pyuncertainnumber.calibration.data_peeling.fuzzy¶

Functions¶

`samples_to_fuzzy_projection`(→ numpy.typing.NDArray)
`samples_to_fuzzy_multivariate`(u, levels[, p])
`boxes_to_fuzzy_projection`(boxes[, p])
`coverage_samples`(lo, hi[, m])
`width`(→ numpy.typing.NDArray)

pyuncertainnumber.calibration.data_peeling.fuzzy.samples_to_fuzzy_projection(ux: numpy.typing.NDArray, c: list) → numpy.typing.NDArray¶

Parameters:

ux (NDArray) – an (mxd_) array of samples, usually uniform. m is a large integer.
c (list) – a list (of length l) of subindices of coverage samples belonging to each level. len(levels) < m must yield True, sum([sum(len(subi)) for subi in levels])==m must yield True.

Returns:

returns a d-dimensional fuzzy number, i.e. an (lxdx2) array.

Return type:

fx (NDArray)

pyuncertainnumber.calibration.data_peeling.fuzzy.samples_to_fuzzy_multivariate(u: numpy.ndarray, levels: list, p: list = None)¶

pyuncertainnumber.calibration.data_peeling.fuzzy.boxes_to_fuzzy_projection(boxes: list, p: list = None)¶

Parameters:: boxes (list) – sequence of boxes, each box is a (dx2) array. Also iterable of interval objects. Second output of the forward data-peeling algorithm.
Returns:: an (lxdx3) fuzzy projection data structure
Return type:: f (NDArray)

pyuncertainnumber.calibration.data_peeling.fuzzy.coverage_samples(lo: numpy.typing.NDArray, hi: numpy.typing.NDArray, m: int = 1000)¶

Parameters:

lo (NDArray) – an (d,) array (or list) of left endpoints. Coverage means samples are generated using low-discrepancy schemes.
hi (NDArray) – an (d,) array (or list) of right endpoints, with hi > lo.

Returns:

an (mxd_) array of coverage samples

Return type:

u (NDArray)

pyuncertainnumber.calibration.data_peeling.fuzzy.width(x: numpy.typing.NDArray) → numpy.typing.NDArray¶