/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.mathutil.matrices.Matrix;
import org.encog.neural.networks.BasicNetwork;
/**
* A randomizer that attempts to create starting weight values that are
* conducive to propagation training.
*
* This is one of the best randomizers offered in Encog, however, the Nguyen
* Widrow method generally performs better.
*
* From:
*
* Neural Networks - A Comprehensive Foundation, Haykin, chapter 6.7
*
* @author jheaton
*
*/
public class FanInRandomizer extends BasicRandomizer {
/**
* Error message. Can't use fan-in on a single number.
*/
static final String ERROR = "To use FanInRandomizer you must "
+ "present a Matrix or 2D array type value.";
/**
* The default boundary.
*/
private static final double DEFAULT_BOUNDARY = 2.4;
/** The lower bound. */
private final double lowerBound;
/** The upper bound. */
private final double upperBound;
/**
* Should the square root of the number of rows be used?
*/
private final boolean sqrt;
/**
* Create a fan-in randomizer with default values.
*/
public FanInRandomizer() {
this(-FanInRandomizer.DEFAULT_BOUNDARY,
FanInRandomizer.DEFAULT_BOUNDARY, false);
}
/**
* Construct a fan-in randomizer along the specified boundary. The min will
* be -boundary and the max will be boundary.
*
* @param boundary
* The boundary for the fan-in.
* @param sqrt
* Should the square root of the rows to be used in the
* calculation.
*/
public FanInRandomizer(final double boundary, final boolean sqrt) {
this(-boundary, boundary, sqrt);
}
/**
* Construct a fan-in randomizer. Use the specified bounds.
*
* @param aLowerBound
* The lower bound.
* @param anUpperBound
* The upper bound.
* @param sqrt
* True if the square root of the rows should be used in the
* calculation.
*/
public FanInRandomizer(final double aLowerBound, final double anUpperBound,
final boolean sqrt) {
this.lowerBound = aLowerBound;
this.upperBound = anUpperBound;
this.sqrt = sqrt;
}
/**
* Calculate the fan-in value.
*
* @param rows
* The number of rows.
* @return The fan-in value.
*/
private double calculateValue(final int rows) {
double rowValue;
if (this.sqrt) {
rowValue = Math.sqrt(rows);
} else {
rowValue = rows;
}
return (this.lowerBound / rowValue) + nextDouble()
* ((this.upperBound - this.lowerBound) / rowValue);
}
/**
* Throw an error if this class is used improperly.
*/
private void causeError() {
throw new EncogError(FanInRandomizer.ERROR);
}
/**
* Starting with the specified number, randomize it to the degree specified
* by this randomizer. This could be a totally new random number, or it
* could be based on the specified number.
*
* @param d
* The number to randomize.
* @return A randomized number.
*/
public double randomize(final double d) {
causeError();
return 0;
}
/**
* Randomize the array based on an array, modify the array. Previous values
* may be used, or they may be discarded, depending on the randomizer.
*
* @param d
* An array to randomize.
*/
@Override
public void randomize(final double[] d) {
for (int i = 0; i < d.length; i++) {
d[i] = calculateValue(1);
}
}
/**
* Randomize the 2d array based on an array, modify the array. Previous
* values may be used, or they may be discarded, depending on the
* randomizer.
*
* @param d
* An array to randomize.
*/
@Override
public void randomize(final double[][] d) {
for (int row = 0; row < d.length; row++) {
for (int col = 0; col < d[0].length; col++) {
d[row][col] = calculateValue(d.length);
}
}
}
/**
* Randomize the matrix based on an array, modify the array. Previous values
* may be used, or they may be discarded, depending on the randomizer.
*
* @param m
* A matrix to randomize.
*/
@Override
public void randomize(final Matrix m) {
for (int row = 0; row < m.getRows(); row++) {
for (int col = 0; col < m.getCols(); col++) {
m.set(row, col, calculateValue(m.getRows()));
}
}
}
/**
* Randomize one level of a neural network.
* @param network The network to randomize
* @param fromLayer The from level to randomize.
*/
public void randomize(final BasicNetwork network, int fromLayer)
{
int fromCount = network.getLayerTotalNeuronCount(fromLayer);
int toCount = network.getLayerNeuronCount(fromLayer+1);
for(int fromNeuron = 0; fromNeuron<fromCount; fromNeuron++)
{
for(int toNeuron = 0; toNeuron<toCount; toNeuron++)
{
double v = network.getWeight(fromLayer, fromNeuron, toNeuron);
v = calculateValue(toCount);
network.setWeight(fromLayer, fromNeuron, toNeuron, v);
}
}
}
}