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Java example source code file (ChineseRingsClassifier.java)

This example Java source code file (ChineseRingsClassifier.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

chineseringsclassifier, featureinitializer, ioexception, iterable, iterator, kohonenupdateaction, mathunsupportedoperationexception, neuronsquaremesh2d, printwriter, quantization, randomgenerator, runnable, summarystatistics, util, vector3d

The ChineseRingsClassifier.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.userguide.sofm;

import java.util.Iterator;
import java.io.PrintWriter;
import java.io.IOException;
import org.apache.commons.math3.ml.neuralnet.SquareNeighbourhood;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory;
import org.apache.commons.math3.ml.neuralnet.MapUtils;
import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
import org.apache.commons.math3.ml.neuralnet.sofm.LearningFactorFunction;
import org.apache.commons.math3.ml.neuralnet.sofm.LearningFactorFunctionFactory;
import org.apache.commons.math3.ml.neuralnet.sofm.NeighbourhoodSizeFunction;
import org.apache.commons.math3.ml.neuralnet.sofm.NeighbourhoodSizeFunctionFactory;
import org.apache.commons.math3.ml.neuralnet.sofm.KohonenUpdateAction;
import org.apache.commons.math3.ml.neuralnet.sofm.KohonenTrainingTask;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.distance.EuclideanDistance;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.Well19937c;
import org.apache.commons.math3.stat.descriptive.SummaryStatistics;
import org.apache.commons.math3.geometry.euclidean.threed.Vector3D;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.exception.MathUnsupportedOperationException;

/**
 * SOFM for categorizing points that belong to each of two intertwined rings.
 *
 * The output currently consists in 3 text files:
 * <ul>
 *  <li>"before.chinese.U.seq.dat": U-matrix of the SOFM before training
 *  <li>"after.chinese.U.seq.dat": U-matrix of the SOFM after training
 *  <li>"after.chinese.hit.seq.dat": Hit histogram after training
 * <ul> 
 */
public class ChineseRingsClassifier {
    /** SOFM. */
    private final NeuronSquareMesh2D sofm;
    /** Rings. */
    private final ChineseRings rings;
    /** Distance function. */
    private final DistanceMeasure distance = new EuclideanDistance();

    public static void main(String[] args) {
        final ChineseRings rings = new ChineseRings(new Vector3D(1, 2, 3),
                                                    25, 2,
                                                    20, 1,
                                                    2000, 1500);
        final ChineseRingsClassifier classifier = new ChineseRingsClassifier(rings, 15, 15);
        printU("before.chinese.U.seq.dat", classifier);
        classifier.createSequentialTask(100000).run();
        printU("after.chinese.U.seq.dat", classifier);
        printHit("after.chinese.hit.seq.dat", classifier);
    }

    /**
     * @param rings Training data.
     * @param dim1 Number of rows of the SOFM.
     * @param dim2 Number of columns of the SOFM.
     */
    public ChineseRingsClassifier(ChineseRings rings,
                                  int dim1,
                                  int dim2) {
        this.rings = rings;
        sofm = new NeuronSquareMesh2D(dim1, false,
                                      dim2, false,
                                      SquareNeighbourhood.MOORE,
                                      makeInitializers());
    }

    /**
     * Creates training tasks.
     *
     * @param numTasks Number of tasks to create.
     * @param numSamplesPerTask Number of training samples per task.
     * @return the created tasks.
     */
    public Runnable[] createParallelTasks(int numTasks,
                                          long numSamplesPerTask) {
        final Runnable[] tasks = new Runnable[numTasks];
        final LearningFactorFunction learning
            = LearningFactorFunctionFactory.exponentialDecay(1e-1,
                                                             5e-2,
                                                             numSamplesPerTask / 2);
        final double numNeurons = FastMath.sqrt(sofm.getNumberOfRows() * sofm.getNumberOfColumns());
        final NeighbourhoodSizeFunction neighbourhood
            = NeighbourhoodSizeFunctionFactory.exponentialDecay(0.5 * numNeurons,
                                                                0.2 * numNeurons,
                                                                numSamplesPerTask / 2);

        for (int i = 0; i < numTasks; i++) {
            final KohonenUpdateAction action = new KohonenUpdateAction(distance,
                                                                       learning,
                                                                       neighbourhood);
            tasks[i] = new KohonenTrainingTask(sofm.getNetwork(),
                                               createRandomIterator(numSamplesPerTask),
                                               action);
        }
        
        return tasks;
    }

    /**
     * Creates a training task.
     *
     * @param numSamples Number of training samples.
     * @return the created task.
     */
    public Runnable createSequentialTask(long numSamples) {
        return createParallelTasks(1, numSamples)[0];
    }

    /**
     * Computes the U-matrix.
     *
     * @return the U-matrix of the network.
     */
    public double[][] computeU() {
        return MapUtils.computeU(sofm, distance);
    }

    /**
     * Computes the hit histogram.
     *
     * @return the histogram.
     */
    public int[][] computeHitHistogram() {
        return MapUtils.computeHitHistogram(createIterable(),
                                            sofm,
                                            distance);
    }

    /**
     * Computes the quantization error.
     *
     * @return the quantization error.
     */
    public double computeQuantizationError() {
        return MapUtils.computeQuantizationError(createIterable(),
                                                 sofm.getNetwork(),
                                                 distance);
    }

    /**
     * Computes the topographic error.
     *
     * @return the topographic error.
     */
    public double computeTopographicError() {
        return MapUtils.computeTopographicError(createIterable(),
                                                sofm.getNetwork(),
                                                distance);
    }

    /**
     * Creates the features' initializers.
     * They are sampled from a uniform distribution around the barycentre of
     * the rings.
     *
     * @return an array containing the initializers for the x, y and
     * z coordinates of the features array of the neurons.
     */
    private FeatureInitializer[] makeInitializers() {
        final SummaryStatistics[] centre = new SummaryStatistics[] {
            new SummaryStatistics(),
            new SummaryStatistics(),
            new SummaryStatistics()
        };
        for (Vector3D p : rings.getPoints()) {
            centre[0].addValue(p.getX());
            centre[1].addValue(p.getY());
            centre[2].addValue(p.getZ());
        }

        final double[] mean = new double[] {
            centre[0].getMean(),
            centre[1].getMean(),
            centre[2].getMean()
        };
        final double s = 0.1;
        final double[] dev = new double[] {
            s * centre[0].getStandardDeviation(),
            s * centre[1].getStandardDeviation(),
            s * centre[2].getStandardDeviation()
        };

        return new FeatureInitializer[] {
            FeatureInitializerFactory.uniform(mean[0] - dev[0], mean[0] + dev[0]),
            FeatureInitializerFactory.uniform(mean[1] - dev[1], mean[1] + dev[1]),
            FeatureInitializerFactory.uniform(mean[2] - dev[2], mean[2] + dev[2])
        };
    }

    /**
     * Creates an iterable that will present the points coordinates.
     *
     * @return the iterable.
     */
    private Iterable<double[]> createIterable() {
        return new Iterable<double[]>() {
            public Iterator<double[]> iterator() {
                return new Iterator<double[]>() {
                    /** Data. */
                    final Vector3D[] points = rings.getPoints();
                    /** Number of samples. */
                    private int n = 0;

                    /** {@inheritDoc} */
                    public boolean hasNext() {
                        return n < points.length;
                    }

                    /** {@inheritDoc} */
                    public double[] next() {
                        return points[n++].toArray();
                    }

                    /** {@inheritDoc} */
                    public void remove() {
                        throw new MathUnsupportedOperationException();
                    }
                };
            }
        };
    }

    /**
     * Creates an iterator that will present a series of points coordinates in
     * a random order.
     *
     * @param numSamples Number of samples.
     * @return the iterator.
     */
    private Iterator<double[]> createRandomIterator(final long numSamples) {
        return new Iterator<double[]>() {
            /** Data. */
            final Vector3D[] points = rings.getPoints();
            /** RNG. */
            final RandomGenerator rng = new Well19937c();
            /** Number of samples. */
            private long n = 0;

            /** {@inheritDoc} */
            public boolean hasNext() {
                return n < numSamples;
            }

            /** {@inheritDoc} */
            public double[] next() {
                ++n;
                return points[rng.nextInt(points.length)].toArray();
            }

            /** {@inheritDoc} */
            public void remove() {
                throw new MathUnsupportedOperationException();
            }
        };
    }

    /**
     * Prints the U-matrix of the map to the given filename.
     *
     * @param filename File.
     * @param sofm Classifier.
     */
    private static void printU(String filename,
                               ChineseRingsClassifier sofm) {
        PrintWriter out = null;
        try {
            out = new PrintWriter(filename);

            final double[][] uMatrix = sofm.computeU();
            for (int i = 0; i < uMatrix.length; i++) {
                for (int j = 0; j < uMatrix[0].length; j++) {
                    out.print(uMatrix[i][j] + " ");
                }
                out.println();
            }
            out.println("# Quantization error: " + sofm.computeQuantizationError());
            out.println("# Topographic error: " + sofm.computeTopographicError());
        } catch (IOException e) {
            // Do nothing.
        } finally {
            if (out != null) {
                out.close();
            }
        }
    }

    /**
     * Prints the hit histogram of the map to the given filename.
     *
     * @param filename File.
     * @param sofm Classifier.
     */
    private static void printHit(String filename,
                                 ChineseRingsClassifier sofm) {
        PrintWriter out = null;
        try {
            out = new PrintWriter(filename);

            final int[][] histo = sofm.computeHitHistogram();
            for (int i = 0; i < histo.length; i++) {
                for (int j = 0; j < histo[0].length; j++) {
                    out.print(histo[i][j] + " ");
                }
                out.println();
            }
        } catch (IOException e) {
            // Do nothing.
        } finally {
            if (out != null) {
                out.close();
            }
        }
    }
}

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