add vonmises and edit readme

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,75 @@
+# Implicit Reparametrization Trick
+
+<div align="center">  
+    <img src="images/implicit.webp" width="500px" />
+</div>
+
+
+<table>
+    <tr>
+        <td align="left"> <b> Title </b> </td>
+        <td> Implicit Reparametrization Trick for BMM </td>
+    </tr>
+    <tr>
+        <td align="left"> <b> Authors </b> </td>
+        <td> Matvei Kreinin, Maria Nikitina, Petr Babkin, Iryna Zabarianska </td>
+    </tr>
+    <tr>
+        <td align="left"> <b> Consultant </b> </td>
+        <td> Oleg Bakhteev, PhD </td>
+    </tr>
+</table>
+
+
+![Testing](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/testing.yml/badge.svg)
+![Docs](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/docs.yml/badge.svg)
+
+
+## Description
+
+This repository implements an educational project for the Bayesian Multimodeling course. It implements algorithms for sampling from various distributions, using the implicit reparameterization trick.
+
+## Scope
+We plan to implement the following distributions in our library:
+- [x] Gaussian normal distribution (*)
+- [x] Dirichlet distribution (Beta distributions)(\*)
+- [x] Mixture of the same family distributions (**)
+- [x] Student's t-distribution (**) (\*)
+- [x] VonMises 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)
+- [Plan of project](https://github.com/intsystems/implitic-reparametrization-trick/blob/main/planning.md)
+- [BlogPost](blogpost/Blog_post_sketch.pdf)
+- [Documentation](https://intsystems.github.io/implicit-reparameterization-trick/)
+- [Matvei Kreinin](https://github.com/kreininmv), [Maria Nikitina](https://github.com/NikitinaMaria), [Petr Babkin](https://github.com/petr-parker), [Iryna Zabarianska](https://github.com/Akshiira)
+

README.md

@@ -1,5 +1,10 @@
 # Implicit Reparametrization Trick
 
+<div align="center">  
+    <img src="images/implicit.webp" width="500px" />
+</div>
+
+
 <table>
     <tr>
         <td align="left"> <b> Title </b> </td>
@@ -15,6 +20,7 @@
     </tr>
 </table>
 
+
 ![Testing](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/testing.yml/badge.svg)
 ![Docs](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/docs.yml/badge.svg)
 
@@ -25,11 +31,12 @@ This repository implements an educational project for the Bayesian Multimodeling
 
 ## 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 (***)
+- [x] Gaussian normal distribution (*)
+- [x] Dirichlet distribution (Beta distributions)(\*)
+- [x] Mixture of the same family distributions (**)
+- [x] Student's t-distribution (**) (\*)
+- [x] VonMises 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
 
@@ -64,4 +71,5 @@ In this example, we demonstrate the use of a mixture of distributions using our
 - [Plan of project](https://github.com/intsystems/implitic-reparametrization-trick/blob/main/planning.md)
 - [BlogPost](blogpost/Blog_post_sketch.pdf)
 - [Documentation](https://intsystems.github.io/implicit-reparameterization-trick/)
+- [Matvei Kreinin](https://github.com/kreininmv), [Maria Nikitina](https://github.com/NikitinaMaria), [Petr Babkin](https://github.com/petr-parker), [Iryna Zabarianska](https://github.com/Akshiira)