Package org.nd4j.linalg.api.rng.distribution

Interface Distribution

    • Method Detail

      • probability

        double probability​(double x)
        For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
        Parameters:
        x - the point at which the PMF is evaluated
        Returns:
        the value of the probability mass function at point x

      • density

        double density​(double x)
        Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
        Parameters:
        x - the point at which the PDF is evaluated
        Returns:
        the value of the probability density function at point x

      • cumulativeProbability

        double cumulativeProbability​(double x)
        For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
        Parameters:
        x - the point at which the CDF is evaluated
        Returns:
        the probability that a random variable with this distribution takes a value less than or equal to x

      • cumulativeProbability

        @Deprecated
double cumulativeProbability​(double x0,
                             double x1)
                      throws org.apache.commons.math3.exception.NumberIsTooLargeException
        Deprecated.
        As of 3.1. In 4.0, this method will be renamed probability(double x0, double x1).
        For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
        Parameters:
        x0 - the exclusive lower bound
        x1 - the inclusive upper bound
        Returns:
        the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint
        Throws:
        org.apache.commons.math3.exception.NumberIsTooLargeException - if x0 > x1

      • inverseCumulativeProbability

        double inverseCumulativeProbability​(double p)
                             throws org.apache.commons.math3.exception.OutOfRangeException
        Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
        • inf{x in R | P(X<=x) >= p} for 0 < p <= 1,
        • inf{x in R | P(X<=x) > 0} for p = 0.
        Parameters:
        p - the cumulative probability
        Returns:
        the smallest p-quantile of this distribution (largest 0-quantile for p = 0)
        Throws:
        org.apache.commons.math3.exception.OutOfRangeException - if p < 0 or p > 1

      • getNumericalMean

        double getNumericalMean()
        Use this method to get the numerical value of the mean of this distribution.
        Returns:
        the mean or Double.NaN if it is not defined

      • getNumericalVariance

        double getNumericalVariance()
        Use this method to get the numerical value of the variance of this distribution.
        Returns:
        the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined

      • getSupportLowerBound

        double getSupportLowerBound()
        Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

        inf {x in R | P(X <= x) > 0}.

        Returns:
        lower bound of the support (might be Double.NEGATIVE_INFINITY)

      • getSupportUpperBound

        double getSupportUpperBound()
        Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

        inf {x in R | P(X <= x) = 1}.

        Returns:
        upper bound of the support (might be Double.POSITIVE_INFINITY)

      • isSupportLowerBoundInclusive

        boolean isSupportLowerBoundInclusive()
        Deprecated.
        to be removed in 4.0
        Whether or not the lower bound of support is in the domain of the density function. Returns true iff getSupporLowerBound() is finite and density(getSupportLowerBound()) returns a non-NaN, non-infinite value.
        Returns:
        true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there

      • isSupportUpperBoundInclusive

        boolean isSupportUpperBoundInclusive()
        Deprecated.
        to be removed in 4.0
        Whether or not the upper bound of support is in the domain of the density function. Returns true iff getSupportUpperBound() is finite and density(getSupportUpperBound()) returns a non-NaN, non-infinite value.
        Returns:
        true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there

      • isSupportConnected

        boolean isSupportConnected()
        Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support.
        Returns:
        whether the support is connected or not

      • reseedRandomGenerator

        void reseedRandomGenerator​(long seed)
        Reseed the random generator used to generate samples.
        Parameters:
        seed - the new seed

      • sample

        double sample()
        Generate a random value sampled from this distribution.
        Returns:
        a random value.

      • sample

        double[] sample​(long sampleSize)
        Generate a random sample from the distribution.
        Parameters:
        sampleSize - the number of random values to generate
        Returns:
        an array representing the random sample
        Throws:
        org.apache.commons.math3.exception.NotStrictlyPositiveException - if sampleSize is not positive

      • sample

        INDArray sample​(int[] shape)
        Sample the given shape
        Parameters:
        shape - the given shape
        Returns:
        an ndarray with random samples from this distribution

      • sample

        INDArray sample​(long[] shape)

      • sample

        INDArray sample​(INDArray target)
        Fill the target array by sampling from the distribution
        Parameters:
        target - target array
        Returns:
        an ndarray with random samples from this distribution