Example usage for org.apache.commons.math3.exception NotStrictlyPositiveException NotStrictlyPositiveException

Introduction

In this page you can find the example usage for org.apache.commons.math3.exception NotStrictlyPositiveException NotStrictlyPositiveException.

Prototype

public NotStrictlyPositiveException(Localizable specific, Number value) 

Source Link

Document

Construct the exception with a specific context.

Usage

From source file:experiment.PascalDistribution_bug.java

/**
  * Create a Pascal distribution with the given number of successes and
  * probability of success.//w  w  w . j  av a  2 s .  c om
  *
  * @param rng Random number generator.
  * @param r Number of successes.
  * @param p Probability of success.
  * @throws NotStrictlyPositiveException if the number of successes is not positive
  * @throws OutOfRangeException if the probability of success is not in the
  * range {@code [0, 1]}.
  * @since 3.1
  */
public PascalDistribution_bug(RandomGenerator rng, int r, double p, int id)
        throws NotStrictlyPositiveException, OutOfRangeException {
    super(rng);

    if (r <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SUCCESSES, r);
    }
    if (p < 0 || p > 1) {
        throw new OutOfRangeException(p, 0, 1);
    }

    numberOfSuccesses = r;
    probabilityOfSuccess = p;
}

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From source file:gmc_hdfs.distribution.ParetoDistribution.java

/**
 * Creates a Pareto distribution.// ww  w .  j  av  a2  s.  co  m
 *
 * @param rng Random number generator.
 * @param scale Scale parameter of this distribution.
 * @param shape Shape parameter of this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code scale <= 0} or {@code shape <= 0}.
 */
public ParetoDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (scale <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale);
    }

    if (shape <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.SHAPE, shape);
    }

    this.scale = scale;
    this.shape = shape;
    this.solverAbsoluteAccuracy = inverseCumAccuracy;
}

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From source file:org.nd4j.linalg.api.rng.distribution.BaseDistribution.java

/**
 * {@inheritDoc}/*from w w  w. ja v  a  2  s.  c o m*/
 * <p/>
 * The default implementation generates the sample by calling
 * {@link #sample()} in a loop.
 */
@Override
public double[] sample(int sampleSize) {
    if (sampleSize <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.NUMBER_OF_SAMPLES, sampleSize);
    }
    double[] out = new double[sampleSize];
    for (int i = 0; i < sampleSize; i++) {
        out[i] = sample();
    }
    return out;
}

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From source file:org.nd4j.linalg.api.rng.distribution.impl.LogNormalDistribution.java

/**
 * Creates a normal distribution./*from w  ww . ja  va 2  s  .  c om*/
 *
 * @param rng                Random number generator.
 * @param mean               Mean for this distribution.
 * @param sd                 Standard deviation for this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code sd <= 0}.
 * @since 3.1
 */
public LogNormalDistribution(Random rng, double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (sd <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sd);
    }

    this.mean = mean;
    standardDeviation = sd;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}

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From source file:org.nd4j.linalg.api.rng.distribution.impl.NormalDistribution.java

/**
 * Creates a normal distribution.//from  ww  w .  j  a  v  a  2  s .co m
 *
 * @param rng                Random number generator.
 * @param mean               Mean for this distribution.
 * @param sd                 Standard deviation for this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code sd <= 0}.
 * @since 3.1
 */
public NormalDistribution(Random rng, double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (sd <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sd);
    }

    this.mean = mean;
    standardDeviation = sd;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}

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From source file:org.nd4j.linalg.api.rng.distribution.impl.TruncatedNormalDistribution.java

/**
 * Creates a normal distribution./*from  www  .j  a  v a  2s  . co m*/
 *
 * @param rng                Random number generator.
 * @param mean               Mean for this distribution.
 * @param sd                 Standard deviation for this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code sd <= 0}.
 * @since 3.1
 */
public TruncatedNormalDistribution(Random rng, double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (sd <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sd);
    }

    this.mean = mean;
    standardDeviation = sd;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}

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From source file:org.nd4j.linalg.jcublas.rng.distribution.NormalDistribution.java

/**
 * Creates a normal distribution.// w  w w  .j  a v  a  2 s.com
 *
 * @param rng                Random number generator.
 * @param mean               Mean for this distribution.
 * @param sd                 Standard deviation for this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code sd <= 0}.
 * @since 3.1
 */
public NormalDistribution(Random rng, double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super((JcudaRandom) rng);

    if (sd <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sd);
    }

    this.mean = mean;
    standardDeviation = sd;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}

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From source file:statalign.utils.GammaDistribution.java

/**
 * Creates a Gamma distribution.//from ww w.  j  av a 2s  .  co  m
 *
 * @param rng Random number generator.
 * @param shape the shape parameter
 * @param scale the scale parameter
 * @param inverseCumAccuracy the maximum absolute error in inverse
 * cumulative probability estimates (defaults to
 * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
 * @throws NotStrictlyPositiveException if {@code shape <= 0} or
 * {@code scale <= 0}.
 * @since 3.1
 */
public GammaDistribution(RandomGenerator rng, double shape, double scale, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (shape <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.SHAPE, shape);
    }
    if (scale <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.SCALE, scale);
    }

    this.shape = shape;
    this.scale = scale;
    this.solverAbsoluteAccuracy = inverseCumAccuracy;
    this.shiftedShape = shape + Gamma.LANCZOS_G + 0.5;
    final double aux = FastMath.E / (2.0 * FastMath.PI * shiftedShape);
    this.densityPrefactor2 = shape * FastMath.sqrt(aux) / Gamma.lanczos(shape);
    this.densityPrefactor1 = this.densityPrefactor2 / scale * FastMath.pow(shiftedShape, -shape)
            * FastMath.exp(shape + Gamma.LANCZOS_G);
    this.minY = shape + Gamma.LANCZOS_G - FastMath.log(Double.MAX_VALUE);
    this.maxLogY = FastMath.log(Double.MAX_VALUE) / (shape - 1.0);
}

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From source file:statalign.utils.NormalDistribution.java

/**
 * Creates a normal distribution.//from w  w  w .ja  v a  2  s .  c o  m
 *
 * @param rng Random number generator.
 * @param mean Mean for this distribution.
 * @param sd Standard deviation for this distribution.
 * @param inverseCumAccuracy Inverse cumulative probability accuracy.
 * @throws NotStrictlyPositiveException if {@code sd <= 0}.
 * @since 3.1
 */
public NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy)
        throws NotStrictlyPositiveException {
    super(rng);

    if (sd <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.STANDARD_DEVIATION, sd);
    }

    this.mean = mean;
    standardDeviation = sd;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}

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