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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 a distance version of the popular Rand Index.  Instead of
 * computing the number of agreements between two {@link Partition}s, this
 * computes the number of disagreements between two {@link Partition}s.
 *
 * @author Keith Stevens
 */
public class RandDistance extends PartitionOverlapComparison {

    /**
     * {@inheritDoc}
     */
    public double compare(Partition p1, Partition p2) {
        // Compute the number of co-clustered elements in p1 and p2.  This runs
        // in O(n log n) time where n in the number of elements in p1.
        double overlap = super.compare(p1, p2);

        // Compute the number of co-clustered elements in each partition.  This
        // runs in O(numClusters) for both p1 and p2.
        int p1Pairs = p1.numPairs();
        int p2Pairs = p2.numPairs();

        // The sum of co-clustered elements in p1 and p2 account for the
        // pairings where each partition disagree AND they each account for the
        // overlapping co-clustered pairs.  So sum the two sums and subtract the
        // overlap count twice to compute the final distance.
        return p1Pairs + p2Pairs - 2*overlap;
    }

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