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mi

Introduction

This project provides a small utility, compute-mi to compute mutual information between two or more random variables. This utility is optimized for:

  • Memory use: since mutual information is typically computed over large data sets, we try to keep memory use small. This is done in two ways: (1) strings from the input are internally represented as numbers; (2) counts of joint events are stored in a hash map where the keys are tuples (fixed-length arrays), avoiding the overhead of regular dynamic arrays.

  • Flexibility: any file that has whitespace-separated columns can be used as the input. You can specify which columns mutual information should be computed for.

Measures

Currently, the following mutual information measures are supported:

  • Specific correlation: this is a generalization of mutual information for multiple variables (Van de Cruys, 2011).
  • Normalized specific correlation: This is our extension of normalized PMI (Bouma, 2009) to specific correlation.
  • Positive mutual information: all measures can be used in the positive variant, where negative mutual information is set to 0.

Installation

To compile and install mi, you need the Rust compiler and the Cargo package manager. Using rustup.rs is the recommended method for installing both. After installing Rust, you can compile and install this package by simply running

cargo install

in the main directory. Afterwards, compute-mi should be in your path.

Usage

The basic usage of compute-mi is:

target/release/compute-mi [options] VARS [INPUT] [OUTPUT]

where VARS is a comma-delimited list of columns that should be used as variables. The input and output files are optional. stdin/stdout are used when they are not provided.

This is an example run using two variables:

zcat < taz-obja-words.gz | target/release/compute-mi -m nsc 1,2

Caveats

Double counting of events

The events in each joint event (line in the input) are considered to be unique occurrences. E.g. if we are computing specific correlations on triples of head-prep-prep_obj:

walk on pavement
walk in city

walk will be counted twice, although it may actually be a single token which has the two dependents on and in.