/*
* 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.pattern;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.ml.MLMethod;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
/**
* This class is used to generate an Jordan style recurrent neural network. This
* network type consists of three regular layers, an input output and hidden
* layer. There is also a context layer which accepts output from the output
* layer and outputs back to the hidden layer. This makes it a recurrent neural
* network.
*
* The Jordan neural network is useful for temporal input data. The specified
* activation function will be used on all layers. The Jordan neural network is
* similar to the Elman neural network.
*
* @author jheaton
*
*/
public class JordanPattern implements NeuralNetworkPattern {
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* The number of hidden neurons.
*/
private int hiddenNeurons;
/**
* The activation function.
*/
private ActivationFunction activation;
/**
* Construct an object to create a Jordan type neural network.
*/
public JordanPattern() {
this.inputNeurons = -1;
this.outputNeurons = -1;
this.hiddenNeurons = -1;
}
/**
* Add a hidden layer, there should be only one.
*
* @param count
* The number of neurons in this hidden layer.
*/
@Override
public final void addHiddenLayer(final int count) {
if (this.hiddenNeurons != -1) {
throw new PatternError(
"A Jordan neural network should have only one hidden "
+ "layer.");
}
this.hiddenNeurons = count;
}
/**
* Clear out any hidden neurons.
*/
@Override
public final void clear() {
this.hiddenNeurons = -1;
}
/**
* Generate a Jordan neural network.
*
* @return A Jordan neural network.
*/
@Override
public final MLMethod generate() {
BasicLayer hidden, output;
final BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(null, true,
this.inputNeurons));
network.addLayer(hidden = new BasicLayer(this.activation, true,
this.hiddenNeurons));
network.addLayer(output = new BasicLayer(this.activation, false,
this.outputNeurons));
hidden.setContextFedBy(output);
network.getStructure().finalizeStructure();
network.reset();
return network;
}
/**
* Set the activation function to use on each of the layers.
*
* @param activation
* The activation function.
*/
@Override
public final void setActivationFunction(final ActivationFunction activation) {
this.activation = activation;
}
/**
* Set the number of input neurons.
*
* @param count
* Neuron count.
*/
@Override
public final void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the number of output neurons.
*
* @param count
* Neuron count.
*/
@Override
public final void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
}