/*
* Artificial Intelligence for Humans
* Volume 2: Nature Inspired Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
*
* Copyright 2014 by Jeff Heaton
*
* 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 com.heatonresearch.aifh.examples.gp;
import com.heatonresearch.aifh.error.ErrorCalculation;
import com.heatonresearch.aifh.error.ErrorCalculationMSE;
import com.heatonresearch.aifh.evolutionary.genome.Genome;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.learning.MLMethod;
import com.heatonresearch.aifh.learning.RegressionAlgorithm;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import java.util.List;
/**
* Score regression data. The score is done using an error calculation method.
*/
public class ScoreSmallExpression implements ScoreFunction {
/**
* The error calculator.
*/
private ErrorCalculation errorCalc = new ErrorCalculationMSE();
/**
* The training data.
*/
private final List<BasicData> trainingData;
/**
* The maximum tree size.
*/
private final int maxLength;
/**
* Construct the function.
*
* @param theTrainingData The training data.
* @param theMaxLength The maximum tree size.
*/
public ScoreSmallExpression(final List<BasicData> theTrainingData, int theMaxLength) {
this.trainingData = theTrainingData;
this.maxLength = theMaxLength;
}
/**
* {@inheritDoc}
*/
@Override
public double calculateScore(final MLMethod algo) {
ErrorCalculation ec = this.errorCalc.create();
final RegressionAlgorithm ralgo = (RegressionAlgorithm) algo;
final Genome genome = (Genome)ralgo;
if( genome.size()>this.maxLength ) {
return Double.POSITIVE_INFINITY;
}
// evaulate
ec.clear();
for (final BasicData pair : this.trainingData) {
final double[] output = ralgo.computeRegression(pair.getInput());
ec.updateError(output, pair.getIdeal(), 1.0);
}
return ec.calculate();
}
/**
* @return The error calculation method.
*/
public ErrorCalculation getErrorCalc() {
return errorCalc;
}
/**
* Set the error calculation method.
*
* @param errorCalc The error calculation method.
*/
public void setErrorCalc(final ErrorCalculation errorCalc) {
this.errorCalc = errorCalc;
}
/**
* @return The training data.
*/
public List<BasicData> getTrainingData() {
return trainingData;
}
/**
* @return True, this scoring method seeks to minimize.
*/
@Override
public boolean shouldMinimize() {
return true;
}
}