/*
* 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.som.SOM;
/**
* A self organizing map is a neural network pattern with an input and output
* layer. There is no hidden layer. The winning neuron, which is that neuron
* with the higest output is the winner, this winning neuron is often used to
* classify the input into a group.
*
* @author jheaton
*
*/
public class SOMPattern implements NeuralNetworkPattern {
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* 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 final void addHiddenLayer(final int count) {
throw new PatternError( "A SOM network does not have hidden layers." );
}
/**
* Clear out any hidden neurons.
*/
public void clear() {
}
/**
* Generate the RSOM network.
*
* @return The neural network.
*/
public final MLMethod generate() {
SOM som = new SOM(this.inputNeurons,this.outputNeurons);
som.reset();
return som;
}
/**
* 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 final void setActivationFunction(final ActivationFunction activation) {
throw new PatternError( "A SOM network can't define an activation function.");
}
/**
* Set the input neuron count.
*
* @param count
* The number of neurons.
*/
public final void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the output neuron count.
*
* @param count
* The number of neurons.
*/
public final void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
}