Metadata-Version: 2.1
Name: embo
Version: 0.3.0
Summary: Empirical Information Bottleneck
Home-page: https://gitlab.com/epiasini/embo
Author: Eugenio Piasini
Author-email: epiasini@sas.upenn.edu
License: GPLv3+
Description: # EMBO - Empirical Bottleneck
        [![License](https://img.shields.io/pypi/l/embo)](https://www.gnu.org/licenses/gpl-3.0.txt)
        [![PyPI version](https://img.shields.io/pypi/v/embo.svg)](https://pypi.python.org/pypi/embo/)
        [![Build status](https://img.shields.io/gitlab/pipeline/epiasini/embo)](https://gitlab.com/epiasini/embo/pipelines)
        
        A Python implementation of the Information Bottleneck analysis
        framework (Tishby, Pereira, Bialek 2000), especially geared towards
        the analysis of concrete, finite-size data sets.
        
        ## Requirements
        
        `embo` requires Python 3, `numpy` and `scipy`.
        
        ## Installation
        To install the latest release, run:
        ``` bash
        pip install embo
        ```
        (depending on your system, you may need to use `pip3` instead of `pip`
        in the command above).
        
        ### Testing
        (requires `setuptools`). If `embo` is already installed on your
        system, look for the copy of the `test_embo.py` script installed
        alongside the rest of the `embo` files and execute it. For example:
        
        ``` bash
        python /usr/lib/python3.X/site-packages/embo/test_embo.py
        ```
        
        **Alternatively**, if you have downloaded the source, from within the
        root folder of the source distribution run:
        
        ``` bash
        python setup.py test
        ```
        
        This should run through all tests specified in `embo/test`.
        
        ## Usage
        
        You probably want to do something like this:
        ``` python
        import numpy as np
        from embo import empirical_bottleneck
        
        # data sequences
        x = np.array([0,0,0,1,0,1,0,1,0,1]*300)
        y = np.array([1,0,1,0,1,0,1,0,1,0]*300)
        
        # IB bound for different values of beta
        i_p,i_f,beta,mi,H_x,H_y = empirical_bottleneck(x,y)
        ```
        
        ## More examples
        A simple example of usage with synthetic data can be found in the
        source distribution, located at `embo/examples/embo_example.ipynb`.
        
        ## Authors
        `embo` is maintained by Eugenio Piasini, Alexandre Filipowicz and
        Jonathan Levine.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
Requires-Python: >=3
Description-Content-Type: text/markdown
