 7-10
 
 .. code-block:: Python
 
 
     # sphinx_gallery_thumbnail_path = 'imgs/juice.png'
 
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-12
+
+Let's start by importing the necessary packages.
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-17
+
+.. code-block:: Python
+
+
     import torch
     import pyjuice as juice
     import pyjuice.nodes.distributions as dists
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 15-17
+.. GENERATED FROM PYTHON SOURCE LINES 18-20
 
 A PC can be created using :code:`pyjuice.inputs`, :code:`pyjuice.multiply`, and :code:`pyjuice.summate`, which create input node vectors, product node vectors, and sum node vectors, respectively.
 The goal of this tutorial is to get you familiar with these functions.
 
-.. GENERATED FROM PYTHON SOURCE LINES 19-21
+.. GENERATED FROM PYTHON SOURCE LINES 22-24
 
 Input nodes
 -----------
 
-.. GENERATED FROM PYTHON SOURCE LINES 23-24
+.. GENERATED FROM PYTHON SOURCE LINES 26-27
 
 Let us start with :code:`pyjuice.inputs`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 24-27
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
 
 .. code-block:: Python
 
 
     ni0 = juice.inputs(var = 0, num_node_blocks = 4, block_size = 2, dist = dists.Categorical(num_cats = 4))
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 28-35
+.. GENERATED FROM PYTHON SOURCE LINES 31-38
 
 The above line defines :code:`num_node_blocks * block_size = 8` input nodes on variable ID :code:`var = 0` featuring Categorical distributions with 4 categories (i.e., :code:`dists.Categorical(num_cats = 4)`). The set of input distributions are defined under :code:`pyjuice.nodes.distributions`.
 
@@ -67,16 +77,16 @@ Although using larger block sizes when defining input node vectors do not have a
 
 Note that :code:`block_size` has to be a power of 2.
 
-.. GENERATED FROM PYTHON SOURCE LINES 37-39
+.. GENERATED FROM PYTHON SOURCE LINES 40-42
 
 Product nodes
 -------------
 
-.. GENERATED FROM PYTHON SOURCE LINES 41-42
+.. GENERATED FROM PYTHON SOURCE LINES 44-45
 
 Let us define another input node vector and a product node vector that take the input nodes as children.
 
-.. GENERATED FROM PYTHON SOURCE LINES 42-48
+.. GENERATED FROM PYTHON SOURCE LINES 45-51
 
 .. code-block:: Python
 
@@ -87,7 +97,7 @@ Let us define another input node vector and a product node vector that take the
     ms = juice.multiply(ni0, ni1, edge_ids = edge_ids)
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 49-59
+.. GENERATED FROM PYTHON SOURCE LINES 52-62
 
 :code:`ms` defines a product node vector where every node have two children: the first child is a node in :code:`ni0` and the second child is a node in :code:`ni1`.
 :code:`edge_ids` specifies which child nodes do every node in :code:`ms` connects to. Specifically, :code:`edge_ids` has size :code:`[# product node blocks, # child node vectors]`, so in this case, it should has size :code:`[4, 2]`.
@@ -100,7 +110,7 @@ For :code:`pyjuice.multiply`, if two node blocks are connected (as defined by :c
 
 When we do not provide the :code:`edge_ids`, PyJuice assumes it to be the following (we can only use this shortcut when the child node vectors have the same :code:`num_node_blocks` and :code:`block_size`):
 
-.. GENERATED FROM PYTHON SOURCE LINES 59-64
+.. GENERATED FROM PYTHON SOURCE LINES 62-67
 
 .. code-block:: Python
 
@@ -110,16 +120,16 @@ When we do not provide the :code:`edge_ids`, PyJuice assumes it to be the follow
     edge_ids = torch.arange(0, num_node_blocks)[:,None].repeat(1, num_child_node_vectors)
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 65-67
+.. GENERATED FROM PYTHON SOURCE LINES 68-70
 
 Sum nodes
 ---------
 
-.. GENERATED FROM PYTHON SOURCE LINES 69-70
+.. GENERATED FROM PYTHON SOURCE LINES 72-73
 
 Finally, we introduce :code:`pyjuice.summate`, which is used to define sum node vectors.
 
-.. GENERATED FROM PYTHON SOURCE LINES 70-74
+.. GENERATED FROM PYTHON SOURCE LINES 73-77
 
 .. code-block:: Python
 
@@ -128,7 +138,7 @@ Finally, we introduce :code:`pyjuice.summate`, which is used to define sum node
     ns = juice.summate(ms, num_node_blocks = 6, block_size = 2, edge_ids = edge_ids)
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 75-85
+.. GENERATED FROM PYTHON SOURCE LINES 78-88
 
 Similar to :code:`pyjuice.multiply`, the positional arguments to :code:`pyjuice.summate` are the list of input node vectors (here we only provide :code:`ms`).
 :code:`num_node_blocks` and :code:`block_size` are the number of node blocks and the size of each node block, respectively.
@@ -141,7 +151,7 @@ That is, in the case where both :code:`ns` and :code:`ms` have block size 2, eve
 
 If :code:`edge_ids` is not provided, we assume that all nodes in the sum node vector are connected to all child nodes:
 
-.. GENERATED FROM PYTHON SOURCE LINES 85-86
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
 
 .. code-block:: Python
 

_sources/getting-started/tutorials/03_construct_hmm.rst.txt

@@ -23,23 +23,33 @@ Construct an HMM
 
 This tutorial demonstrates how to construct an HMM with :code:`pyjuice.inputs`, :code:`pyjuice.multiply`, and :code:`pyjuice.summate`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 7-14
+.. GENERATED FROM PYTHON SOURCE LINES 7-10
 
 .. code-block:: Python
 
 
     # sphinx_gallery_thumbnail_path = 'imgs/juice.png'
 
+
+.. GENERATED FROM PYTHON SOURCE LINES 11-12
+
+Let's start by importing the necessary packages.
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-17
+
+.. code-block:: Python
+
+
     import torch
     import pyjuice as juice
     import pyjuice.nodes.distributions as dists
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 15-16
+.. GENERATED FROM PYTHON SOURCE LINES 18-19
 
 We start with specifying the structural parameters of the HMM
 
-.. GENERATED FROM PYTHON SOURCE LINES 16-21
+.. GENERATED FROM PYTHON SOURCE LINES 19-24
 
 .. code-block:: Python
 
@@ -49,37 +59,37 @@ We start with specifying the structural parameters of the HMM
     num_emits = 4023
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 22-24
+.. GENERATED FROM PYTHON SOURCE LINES 25-27
 
 An important parameter to be determined is the block size, which is crucial for PyJuice to compile efficient models.
 Specifically, we want the block size to be large enough so that PyJuice can leverage block-based parallelization.
 
-.. GENERATED FROM PYTHON SOURCE LINES 24-27
+.. GENERATED FROM PYTHON SOURCE LINES 27-30
 
 .. code-block:: Python
 
 
     block_size = min(juice.utils.util.max_cdf_power_of_2(num_latents), 1024)
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 28-29
+.. GENERATED FROM PYTHON SOURCE LINES 31-32
 
 The number of node blocks is derived accordingly
 
-.. GENERATED FROM PYTHON SOURCE LINES 29-32
+.. GENERATED FROM PYTHON SOURCE LINES 32-35
 
 .. code-block:: Python
 
 
     num_node_blocks = num_latents // block_size
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 33-35
+.. GENERATED FROM PYTHON SOURCE LINES 36-38
 
 We use the context manager `set_block_size` to set the block size of all PC nodes.
 In the following we assume `T = seq_length` and `K = num_latents`
 
-.. GENERATED FROM PYTHON SOURCE LINES 35-61
+.. GENERATED FROM PYTHON SOURCE LINES 38-64
 
 .. code-block:: Python
 
@@ -110,7 +120,7 @@ In the following we assume `T = seq_length` and `K = num_latents`
         ns = juice.summate(curr_zs, num_node_blocks = 1, block_size = 1)
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 62-68
+.. GENERATED FROM PYTHON SOURCE LINES 65-71
 
 Note that :code:`ns.duplication` is a handy function to create duplications of existing node vectors. 
 For input nodes (e.g., :code:`ns_input.duplicate(var, tie_params = True)`) we can specify it to define on a new variable.