helpers.permute_rows

helpers.permute_rows(arr, rows=None, seed=None)

Randomly permute rows in a numpy ndarray

Parameters

Name	Type	Description	Default
`arr`	numpy.numpy.ArrayLike	A numpy ndarray	required
`rows`	typing.Optional[typing.List[int]]	If `rows` is None all columns will be randomly permuted, otherwise provide a list of rows to permute. Defaults to None.	`None`
`seed`	Optional[Union[int, np.random.Generator]])	If `seed`is an integer or None, a new numpy.random.Generator is created using np.random.default_rng(seed). If `seed` is already a ``Generator` instance, then the provided instance is used. Defaults to None.	`None`

Returns: numpy ndarray with rows of choice randomly permuted

Examples:

import pyLHD
x = pyLHD.LatinHypercube(size = (5,3), seed = 1)
x

array([[0.70930042, 0.30930042, 0.10930042],
       [0.90930042, 0.50930042, 0.90930042],
       [0.10930042, 0.70930042, 0.30930042],
       [0.50930042, 0.90930042, 0.70930042],
       [0.30930042, 0.10930042, 0.50930042]])

Permute all columns

pyLHD.permute_rows(x)

array([[0.70930042, 0.10930042, 0.30930042],
       [0.90930042, 0.90930042, 0.50930042],
       [0.10930042, 0.30930042, 0.70930042],
       [0.90930042, 0.50930042, 0.70930042],
       [0.10930042, 0.30930042, 0.50930042]])

Permute columns [0,1] with seed=1

pyLHD.permute_rows(x, rows = [0,1], seed = 1)

array([[0.70930042, 0.30930042, 0.10930042],
       [0.90930042, 0.90930042, 0.50930042],
       [0.10930042, 0.70930042, 0.30930042],
       [0.50930042, 0.90930042, 0.70930042],
       [0.30930042, 0.10930042, 0.50930042]])