Java Code Examples for org.apache.commons.math.exception.util.LocalizedFormats#MEAN

/**
 * Create a new Poisson distribution with the given mean, convergence criterion
 * and maximum number of iterations.
 *
 * @param p the Poisson mean
 * @param epsilon the convergence criteria for cumulative probabilites
 * @param maxIterations the maximum number of iterations for cumulative probabilites
 * @since 2.1
 */
public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
    if (p <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, p);
    }
    mean = p;
    normal = new NormalDistributionImpl(p, FastMath.sqrt(p));
    this.epsilon = epsilon;
    this.maxIterations = maxIterations;
}
 

/**
 * Returns a random value from an Exponential distribution with the given
 * mean.
 * <p>
 * <strong>Algorithm Description</strong>: Uses the Algorithm SA (Ahrens)
 * from p. 876 in:
 * [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for
 * sampling from the exponential and normal distributions.
 * Communications of the ACM, 15, 873-882.
 * </p>
 *
 * @param mean the mean of the distribution
 * @return the random Exponential value
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 */
public double nextExponential(double mean) {
    if (mean <= 0.0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }

    // Step 1:
    double a = 0;
    double u = this.nextUniform(0, 1);

    // Step 2 and 3:
    while (u < 0.5) {
        a += EXPONENTIAL_SA_QI[0];
        u *= 2;
    }

    // Step 4 (now u >= 0.5):
    u += u - 1;

    // Step 5:
    if (u <= EXPONENTIAL_SA_QI[0]) {
        return mean * (a + u);
    }

    // Step 6:
    int i = 0; // Should be 1, be we iterate before it in while using 0
    double u2 = this.nextUniform(0, 1);
    double umin = u2;

    // Step 7 and 8:
    do {
        ++i;
        u2 = this.nextUniform(0, 1);

        if (u2 < umin) {
            umin = u2;
        }

        // Step 8:
    } while (u > EXPONENTIAL_SA_QI[i]); // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1

    return mean * (a + umin * EXPONENTIAL_SA_QI[0]);
}
 

/**
 * Returns a random value from an Exponential distribution with the given
 * mean.
 * <p>
 * <strong>Algorithm Description</strong>: Uses the Algorithm SA (Ahrens)
 * from p. 876 in:
 * [1]: Ahrens, J. H. and Dieter, U. (1972). Computer methods for
 * sampling from the exponential and normal distributions.
 * Communications of the ACM, 15, 873-882.
 * </p>
 *
 * @param mean the mean of the distribution
 * @return the random Exponential value
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 */
public double nextExponential(double mean) {
    if (mean <= 0.0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }

    // Step 1:
    double a = 0;
    double u = this.nextUniform(0, 1);

    // Step 2 and 3:
    while (u < 0.5) {
        a += EXPONENTIAL_SA_QI[0];
        u *= 2;
    }

    // Step 4 (now u >= 0.5):
    u += u - 1;

    // Step 5:
    if (u <= EXPONENTIAL_SA_QI[0]) {
        return mean * (a + u);
    }

    // Step 6:
    int i = 0; // Should be 1, be we iterate before it in while using 0
    double u2 = this.nextUniform(0, 1);
    double umin = u2;

    // Step 7 and 8:
    do {
        ++i;
        u2 = this.nextUniform(0, 1);

        if (u2 < umin) {
            umin = u2;
        }

        // Step 8:
    } while (u > EXPONENTIAL_SA_QI[i]); // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1

    return mean * (a + umin * EXPONENTIAL_SA_QI[0]);
}
 

/**
 * Create a exponential distribution with the given mean.
 *
 * @param mean Mean of this distribution.
 * @param inverseCumAccuracy Maximum absolute error in inverse
 * cumulative probability estimates (defaults to
 * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 * @since 2.1
 */
public ExponentialDistributionImpl(double mean, double inverseCumAccuracy) {
    if (mean <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }
    this.mean = mean;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}
 

/**
 * Create a new Poisson distribution with the given mean, convergence criterion
 * and maximum number of iterations.
 *
 * @param p Poisson mean.
 * @param epsilon Convergence criterion for cumulative probabilities.
 * @param maxIterations the maximum number of iterations for cumulative
 * probabilities.
 * @since 2.1
 */
public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
    if (p <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, p);
    }
    mean = p;
    normal = new NormalDistributionImpl(p, FastMath.sqrt(p));
    this.epsilon = epsilon;
    this.maxIterations = maxIterations;
}
 

/**
 * Returns a random value from an Exponential distribution with the given
 * mean.
 * <p>
 * <strong>Algorithm Description</strong>: Uses the <a
 * href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion
 * Method</a> to generate exponentially distributed random values from
 * uniform deviates.
 * </p>
 *
 * @param mean the mean of the distribution
 * @return the random Exponential value
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 */
public double nextExponential(double mean) {
    if (mean <= 0.0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }
    final RandomGenerator generator = getRan();
    double unif = generator.nextDouble();
    while (unif == 0.0d) {
        unif = generator.nextDouble();
    }
    return -mean * FastMath.log(unif);
}
 

/**
 * Returns a random value from an Exponential distribution with the given
 * mean.
 * <p>
 * <strong>Algorithm Description</strong>: Uses the <a
 * href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion
 * Method</a> to generate exponentially distributed random values from
 * uniform deviates.
 * </p>
 *
 * @param mean the mean of the distribution
 * @return the random Exponential value
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 */
public double nextExponential(double mean) {
    if (mean <= 0.0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }
    final RandomGenerator generator = getRan();
    double unif = generator.nextDouble();
    while (unif == 0.0d) {
        unif = generator.nextDouble();
    }
    return -mean * FastMath.log(unif);
}
 

/**
 * Create a exponential distribution with the given mean.
 *
 * @param mean Mean of this distribution.
 * @param inverseCumAccuracy Maximum absolute error in inverse
 * cumulative probability estimates (defaults to
 * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 * @since 2.1
 */
public ExponentialDistributionImpl(double mean, double inverseCumAccuracy) {
    if (mean <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }
    this.mean = mean;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}
 

/**
 * Create a new Poisson distribution with the given mean, convergence criterion
 * and maximum number of iterations.
 *
 * @param p Poisson mean.
 * @param epsilon Convergence criterion for cumulative probabilities.
 * @param maxIterations the maximum number of iterations for cumulative
 * probabilities.
 * @since 2.1
 */
public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
    if (p <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, p);
    }
    mean = p;
    normal = new NormalDistributionImpl(p, FastMath.sqrt(p));
    this.epsilon = epsilon;
    this.maxIterations = maxIterations;
}
 

/**
 * Create a exponential distribution with the given mean.
 *
 * @param mean Mean of this distribution.
 * @param inverseCumAccuracy Maximum absolute error in inverse
 * cumulative probability estimates (defaults to
 * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
 * @throws NotStrictlyPositiveException if {@code mean <= 0}.
 * @since 2.1
 */
public ExponentialDistributionImpl(double mean, double inverseCumAccuracy) {
    if (mean <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean);
    }
    this.mean = mean;
    solverAbsoluteAccuracy = inverseCumAccuracy;
}
 

/**
 * Create a new Poisson distribution with the given mean, convergence criterion
 * and maximum number of iterations.
 *
 * @param p Poisson mean.
 * @param epsilon Convergence criterion for cumulative probabilities.
 * @param maxIterations the maximum number of iterations for cumulative
 * probabilities.
 * @since 2.1
 */
public PoissonDistributionImpl(double p, double epsilon, int maxIterations) {
    if (p <= 0) {
        throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, p);
    }
    mean = p;
    normal = new NormalDistributionImpl(p, FastMath.sqrt(p));
    this.epsilon = epsilon;
    this.maxIterations = maxIterations;
}