/*
* 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.neural.networks.structure;
import java.util.ArrayList;
import java.util.List;
import org.encog.mathutil.NumericRange;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.Layer;
import org.encog.util.EngineArray;
import org.encog.util.Format;
/**
* Allows the weights and bias values of the neural network to be analyzed.
*/
public class AnalyzeNetwork {
/**
* The ranges of the weights.
*/
private final NumericRange weights;
/**
* The ranges of the bias values.
*/
private final NumericRange bias;
/**
* The ranges of both the weights and biases.
*/
private final NumericRange weightsAndBias;
/**
* The number of disabled connections.
*/
private final int disabledConnections;
/**
* The total number of connections.
*/
private final int totalConnections;
/**
* All of the values in the neural network.
*/
private final double[] allValues;
/**
* The weight values in the neural network.
*/
private final double[] weightValues;
/**
* The bias values in the neural network.
*/
private final double[] biasValues;
/**
* Construct a network analyze class. Analyze the specified network.
*
* @param network
* The network to analyze.
*/
public AnalyzeNetwork(final BasicNetwork network) {
final int assignDisabled = 0;
final int assignedTotal = 0;
final List<Double> biasList = new ArrayList<Double>();
final List<Double> weightList = new ArrayList<Double>();
final List<Double> allList = new ArrayList<Double>();
for (int layerNumber = 0; layerNumber < network.getLayerCount() - 1; layerNumber++) {
final int fromCount = network.getLayerNeuronCount(layerNumber);
final int fromBiasCount = network
.getLayerTotalNeuronCount(layerNumber);
final int toCount = network.getLayerNeuronCount(layerNumber + 1);
// weights
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++) {
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
final double v = network.getWeight(layerNumber, fromNeuron,
toNeuron);
weightList.add(v);
allList.add(v);
}
}
// bias
if (fromCount != fromBiasCount) {
final int biasNeuron = fromCount;
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
final double v = network.getWeight(layerNumber, biasNeuron,
toNeuron);
biasList.add(v);
allList.add(v);
}
}
}
for (final Layer layer : network.getStructure().getLayers()) {
if (layer.hasBias()) {
for (int i = 0; i < layer.getNeuronCount(); i++) {
}
}
}
this.disabledConnections = assignDisabled;
this.totalConnections = assignedTotal;
this.weights = new NumericRange(weightList);
this.bias = new NumericRange(biasList);
this.weightsAndBias = new NumericRange(allList);
this.weightValues = EngineArray.listToDouble(weightList);
this.allValues = EngineArray.listToDouble(allList);
this.biasValues = EngineArray.listToDouble(biasList);
}
/**
* @return All of the values in the neural network.
*/
public final double[] getAllValues() {
return this.allValues;
}
/**
* @return The numeric range of the bias values.
*/
public final NumericRange getBias() {
return this.bias;
}
/**
* @return The bias values in the neural network.
*/
public final double[] getBiasValues() {
return this.biasValues;
}
/**
* @return The number of disabled connections in the network.
*/
public final int getDisabledConnections() {
return this.disabledConnections;
}
/**
* @return The total number of connections in the network.
*/
public final int getTotalConnections() {
return this.totalConnections;
}
/**
* @return The numeric range of the weights values.
*/
public final NumericRange getWeights() {
return this.weights;
}
/**
* @return The numeric range of the weights and bias values.
*/
public final NumericRange getWeightsAndBias() {
return this.weightsAndBias;
}
/**
* @return The weight values in the neural network.
*/
public final double[] getWeightValues() {
return this.weightValues;
}
/**
* {@inheritDoc}
*/
@Override
public final String toString() {
final StringBuilder result = new StringBuilder();
result.append("All Values : ");
result.append(this.weightsAndBias.toString());
result.append("\n");
result.append("Bias : ");
result.append(this.bias.toString());
result.append("\n");
result.append("Weights : ");
result.append(this.weights.toString());
result.append("\n");
result.append("Disabled : ");
result.append(Format.formatInteger(this.disabledConnections));
result.append("\n");
return result.toString();
}
}