chaospy.Logn

class chaospy.Logn(dist, base=2)

Logarithm with base N.

Args:

dist (Distribution):

Distribution to perform transformation on.

base (int, float):

the logarithm base.

Example:

>>> distribution = chaospy.Logn(chaospy.Uniform(1, 2), 3)
>>> distribution
Logn(Uniform(lower=1, upper=2), 3)
>>> q = numpy.linspace(0,1,6)[1:-1]
>>> distribution.inv(q).round(4)
array([0.166 , 0.3063, 0.4278, 0.535 ])
>>> distribution.fwd(distribution.inv(q)).round(4)
array([0.2, 0.4, 0.6, 0.8])
>>> distribution.pdf(distribution.inv(q)).round(4)
array([1.3183, 1.5381, 1.7578, 1.9775])
>>> distribution.sample(4).round(4)
array([0.4578, 0.0991, 0.608 , 0.3582])
>>> distribution.mom(1).round(4)
0.3516

__init__(dist, base=2)

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.