pyuncertainnumber.propagation.p¶

Classes¶

`P`	Base class blueprint. Not for direct use
`AleatoryPropagation`	Aleatoric uncertainty propagation class for Distribution constructs only
`EpistemicPropagation`	Epistemic uncertainty propagation class for construct
`MixedPropagation`	Mixed uncertainty propagation class for construct
`Propagation`	High-level integrated class for the propagation of uncertain numbers

Module Contents¶

class pyuncertainnumber.propagation.p.P(vars, func, method, dependency=None)¶

Bases: abc.ABC

Base class blueprint. Not for direct use

_vars¶

func¶

method¶

dependency = None¶

post_init_check()¶: some checks

abstractmethod type_check()¶: if the nature of the UN suitable for the method

abstractmethod method_check()¶: if the method is suitable for the nature of the UN

class pyuncertainnumber.propagation.p.AleatoryPropagation(vars, func, method, dependency=None)¶

Bases: P

Aleatoric uncertainty propagation class for Distribution constructs only

Parameters:

vars (Distribution) – a list of uncertain numbers objects
func (callable) – the response or performance function applied to the uncertain numbers
method (str) – a string indicating the method to be used for propagation.
dependency (string or Dependency) – a Dependency object(i.e. a copula function) to model the dependency structure among input variables. Strings such as “independence” accepted for independence.

Note

Supported methods include “monte_carlo”. Note that “taylor_expansion” is not supported herein but implemented as a standalone function in the module taylor_expansion.py.

Caution

This function supports with low-level constructs NOT the high-level UN (uncertain number) objects. For UN objects, use Propagation class as an high-level API.

See also

Propagation() : the high-level API for uncertain number propagation.

Warning

The computation cost increases exponentially with the number of input variables and the number of slices. Be cautious with the choice of number of slices n_slices given the number of input variables vars of the response function.

Note

Discussion of the methods and strategies. When choosing interval_strategy, “direct” requires function signature to take a list of inputs, whereas “subinterval” and “endpoints” require the function to take a vectorised signature. Currently, only “interval_monte_carlo” supports with dependency structures (e.g. copulas).

When calling the run function to do propagation, extra keyword arguments are needed to be passed down to the selected method. For example, n_sam for “interval_monte_carlo”; n_slices for “slicing”; n_outer, n_inner for “double_monte_carlo”.

Example

>>> from pyuncertainnumber import pba
>>> from pyuncertainnumber.propagation.p import MixedPropagation
>>> def foo(x): return x[0] ** 3 + x[1] + x[2]
>>> a = pba.normal([2, 3], [1])
>>> b = pba.normal([10, 14], [1])
>>> c = pba.normal([4, 5], [1])
>>> mix = MixedPropagation(vars=[a,b,c], func=foo, method='slicing', interval_strategy='subinterval')
>>> result = mix.run(n_slices=20, n_sub=2, style='endpoints')

interval_strategy = None¶

type_check()¶: Inspection if inputs are mixed uncertainy model

method_check()¶: Check if the method is suitable for mixed uncertainty propagation

run(**kwargs)¶: doing the propagation. Extra keyword are needed to be passed down to the selected method

class pyuncertainnumber.propagation.p.Propagation(vars: list[pyuncertainnumber.characterisation.uncertainNumber.UncertainNumber], func: callable, method: str, dependency: str | pyuncertainnumber.pba.dependency.Dependency = None, interval_strategy: str = None)¶

High-level integrated class for the propagation of uncertain numbers

Parameters:

vars (UncertainNumber) – a list of uncertain numbers objects
func (Callable) – the response or performance function applied to the uncertain numbers
method (str) – a string indicating the method to be used for propagation (e.g. “monte_carlo”, “endpoint”, etc.) which may depend on the constructs of the uncertain numbers. See notes about function signature.
dependency (string or Dependency) – a Dependency object(i.e. a copula function) to model the dependency structure among input variables. Strings such as “independence” accepted for independence.
interval_strategy (str) – a strategy for interval propagation, including {‘direct’, ‘subinterval’, ‘endpoints’} which will affect the function signature of the response function. See notes about function signature.

Caution

This class supports with high-level computation with UncertainNumber objects.

Note

Warning

Example

>>> import pyuncertainnumber as pun
>>> # construction of uncertain number objects
>>> a = pun.I(2, 3)
>>> b = pun.normal(4, 1)
>>> c = pun.uniform([4,5], [9,10])

>>> # vectorised function signature with matrix input (2D np.ndarray)
>>> def foo_vec(x): return x[:, 0] ** 3 + x[:, 1] + x[:, 2]

>>> # high-level propagation API
>>> p = Propagation(vars=[a,b,c],
>>>     func=foo,
>>>     method='slicing',
>>>     interval_strategy='subinterval'
>>> )

>>> # heavy-lifting of propagation
>>> t = p.run(n_sam=20, n_sub=2, style='endpoints')

_vars¶

_func¶

method¶

dependency = None¶

interval_strategy = None¶

_post_init_check()¶: Some checks after initialisation

assign_method()¶: Assign the propagation method based on the essence of constructs

property constructs¶: return the underlying constructs

run(**kwargs)¶

Doing the propagation and return UN

Returns:: the result of the propagation as an uncertain number object
Return type:: UncertainNumber