Merge pull request #3 from intsystems/docs

Docs

Author: NikitinaMaria

doc/source/conf.py

@@ -1,10 +1,38 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../..'))
+
+sys.path.insert(0, os.path.abspath('../../src/'))
+
+from mylib import __version__
+
+
+# -- Project information -----------------------------------------------------
 
 project = 'Implicit Reparametrization Trick'
+copyright = '2024, Matvei Kreinin, Maria Nikitina, Petr Babkin, Irina Zaboryanskaya'
 author = 'Matvei Kreinin, Maria Nikitina, Petr Babkin, Irina Zaboryanskaya'
-release = '0.1'
+
+version = __version__
+master_doc = 'index'
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+=======
 
 extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 
               'sphinx.ext.intersphinx', 'sphinx.ext.todo',
@@ -13,20 +41,37 @@
               'sphinx.ext.autosummary', 'sphinx.ext.mathjax',
               'sphinx_rtd_theme']
 
+=======
+
 autodoc_mock_imports = ["numpy", "scipy", "sklearn", "torch"]
 
+# Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = []
 
 html_extra_path = []
 
 html_context = {
-    "display_github": True,
-    "github_user": "Intelligent-Systems-Phystech",
-    "github_repo": "ProjectTemplate",
-    "github_version": "master",
-    "conf_py_path": "/doc/source/",
+    "display_github": True, # Integrate GitHub
+    "github_user": "Intelligent-Systems-Phystech", # Username
+    "github_repo": "ProjectTemplate", # Repo name
+    "github_version": "master", # Version
+    "conf_py_path": "/doc/source/", # Path in the checkout to the docs root
 }
 
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
 html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']

doc/source/distributions.rst

@@ -0,0 +1,8 @@
+*****
+Distributions
+*****
+
+.. automodule:: irt.distributions
+    :members:
+
+

doc/source/index.rst

@@ -1,13 +1,30 @@
-.. Implicit Reparametrization Trick documentation master file, created by
-   sphinx-quickstart on Mon Oct 10 10:00:00 2022.
+.. MixtureLib documentation master file, created by
+   sphinx-quickstart on Fri Mar 20 22:58:28 2020.
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to Implicit Reparametrization Trick's documentation!
-===========================================================
+Welcome to Mixture Lib!
+=======================
 
 .. toctree::
-   :maxdepth: 2
-   :caption: Contents:
+   :maxdepth: 1
+   :caption: Main Info:
+   
+   info.rst
 
-   modules
+.. toctree::
+   :maxdepth: 1
+   :caption: Get Started:
+   
+   installation.rst
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Packages:
+
+   distributions.rst
+
+
+Indices and tables
+==================

doc/source/installation.rst

@@ -0,0 +1 @@
+.. include:: ../../src/README.md

doc/source/modules.rst

@@ -0,0 +1,21 @@
+************
+Installation
+************
+
+Requirements
+============
+
+- Python 3.*
+- pip 20.0.2
+
+Installing by using PyPi
+========================
+
+Install
+-------
+	git clone https://github.com/Intelligent-Systems-Phystech/implicit-reparameterization-trick.git /tmp/implicit-reparameterization-trick
+	python3 -m pip install /tmp/implicit-reparameterization-trick/src/
+
+Uninstall
+---------
+	python3 -m pip uninstall irt

src/README.md

@@ -0,0 +1 @@
+__version__ = '0.0.1'

src/irt/__init__.py

@@ -0,0 +1,98 @@
+import torch
+
+class torch.distributions.Distribution:
+	'''
+	The abstract base class for probability distributions, which we inherit from. These methods are implied
+	to be implemented for each subclass.
+	'''
+	def __init__(batch_shape=torch.Size([]), event_shape=torch.Size([])):
+		'''
+		Basic constructer of distribution.
+		'''
+	
+	@property
+	def arg_constraints():
+		'''
+		Returns a dictionary from argument names to Constraint objects that should
+		be satisfied by each argument of this distribution. Args that are not tensors need not appear
+		in this dict.
+		'''
+	
+	def cdf(value):
+		'''
+		Returns the cumulative density/mass function evaluated at value.
+		'''
+		
+	def entropy():
+		'''
+		Returns entropy of distribution, batched over batch_shape.
+		'''
+
+	def enumerate_support(expand=True):
+		'''
+		Returns tensor containing all values supported by a discrete distribution. The result will
+		enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape
+		+ event_shape (where event_shape = () for univariate distributions).
+		'''
+	
+	@property
+	def mean(expand=True):
+		'''
+		Returns mean of the distributio.
+		'''
+
+	@property
+	def mode(expand=True):
+		'''
+		Returns mean of the distributio.
+		'''
+	def perplexity():
+		'''
+		Returns perplexity of distribution, batched over batch_shape.
+		'''
+	
+	def rsample(sample_shape=torch.Size([])):
+		'''
+		Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution
+		parameters are batched.
+		'''
+
+	def sample(sample_shape=torch.Size([])):
+		'''
+		Generates a sample_shape shaped sample or sample_shape shaped batch of reparameterized samples
+		if the distribution parameters are batched.
+		'''
+
+class torch.distributions.implicit.Normal(Distribution):
+	'''
+	A Gaussian distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(mean_matrix, covariance_matrix=None):
+		pass
+
+class torch.distributions.implicit.Dirichlet(Distribution):
+	'''
+	A Dirichlet distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(concentration, validate_args=None):
+		pass
+		
+class torch.distributions.implicit.Mixture(Distribution):
+	'''
+	A Mixture of distributions class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(distributions : List[Distribution]):
+		pass
+
+class torch.distributions.implicit.Student(Distribution):
+	'''
+	A Student's distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__():
+		pass
+
+class torch.distributions.implicit.Factorized(Distribution):
+	'''
+	A class for an arbitrary factorized distribution with backpropagation capability for the rsample
+	function through IRT.
+	'''