com.jgaap.distances.HistogramDistance.java Source code

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Here is the source code for com.jgaap.distances.HistogramDistance.java

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/*
 * JGAAP -- a graphical program for stylometric authorship attribution
 * Copyright (C) 2009,2011 by Patrick Juola
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as
 * published by the Free Software Foundation, either version 3 of the
 * License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
/**
 **/
package com.jgaap.distances;

import java.util.Set;

import com.google.common.collect.Sets;
import com.jgaap.generics.DistanceFunction;
import com.jgaap.util.Event;
import com.jgaap.util.Histogram;

/**
 * Histogram distance using L2 metric,(defined as D(x,y) = sum ((xi -yi)^2) This
 * is YA distance for Nearest Neighbor algorithms
 * 
 * @author Juola
 * @version 1.0
 */
public class HistogramDistance extends DistanceFunction {
    public String displayName() {
        return "Histogram Distance";
    }

    public String tooltipText() {
        return "Histogram Distance (also known as Euclidean or L2 Norm)";
    }

    public boolean showInGUI() {
        return true;
    }

    /**
     * Returns histogram distance between event sets es1 and es2.
     * 
     * @param unknownEventSet
     *            The first EventSet
     * @param knownEventSet
     *            The second EventSet
     * @return the (Euclidean) histogram distance between them
     */

    @Override
    public double distance(Histogram unknownHistogram, Histogram knownHistogram) {
        double distance = 0.0;

        Set<Event> events = Sets.union(unknownHistogram.uniqueEvents(), knownHistogram.uniqueEvents());

        for (Event event : events) {
            distance += Math
                    .pow(unknownHistogram.relativeFrequency(event) - knownHistogram.relativeFrequency(event), 2);
        }

        return distance;
    }
}