Ruby Vector Space Model (VSM) with tf*idf weights

Calculates the similarity between texts using a bag-of-words Vector Space Model with Term Frequency-Inverse Document Frequency weights. If your use case demands performance, use Lucene or similar (see below).

Usage

require 'tf-idf-similarity'

corpus = TfIdfSimilarity::Collection.new
corpus << TfIdfSimilarity::Document.new("Lorem ipsum dolor sit amet...")
corpus << TfIdfSimilarity::Document.new("Pellentesque sed ipsum dui...")
corpus << TfIdfSimilarity::Document.new("Nam scelerisque dui sed leo...")

p corpus.similarity_matrix

This gem will use the gsl gem if available, for faster matrix multiplication.

Optimizations

NArray

gem install narray

GNU Scientific Library (GSL)

The latest gsl gem (1.14.7) is not compatible with the gsl package (1.15) in Homebrew:

cd /usr/local
git checkout -b gsl-1.14 83ed49411f076e30ced04c2cbebb054b2645a431
brew install gsl
git checkout master
git branch -d gsl-1.14
gem install gsl

Automatically Tuned Linear Algebra Software (ATLAS)

You may know this software through Linear Algebra PACKage (LAPACK) or Basic Linear Algebra Subprograms (BLAS). You can use it through version 0.0.2 of the nmatrix gem. As of writing, 0.0.2 is not released, so follow these instructions to install it. You may need additional instructions for Mac OS X Lion.

Other Options

The nmatrix gem has no easy way to normalize all columns to unit vectors. Ruby-LAPACK is a very thin wrapper around LAPACK, which has an opaque Fortran-style naming scheme. Linalg and RNum are old and not available as gems.

Extras

You can access more term frequency, document frequency, and normalization formulas with:

require 'tf-idf-similarity/extras/collection'
require 'tf-idf-similarity/extras/document'

The default tf*idf formula follows the Lucene Conceptual Scoring Formula.

Why?

The treat, tf-idf, similarity and rsimilarity gems normalize the frequency of a term in a document to the number of terms in that document (which, as far as I can tell, never occurs in the academic literature) and have no normalization component. vss uses plain term and document frequencies, with no damping or normalization.

Reference

Bugs? Questions?

This gem's main repository is on GitHub: http://github.com/opennorth/tf-idf-similarity, where your contributions, forks, bug reports, feature requests, and feedback are greatly welcomed.