chaospy.Uniform

class chaospy.Uniform(lower=0.0, upper=1.0)[source]

Uniform probability distribution.

Args:
lower (float, Distribution):

Lower threshold of distribution. Must be smaller than upper.

upper (float, Distribution):

Upper threshold of distribution.

Examples:
>>> distribution = chaospy.Uniform(2, 4)
>>> distribution
Uniform(lower=2, upper=4)
>>> uloc = numpy.linspace(0, 1, 6)
>>> uloc
array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])
>>> xloc = distribution.inv(uloc)
>>> xloc.round(3)
array([2. , 2.4, 2.8, 3.2, 3.6, 4. ])
>>> numpy.allclose(distribution.fwd(xloc), uloc)
True
>>> distribution.pdf(xloc).round(3)
array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
>>> distribution.sample(4).round(3)
array([3.307, 2.23 , 3.901, 2.964])
>>> distribution.mom(1).round(4)
3.0
>>> distribution.ttr([0, 1, 2, 3]).round(4)
array([[ 3.    ,  3.    ,  3.    ,  3.    ],
       [-0.    ,  0.3333,  0.2667,  0.2571]])
__init__(lower=0.0, upper=1.0)[source]

Distribution initializer.

In addition to assigning some object variables, also checks for some consistency issues.

Args:
parameters (Optional[Distribution[str, Union[ndarray, Distribution]]]):

Collection of model parameters.

dependencies (Optional[Sequence[Set[int]]]):

Dependency identifiers. One collection for each dimension.

rotation (Optional[Sequence[int]]):

The order of which to resolve dependencies.

exclusion (Optional[Sequence[int]]):

Distributions that has been “taken out of play” and therefore can not be reused other places in the dependency hierarchy.

repr_args (Optional[Sequence[str]]):

Positional arguments to place in the object string representation. The repr output will then be: <class name>(<arg1>, <arg2>, …).

Raises:
StochasticallyDependentError:

For dependency structures that can not later be rectified. This include under-defined distributions, and inclusion of distributions that should be exclusion.

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.