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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 David Jurgens
 *
 * 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;

// javadoc includes
import edu.ucla.sspace.clustering.HierarchicalAgglomerativeClustering.ClusterLinkage;
import edu.ucla.sspace.common.Similarity.SimType;

/**
 * A status object that represents the result of agglomeratively merging two
 * clusters.  This class provides the information on which clusters were merged,
 * what the id of the remaining cluster is, and the similarity of the two
 * clusters at the point at which they were merged.
 *
 * @see HierarchicalAgglomerativeClustering#buildDendogram(Matrix,edu.ucla.sspace.clustering.HierarchicalAgglomerativeClustering.ClusterLinkage,edu.ucla.sspace.common.Similarity.SimType)
 */
public class Merge implements java.io.Serializable {

    private static final long serialVersionUID = 1L;

    private final int remainingCluster;
    
    private final int mergedCluster;
    
    private final double similarity;

    public Merge(int remainingCluster, int mergedCluster, double similarity) {
        this.remainingCluster = remainingCluster;
        this.mergedCluster = mergedCluster;
        this.similarity = similarity;
    }

    public boolean equals(Object o) {
        if (o instanceof Merge) {
            Merge m = (Merge)o;
            return m.remainingCluster == remainingCluster
                && m.mergedCluster == mergedCluster
                && m.similarity == similarity;
        }
        return false;
    }

    public int hashCode() {
        return remainingCluster ^ mergedCluster;
    }

    /**
     * Returns the ID of the cluster that was merged into another cluster.  
     */
    public int mergedCluster() {
        return mergedCluster;
    }

    /**
     * Returns the ID of the clusters into which another cluster was merged,
     * i.e. all the data points in the merged cluster would now have this ID.
     */
    public int remainingCluster() {
        return remainingCluster;
    }

    /**
     * Returns the similarity of the two clusters at the time of their merging.
     */
    public double similarity() {
        return similarity;
    }

    public String toString() {
        return "(" + mergedCluster + " -> " + remainingCluster + ": "
            + similarity + ")";
    }
}