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

Class BinomialDistribution

    • Constructor Detail

      • BinomialDistribution

        public BinomialDistribution​(int trials,
                            double p)
        Create a binomial distribution with the given number of trials and probability of success.
        Parameters:
        trials - Number of trials.
        p - Probability of success.
        Throws:
        org.apache.commons.math3.exception.NotPositiveException - if trials < 0.
        org.apache.commons.math3.exception.OutOfRangeException - if p < 0 or p > 1.

      • BinomialDistribution

        public BinomialDistribution​(Random rng,
                            int trials,
                            double p)
        Creates a binomial distribution.
        Parameters:
        rng - Random number generator.
        trials - Number of trials.
        p - Probability of success.
        Throws:
        org.apache.commons.math3.exception.NotPositiveException - if trials < 0.
        org.apache.commons.math3.exception.OutOfRangeException - if p < 0 or p > 1.
        Since:
        3.1

      • BinomialDistribution

        public BinomialDistribution​(int n,
                            INDArray p)

    • Method Detail

      • getNumberOfTrials

        public int getNumberOfTrials()
        Access the number of trials for this distribution.
        Returns:
        the number of trials.

      • getProbabilityOfSuccess

        public double getProbabilityOfSuccess()
        Access the probability of success for this distribution.
        Returns:
        the probability of success.

      • probability

        public double probability​(int x)

      • cumulativeProbability

        public double cumulativeProbability​(int x)

      • density

        public double density​(double x)
        Description copied from interface: Distribution
        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

        public double cumulativeProbability​(double x)
        Description copied from interface: Distribution
        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

        public double cumulativeProbability​(double x0,
                                    double x1)
                             throws org.apache.commons.math3.exception.NumberIsTooLargeException
        Description copied from interface: Distribution
        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

      • getNumericalMean

        public double getNumericalMean()
        Use this method to get the numerical value of the mean of this distribution.

        For n trials and probability parameter p, the mean is n * p.

        Returns:
        the mean or Double.NaN if it is not defined

      • getNumericalVariance

        public double getNumericalVariance()
        Use this method to get the numerical value of the variance of this distribution.

        For n trials and probability parameter p, the variance is n * p * (1 - p).

        Returns:
        the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistribution) or Double.NaN if it is not defined

      • getSupportLowerBound

        public 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}.

        The lower bound of the support is always 0 except for the probability parameter p = 1.

        Returns:
        lower bound of the support (0 or the number of trials)

      • getSupportUpperBound

        public 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}.

        The upper bound of the support is the number of trials except for the probability parameter p = 0.

        Returns:
        upper bound of the support (number of trials or 0)

      • isSupportLowerBoundInclusive

        public boolean isSupportLowerBoundInclusive()
        Description copied from interface: Distribution
        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

        public boolean isSupportUpperBoundInclusive()
        Description copied from interface: Distribution
        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

        public 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.

        The support of this distribution is connected.

        Returns:
        true

      • sample

        public INDArray sample​(int[] shape)
        Description copied from interface: Distribution
        Sample the given shape
        Specified by:
        sample in interface Distribution
        Overrides:
        sample in class BaseDistribution
        Parameters:
        shape - the given shape
        Returns:
        an ndarray with random samples from this distribution

      • sample

        public INDArray sample​(INDArray ret)
        Description copied from interface: Distribution
        Fill the target array by sampling from the distribution
        Specified by:
        sample in interface Distribution
        Overrides:
        sample in class BaseDistribution
        Parameters:
        ret - target array
        Returns:
        an ndarray with random samples from this distribution