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/*
 * File:                ClusterSingleLinkDivergenceFunction.java
 * Authors:             Justin Basilico
 * Company:             Sandia National Laboratories
 * Project:             Cognitive Foundry
 *
 * Copyright June 28, 2006, Sandia Corporation.  Under the terms of Contract
 * DE-AC04-94AL85000, there is a non-exclusive license for use of this work by
 * or on behalf of the U.S. Government. Export of this program may require a
 * license from the United States Government. See CopyrightHistory.txt for
 * complete details.
 *
 */

package gov.sandia.cognition.learning.algorithm.clustering.divergence;

import gov.sandia.cognition.annotation.CodeReview;
import gov.sandia.cognition.learning.algorithm.clustering.cluster.Cluster;
import gov.sandia.cognition.math.DivergenceFunction;
import java.util.Collection;

/**
 * The ClusterSingleLinkDivergenceFunction class implements the complete 
 * linkage distance metric between two clusters. That is, the value returned 
 * by the metric is the minimum distance between any point in the first 
 * cluster and any point in the second cluster.
 *
 * @param  type of {@code Cluster} used in the
 * {@code learn()} method
 * @param  The algorithm operates on a {@code Collection},
 * so {@code DataType} will be something like Vector or String
 * @author Justin Basilico
 * @since 1.0
 */
@CodeReview(
    reviewer="Kevin R. Dixon",
    date="2008-07-23",
    changesNeeded=false,
    comments={
        "Cleaned up javadoc a little bit with code annotations.",
        "Otherwise, looks fine."
    }
)
public class ClusterSingleLinkDivergenceFunction
    , DataType>
    extends AbstractClusterToClusterDivergenceFunction
{
    /**
     * Creates a new instance of ClusterSingleLinkDivergenceFunction.
     */
    public ClusterSingleLinkDivergenceFunction()
    {
        this(null);
    }
    
    /**
     * Creates a new instance of ClusterSingleLinkDivergenceFunction using
     * the given divergence function for elements.
     *
     * @param  divergenceFunction The divergence function for elements.
     */
    public ClusterSingleLinkDivergenceFunction(
        DivergenceFunction 
            divergenceFunction)
    {
        super(divergenceFunction);
    }
    
    /**
     * This method computes the complete link distance between the two given 
     * Clusters. The distance returned is the minimum distance between a 
     * member of the first cluster and a member of the second cluster.
     *
     * @param  from The first Cluster.
     * @param  to The second Cluster.
     * @return The single link distance between the two given Clusters.
     */
    public double evaluate(
        ClusterType from,
        ClusterType to)
    {
        // Get the members of each cluster.
        Collection fromMembers = from.getMembers();
        Collection toMembers   = to.getMembers();
        
        // We are going to compute the minimum distance between the
        // pairs.
        double minDistance = Double.MAX_VALUE;

        // Double loop over the members of each cluster.
        for ( DataType first : fromMembers )
        {
            for ( DataType second : toMembers )
            {
                // Compute the distance between the two members.
                double distance =
                    this.divergenceFunction.evaluate(first, second);

                if ( distance < minDistance )
                {
                    // This is the smallest distance seen so far so save 
                    // it.
                    minDistance = distance;
                }
            }
        }

        // Return the minimum distance discovered.
        return minDistance;
    }
}