public class TriangularDistribution extends AbstractRealDistribution
random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY| Constructor and Description | 
|---|
| TriangularDistribution(double a,
                      double c,
                      double b)Creates a triangular real distribution using the given lower limit,
 upper limit, and mode. | 
| TriangularDistribution(RandomGenerator rng,
                      double a,
                      double c,
                      double b)Creates a triangular distribution. | 
| Modifier and Type | Method and Description | 
|---|---|
| double | cumulativeProbability(double x)For a random variable  Xwhose values are distributed according
 to this distribution, this method returnsP(X <= x). | 
| double | density(double x)Returns the probability density function (PDF) of this distribution
 evaluated at the specified point  x. | 
| double | getMode()Returns the mode  cof this distribution. | 
| double | getNumericalMean()Use this method to get the numerical value of the mean of this
 distribution. | 
| double | getNumericalVariance()Use this method to get the numerical value of the variance of this
 distribution. | 
| protected double | getSolverAbsoluteAccuracy()Returns the solver absolute accuracy for inverse cumulative computation. | 
| double | getSupportLowerBound()Access the lower bound of the support. | 
| double | getSupportUpperBound()Access the upper bound of the support. | 
| double | inverseCumulativeProbability(double p)Computes the quantile function of this distribution. | 
| boolean | isSupportConnected()Use this method to get information about whether the support is connected,
 i.e. | 
| boolean | isSupportLowerBoundInclusive()Whether or not the lower bound of support is in the domain of the density
 function. | 
| boolean | isSupportUpperBoundInclusive()Whether or not the upper bound of support is in the domain of the density
 function. | 
cumulativeProbability, logDensity, probability, probability, reseedRandomGenerator, sample, samplepublic TriangularDistribution(double a,
                      double c,
                      double b)
                       throws NumberIsTooLargeException,
                              NumberIsTooSmallException
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 AbstractRealDistribution.sample() and AbstractRealDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
a - Lower limit of this distribution (inclusive).b - Upper limit of this distribution (inclusive).c - Mode of this distribution.NumberIsTooLargeException - if a >= b or if c > b.NumberIsTooSmallException - if c < a.public TriangularDistribution(RandomGenerator rng, double a, double c, double b) throws NumberIsTooLargeException, NumberIsTooSmallException
rng - Random number generator.a - Lower limit of this distribution (inclusive).b - Upper limit of this distribution (inclusive).c - Mode of this distribution.NumberIsTooLargeException - if a >= b or if c > b.NumberIsTooSmallException - if c < a.public double getMode()
c of this distribution.c of this distributionprotected double getSolverAbsoluteAccuracy()
 For this distribution, the returned value is not really meaningful,
 since exact formulas are implemented for the computation of the
 inverseCumulativeProbability(double) (no solver is invoked).
 
 For lower limit a and upper limit b, the current
 implementation returns max(ulp(a), ulp(b).
 
getSolverAbsoluteAccuracy in class AbstractRealDistributionpublic double density(double x)
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.
 For lower limit a, upper limit b and mode c, the
 PDF is given by
 2 * (x - a) / [(b - a) * (c - a)] if a <= x < c,2 / (b - a) if x = c,2 * (b - x) / [(b - a) * (b - c)] if c < x <= b,0 otherwise.
 x - the point at which the PDF is evaluatedxpublic double cumulativeProbability(double x)
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.
 For lower limit a, upper limit b and mode c, the
 CDF is given by
 0 if x < a,(x - a)^2 / [(b - a) * (c - a)] if a <= x < c,(c - a) / (b - a) if x = c,1 - (b - x)^2 / [(b - a) * (b - c)] if c < x <= b,1 if x > b.x - the point at which the CDF is evaluatedxpublic double getNumericalMean()
a, upper limit b, and mode c,
 the mean is (a + b + c) / 3.Double.NaN if it is not definedpublic double getNumericalVariance()
a, upper limit b, and mode c,
 the variance is (a^2 + b^2 + c^2 - a * b - a * c - b * c) / 18.Double.POSITIVE_INFINITY as
 for certain cases in TDistribution) or Double.NaN if it
 is not definedpublic double getSupportLowerBound()
inverseCumulativeProbability(0). In other words, this
 method must return
 inf {x in R | P(X <= x) > 0}.
a of the distribution.public double getSupportUpperBound()
inverseCumulativeProbability(1). In other words, this
 method must return
 inf {x in R | P(X <= x) = 1}.
b of the distribution.public boolean isSupportLowerBoundInclusive()
getSupporLowerBound() is finite and
 density(getSupportLowerBound()) returns a non-NaN, non-infinite
 value.public boolean isSupportUpperBoundInclusive()
getSupportUpperBound() is finite and
 density(getSupportUpperBound()) returns a non-NaN, non-infinite
 value.public boolean isSupportConnected()
truepublic double inverseCumulativeProbability(double p)
                                    throws OutOfRangeException
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.RealDistribution.getSupportLowerBound() for p = 0,RealDistribution.getSupportUpperBound() for p = 1.inverseCumulativeProbability in interface RealDistributioninverseCumulativeProbability in class AbstractRealDistributionp - the cumulative probabilityp-quantile of this distribution
 (largest 0-quantile for p = 0)OutOfRangeException - if p < 0 or p > 1Copyright © 2003–2016 The Apache Software Foundation. All rights reserved.