/*
* 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:
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*/
package org.encog.neural.networks.training;
import org.encog.mathutil.error.ErrorCalculation;
import org.encog.mathutil.randomize.generate.GenerateRandom;
import org.encog.mathutil.randomize.generate.MersenneTwisterGenerateRandom;
import org.encog.ml.CalculateScore;
import org.encog.ml.MLMethod;
import org.encog.ml.MLRegression;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.buffer.BufferedMLDataSet;
import org.encog.util.error.CalculateRegressionError;
/**
* Calculate a score based on a training set. This class allows simulated
* annealing or genetic algorithms just as you would any other training set
* based training method. The method must support regression (MLRegression).
* A random sample is used to score the model.
*/
public class StochasticTrainingSetScore implements CalculateScore {
/**
* The training set.
*/
private final MLDataSet training;
/**
* The sample size.
*/
private final int size;
/**
* Random number generator.
*/
private GenerateRandom rnd = new MersenneTwisterGenerateRandom();
/**
* Construct a training set score calculation.
*
* @param training
* The training data to use.
*/
public StochasticTrainingSetScore(final MLDataSet training, final int theSize)
{
this.training = training;
this.size = theSize;
}
/**
* Calculate the score for the network.
* @param method The network to calculate for.
* @return The score.
*/
public double calculateScore(final MLMethod method) {
ErrorCalculation error = new ErrorCalculation();
for(int i=0;i<this.size;i++) {
int idx = this.rnd.nextInt(this.training.size());
MLDataPair pair = this.training.get(idx);
MLData output = ((MLRegression)method).compute(pair.getInput());
error.updateError(output.getData(),pair.getIdealArray(),1.0);
}
return error.calculate();
}
/**
* A training set based score should always seek to lower the error,
* as a result, this method always returns true.
* @return Returns true.
*/
public boolean shouldMinimize() {
return true;
}
@Override
public boolean requireSingleThreaded() {
if( this.training instanceof BufferedMLDataSet ) {
return true;
}
return false;
}
public MLDataSet getTraining() {
return training;
}
public int getSize() {
return size;
}
public GenerateRandom getRnd() {
return rnd;
}
public void setRnd(GenerateRandom rnd) {
this.rnd = rnd;
}
}