/*
* 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.neural.pattern;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.ContextLayer;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.logic.SOMLogic;
import org.encog.neural.networks.synapse.SynapseType;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* A recurrent self organizing map is a self organizing map that has a recurrent
* context connection on the hidden layer. This type of neural network is adept
* at classifying temporal data.
*
* @author jheaton
*
*/
public class RSOMPattern implements NeuralNetworkPattern {
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* The logging object.
*/
@SuppressWarnings("unused")
private final Logger logger = LoggerFactory.getLogger(this.getClass());
/**
* Add a hidden layer. SOM networks do not have hidden layers, so this will
* throw an error.
*
* @param count
* The number of hidden neurons.
*/
public void addHiddenLayer(final int count) {
final String str = "A SOM network does not have hidden layers.";
if (this.logger.isErrorEnabled()) {
this.logger.error(str);
}
throw new PatternError(str);
}
/**
* Clear out any hidden neurons.
*/
public void clear() {
}
/**
* Generate the RSOM network.
*
* @return The neural network.
*/
public BasicNetwork generate() {
final Layer output = new BasicLayer(new ActivationLinear(), false,
this.outputNeurons);
final Layer input = new BasicLayer(new ActivationLinear(), false,
this.inputNeurons);
final BasicNetwork network = new BasicNetwork(new SOMLogic());
final Layer context = new ContextLayer(this.outputNeurons);
network.addLayer(input);
network.addLayer(output);
output.addNext(context, SynapseType.OneToOne);
context.addNext(input);
int y = PatternConst.START_Y;
input.setX(PatternConst.START_X);
input.setY(y);
context.setX(PatternConst.INDENT_X);
context.setY(y);
y += PatternConst.INC_Y;
output.setX(PatternConst.START_X);
output.setY(y);
network.getStructure().finalizeStructure();
network.reset();
return network;
}
/**
* Set the activation function. A SOM uses a linear activation function, so
* this method throws an error.
*
* @param activation
* The activation function to use.
*/
public void setActivationFunction(final ActivationFunction activation) {
final String str = "A SOM network can't define an activation function.";
if (this.logger.isErrorEnabled()) {
this.logger.error(str);
}
throw new PatternError(str);
}
/**
* Set the input neuron count.
*
* @param count
* The number of neurons.
*/
public void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the output neuron count.
*
* @param count
* The number of neurons.
*/
public void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
}