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Java example source code file (NeuronString.java)

This example Java source code file (NeuronString.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

featureinitializer, illegalstateexception, network, neuronstring, numberistoosmallexception, object, outofrangeexception, serializable, serializationproxy

The NeuronString.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.oned;

import java.io.Serializable;
import java.io.ObjectInputStream;
import org.apache.commons.math3.ml.neuralnet.Network;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.OutOfRangeException;

/**
 * Neural network with the topology of a one-dimensional line.
 * Each neuron defines one point on the line.
 *
 * @since 3.3
 */
public class NeuronString implements Serializable {
    /** Serial version ID */
    private static final long serialVersionUID = 1L;
    /** Underlying network. */
    private final Network network;
    /** Number of neurons. */
    private final int size;
    /** Wrap. */
    private final boolean wrap;

    /**
     * Mapping of the 1D coordinate to the neuron identifiers
     * (attributed by the {@link #network} instance).
     */
    private final long[] identifiers;

    /**
     * Constructor with restricted access, solely used for deserialization.
     *
     * @param wrap Whether to wrap the dimension (i.e the first and last
     * neurons will be linked together).
     * @param featuresList Arrays that will initialize the features sets of
     * the network's neurons.
     * @throws NumberIsTooSmallException if {@code num < 2}.
     */
    NeuronString(boolean wrap,
                 double[][] featuresList) {
        size = featuresList.length;

        if (size < 2) {
            throw new NumberIsTooSmallException(size, 2, true);
        }

        this.wrap = wrap;

        final int fLen = featuresList[0].length;
        network = new Network(0, fLen);
        identifiers = new long[size];

        // Add neurons.
        for (int i = 0; i < size; i++) {
            identifiers[i] = network.createNeuron(featuresList[i]);
        }

        // Add links.
        createLinks();
    }

    /**
     * Creates a one-dimensional network:
     * Each neuron not located on the border of the mesh has two
     * neurons linked to it.
     * <br/>
     * The links are bi-directional.
     * Neurons created successively are neighbours (i.e. there are
     * links between them).
     * <br/>
     * The topology of the network can also be a circle (if the
     * dimension is wrapped).
     *
     * @param num Number of neurons.
     * @param wrap Whether to wrap the dimension (i.e the first and last
     * neurons will be linked together).
     * @param featureInit Arrays that will initialize the features sets of
     * the network's neurons.
     * @throws NumberIsTooSmallException if {@code num < 2}.
     */
    public NeuronString(int num,
                        boolean wrap,
                        FeatureInitializer[] featureInit) {
        if (num < 2) {
            throw new NumberIsTooSmallException(num, 2, true);
        }

        size = num;
        this.wrap = wrap;
        identifiers = new long[num];

        final int fLen = featureInit.length;
        network = new Network(0, fLen);

        // Add neurons.
        for (int i = 0; i < num; i++) {
            final double[] features = new double[fLen];
            for (int fIndex = 0; fIndex < fLen; fIndex++) {
                features[fIndex] = featureInit[fIndex].value();
            }
            identifiers[i] = network.createNeuron(features);
        }

        // Add links.
        createLinks();
    }

    /**
     * Retrieves the underlying network.
     * A reference is returned (enabling, for example, the network to be
     * trained).
     * This also implies that calling methods that modify the {@link Network}
     * topology may cause this class to become inconsistent.
     *
     * @return the network.
     */
    public Network getNetwork() {
        return network;
    }

    /**
     * Gets the number of neurons.
     *
     * @return the number of neurons.
     */
    public int getSize() {
        return size;
    }

    /**
     * Retrieves the features set from the neuron at location
     * {@code i} in the map.
     *
     * @param i Neuron index.
     * @return the features of the neuron at index {@code i}.
     * @throws OutOfRangeException if {@code i} is out of range.
     */
    public double[] getFeatures(int i) {
        if (i < 0 ||
            i >= size) {
            throw new OutOfRangeException(i, 0, size - 1);
        }

        return network.getNeuron(identifiers[i]).getFeatures();
    }

    /**
     * Creates the neighbour relationships between neurons.
     */
    private void createLinks() {
        for (int i = 0; i < size - 1; i++) {
            network.addLink(network.getNeuron(i), network.getNeuron(i + 1));
        }
        for (int i = size - 1; i > 0; i--) {
            network.addLink(network.getNeuron(i), network.getNeuron(i - 1));
        }
        if (wrap) {
            network.addLink(network.getNeuron(0), network.getNeuron(size - 1));
            network.addLink(network.getNeuron(size - 1), network.getNeuron(0));
        }
    }

    /**
     * Prevents proxy bypass.
     *
     * @param in Input stream.
     */
    private void readObject(ObjectInputStream in) {
        throw new IllegalStateException();
    }

    /**
     * Custom serialization.
     *
     * @return the proxy instance that will be actually serialized.
     */
    private Object writeReplace() {
        final double[][] featuresList = new double[size][];
        for (int i = 0; i < size; i++) {
            featuresList[i] = getFeatures(i);
        }

        return new SerializationProxy(wrap,
                                      featuresList);
    }

    /**
     * Serialization.
     */
    private static class SerializationProxy implements Serializable {
        /** Serializable. */
        private static final long serialVersionUID = 20130226L;
        /** Wrap. */
        private final boolean wrap;
        /** Neurons' features. */
        private final double[][] featuresList;

        /**
         * @param wrap Whether the dimension is wrapped.
         * @param featuresList List of neurons features.
         * {@code neuronList}.
         */
        SerializationProxy(boolean wrap,
                           double[][] featuresList) {
            this.wrap = wrap;
            this.featuresList = featuresList;
        }

        /**
         * Custom serialization.
         *
         * @return the {@link Neuron} for which this instance is the proxy.
         */
        private Object readResolve() {
            return new NeuronString(wrap,
                                    featuresList);
        }
    }
}

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