ifa 0.1.1

A library for information flow analysis

Information Flow Analysis
=========================

IFA is a simple and fast library for information theory research and information flow analysis. It's a Python module written C++, Cython.


Installation
============
Dependencies:
* numpy

If you have Cython some cpp files will get regenerated during installation
.. code-block:: bash

pip install ifa

Or if you want the developmen version:
.. code-block:: bash

git clone https://github.com/janekolszak/ifa.git;
cd ifa;
sudo make install;

Usage
=====
Computing Jensen–Shannon divergence:
.. code-block:: python

from ifa.distribution import Distribution
from ifa.divergence import jsd

from numpy.testing import assert_allclose

p = Distribution(["A", "B"], [0.5, 0.5])
q = Distribution(["A", "C"], [0.5, 0.5])

assert_allclose(jsd(p, 0.5, q, 0.5), [0.5])

What's inside:
==============
* Distribution class with some basic operations
* Divergences:
* Jensen–Shannon divergence
* Kullback–Leibler divergence
* Functions to compute information flow between distributions