/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.mathutil.randomize;
import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.mathutil.matrices.Matrix;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
/**
* Implementation of <i>Nguyen-Widrow</i> weight initialization. This is the
* default weight initialization used by Encog, as it generally provides the
* most train-able neural network.
*/
public class NguyenWidrowRandomizer extends BasicRandomizer {
public static String MSG = "This type of randomization is not supported by Nguyen-Widrow";
@Override
public void randomize(MLMethod method) {
if( !(method instanceof BasicNetwork) ) {
throw new EncogError("Nguyen-Widrow only supports BasicNetwork.");
}
BasicNetwork network = (BasicNetwork)method;
for(int fromLayer=0; fromLayer<network.getLayerCount()-1; fromLayer++) {
randomizeSynapse(network, fromLayer);
}
}
private double calculateRange(ActivationFunction af, double r) {
double[] d = { r };
af.activationFunction(d, 0, 1);
return d[0];
}
private void randomizeSynapse(BasicNetwork network, int fromLayer) {
int toLayer = fromLayer+1;
int toCount = network.getLayerNeuronCount(toLayer);
int fromCount = network.getLayerNeuronCount(fromLayer);
int fromCountTotalCount = network.getLayerTotalNeuronCount(fromLayer);
ActivationFunction af = network.getActivation(toLayer);
double low = calculateRange(af,Double.MIN_VALUE);
double high = calculateRange(af,Double.MAX_VALUE);
double b = 0.7d * Math.pow(toCount, (1d / fromCount)) / (high-low);
for(int toNeuron=0; toNeuron<toCount;toNeuron++) {
if( fromCount!=fromCountTotalCount ) {
double w = nextDouble(-b, b);
network.setWeight(fromLayer, fromCount, toNeuron, w);
}
for(int fromNeuron=0; fromNeuron<fromCount;fromNeuron++) {
double w = nextDouble(0, b);
network.setWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
@Override
public double randomize(double d) {
throw new EncogError(MSG);
}
@Override
public void randomize(double[] d) {
throw new EncogError(MSG);
}
@Override
public void randomize(double[][] d) {
throw new EncogError(MSG);
}
@Override
public void randomize(Matrix m) {
throw new EncogError(MSG);
}
@Override
public void randomize(double[] d, int begin, int size) {
throw new EncogError(MSG);
}
}