/*
* 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.evolutionary.score.parallel;
import com.heatonresearch.aifh.AIFHError;
import com.heatonresearch.aifh.evolutionary.genome.Genome;
import com.heatonresearch.aifh.evolutionary.score.AdjustScore;
import com.heatonresearch.aifh.evolutionary.train.basic.BasicEA;
import com.heatonresearch.aifh.learning.MLMethod;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import java.util.List;
/**
* An individual threadable task for the parallel score calculation.
*/
public class ParallelScoreTask implements Runnable {
/**
* The genome to calculate the score for.
*/
private final Genome genome;
/**
* The score function.
*/
private final ScoreFunction scoreFunction;
/**
* The score adjusters.
*/
private final List<AdjustScore> adjusters;
/**
* The owners.
*/
private final ParallelScore owner;
/**
* Construct the parallel task.
*
* @param genome The genome.
* @param theOwner The owner.
*/
public ParallelScoreTask(Genome genome, ParallelScore theOwner) {
super();
this.owner = theOwner;
this.genome = genome;
this.scoreFunction = theOwner.getScoreFunction();
this.adjusters = theOwner.getAdjusters();
}
/**
* Perform the task.
*/
@Override
public void run() {
MLMethod phenotype = this.owner.getCodec().decode(this.genome);
if (phenotype != null) {
double score;
try {
score = this.scoreFunction.calculateScore(phenotype);
} catch (AIFHError e) {
score = Double.NaN;
}
genome.setScore(score);
genome.setAdjustedScore(score);
BasicEA.calculateScoreAdjustment(genome, adjusters);
}
}
}