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// Copyright 2016 Yahoo Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.searchlib.mlr.ga;
import com.yahoo.searchlib.rankingexpression.RankingExpression;
import java.util.Collections;
import java.util.List;
/**
* An individual in an evolving population - a genome with a fitness score.
* Individuals are comparable by decreasing fitness.
* <p>
* As we are training ranking expressions, the genome, here, is the ranking expression.
*
* @author bratseth
*/
public class Individual extends Evolvable {
private final RankingExpression genome;
private final TrainingSet trainingSet;
private final double fitness;
public Individual(RankingExpression genome, TrainingSet trainingSet) {
this.genome = genome;
this.trainingSet = trainingSet;
this.fitness = trainingSet.evaluate(genome);
}
public RankingExpression getGenome() { return genome; }
public double calculateAverageError() {
return trainingSet.calculateAverageError(genome);
}
public double calculateAverageErrorPercentage() {
return trainingSet.calculateAverageErrorPercentage(genome);
}
@Override
public double getFitness() { return fitness; }
@Override
public Individual makeSuccessor(int memberNumber, List<RankingExpression> genepool, TrainingEnvironment environment) {
return new Individual(environment.recombiner().recombine(genome, genepool), trainingSet);
}
@Override
public RankingExpression getGenepool() {
return genome;
}
@Override
public String toString() {
return toSomewhatShortString() + ", expression: " + genome;
}
/** Returns a shorter string describing this (not including the expression */
public String toSomewhatShortString() {
return "Error % " + calculateAverageErrorPercentage() +
" average error " + calculateAverageError() +
" fitness " + getFitness();
}
/** Returns a shorter string describing this (not including the expression */
public String toShortString() {
return "Error: " + calculateAverageErrorPercentage() + " %";
}
}
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