Free Energy Minimization

Quick Start

Interactive notebook

Launch an interactive Jupyter notebook using Binder to run and edit the examples in the documentation:

Installation

• To install fem on your computer using pip, execute

  pip install fem
  

  Dependencies:

  • Fortran compiler such as gfortran
  • LAPACK development files
  • OpenMP development files (for parallel computing support)

• Load fem in your Python script:

  import fem
  

Links

Online documentation:

http://nihcompmed.github.io/fem

Source code repository:

https://github.com/nihcompmed/fem

Python package index:

https://pypi.python.org/pypi/fem

Introduction

Free energy minimization (FEM) is a method for learning from data the probability distribution \(p\), with a form inspired by statistical physics, of an output variable \(y\) given input variables \(x_i\). We use \(p\) to both 1) understand the relations among the data variables \(x_i,y\), for example, to identify pairs or groups of variables that vary together and 2) to predict the output given new inputs. We are actively developing variations of the method that are conducive to modeling different types of data.

Table of Contents

• FEM for discrete data
  • Model
  • Method
• Examples
  • Discrete data examples
    • Simulated time series
    • Anagram solver
    • Direct information from a multiple sequence alignment
    • Currency trading strategies
  • Continuous data examples
    • Simulated time series
    • Object detection from fMRI

Source Code

• fem package
  • Subpackages
    • fem.continuous package
    • fem.discrete package
  • Module contents
• fortran_module extension