/*
* Encog(tm) Core v2.5 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2010 Heaton Research, Inc.
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.engine.network.train.prop;
import org.encog.engine.data.EngineDataSet;
import org.encog.engine.network.flat.FlatNetwork;
/**
* Train a flat network using RPROP.
*/
public class TrainFlatNetworkResilient extends TrainFlatNetworkProp {
/**
* The update values, for the weights and thresholds.
*/
private final double[] updateValues;
/**
* The zero tolerance.
*/
private final double zeroTolerance;
/**
* The maximum step value for rprop.
*/
private final double maxStep;
/**
* Construct a resilient trainer for flat networks.
*
* @param network
* The network to train.
* @param training
* The training data to use.
* @param zeroTolerance
* How close a number should be to zero to be counted as zero.
* @param initialUpdate
* The initial update value.
* @param maxStep
* The maximum step value.
*/
public TrainFlatNetworkResilient(final FlatNetwork network,
final EngineDataSet training, final double zeroTolerance,
final double initialUpdate, final double maxStep) {
super(network, training);
this.updateValues = new double[network.getWeights().length];
this.zeroTolerance = zeroTolerance;
this.maxStep = maxStep;
for (int i = 0; i < this.updateValues.length; i++) {
this.updateValues[i] = initialUpdate;
}
}
/**
* Tran a network using RPROP.
* @param flat
* The network to train.
* @param trainingSet
* The training data to use.
*/
public TrainFlatNetworkResilient(final FlatNetwork flat,
final EngineDataSet trainingSet) {
this(flat, trainingSet, RPROPConst.DEFAULT_ZERO_TOLERANCE,
RPROPConst.DEFAULT_INITIAL_UPDATE, RPROPConst.DEFAULT_MAX_STEP);
}
/**
* Determine the sign of the value.
*
* @param value
* The value to check.
* @return -1 if less than zero, 1 if greater, or 0 if zero.
*/
private int sign(final double value) {
if (Math.abs(value) < this.zeroTolerance) {
return 0;
} else if (value > 0) {
return 1;
} else {
return -1;
}
}
/**
* Calculate the amount to change the weight by.
*
* @param gradients
* The gradients.
* @param lastGradient
* The last gradients.
* @param index
* The index to update.
* @return The amount to change the weight by.
*/
@Override
public double updateWeight(final double[] gradients,
final double[] lastGradient, final int index) {
// multiply the current and previous gradient, and take the
// sign. We want to see if the gradient has changed its sign.
final int change = sign(gradients[index] * lastGradient[index]);
double weightChange = 0;
// if the gradient has retained its sign, then we increase the
// delta so that it will converge faster
if (change > 0) {
double delta = this.updateValues[index]
* RPROPConst.POSITIVE_ETA;
delta = Math.min(delta, this.maxStep);
weightChange = sign(gradients[index]) * delta;
this.updateValues[index] = delta;
lastGradient[index] = gradients[index];
} else if (change < 0) {
// if change<0, then the sign has changed, and the last
// delta was too big
double delta = this.updateValues[index]
* RPROPConst.NEGATIVE_ETA;
delta = Math.max(delta, RPROPConst.DELTA_MIN);
this.updateValues[index] = delta;
// set the previous gradent to zero so that there will be no
// adjustment the next iteration
lastGradient[index] = 0;
} else if (change == 0) {
// if change==0 then there is no change to the delta
final double delta = this.updateValues[index];
weightChange = sign(gradients[index]) * delta;
lastGradient[index] = gradients[index];
}
// apply the weight change, if any
return weightChange;
}
/**
* @return The RPROP update values.
*/
public double[] getUpdateValues() {
return updateValues;
}
}