net.sourceforge.cilib.measurement.single.ConvergedDimensions Maven / Gradle / Ivy

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/**           __  __
 *    _____ _/ /_/ /_    Computational Intelligence Library (CIlib)
 *   / ___/ / / / __ \   (c) CIRG @ UP
 *  / /__/ / / / /_/ /   http://cilib.net
 *  \___/_/_/_/_.___/
 */
package net.sourceforge.cilib.measurement.single;

import net.sourceforge.cilib.algorithm.Algorithm;
import net.sourceforge.cilib.algorithm.population.SinglePopulationBasedAlgorithm;
import net.sourceforge.cilib.controlparameter.ConstantControlParameter;
import net.sourceforge.cilib.controlparameter.ControlParameter;
import net.sourceforge.cilib.entity.Entity;
import net.sourceforge.cilib.measurement.Measurement;
import net.sourceforge.cilib.type.parser.DomainParser;
import net.sourceforge.cilib.type.types.Numeric;
import net.sourceforge.cilib.type.types.Real;
import net.sourceforge.cilib.type.types.container.StructuredType;
import net.sourceforge.cilib.type.types.container.Vector;

/**
 * Calculates the average number of dimensions over all
 * particles that have converged within a specified error
 * from the specified optimum. This measurement will be
 * useful to see if convergence in certain dimensions is
 * too slow.
 *
 */
public class ConvergedDimensions implements Measurement {

    private static final long serialVersionUID = 7322191932976445577L;
    private ControlParameter errorThreshold;
    private Vector targetSolution;

    /** Creates a new instance of ConvergedDimensions. */
    public ConvergedDimensions() {
        errorThreshold = ConstantControlParameter.of(0.0);
    }

    /**
     * Copy constructor. Creates a copy of the provided instance.
     * @param copy The instance to copy.
     */
    public ConvergedDimensions(ConvergedDimensions copy) {
        this.errorThreshold = copy.errorThreshold.getClone();
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public ConvergedDimensions getClone() {
        return new ConvergedDimensions(this);
    }

    /**
     * @return The solution error threshold.
     */
    public ControlParameter getErrorThreshold() {
        return this.errorThreshold;
    }

    /**
     * @return The target solution.
     */
    public StructuredType getTargetSolution() {
        return this.targetSolution;
    }

    /**
     * Set the error threshold
     * @param error
     */
    public void setErrorThreshold(ControlParameter error) {
        this.errorThreshold = error;
    }

    /**
     * Set the target solution string representation
     * @param stringRepresentation
     */
    public void setTargetSolution(String stringRepresentation) {
        this.targetSolution = (Vector) DomainParser.parse(stringRepresentation);
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public Real getValue(Algorithm algorithm) {
        SinglePopulationBasedAlgorithm populationBasedAlgorithm = (SinglePopulationBasedAlgorithm) algorithm;
        int populationSize = populationBasedAlgorithm.getTopology().length();

        int dimensions = 0;
        double sumOfAverageConvergedDimensions = 0.0;
        
        fj.data.List local = populationBasedAlgorithm.getTopology();
        for (Entity populationEntity : local) {
            dimensions = populationEntity.getDimension();

            int dimension = 0;
            int numberConvergedDimensions = 0;
            for (Numeric position : (Vector) populationEntity.getCandidateSolution()) {
                double lowerBound = targetSolution.doubleValueOf(dimension) - this.errorThreshold.getParameter();
                double upperBound = targetSolution.doubleValueOf(dimension) + this.errorThreshold.getParameter();
                double value = position.doubleValue();

                if ((value >= lowerBound) && (value <= upperBound)) {
                    numberConvergedDimensions++;
                }
                dimension++;
            }
            sumOfAverageConvergedDimensions += (double) numberConvergedDimensions / (double) dimensions;
        }

        return Real.valueOf(sumOfAverageConvergedDimensions / (double) populationSize * (double) dimensions);
    }
}