Example usage for org.apache.commons.math3.optimization.direct CMAESOptimizer CMAESOptimizer

Introduction

In this page you can find the example usage for org.apache.commons.math3.optimization.direct CMAESOptimizer CMAESOptimizer.

Prototype

@Deprecated
public CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness,
        boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random,
        boolean generateStatistics) 

Source Link

Usage

From source file:edu.stevens.cpe.reservior.readout.CMAES.java

public double[] train(int numberWeightsToTrain) {
    this.epoch = 0;

    //Number of weights to evolve
    final double[] start = new double[numberWeightsToTrain];
    Arrays.fill(start, startValue);
    final double[] lower = new double[numberWeightsToTrain];
    Arrays.fill(lower, lowerBound);
    final double[] upper = new double[numberWeightsToTrain];
    Arrays.fill(upper, upperBound);

    //Set to 1/3 the initial search volume
    final double[] sigma = new double[numberWeightsToTrain];
    Arrays.fill(sigma, insigma);/*from w  ww.  jav a 2 s. c o m*/

    int lambda = 4 + (int) (3. * Math.log(numberWeightsToTrain));
    int maxEvals = CMAESOptimizer.DEFAULT_MAXITERATIONS;
    double stopValue = .01;
    boolean isActive = true; //Chooses the covariance matrix update method.
    int diagonalOnly = 0;
    int checkFeasable = 0;
    final CMAESOptimizer optimizer = new CMAESOptimizer(lambda, sigma, CMAESOptimizer.DEFAULT_MAXITERATIONS,
            stopValue, isActive, diagonalOnly, checkFeasable, new MersenneTwister(), false);

    final PointValuePair result = optimizer.optimize(maxEvals, fitnessFuntion, GoalType.MINIMIZE, start, lower,
            upper);
    logger.info(Arrays.toString(result.getPoint()));
    logger.info("Best weights: " + Arrays.toString(bestWeights));

    updateReadoutWeights(result.getPoint());
    logger.info("done training.");
    return bestWeights;
}

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From source file:edu.stevens.cpe.reservior.demo.EvolvedReservoir.java

/**
 * Optimize the convolution filter/*  w w  w  .jav  a2 s  .c  o m*/
 */
public void optimize(int size) {
    this.epoch = 0;

    //Number of weights to evolve
    final double[] start = new double[] { 3, 0, 1, 20, .1 };
    final double[] lower = new double[] { 1, 0, 0, 15, .08 };
    final double[] upper = new double[] { 5, 5, 3, 120, .3 };

    //Set to 1/3 the initial search volume
    final double[] sigma = new double[size];
    Arrays.fill(sigma, insigma);

    int lambda = 4 + (int) (3. * Math.log(size));
    int maxEvals = 100000;//CMAESOptimizer.DEFAULT_MAXITERATIONS;
    double stopValue = .01;
    boolean isActive = true; //Chooses the covariance matrix update method.
    int diagonalOnly = 0;
    int checkFeasable = 0;

    final CMAESOptimizer optimizer = new CMAESOptimizer(lambda, sigma, 100000, //CMAESOptimizer.DEFAULT_MAXITERATIONS,
            stopValue, isActive, diagonalOnly, checkFeasable, new MersenneTwister(), false);

    final PointValuePair result = optimizer.optimize(maxEvals, fitnessFuntion, GoalType.MINIMIZE, start, lower,
            upper);
    logger.info(Arrays.toString(result.getPoint()));

}

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