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Java example source code file (TravellingSalesmanSolver.java)

This example Java source code file (TravellingSalesmanSolver.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, city, constant, featureinitializer, harmonicoscillator, iterator, kohonenupdateaction, list, neuron, runnable, set, string, travellingsalesmansolver, univariatefunction, util

The TravellingSalesmanSolver.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.sofm;

import java.util.List;
import java.util.ArrayList;
import java.util.Set;
import java.util.HashSet;
import java.util.Collection;
import java.util.Iterator;

import org.apache.commons.math3.analysis.FunctionUtils;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.function.Constant;
import org.apache.commons.math3.analysis.function.HarmonicOscillator;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.distribution.UniformRealDistribution;
import org.apache.commons.math3.exception.MathUnsupportedOperationException;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.distance.EuclideanDistance;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializerFactory;
import org.apache.commons.math3.ml.neuralnet.Network;
import org.apache.commons.math3.ml.neuralnet.Neuron;
import org.apache.commons.math3.ml.neuralnet.oned.NeuronString;
import org.apache.commons.math3.ml.neuralnet.sofm.KohonenTrainingTask;
import org.apache.commons.math3.ml.neuralnet.sofm.KohonenUpdateAction;
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.random.RandomGenerator;
import org.apache.commons.math3.random.Well44497b;
import org.apache.commons.math3.util.FastMath;

/**
 * Solves the "Travelling Salesman's Problem" (i.e. trying to find the
 * sequence of cities that minimizes the travel distance) using a 1D
 * SOFM.
 */
public class TravellingSalesmanSolver {
    private static final long FIRST_NEURON_ID = 0;
    /** RNG. */
    private final RandomGenerator random;
    /** Set of cities. */
    private final Set<City> cities = new HashSet();
    /** SOFM. */
    private final Network net;
    /** Distance function. */
    private final DistanceMeasure distance = new EuclideanDistance();
    /** Total number of neurons. */
    private final int numberOfNeurons;

    /**
     * @param cityList List of cities to visit in a single travel.
     * @param numNeuronsPerCity Number of neurons per city.
     */
    public TravellingSalesmanSolver(City[] cityList,
                                    double numNeuronsPerCity) {
        this(cityList, numNeuronsPerCity, new Well44497b().nextLong());
    }

    /**
     * @param cityList List of cities to visit in a single travel.
     * @param numNeuronsPerCity Number of neurons per city.
     * @param seed Seed for the RNG that is used to present the samples
     * to the trainer.
     */
    public TravellingSalesmanSolver(City[] cityList,
                                    double numNeuronsPerCity,
                                    long seed) {
        random = new Well44497b(seed);

        // Make sure that each city will appear only once in the list.
        for (City city : cityList) {
            cities.add(city);
        }

        // Total number of neurons.
        numberOfNeurons = (int) numNeuronsPerCity * cities.size();

        // Create a network with circle topology.
        net = new NeuronString(numberOfNeurons, true, makeInitializers()).getNetwork();
    }

    /**
     * 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(2e-1,
                                                             5e-2,
                                                             numSamplesPerTask / 2);
        final NeighbourhoodSizeFunction neighbourhood
            = NeighbourhoodSizeFunctionFactory.exponentialDecay(0.5 * numberOfNeurons,
                                                                0.1 * numberOfNeurons,
                                                                numSamplesPerTask / 2);

        for (int i = 0; i < numTasks; i++) {
            final KohonenUpdateAction action = new KohonenUpdateAction(distance,
                                                                       learning,
                                                                       neighbourhood);
            tasks[i] = new KohonenTrainingTask(net,
                                               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];
    }

    /**
     * Measures the network's concurrent update performance.
     *
     * @return the ratio between the number of succesful network updates
     * and the number of update attempts.
     */
    public double getUpdateRatio() {
        return computeUpdateRatio(net);
    }

    /**
     * Measures the network's concurrent update performance.
     *
     * @param net Network to be trained with the SOFM algorithm.
     * @return the ratio between the number of successful network updates
     * and the number of update attempts.
     */
    private static double computeUpdateRatio(Network net) {
        long numAttempts = 0;
        long numSuccesses = 0;

        for (Neuron n : net) {
            numAttempts += n.getNumberOfAttemptedUpdates();
            numSuccesses += n.getNumberOfSuccessfulUpdates();
        }

        return (double) numSuccesses / (double) numAttempts;
    }

    /**
     * Creates an iterator that will present a series of city's coordinates in
     * a random order.
     *
     * @param numSamples Number of samples.
     * @return the iterator.
     */
    private Iterator<double[]> createRandomIterator(final long numSamples) {
        final List<City> cityList = new ArrayList();
        cityList.addAll(cities);

        return new Iterator<double[]>() {
            /** Number of samples. */
            private long n = 0;
            /** {@inheritDoc} */
            public boolean hasNext() {
                return n < numSamples;
            }
            /** {@inheritDoc} */
            public double[] next() {
                ++n;
                return cityList.get(random.nextInt(cityList.size())).getCoordinates();
            }
            /** {@inheritDoc} */
            public void remove() {
                throw new MathUnsupportedOperationException();
            }
        };
    }

    /**
     * @return the list of linked neurons (i.e. the one-dimensional
     * SOFM).
     */
    private List<Neuron> getNeuronList() {
        // Sequence of coordinates.
        final List<Neuron> list = new ArrayList();

        // First neuron.
        Neuron current = net.getNeuron(FIRST_NEURON_ID);
        while (true) {
            list.add(current);
            final Collection<Neuron> neighbours
                = net.getNeighbours(current, list);

            final Iterator<Neuron> iter = neighbours.iterator();
            if (!iter.hasNext()) {
                // All neurons have been visited.
                break;
            }

            current = iter.next();
        }

        return list;
    }

    /**
     * @return the list of features (coordinates) of linked neurons.
     */
    public List<double[]> getCoordinatesList() {
        // Sequence of coordinates.
        final List<double[]> coordinatesList = new ArrayList();

        for (Neuron n : getNeuronList()) {
            coordinatesList.add(n.getFeatures());
        }

        return coordinatesList;
    }

    /**
     * Returns the travel proposed by the solver.
     * Note: cities can be missing or duplicated.
     *
     * @return the list of cities in travel order.
     */
    public City[] getCityList() {
        final List<double[]> coord = getCoordinatesList();
        final City[] cityList = new City[coord.size()];
        for (int i = 0; i < cityList.length; i++) {
            final double[] c = coord.get(i);
            cityList[i] = getClosestCity(c[0], c[1]);
        }
        return cityList;
    }

    /**
     * @param x x-coordinate.
     * @param y y-coordinate.
     * @return the city whose coordinates are closest to {@code (x, y)}.
     */
    public City getClosestCity(double x,
                               double y) {
        City closest = null;
        double min = Double.POSITIVE_INFINITY;
        for (City c : cities) {
            final double d = c.distance(x, y);
            if (d < min) {
                min = d;
                closest = c;
            }
        }
        return closest;
    }

    /**
     * Computes the barycentre of all city locations.
     *
     * @param cities City list.
     * @return the barycentre.
     */
    private static double[] barycentre(Set<City> cities) {
        double xB = 0;
        double yB = 0;

        int count = 0;
        for (City c : cities) {
            final double[] coord = c.getCoordinates();
            xB += coord[0];
            yB += coord[1];

            ++count;
        }

        return new double[] { xB / count, yB / count };
    }

    /**
     * Computes the largest distance between the point at coordinates
     * {@code (x, y)} and any of the cities.
     *
     * @param x x-coodinate.
     * @param y y-coodinate.
     * @param cities City list.
     * @return the largest distance.
     */
    private static double largestDistance(double x,
                                          double y,
                                          Set<City> cities) {
        double maxDist = 0;
        for (City c : cities) {
            final double dist = c.distance(x, y);
            if (dist > maxDist) {
                maxDist = dist;
            }
        }

        return maxDist;
    }

    /**
     * Creates the features' initializers: an approximate circle around the
     * barycentre of the cities.
     *
     * @return an array containing the two initializers.
     */
    private FeatureInitializer[] makeInitializers() {
        // Barycentre.
        final double[] centre = barycentre(cities);
        // Largest distance from centre.
        final double radius = 0.5 * largestDistance(centre[0], centre[1], cities);

        final double omega = 2 * Math.PI / numberOfNeurons;
        final UnivariateFunction h1 = new HarmonicOscillator(radius, omega, 0);
        final UnivariateFunction h2 = new HarmonicOscillator(radius, omega, 0.5 * Math.PI);

        final UnivariateFunction f1 = FunctionUtils.add(h1, new Constant(centre[0]));
        final UnivariateFunction f2 = FunctionUtils.add(h2, new Constant(centre[1]));

        final RealDistribution u
            = new UniformRealDistribution(random, -0.05 * radius, 0.05 * radius);

        return new FeatureInitializer[] {
            FeatureInitializerFactory.randomize(u, FeatureInitializerFactory.function(f1, 0, 1)),
            FeatureInitializerFactory.randomize(u, FeatureInitializerFactory.function(f2, 0, 1))
        };
    }
}

/**
 * A city, represented by a name and two-dimensional coordinates.
 */
class City {
    /** Identifier. */
    final String name;
    /** x-coordinate. */
    final double x;
    /** y-coordinate. */
    final double y;

    /**
     * @param name Name.
     * @param x Cartesian x-coordinate.
     * @param y Cartesian y-coordinate.
     */
    public City(String name,
                double x,
                double y) {
        this.name = name;
        this.x = x;
        this.y = y;
    }

    /**
     * @retun the name.
     */
    public String getName() {
        return name;
    }

    /**
     * @return the (x, y) coordinates.
     */
    public double[] getCoordinates() {
        return new double[] { x, y };
    }

    /**
     * Computes the distance between this city and
     * the given point.
     *
     * @param x x-coodinate.
     * @param y y-coodinate.
     * @return the distance between {@code (x, y)} and this
     * city.
     */
    public double distance(double x,
                           double y) {
        final double xDiff = this.x - x;
        final double yDiff = this.y - y;

        return FastMath.sqrt(xDiff * xDiff + yDiff * yDiff);
    }

    /** {@inheritDoc} */
    public boolean equals(Object o) {
        if (o instanceof City) {
            final City other = (City) o;
            return x == other.x &&
                y == other.y;
        }
        return false;
    }

    /** {@inheritDoc} */
    public int hashCode() {
        int result = 17;

        final long c1 = Double.doubleToLongBits(x);
        result = 31 * result + (int) (c1 ^ (c1 >>> 32));

        final long c2 = Double.doubleToLongBits(y);
        result = 31 * result + (int) (c2 ^ (c2 >>> 32));

        return result;
    }
}

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