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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 (c) 2012, Lawrence Livermore National Security, LLC. Produced at
 * the Lawrence Livermore National Laboratory. Written by Keith Stevens,
 * [email protected] OCEC-10-073 All rights reserved. 
 *
 * 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.clustering;


/**
 * Computes the popular Rand Index, which reports
 * the number of agreements between two {@link Partition}s.
 *
 * @author Keith Stevens
 */
public class RandIndex extends RandDistance {

    public double compare(Partition p1, Partition p2) {
        // Compute the raw number of disagreements between p1 and p2.
        double distance = super.compare(p1, p2);

        // Compute the total number of pairings possible in any partition of
        // this size.
        int numPoints = p1.numPoints();
        int totalPairs = numPoints * (numPoints - 1) / 2;
        
        // Compute the number of total agreements, i.e. the number of
        // co-clustered pairs and number of pairs no co-clustered in both
        // partitions by subtracting the number of disagreements from the total
        // number of possible pairs.  
        double numAgreements = totalPairs - distance;

        // Normalize this by the total number of pairs to make this a metric.
        return numAgreements / totalPairs;
    }

    /**
     * Returns false.
     */
    public boolean isDistance() {
        return false;
    }
}