/*
* 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.ml.svm.KernelType;
import org.encog.ml.svm.SVM;
import org.encog.ml.svm.SVMType;
/**
* A pattern to create support vector machines.
*
*/
public class SVMPattern implements NeuralNetworkPattern {
/**
* The number of neurons in the first layer.
*/
private int inputNeurons;
/**
* The number of neurons in the second layer.
*/
private int outputNeurons;
/**
* True, if using regression.
*/
private boolean regression = true;
/**
* The kernel type.
*/
private KernelType kernelType = KernelType.RadialBasisFunction;
/**
* The SVM type.
*/
private SVMType svmType = SVMType.EpsilonSupportVectorRegression;
/**
* Unused, a BAM has no hidden layers.
*
* @param count
* Not used.
*/
@Override
public final void addHiddenLayer(final int count) {
throw new PatternError("A SVM network has no hidden layers.");
}
/**
* Clear any settings on the pattern.
*/
@Override
public final void clear() {
this.inputNeurons = 0;
this.outputNeurons = 0;
}
/**
* @return The generated network.
*/
@Override
public final MLMethod generate() {
if (this.outputNeurons != 1) {
throw new PatternError("A SVM may only have one output.");
}
final SVM network = new SVM(this.inputNeurons, this.svmType,
this.kernelType);
return network;
}
/**
* @return The input neuron count.
*/
public final int getInputNeurons() {
return this.inputNeurons;
}
/**
* @return The input output count.
*/
public final int getOutputNeurons() {
return this.outputNeurons;
}
/**
* @return True, if this is regression.
*/
public final boolean isRegression() {
return this.regression;
}
/**
* Not used, the BAM uses a bipoloar activation function.
*
* @param activation
* Not used.
*/
@Override
public final void setActivationFunction(
final ActivationFunction activation) {
throw new PatternError(
"A SVM network can't specify a custom activation function.");
}
/**
* Set the number of input neurons.
*
* @param count
* The number of input neurons.
*/
@Override
public final void setInputNeurons(final int count) {
this.inputNeurons = count;
}
/**
* Set the kernel type.
* @param kernelType The kernel type.
*/
public final void setKernelType(final KernelType kernelType) {
this.kernelType = kernelType;
}
/**
* Set the number of output neurons.
*
* @param count
* The output neuron count.
*/
@Override
public final void setOutputNeurons(final int count) {
this.outputNeurons = count;
}
/**
* Set if regression is used.
* @param regression True if regression is used.
*/
public final void setRegression(final boolean regression) {
this.regression = regression;
}
/**
* Set the SVM type.
* @param svmType The SVM type.
*/
public final void setSVMType(final SVMType svmType) {
this.svmType = svmType;
}
}