/*
* 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;
import org.encog.Encog;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.plugin.EncogPluginBase;
import org.encog.plugin.EncogPluginService1;
/**
* This factory is used to create machine learning methods.
*/
public class MLMethodFactory {
/**
* String constant for a bayesian neural network.
*/
public static final String TYPE_BAYESIAN = "bayesian";
/**
* String constant for feedforward neural networks.
*/
public static final String TYPE_FEEDFORWARD = "feedforward";
/**
* String constant for RBF neural networks.
*/
public static final String TYPE_RBFNETWORK = "rbfnetwork";
/**
* String constant for support vector machines.
*/
public static final String TYPE_SVM = "svm";
/**
* String constant for SOMs.
*/
public static final String TYPE_SOM = "som";
/**
* A probabilistic neural network. Supports both PNN & GRNN.
*/
public static final String TYPE_PNN = "pnn";
/**
* A NEAT neural network.
*/
public static final String TYPE_NEAT = "neat";
/**
* A Encog program.
*/
public static final String TYPE_EPL = "epl";
public static final String PROPERTY_AF = "AF";
/**
* Population size.
*/
public static final String PROPERTY_POPULATION_SIZE = "population";
public static final String PROPERTY_CYCLES = "cycles";
/**
* Create a new machine learning method.
*
* @param methodType
* The method to create.
* @param architecture
* The architecture string.
* @param input
* The input count.
* @param output
* The output count.
* @return The newly created machine learning method.
*/
public MLMethod create(final String methodType,
final String architecture, final int input, final int output) {
for (EncogPluginBase plugin : Encog.getInstance().getPlugins()) {
if (plugin instanceof EncogPluginService1) {
MLMethod result = ((EncogPluginService1) plugin).createMethod(
methodType, architecture, input, output);
if (result != null) {
return result;
}
}
}
throw new EncogError("Unknown method type: " + methodType);
}
}