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A library for information flow analysis

Project Description

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.

* numpy

If you have Cython some cpp files will get regenerated during installation
pip install ifa


Or if you want the developmen version:
git clone;
cd ifa;
sudo make install;
Computing Jensen–Shannon divergence:
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

Release History

This version
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Source None Feb 5, 2015

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