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Java example source code file (MapUtils.java)

This example Java source code file (MapUtils.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

arraylist, collection, comparator, distancemeasure, hashmap, integer, iterable, list, maputils, network, neuron, neuronsquaremesh2d, nodataexception, pairneurondouble, util

The MapUtils.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;

import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Comparator;

import org.apache.commons.math3.exception.NoDataException;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
import org.apache.commons.math3.util.Pair;

/**
 * Utilities for network maps.
 *
 * @since 3.3
 */
public class MapUtils {
    /**
     * Class contains only static methods.
     */
    private MapUtils() {}

    /**
     * Finds the neuron that best matches the given features.
     *
     * @param features Data.
     * @param neurons List of neurons to scan. If the list is empty
     * {@code null} will be returned.
     * @param distance Distance function. The neuron's features are
     * passed as the first argument to {@link DistanceMeasure#compute(double[],double[])}.
     * @return the neuron whose features are closest to the given data.
     * @throws org.apache.commons.math3.exception.DimensionMismatchException
     * if the size of the input is not compatible with the neurons features
     * size.
     */
    public static Neuron findBest(double[] features,
                                  Iterable<Neuron> neurons,
                                  DistanceMeasure distance) {
        Neuron best = null;
        double min = Double.POSITIVE_INFINITY;
        for (final Neuron n : neurons) {
            final double d = distance.compute(n.getFeatures(), features);
            if (d < min) {
                min = d;
                best = n;
            }
        }

        return best;
    }

    /**
     * Finds the two neurons that best match the given features.
     *
     * @param features Data.
     * @param neurons List of neurons to scan. If the list is empty
     * {@code null} will be returned.
     * @param distance Distance function. The neuron's features are
     * passed as the first argument to {@link DistanceMeasure#compute(double[],double[])}.
     * @return the two neurons whose features are closest to the given data.
     * @throws org.apache.commons.math3.exception.DimensionMismatchException
     * if the size of the input is not compatible with the neurons features
     * size.
     */
    public static Pair<Neuron, Neuron> findBestAndSecondBest(double[] features,
                                                             Iterable<Neuron> neurons,
                                                             DistanceMeasure distance) {
        Neuron[] best = { null, null };
        double[] min = { Double.POSITIVE_INFINITY,
                         Double.POSITIVE_INFINITY };
        for (final Neuron n : neurons) {
            final double d = distance.compute(n.getFeatures(), features);
            if (d < min[0]) {
                // Replace second best with old best.
                min[1] = min[0];
                best[1] = best[0];

                // Store current as new best.
                min[0] = d;
                best[0] = n;
            } else if (d < min[1]) {
                // Replace old second best with current.
                min[1] = d;
                best[1] = n;
            }
        }

        return new Pair<Neuron, Neuron>(best[0], best[1]);
    }

    /**
     * Creates a list of neurons sorted in increased order of the distance
     * to the given {@code features}.
     *
     * @param features Data.
     * @param neurons List of neurons to scan. If it is empty, an empty array
     * will be returned.
     * @param distance Distance function.
     * @return the neurons, sorted in increasing order of distance in data
     * space.
     * @throws org.apache.commons.math3.exception.DimensionMismatchException
     * if the size of the input is not compatible with the neurons features
     * size.
     *
     * @see #findBest(double[],Iterable,DistanceMeasure)
     * @see #findBestAndSecondBest(double[],Iterable,DistanceMeasure)
     *
     * @since 3.6
     */
    public static Neuron[] sort(double[] features,
                                Iterable<Neuron> neurons,
                                DistanceMeasure distance) {
        final List<PairNeuronDouble> list = new ArrayList();

        for (final Neuron n : neurons) {
            final double d = distance.compute(n.getFeatures(), features);
            list.add(new PairNeuronDouble(n, d));
        }

        Collections.sort(list, PairNeuronDouble.COMPARATOR);

        final int len = list.size();
        final Neuron[] sorted = new Neuron[len];

        for (int i = 0; i < len; i++) {
            sorted[i] = list.get(i).getNeuron();
        }
        return sorted;
    }

    /**
     * Computes the <a href="http://en.wikipedia.org/wiki/U-Matrix">
     *  U-matrix</a> of a two-dimensional map.
     *
     * @param map Network.
     * @param distance Function to use for computing the average
     * distance from a neuron to its neighbours.
     * @return the matrix of average distances.
     */
    public static double[][] computeU(NeuronSquareMesh2D map,
                                      DistanceMeasure distance) {
        final int numRows = map.getNumberOfRows();
        final int numCols = map.getNumberOfColumns();
        final double[][] uMatrix = new double[numRows][numCols];

        final Network net = map.getNetwork();

        for (int i = 0; i < numRows; i++) {
            for (int j = 0; j < numCols; j++) {
                final Neuron neuron = map.getNeuron(i, j);
                final Collection<Neuron> neighbours = net.getNeighbours(neuron);
                final double[] features = neuron.getFeatures();

                double d = 0;
                int count = 0;
                for (Neuron n : neighbours) {
                    ++count;
                    d += distance.compute(features, n.getFeatures());
                }

                uMatrix[i][j] = d / count;
            }
        }

        return uMatrix;
    }

    /**
     * Computes the "hit" histogram of a two-dimensional map.
     *
     * @param data Feature vectors.
     * @param map Network.
     * @param distance Function to use for determining the best matching unit.
     * @return the number of hits for each neuron in the map.
     */
    public static int[][] computeHitHistogram(Iterable<double[]> data,
                                              NeuronSquareMesh2D map,
                                              DistanceMeasure distance) {
        final HashMap<Neuron, Integer> hit = new HashMap();
        final Network net = map.getNetwork();

        for (double[] f : data) {
            final Neuron best = findBest(f, net, distance);
            final Integer count = hit.get(best);
            if (count == null) {
                hit.put(best, 1);
            } else {
                hit.put(best, count + 1);
            }
        }

        // Copy the histogram data into a 2D map.
        final int numRows = map.getNumberOfRows();
        final int numCols = map.getNumberOfColumns();
        final int[][] histo = new int[numRows][numCols];

        for (int i = 0; i < numRows; i++) {
            for (int j = 0; j < numCols; j++) {
                final Neuron neuron = map.getNeuron(i, j);
                final Integer count = hit.get(neuron);
                if (count == null) {
                    histo[i][j] = 0;
                } else {
                    histo[i][j] = count;
                }
            }
        }

        return histo;
    }

    /**
     * Computes the quantization error.
     * The quantization error is the average distance between a feature vector
     * and its "best matching unit" (closest neuron).
     *
     * @param data Feature vectors.
     * @param neurons List of neurons to scan.
     * @param distance Distance function.
     * @return the error.
     * @throws NoDataException if {@code data} is empty.
     */
    public static double computeQuantizationError(Iterable<double[]> data,
                                                  Iterable<Neuron> neurons,
                                                  DistanceMeasure distance) {
        double d = 0;
        int count = 0;
        for (double[] f : data) {
            ++count;
            d += distance.compute(f, findBest(f, neurons, distance).getFeatures());
        }

        if (count == 0) {
            throw new NoDataException();
        }

        return d / count;
    }

    /**
     * Computes the topographic error.
     * The topographic error is the proportion of data for which first and
     * second best matching units are not adjacent in the map.
     *
     * @param data Feature vectors.
     * @param net Network.
     * @param distance Distance function.
     * @return the error.
     * @throws NoDataException if {@code data} is empty.
     */
    public static double computeTopographicError(Iterable<double[]> data,
                                                 Network net,
                                                 DistanceMeasure distance) {
        int notAdjacentCount = 0;
        int count = 0;
        for (double[] f : data) {
            ++count;
            final Pair<Neuron, Neuron> p = findBestAndSecondBest(f, net, distance);
            if (!net.getNeighbours(p.getFirst()).contains(p.getSecond())) {
                // Increment count if first and second best matching units
                // are not neighbours.
                ++notAdjacentCount;
            }
        }

        if (count == 0) {
            throw new NoDataException();
        }

        return ((double) notAdjacentCount) / count;
    }

    /**
     * Helper data structure holding a (Neuron, double) pair.
     */
    private static class PairNeuronDouble {
        /** Comparator. */
        static final Comparator<PairNeuronDouble> COMPARATOR
            = new Comparator<PairNeuronDouble>() {
            /** {@inheritDoc} */
            public int compare(PairNeuronDouble o1,
                               PairNeuronDouble o2) {
                return Double.compare(o1.value, o2.value);
            }
        };
        /** Key. */
        private final Neuron neuron;
        /** Value. */
        private final double value;

        /**
         * @param neuron Neuron.
         * @param value Value.
         */
        PairNeuronDouble(Neuron neuron, double value) {
            this.neuron = neuron;
            this.value = value;
        }

        /** @return the neuron. */
        public Neuron getNeuron() {
            return neuron;
        }

    }
}

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