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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;

import java.util.List;


/**
 * An implementation of the Best of K {@link ConsensusFunction} which finds the
 * input {@link Partition} that is most similar to all given {@link Partition}s.
 * Formally, this returns the {@link Partition} that optimizes a {@link
 * PartitionComparison} with respect to all other {@link Partition}s.  If the
 * given {@link PartitionComparison} is a distance, this will minimize the
 * distance, otherwise it will maximize the similarity.  This method is based on
 * 
 *   
 *    
Vladimir Filkov and
 *    Steven Skiena.  Integerating Microarray Data by Consensus Clustering. 
 *    Proceedings of the 15th IEEE Internation Conference on Tools with
 *    Artificial Intelligence..  Available here
 * 
 *
 * @author Keith Stevens
 */
public class BestOfKConsensusFunction implements ConsensusFunction {

    /**
     * The partition function used to compare two {@link Partition}s.
     */
    private final PartitionComparison comp;

    /**
     * Constructs a new {@link BestOfKConsensusFunction} using the {@link
     * RandDistance} comparison.
     */
    public BestOfKConsensusFunction() {
        this(new RandDistance());
    }

    /**
     * Constructs a new {@link BestOfKConsensusFunction} using the given {@link
     * PartitionComparison} function.
     */
    public BestOfKConsensusFunction(PartitionComparison comp) {
        this.comp = comp;
    }

    public Partition consensus(List partitions, int numClusters) {
        Partition best = null;
        double bestScore = (comp.isDistance())
            ? Double.MAX_VALUE 
            : -Double.MAX_VALUE;
        for (int i = 0; i < partitions.size(); ++i) {
            Partition curr = partitions.get(i);
            double totalScore = 0;
            for (int j = 0; j < partitions.size(); ++j)
                if (i != j) 
                    totalScore += comp.compare(curr, partitions.get(j));
            if ((comp.isDistance() && totalScore < bestScore) ||
                (!comp.isDistance() && totalScore > bestScore)) {
                best = curr;
                bestScore = totalScore;
            }
        }

        return Partition.copyOf(best);
    }
}