/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.mathutil.rbf.RBFEnum;
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.rbf.RBFNetwork;
import org.encog.util.ParamsHolder;
/**
* A factory to create RBF networks.
*/
public class RBFNetworkFactory {
/**
* The max layer count.
*/
public static final int MAX_LAYERS = 3;
/**
* Create a RBF network.
* @param architecture THe architecture string to use.
* @param input The input count.
* @param output The output count.
* @return The RBF network.
*/
public 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(
"RBF Networks must have exactly three elements, "
+ "separated by ->.");
}
final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
layers.get(0), input);
final ArchitectureLayer rbfLayer = 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();
RBFEnum t;
if (rbfLayer.getName().equalsIgnoreCase("Gaussian")) {
t = RBFEnum.Gaussian;
} else if (rbfLayer.getName().equalsIgnoreCase("Multiquadric")) {
t = RBFEnum.Multiquadric;
} else if (rbfLayer.getName().equalsIgnoreCase("InverseMultiquadric")) {
t = RBFEnum.InverseMultiquadric;
} else if (rbfLayer.getName().equalsIgnoreCase("MexicanHat")) {
t = RBFEnum.MexicanHat;
} else {
throw new NeuralNetworkError("Unknown RBF: " + rbfLayer.getName());
}
final ParamsHolder holder = new ParamsHolder(rbfLayer.getParams());
final int rbfCount = holder.getInt("C", true, 0);
final RBFNetwork result = new RBFNetwork(inputCount, rbfCount,
outputCount, t);
return result;
}
}