Metadata-Version: 2.1
Name: SBM_VBEM
Version: 0.0.11
Summary: Implementation of Stochastic Block Model with several inference strategy (VBEM, VEM, Natural conjugate gradients)
Project-URL: Homepage, https://github.com/Remi-Boutin/SBM_package
Project-URL: Bug Tracker, https://github.com/Remi-Boutin/SBM_package/issues
Author-email: Remi Boutin <remi.boutin.stat@gmail.com>
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.1
Description-Content-Type: text/markdown

# SBM package

## Installation

To install this package, run 

``python -m pip install sbm-vbem``


## Quick use of the SBM function

In this package, I implemented 2 different estimations : *a variational-bayes EM (it's on the way)* and a variational-EM.
The variational-bayes EM can be optimized in two ways : setting the lagrangian to zero, 
referred to as the **VBEM** algorithm, or using a natural-conjugate gradient method,
referred to as the **NCG** algorithm.

The main function ``sbm`` is only implemented for directed graphs (for now) and can be used this way:

- with no init given : ``sbm(adj, algo='vbem', init='kmeans') ``
- with an already computed init : ``sbm(adj, algo='vbem', tau_init=tau) ``

This is a joint work with Pr. Pierre Latouche and Pr. Charles Bouveyron.