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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 .
*/
/*
* 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 ([email protected])
* @version $Revision: 8034 $
*/
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: 8034 $");
}
}