Merge pull request #1 from intsystems/docs

Docs

Author: NikitinaMaria

README.md

@@ -19,9 +19,41 @@
 
 This repository implements an educational project for the Bayesian Multimodeling course. It implements algorithms for sampling from various distributions, using the implicit reparameterization trick.
 
-## Описание
-
-В этом репозитории реализован учебный проект для курса Байесовское мультимоделирование. В нем реализуются алгоритмы сэмплирования из различных распределений, используя implicit reparametriation trick.
+## Scope
+We plan to implement the following distributions in our library:
+- Gaussian normal distribution (*)
+- Dirichlet distribution (Beta distributions)(\*)
+- Sampling from a mixture of distributions
+- Sampling from the Student's t-distribution (**) (\*)
+- Sampling from an arbitrary factorized distribution (***)
+
+(\*) - this distribution is already implemented in torch using the explicit reparameterization trick, we will implement it for comparison
+
+(\*\*) - this distribution is added as a backup, their inclusion is questionable
+
+(\*\*\*) - this distribution is not very clear in implementation, its inclusion is questionable
+
+## Stack
+
+We plan to inherit from the torch.distribution.Distribution class, so we need to implement all the methods that are present in that class.
+
+##  Usage
+In this example, we demonstrate the application of our library using a Variational Autoencoder (VAE) model, where the latent layer is modified by a normal distribution.
+```
+>>> import torch.distributions.implicit as irt
+>>> params = Encoder(inputs)
+>>> gauss = irt.Normal(*params)
+>>> deviated = gauss.rsample()
+>>> outputs = Decoder(deviated)
+```
+In this example, we demonstrate the use of a mixture of distributions using our library.
+```
+>>> import irt
+>>> params = Encoder(inputs)
+>>> mix = irt.Mixture([irt.Normal(*params), irt.Dirichlet(*params)])
+>>> deviated = mix.rsample()
+>>> outputs = Decoder(deviated)
+```
 
 ## Links
 - [LinkReview](https://github.com/intsystems/implitic-reparametrization-trick/blob/main/linkreview.md)

doc/source/conf.py

@@ -0,0 +1,19 @@
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../..'))
+
+project = 'Implicit Reparametrization Trick'
+author = 'Matvei Kreinin, Maria Nikitina, Petr Babkin, Irina Zaboryanskaya'
+release = '0.1'
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+]
+
+templates_path = ['_templates']
+exclude_patterns = []
+
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']

doc/source/index.rst

@@ -0,0 +1,13 @@
+.. Implicit Reparametrization Trick documentation master file, created by
+   sphinx-quickstart on Mon Oct 10 10:00:00 2022.
+   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!
+===========================================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   modules

doc/source/info.rst

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

doc/source/modules.rst

@@ -0,0 +1,5 @@
+Modules
+=======
+
+.. automodule:: torch.distributions.implicit
+   :members: