/*
* 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.train;
import java.util.Map;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.ml.svm.SVM;
import org.encog.ml.svm.training.SVMTrain;
import org.encog.ml.train.MLTrain;
import org.encog.util.ParamsHolder;
/**
* A factory to create SVM trainers.
*/
public class SVMFactory {
/**
* Create a SVM trainer.
*
* @param method
* The method to use.
* @param training
* The training data to use.
* @param argsStr
* The arguments to use.
* @return The newly created trainer.
*/
public final MLTrain create(final MLMethod method,
final MLDataSet training,
final String argsStr) {
if (!(method instanceof SVM)) {
throw new EncogError(
"SVM Train training cannot be used on a method of type: "
+ method.getClass().getName());
}
final double defaultGamma = 1.0 / ((SVM) method).getInputCount();
final double defaultC = 1.0;
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA,
false, defaultGamma);
final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false,
defaultC);
final SVMTrain result = new SVMTrain((SVM) method, training);
result.setGamma(gamma);
result.setC(c);
return result;
}
}