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

Class ConstantDistribution

    • Field Detail

      • DEFAULT_INVERSE_ABSOLUTE_ACCURACY

        public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
        Default inverse cumulative probability accuracy.
        Since:
        2.1
        See Also:
        Constant Field Values

    • Constructor Detail

      • ConstantDistribution

        public ConstantDistribution​(double value)

    • Method Detail

      • getMean

        public double getMean()
        Access the mean.
        Returns:
        the mean for this distribution.

      • getStandardDeviation

        public double getStandardDeviation()
        Access the standard deviation.
        Returns:
        the standard deviation for this distribution.

      • density

        public 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

        public 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.

        If x is more than 40 standard deviations from the mean, 0 or 1 is returned, as in these cases the actual value is within Double.MIN_VALUE of 0 or 1.

        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

      • inverseCumulativeProbability

        public 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.

        The default implementation returns

        Specified by:
        inverseCumulativeProbability in interface Distribution
        Overrides:
        inverseCumulativeProbability in class BaseDistribution
        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
        Since:
        3.2

      • cumulativeProbability

        @Deprecated
public double cumulativeProbability​(double x0,
                                    double x1)
                             throws org.apache.commons.math3.exception.NumberIsTooLargeException
        Deprecated.
        See RealDistribution.cumulativeProbability(double, double)
        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

      • probability

        public double probability​(double x0,
                          double x1)
                   throws org.apache.commons.math3.exception.NumberIsTooLargeException
        For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
        Overrides:
        probability in class BaseDistribution
        Parameters:
        x0 - Lower bound (excluded).
        x1 - Upper bound (included).
        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.

        The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)

      • getSolverAbsoluteAccuracy

        protected double getSolverAbsoluteAccuracy()
        Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
        Overrides:
        getSolverAbsoluteAccuracy in class BaseDistribution
        Returns:
        the maximum absolute error in inverse cumulative probability estimates

      • getNumericalMean

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

        For mean parameter mu, the mean is mu.

        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 standard deviation parameter s, the variance is s^2.

        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 negative infinity no matter the parameters.

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

      • 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 always positive infinity no matter the parameters.

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

      • isSupportLowerBoundInclusive

        public boolean isSupportLowerBoundInclusive()
        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()
        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 target)
        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:
        target - target array
        Returns:
        an ndarray with random samples from this distribution