/*
* 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.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.flat.FlatLayer;
import org.encog.neural.flat.FlatNetwork;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
/**
* Holds "cached" information about the structure of the neural network. This is
* a very good performance boost since the neural network does not need to
* traverse itself each time a complete collection of layers or synapses is
* needed.
*
* @author jheaton
*
*/
public class NeuralStructure implements Serializable {
/**
* The serial ID.
*/
private static final long serialVersionUID = -2929683885395737817L;
/**
* The layers in this neural network.
*/
private final List<Layer> layers = new ArrayList<Layer>();
/**
* The neural network this class belongs to.
*/
private final BasicNetwork network;
/**
* The limit, below which a connection is treated as zero.
*/
private double connectionLimit;
/**
* Are connections limited?
*/
private boolean connectionLimited;
/**
* The flattened form of the network.
*/
private FlatNetwork flat;
/**
* Construct a structure object for the specified network.
*
* @param network
* The network to construct a structure for.
*/
public NeuralStructure(final BasicNetwork network) {
this.network = network;
}
/**
* Calculate the size that an array should be to hold all of the weights and
* bias values.
*
* @return The size of the calculated array.
*/
public final int calculateSize() {
return NetworkCODEC.networkSize(this.network);
}
/**
* Enforce that all connections are above the connection limit. Any
* connections below this limit will be severed.
*/
public final void enforceLimit() {
if (!this.connectionLimited) {
return;
}
final double[] weights = this.flat.getWeights();
for (int i = 0; i < weights.length; i++) {
if (Math.abs(weights[i]) < this.connectionLimit) {
weights[i] = 0;
}
}
}
/**
* Parse/finalize the limit value for connections.
*/
private void finalizeLimit() {
// see if there is a connection limit imposed
final String limit = this.network
.getPropertyString(BasicNetwork.TAG_LIMIT);
if (limit != null) {
try {
this.connectionLimited = true;
this.connectionLimit = Double.parseDouble(limit);
} catch (final NumberFormatException e) {
throw new NeuralNetworkError("Invalid property("
+ BasicNetwork.TAG_LIMIT + "):" + limit);
}
} else {
this.connectionLimited = false;
this.connectionLimit = 0;
}
}
/**
* Build the synapse and layer structure. This method should be called after
* you are done adding layers to a network, or change the network's logic
* property.
*/
public final void finalizeStructure() {
if (this.layers.size() < 2) {
throw new NeuralNetworkError(
"There must be at least two layers before the structure is finalized.");
}
final FlatLayer[] flatLayers = new FlatLayer[this.layers.size()];
for (int i = 0; i < this.layers.size(); i++) {
final BasicLayer layer = (BasicLayer) this.layers.get(i);
if (layer.getActivation() == null) {
layer.setActivation(new ActivationLinear());
}
flatLayers[i] = layer;
}
this.flat = new FlatNetwork(flatLayers);
finalizeLimit();
this.layers.clear();
enforceLimit();
}
/**
* @return The connection limit.
*/
public final double getConnectionLimit() {
return this.connectionLimit;
}
/**
* @return The flat network.
*/
public final FlatNetwork getFlat() {
requireFlat();
return this.flat;
}
/**
* @return The layers in this neural network.
*/
public final List<Layer> getLayers() {
return this.layers;
}
/**
* @return The network this structure belongs to.
*/
public final BasicNetwork getNetwork() {
return this.network;
}
/**
* @return True if this is not a fully connected feedforward network.
*/
public final boolean isConnectionLimited() {
return this.connectionLimited;
}
/**
* Throw an error if there is no flat network.
*/
public final void requireFlat() {
if (this.flat == null) {
throw new NeuralNetworkError(
"Must call finalizeStructure before using this network.");
}
}
/**
* Set the flat network.
* @param flat The flat network.
*/
public final void setFlat(final FlatNetwork flat) {
this.flat = flat;
}
/**
* Update any properties from the property map.
*/
public final void updateProperties() {
if (this.network.getProperties().containsKey(BasicNetwork.TAG_LIMIT)) {
this.connectionLimit = this.network
.getPropertyDouble(BasicNetwork.TAG_LIMIT);
this.connectionLimited = true;
} else {
this.connectionLimited = false;
this.connectionLimit = 0;
}
if (this.flat != null) {
this.flat.setConnectionLimit(this.connectionLimit);
}
}
}