weka.classifiers.functions.neural.SigmoidUnit.java Source code

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Here is the source code for weka.classifiers.functions.neural.SigmoidUnit.java

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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    SigmoidUnit.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.functions.neural;

import weka.core.RevisionHandler;
import weka.core.RevisionUtils;

/**
 * This can be used by the 
 * neuralnode to perform all it's computations (as a sigmoid unit).
 *
 * @author Malcolm Ware (mfw4@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class SigmoidUnit implements NeuralMethod, RevisionHandler {

    /** for serialization */
    private static final long serialVersionUID = -5162958458177475652L;

    /**
     * This function calculates what the output value should be.
     * @param node The node to calculate the value for.
     * @return The value.
     */
    public double outputValue(NeuralNode node) {
        double[] weights = node.getWeights();
        NeuralConnection[] inputs = node.getInputs();
        double value = weights[0];
        for (int noa = 0; noa < node.getNumInputs(); noa++) {

            value += inputs[noa].outputValue(true) * weights[noa + 1];
        }

        //this I got from the Neural Network faq to combat overflow
        //pretty simple solution really :)
        if (value < -45) {
            value = 0;
        } else if (value > 45) {
            value = 1;
        } else {
            value = 1 / (1 + Math.exp(-value));
        }
        return value;
    }

    /**
     * This function calculates what the error value should be.
     * @param node The node to calculate the error for.
     * @return The error.
     */
    public double errorValue(NeuralNode node) {
        //then calculate the error.

        NeuralConnection[] outputs = node.getOutputs();
        int[] oNums = node.getOutputNums();
        double error = 0;

        for (int noa = 0; noa < node.getNumOutputs(); noa++) {
            error += outputs[noa].errorValue(true) * outputs[noa].weightValue(oNums[noa]);
        }
        double value = node.outputValue(false);
        error *= value * (1 - value);

        return error;
    }

    /**
     * This function will calculate what the change in weights should be
     * and also update them.
     * @param node The node to update the weights for.
     * @param learn The learning rate to use.
     * @param momentum The momentum to use.
     */
    public void updateWeights(NeuralNode node, double learn, double momentum) {

        NeuralConnection[] inputs = node.getInputs();
        double[] cWeights = node.getChangeInWeights();
        double[] weights = node.getWeights();
        double learnTimesError = 0;
        learnTimesError = learn * node.errorValue(false);
        double c = learnTimesError + momentum * cWeights[0];
        weights[0] += c;
        cWeights[0] = c;

        int stopValue = node.getNumInputs() + 1;
        for (int noa = 1; noa < stopValue; noa++) {

            c = learnTimesError * inputs[noa - 1].outputValue(false);
            c += momentum * cWeights[noa];

            weights[noa] += c;
            cWeights[noa] = c;
        }
    }

    /**
     * Returns the revision string.
     * 
     * @return      the revision
     */
    public String getRevision() {
        return RevisionUtils.extract("$Revision$");
    }
}