/*
* 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.ml.factory.method;
import java.util.List;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.factory.parse.ArchitectureLayer;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.pnn.BasicPNN;
import org.encog.neural.pnn.PNNKernelType;
import org.encog.neural.pnn.PNNOutputMode;
import org.encog.util.ParamsHolder;
/**
* A factory to create PNN networks.
*/
public class PNNFactory {
/**
* The max layer count.
*/
public static final int MAX_LAYERS = 3;
/**
* Create a PNN network.
* @param architecture THe architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The RBF network.
*/
public final MLMethod create(final String architecture, final int input,
final int output) {
final List<String> layers = ArchitectureParse.parseLayers(architecture);
if (layers.size() != MAX_LAYERS) {
throw new EncogError(
"PNN Networks must have exactly three elements, "
+ "separated by ->.");
}
final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
layers.get(0), input);
final ArchitectureLayer pnnLayer = ArchitectureParse.parseLayer(
layers.get(1), -1);
final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
layers.get(2), output);
final int inputCount = inputLayer.getCount();
final int outputCount = outputLayer.getCount();
PNNKernelType kernel;
PNNOutputMode outmodel;
if (pnnLayer.getName().equalsIgnoreCase("c")) {
outmodel = PNNOutputMode.Classification;
} else if (pnnLayer.getName().equalsIgnoreCase("r")) {
outmodel = PNNOutputMode.Regression;
} else if (pnnLayer.getName().equalsIgnoreCase("u")) {
outmodel = PNNOutputMode.Unsupervised;
} else {
throw new NeuralNetworkError("Unknown model: "
+ pnnLayer.getName());
}
final ParamsHolder holder = new ParamsHolder(pnnLayer.getParams());
final String kernelStr = holder.getString("KERNEL", false, "gaussian");
if (kernelStr.equalsIgnoreCase("gaussian")) {
kernel = PNNKernelType.Gaussian;
} else if (kernelStr.equalsIgnoreCase("reciprocal")) {
kernel = PNNKernelType.Reciprocal;
} else {
throw new NeuralNetworkError("Unknown kernel: " + kernelStr);
}
final BasicPNN result = new BasicPNN(kernel, outmodel,
inputCount, outputCount);
return result;
}
}