chaospy.DiscreteUniform

class chaospy.DiscreteUniform(lower, upper)

Discrete uniform probability distribution.

Examples:

>>> distribution = chaospy.DiscreteUniform(2, 4)
>>> distribution
DiscreteUniform(2, 4)
>>> qloc = numpy.linspace(0, 1, 9)
>>> qloc.round(2)
array([0.  , 0.12, 0.25, 0.38, 0.5 , 0.62, 0.75, 0.88, 1.  ])
>>> distribution.inv(qloc).round(2)
array([1.5 , 1.88, 2.25, 2.62, 3.  , 3.38, 3.75, 4.12, 4.5 ])
>>> distribution.fwd(distribution.inv(qloc)).round(2)
array([0.  , 0.12, 0.25, 0.38, 0.5 , 0.62, 0.75, 0.88, 1.  ])
>>> distribution.pdf(distribution.inv(qloc)).round(4)
array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
>>> distribution.sample(4)
array([3, 2, 4, 3])
>>> distribution.mom(1).round(4)
3.0

__init__(lower, upper)

Args:

lower (float, chaospy.Distribution):

Lower threshold of distribution. Must be smaller than upper. Value will be rounded up to closes integer.

upper (float, chaospy.Distribution):

Upper threshold of distribution. Value will be rounded down to closes integer.

Methods

pdf(x_data[, decompose, allow_approx, step_size])	Probability density function.
cdf(x_data)	Cumulative distribution function.
fwd(x_data)	Forward Rosenblatt transformation.
inv(q_data[, max_iterations, tollerance])	Inverse Rosenblatt transformation.
sample([size, rule, antithetic, ...])	Create pseudo-random generated samples.
mom(K[, allow_approx])	Raw statistical moments.
ttr(kloc)	Three terms relation's coefficient generator.

Attributes

interpret_as_integer	Flag indicating that return value from the methods sample, and inv should be interpreted as integers instead of floating point.
lower	Lower bound for the distribution.
stochastic_dependent	True if distribution contains stochastically dependent components.
upper	Upper bound for the distribution.