/*
* 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.thermal.HopfieldNetwork;
/**
* Create a Hopfield pattern. A Hopfield neural network has a single layer that
* functions both as the input and output layers. There are no hidden layers.
* Hopfield networks are used for basic pattern recognition. When a Hopfield
* network recognizes a pattern, it "echos" that pattern on the output.
*
* @author jheaton
*
*/
public class HopfieldPattern implements NeuralNetworkPattern {
/**
* How many neurons in the Hopfield network. Default to -1, which is
* invalid. Therefore this value must be set.
*/
private int neuronCount = -1;
/**
* Add a hidden layer. This will throw an error, because the Hopfield neural
* network has no hidden layers.
*
* @param count
* The number of neurons.
*/
public final void addHiddenLayer(final int count) {
throw new PatternError("A Hopfield network has no hidden layers.");
}
/**
* Nothing to clear.
*/
public void clear() {
}
/**
* Generate the Hopfield neural network.
*
* @return The generated network.
*/
public final MLMethod generate() {
HopfieldNetwork logic = new HopfieldNetwork(this.neuronCount);
return logic;
}
/**
* Set the activation function to use. This function will throw an error,
* because the Hopfield network must use the BiPolar activation function.
*
* @param activation
* The activation function to use.
*/
public final void setActivationFunction(final ActivationFunction activation) {
throw new PatternError( "A Hopfield network will use the BiPolar activation "
+ "function, no activation function needs to be specified.");
}
/**
* Set the number of input neurons, this must match the output neurons.
*
* @param count
* The number of neurons.
*/
public final void setInputNeurons(final int count) {
this.neuronCount = count;
}
/**
* Set the number of output neurons, should not be used with a hopfield
* neural network, because the number of input neurons defines the number of
* output neurons.
*
* @param count
* The number of neurons.
*/
public final void setOutputNeurons(final int count) {
throw new PatternError( "A Hopfield network has a single layer, so no need "
+ "to specify the output count.");
}
}