/*
* 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.train;
import java.util.Map;
import org.encog.ml.MLMethod;
import org.encog.ml.bayesian.BayesianError;
import org.encog.ml.bayesian.BayesianNetwork;
import org.encog.ml.bayesian.training.BayesianInit;
import org.encog.ml.bayesian.training.TrainBayesian;
import org.encog.ml.bayesian.training.estimator.BayesEstimator;
import org.encog.ml.bayesian.training.estimator.EstimatorNone;
import org.encog.ml.bayesian.training.estimator.SimpleEstimator;
import org.encog.ml.bayesian.training.search.SearchNone;
import org.encog.ml.bayesian.training.search.k2.BayesSearch;
import org.encog.ml.bayesian.training.search.k2.SearchK2;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.ml.train.MLTrain;
import org.encog.util.ParamsHolder;
public class TrainBayesianFactory {
/**
* Create a K2 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) {
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final int maxParents = holder.getInt(
MLTrainFactory.PROPERTY_MAX_PARENTS, false, 1);
String searchStr = holder.getString("SEARCH", false, "k2");
String estimatorStr = holder.getString("ESTIMATOR", false, "simple");
String initStr = holder.getString("INIT", false, "naive");
BayesSearch search;
BayesEstimator estimator;
BayesianInit init;
if( searchStr.equalsIgnoreCase("k2")) {
search = new SearchK2();
} else if( searchStr.equalsIgnoreCase("none")) {
search = new SearchNone();
}
else {
throw new BayesianError("Invalid search type: " + searchStr);
}
if( estimatorStr.equalsIgnoreCase("simple")) {
estimator = new SimpleEstimator();
} else if( estimatorStr.equalsIgnoreCase("none")) {
estimator = new EstimatorNone();
}
else {
throw new BayesianError("Invalid estimator type: " + estimatorStr);
}
if( initStr.equalsIgnoreCase("simple")) {
init = BayesianInit.InitEmpty;
} else if( initStr.equalsIgnoreCase("naive")) {
init = BayesianInit.InitNaiveBayes;
} else if( initStr.equalsIgnoreCase("none")) {
init = BayesianInit.InitNoChange;
}
else {
throw new BayesianError("Invalid init type: " + initStr);
}
return new TrainBayesian((BayesianNetwork) method, training, maxParents, init, search, estimator);
}
}