Example usage for org.apache.commons.math.random RandomData nextGaussian

Introduction

In this page you can find the example usage for org.apache.commons.math.random RandomData nextGaussian.

Prototype

double nextGaussian(double mu, double sigma);

Source Link

Document

Generates a random value from the Normal (or Gaussian) distribution with the given mean and standard deviation.

Usage

From source file:br.upe.ecomp.doss.algorithm.apso.APSO.java

protected void doElitistLearningStrategy() {
    RandomData random = new RandomDataImpl();
    APSOParticle[] particles = (APSOParticle[]) getParticles();

    List<APSOParticle> apsoParticles = Arrays.asList(particles);
    APSOParticle bestParticle = Collections.max(apsoParticles, new ComparatorMaximumFitness());

    // Dimension that can be changed if the best particle fitness improves after the change
    int dimensionChange = random.nextInt(0, getProblem().getDimensionsNumber() - 1);
    double[] currentBestPosition = bestParticle.getBestPosition();

    // Standard Deviation of the Gaussian Distribution
    // TODO usar esse decaimento linear pode ser ruim para ambientes dinamicos
    double learningElitistRate = sigmaMax - (sigmaMax - sigmaMin) * (this.getIterations() / getMaxIterations());

    // Changes position in only one dimension
    // if(EnumEvolutionaryState.CONVERGENCE) TODO testar com e sem esta condicao
    double newDimensionPosition = currentBestPosition[dimensionChange]
            + (getProblem().getUpperBound(dimensionChange) - getProblem().getLowerBound(dimensionChange))
                    * random.nextGaussian(0, learningElitistRate);

    // TODO verificar se nao extrapolou o limite do espaco de busca
    double[] newPosition = currentBestPosition.clone();
    newPosition[dimensionChange] = newDimensionPosition;

    double currentBestFitness = bestParticle.getBestFitness();
    double newFitness = getProblem().getFitness(newPosition);

    // If the change improves the fitness then update the best particle with the new position,
    // else update the worst particle with the new position.
    if (getProblem().isFitnessBetterThan(currentBestFitness, newFitness)) {
        bestParticle.updateBestPosition(newPosition.clone(), newFitness);
        bestParticle.updateCurrentPosition(newPosition.clone(), newFitness);
    } else {/*from   w  ww.  j av a  2s . c  o  m*/
        PSOParticle worstParticle = Collections.min(apsoParticles, new ComparatorMaximumFitness());
        worstParticle.updateCurrentPosition(newPosition.clone(), newFitness);
        worstParticle.updatePBest(getProblem());
    }
    calculateGBest(bestParticle);
}

From source file:org.tolven.gen.model.GaussianValueGen.java

public double generate(RandomData rng) {
    return rng.nextGaussian(mean, sigma);
}

From source file:org.tolven.gen.model.RepeatScenario.java

/**
 * To apply this scenario, we will modify the start time of the scenario to repeat to
 * be the selected event time. For example, if the repeat interval is one year (in milliseconds), 
 * and apply is called with a start time around 1992, then the child scenario is called
 * with a start time of 1977, 1978, to the end-time (present day or death being most common
 * for chronic diseases or the duration of the disease for acute diseases).
 * Since most repeat scenarios represent followup activity, the start date is often offset 
 * by some interval. Thus, with a startOffset of 12 (months), and the 
 * startDate were to be 1976, then the first "repeat" will be around 1977. 
 *///from w  ww.  j av a2s  .  c om
public boolean apply(GenMedical patient, Date startTime, Date endTime, RandomData rng) {
    GregorianCalendar clock = new GregorianCalendar();
    clock.setTime(startTime);
    boolean rslt = false;
    int count = -startOffset;
    //      TolvenLogger.info( getTitle() + " of " + getScenarioToRepeat().getTitle() + " Ranging from " + clock.getTime() + " to " + endTime, RepeatScenario.class); 
    while (true) {
        if (count++ >= MAX_REPEAT)
            throw new IllegalArgumentException("Maximum repeat for scenario " + getTitle());
        if (count > maxCount)
            break;
        // Now pick a random time around that clock setting unless this is the first instance or there is no deviation
        Date instanceTime;
        if (getDeviation() == 0.0 || count <= 0) {
            instanceTime = new Date(clock.getTimeInMillis());
        } else {
            instanceTime = new Date(((long) rng.nextGaussian(clock.getTimeInMillis(), getDeviationMs())));
        }
        // Needs to be before the end time, otherwise, we're done.
        if (instanceTime.after(endTime))
            break;
        // Now apply the instance (if we're past the startOffset)
        if (count > 0) {
            getScenarioToRepeat().apply(patient, instanceTime, endTime, rng);
        }
        // Reset the clock if requested
        if (resetClock)
            clock.setTime(instanceTime);
        // Advance the clock with no randomness
        clock.add(intervalType, intervalValue);
        rslt = true;
    }
    // If there's a future count, then we keep going by calling the futureScenario
    // Note: Logic is similar but not identical to above.
    for (int x = 0; x < getFutureCount(); x++) {
        Date instanceTime;
        if (getDeviation() == 0.0) {
            instanceTime = new Date(clock.getTimeInMillis());
        } else {
            instanceTime = new Date(((long) rng.nextGaussian(clock.getTimeInMillis(), getDeviationMs())));
        }
        // 
        // In the future, start and end time are the same.
        getFutureScenario().apply(patient, instanceTime, instanceTime, rng);
        // Reset the clock if requested
        if (resetClock)
            clock.setTime(instanceTime);
        // Advance the clock with no randomness
        clock.add(intervalType, intervalValue);
    }
    return rslt;
}