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Java example source code file (SmoothedDataHistogram.java)

This example Java source code file (SmoothedDataHistogram.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

distancemeasure, iterable, locationfinder, mapdatavisualization, neuron, numberistoosmallexception, smootheddatahistogram

The SmoothedDataHistogram.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math3.ml.neuralnet.twod.util;

import org.apache.commons.math3.ml.neuralnet.MapUtils;
import org.apache.commons.math3.ml.neuralnet.Neuron;
import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.exception.NumberIsTooSmallException;

/**
 * Visualization of high-dimensional data projection on a 2D-map.
 * The method is described in
 * <quote>
 *  <em>Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
 *  <br>
 *  by Elias Pampalk, Andreas Rauber and Dieter Merkl.
 * </quote>
 * @since 3.6
 */
public class SmoothedDataHistogram implements MapDataVisualization {
    /** Smoothing parameter. */
    private final int smoothingBins;
    /** Distance. */
    private final DistanceMeasure distance;
    /** Normalization factor. */
    private final double membershipNormalization;

    /**
     * @param smoothingBins Number of bins.
     * @param distance Distance.
     */
    public SmoothedDataHistogram(int smoothingBins,
                                 DistanceMeasure distance) {
        this.smoothingBins = smoothingBins;
        this.distance = distance;

        double sum = 0;
        for (int i = 0; i < smoothingBins; i++) {
            sum += smoothingBins - i;
        }

        this.membershipNormalization = 1d / sum;
    }

    /**
     * {@inheritDoc}
     *
     * @throws NumberIsTooSmallException if the size of the {@code map}
     * is smaller than the number of {@link #SmoothedDataHistogram(int,DistanceMeasure)
     * smoothing bins}.
     */
    public double[][] computeImage(NeuronSquareMesh2D map,
                                   Iterable<double[]> data) {
        final int nR = map.getNumberOfRows();
        final int nC = map.getNumberOfColumns();

        final int mapSize = nR * nC;
        if (mapSize < smoothingBins) {
            throw new NumberIsTooSmallException(mapSize, smoothingBins, true);
        }

        final LocationFinder finder = new LocationFinder(map);

        // Histogram bins.
        final double[][] histo = new double[nR][nC];

        for (double[] sample : data) {
            final Neuron[] sorted = MapUtils.sort(sample,
                                                  map.getNetwork(),
                                                  distance);
            for (int i = 0; i < smoothingBins; i++) {
                final LocationFinder.Location loc = finder.getLocation(sorted[i]);
                final int row = loc.getRow();
                final int col = loc.getColumn();
                histo[row][col] += (smoothingBins - i) * membershipNormalization;
            }
        }

        return histo;
    }
}

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