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
```bash
pip install ifa
```
Or if you want the developmen version:
```bash
git clone https://github.com/janekolszak/ifa.git;
cd ifa;
sudo make install;
```
Usage
=====
Computing Jensen–Shannon divergence:
```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
=========================
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
```bash
pip install ifa
```
Or if you want the developmen version:
```bash
git clone https://github.com/janekolszak/ifa.git;
cd ifa;
sudo make install;
```
Usage
=====
Computing Jensen–Shannon divergence:
```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
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