/*
* 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.
*
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* and trademarks visit:
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*/
package org.encog.ensemble.aggregator;
import java.util.ArrayList;
import org.encog.ensemble.EnsembleAggregator;
import org.encog.ensemble.EnsembleML;
import org.encog.ensemble.EnsembleMLMethodFactory;
import org.encog.ensemble.EnsembleTrainFactory;
import org.encog.ensemble.GenericEnsembleML;
import org.encog.ensemble.data.EnsembleDataSet;
import org.encog.ml.data.MLData;
import org.encog.ml.data.basic.BasicMLData;
public class MetaClassifier implements EnsembleAggregator {
EnsembleML classifier;
EnsembleMLMethodFactory mlFact;
EnsembleTrainFactory etFact;
double trainError;
int members;
boolean adaptiveError = false;
public MetaClassifier(double trainError, EnsembleMLMethodFactory mlFact, EnsembleTrainFactory etFact, boolean adaptiveError)
{
this.trainError = trainError;
this.mlFact = mlFact;
this.etFact = etFact;
this.adaptiveError = adaptiveError;
members = 1;
}
public MetaClassifier(double trainError, EnsembleMLMethodFactory mlFact, EnsembleTrainFactory etFact)
{
this(trainError, mlFact, etFact, false);
}
public double getTrainingError()
{
return trainError;
}
public void setTrainingError(double trainError)
{
this.trainError = trainError;
}
@Override
public void setNumberOfMembers(int members)
{
this.members = members;
}
@Override
public MLData evaluate(ArrayList<MLData> outputs) {
BasicMLData merged_outputs = new BasicMLData(classifier.getInputCount());
for(MLData output:outputs) {
int index = 0;
for(double val:output.getData()) {
merged_outputs.add(index++,val);
}
}
return classifier.compute(merged_outputs);
}
@Override
public String getLabel()
{
String ret = "metaclassifier-" + mlFact.getLabel() + "-" + trainError + "-" + etFact.getLabel();
if(adaptiveError)
{
ret += "-adaptive";
}
return ret;
}
@Override
public void train()
{
if (classifier != null)
{
double targetError = adaptiveError ? trainError / members : trainError;
classifier.train(targetError);
}
else
{
System.err.println("Trying to train a null classifier in MetaClassifier");
}
}
@Override
public void setTrainingSet(EnsembleDataSet trainingSet)
{
mlFact.setSizeMultiplier(members);
classifier = new GenericEnsembleML(mlFact.createML(trainingSet.getInputSize(), trainingSet.getIdealSize()),mlFact.getLabel());
classifier.setTraining(etFact.getTraining(classifier.getMl(), trainingSet));
classifier.setTrainingSet(trainingSet);
}
@Override
public boolean needsTraining()
{
return true;
}
}