/*
* 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.ensemble.data.factories;
import java.util.Random;
import org.encog.ensemble.data.EnsembleDataSet;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
public class WeightedResamplingDataSetFactory extends EnsembleDataSetFactory {
public WeightedResamplingDataSetFactory(int dataSetSize) {
super(dataSetSize);
}
MLDataSet originalData;
MLDataPair getCandidate(double weight) {
double weightSoFar = 0;
for (int i = 0; i < dataSource.size(); i++) {
weightSoFar += dataSource.get(i).getSignificance();
if (weightSoFar > weight)
return (MLDataPair) dataSource.get(i);
}
return (MLDataPair) dataSource.get(dataSource.size());
}
@Override
public EnsembleDataSet getNewDataSet() {
double weightSum = 0;
for (int i = 0; i < dataSource.size(); i++)
weightSum += dataSource.get(i).getSignificance();
Random generator = new Random();
EnsembleDataSet ds = new EnsembleDataSet(dataSource.getInputSize(), dataSource.getIdealSize());
for (int i = 0; i < dataSetSize; i++)
{
double candidate = generator.nextDouble() * weightSum;
ds.add(getCandidate(candidate));
}
return ds;
}
}