/*
* 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.ensemble.aggregator;
import java.util.ArrayList;
import java.util.List;
import org.encog.ensemble.EnsembleWeightedAggregator;
import org.encog.ensemble.data.EnsembleDataSet;
import org.encog.ml.data.MLData;
import org.encog.ml.data.basic.BasicMLData;
public class WeightedAveraging implements EnsembleWeightedAggregator {
private ArrayList<Double> weights;
public class WeightMismatchException extends Exception {
/**
*
*/
private static final long serialVersionUID = 327652547599703252L;
}
public WeightedAveraging(List<Double> weights)
{
this.weights = (ArrayList<Double>) weights;
}
@Override
public void setWeights(List<Double> weights)
{
this.weights = (ArrayList<Double>) weights;
}
@Override
public List<Double> getWeights()
{
return this.weights;
}
@Override
public MLData evaluate(ArrayList<MLData> outputs) throws WeightMismatchException {
int outputSize = outputs.get(0).size();
double weightSum = 0;
if (weights == null || weights.size() != outputs.size())
{
throw new WeightMismatchException();
}
BasicMLData acc = new BasicMLData(outputSize);
for (int i = 0; i < outputs.size(); i++)
{
BasicMLData out = (BasicMLData) outputs.get(i);
out = (BasicMLData) out.times(weights.get(i));
acc = (BasicMLData) acc.plus(out);
weightSum += weights.get(i);
}
if(weightSum == 0)
{
weightSum = 1;
}
acc = (BasicMLData) acc.times(1.0 / weightSum);
return acc;
}
@Override
public String getLabel() {
return "weightedaveraging";
}
@Override
public void train() {
//This is a no-op in this aggregator
}
@Override
public void setTrainingSet(EnsembleDataSet trainingSet) {
// This is a no-op in this aggregator.
}
@Override
public boolean needsTraining() {
return false;
}
@Override
public void setNumberOfMembers(int members) {
//does nothing
}
}