org.apache.commons.statistics.distribution

## Class TriangularDistribution

• java.lang.Object
• org.apache.commons.statistics.distribution.TriangularDistribution

• ### Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution

ContinuousDistribution.Sampler
• ### Constructor Summary

Constructors
Constructor and Description
TriangularDistribution(double a, double c, double b)
Creates a distribution.
• ### Method Summary

All Methods
Modifier and Type Method and Description
ContinuousDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
double cumulativeProbability(double x)
For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
double density(double x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x.
double getMean()
Gets the mean of this distribution.
double getMode()
Gets the mode.
double getSupportLowerBound()
Gets the lower bound of the support.
double getSupportUpperBound()
Gets the upper bound of the support.
double getVariance()
Gets the variance of this distribution.
double inverseCumulativeProbability(double p)
Computes the quantile function of this distribution.
boolean isSupportConnected()
Indicates whether the support is connected, i.e.
static double[] sample(int n, ContinuousDistribution.Sampler sampler)
Utility function for allocating an array and filling it with n samples generated by the given sampler.
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Methods inherited from interface org.apache.commons.statistics.distribution.ContinuousDistribution

logDensity, probability, probability
• ### Constructor Detail

• #### TriangularDistribution

public TriangularDistribution(double a,
double c,
double b)
Creates a distribution.
Parameters:
a - Lower limit of this distribution (inclusive).
b - Upper limit of this distribution (inclusive).
c - Mode of this distribution.
Throws:
IllegalArgumentException - if a >= b, if c > b or if c < a.
• ### Method Detail

• #### getMode

public double getMode()
Gets the mode.
Returns:
the mode of the 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. 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.
Parameters:
x - Point at which the PDF is evaluated.
Returns:
the value of the probability density function at 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. 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.
Parameters:
x - 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.
• #### getMean

public double getMean()
Gets the mean of this distribution. For lower limit a, upper limit b, and mode c, the mean is (a + b + c) / 3.
Returns:
the mean, or Double.NaN if it is not defined.
• #### getVariance

public double getVariance()
Gets the variance of this distribution. For lower limit a, upper limit b, and mode c, the variance is (a^2 + b^2 + c^2 - a * b - a * c - b * c) / 18.
Returns:
the variance, or Double.NaN if it is not defined.
• #### getSupportLowerBound

public double getSupportLowerBound()
Gets the lower bound of the support. It must return the same value as inverseCumulativeProbability(0), i.e. inf {x in R | P(X <= x) > 0}. The lower bound of the support is equal to the lower limit parameter a of the distribution.
Returns:
lower bound of the support
• #### getSupportUpperBound

public double getSupportUpperBound()
Gets the upper bound of the support. It must return the same value as inverseCumulativeProbability(1), i.e. inf {x in R | P(X <= x) = 1}. The upper bound of the support is equal to the upper limit parameter b of the distribution.
Returns:
upper bound of the support
• #### isSupportConnected

public boolean isSupportConnected()
Indicates 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
• #### inverseCumulativeProbability

public double inverseCumulativeProbability(double p)
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 ContinuousDistribution
Parameters:
p - Cumulative probability.
Returns:
the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
• #### sample

public static double[] sample(int n,
ContinuousDistribution.Sampler sampler)
Utility function for allocating an array and filling it with n samples generated by the given sampler.
Parameters:
n - Number of samples.
sampler - Sampler.
Returns:
an array of size n.
• #### createSampler

public ContinuousDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
Specified by:
createSampler in interface ContinuousDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.