/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 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 org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.lma.LevenbergMarquardtTraining;
/**
* This class is a factory to create the LMA training method.
*/
public class LMAFactory {
/**
* Create a LMA 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 MLTrain create(final MLMethod method,
final MLDataSet training,
final String argsStr) {
if (!(method instanceof BasicNetwork)) {
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.getClass().getName());
}
final LevenbergMarquardtTraining result
= new LevenbergMarquardtTraining(
(BasicNetwork) method, training);
return result;
}
}