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The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

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/*
 * Copyright 2010 Keith Stevens 
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published
 * by the Free Software Foundation and distributed hereunder to you.
 *
 * THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
 * EXPRESS OR IMPLIED ARE MADE.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
 * NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
 * PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
 * WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
 * RIGHTS.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see .
 */

package edu.ucla.sspace.wordsi;

import edu.ucla.sspace.basis.BasisMapping;

import edu.ucla.sspace.hal.WeightingFunction;

import edu.ucla.sspace.text.IteratorFactory;

import edu.ucla.sspace.vector.CompactSparseVector;
import edu.ucla.sspace.vector.SparseDoubleVector;

import java.util.Queue;


/**
 * A raw word co-occurrence {@link ContextGenerator}.  Each co-occurring word is
 * mapped to a unique dimension and feature scores are based on the distance
 * between the co-occurring word and the focus word in a particular context.
 *
 * @author Keith Stevens
 */
public class WordOccrrenceContextGenerator implements ContextGenerator {

    /**
     * The {@link BasisMapping} used to represent the feature space.
     */
    private final BasisMapping basis;

    /**
     * The type of weight to apply to a the co-occurrence word based on its
     * relative location
     */
    private final WeightingFunction weighting;

    /**
     * The number of words to consider in one direction to create the symmetric
     * window
     */
    private final int windowSize;

    /**
     * Creates a new {@link WordOccrrenceContextGenerator}.
     *
     * @param weighting The {@link WeightingFunction} used to score each word
     *                co-occrrence, based on the distance from the focus word
     * @param windowSize The size of the sliding symmetric window composing a
     *                context
     */
    public WordOccrrenceContextGenerator(BasisMapping basis,
                                         WeightingFunction weighting,
                                         int windowSize) {
        this.basis = basis;
        this.weighting = weighting;
        this.windowSize = windowSize;
    }

    /**
     * {@inheritDoc}
     */
    public SparseDoubleVector generateContext(Queue prevWords,
                                              Queue nextWords) {
        SparseDoubleVector meaning = new CompactSparseVector();
        addContextTerms(meaning, prevWords, -1 * prevWords.size());
        addContextTerms(meaning, nextWords, 1);
        return meaning;
    }

    /**
     * {@inheritDoc}
     */
    public int getVectorLength() {
        return basis.numDimensions();
    }

    /**
     * {@inheritDoc}
     */
    public void setReadOnly(boolean readOnly) {
        basis.setReadOnly(readOnly);
    }

    /**
     * Adds a feature for each word in the context that has a valid dimension.
     * Feature are scored based on the context word's distance from the focus
     * word.
     */
    protected void addContextTerms(SparseDoubleVector meaning,
                                   Queue words,
                                   int distance) {
        // Iterate through each of the context words.
        for (String term : words) {
            if (!term.equals(IteratorFactory.EMPTY_TOKEN)) {
                // Ignore any features that have no valid dimension.
                int dimension = basis.getDimension(term);
                if (dimension == -1)
                    continue;

                // Add the feature to the context vector and increase the
                // distance from the focus word.
                meaning.set(dimension, weighting.weight(distance, windowSize));
                ++distance;
            }
        }
    }
}