/*
* 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.pnn.BasicPNN;
import org.encog.neural.pnn.PNNKernelType;
import org.encog.neural.pnn.PNNOutputMode;
/**
* Pattern to create a PNN.
*
*/
public class PNNPattern implements NeuralNetworkPattern {
/**
* The kernel type.
*/
private PNNKernelType kernel = PNNKernelType.Gaussian;
/**
* The output model.
*/
private PNNOutputMode outmodel = PNNOutputMode.Regression;
/**
* The number of input neurons.
*/
private int inputNeurons;
/**
* The number of output neurons.
*/
private int outputNeurons;
/**
* Add a hidden layer. PNN networks do not have hidden layers, so this will
* throw an error.
*
* @param count
* The number of hidden neurons.
*/
@Override
public final void addHiddenLayer(final int count) {
throw new PatternError("A PNN network does not have hidden layers.");
}
/**
* Clear out any hidden neurons.
*/
@Override
public void clear() {
}
/**
* Generate the RSOM network.
*
* @return The neural network.
*/
@Override
public final MLMethod generate() {
final BasicPNN pnn = new BasicPNN(this.kernel, this.outmodel,
this.inputNeurons, this.outputNeurons);
return pnn;
}
/**
* @return The number of input neurons.
*/
public final int getInputNeurons() {
return this.inputNeurons;
}
/**
* @return The kernel.
*/
public final PNNKernelType getKernel() {
return this.kernel;
}
/**
* @return The output model.
*/
public final PNNOutputMode getOutmodel() {
return this.outmodel;
}
/**
* @return The number of output neurons.
*/
public final int getOutputNeurons() {
return this.outputNeurons;
}
/**
* Set the activation function. A PNN uses a linear activation function, so
* this method throws an error.
*
* @param activation
* The activation function to use.
*/
@Override
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.
*/
@Override
public final void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the kernel type.
*
* @param kernel
* The kernel type.
*/
public final void setKernel(final PNNKernelType kernel) {
this.kernel = kernel;
}
/**
* Set the output model.
* @param outmodel The output model.
*/
public final void setOutmodel(final PNNOutputMode outmodel) {
this.outmodel = outmodel;
}
/**
* Set the output neuron count.
*
* @param count
* The number of neurons.
*/
@Override
public final void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
}