mrdja.sampling.sampling_np_arrays_from_enumerable

mrdja.sampling.sampling_np_arrays_from_enumerable(source_list: List | ndarray, cardinality_of_np_arrays: int, number_of_np_arrays: int = 1, num_source_elems: int | None = None, seed: int | None = None) → List[ndarray][source]

Returns a list with number_of_np_arrays numpy arrays of size cardinality_of_np_arrays with random elements from a list or numpy array.

Parameters:

source_list (Union[list, np.ndarray]) – The list or numpy array to sample from.
cardinality_of_np_arrays (int) – The cardinality of the numpy arrays to generate.
number_of_np_arrays (int) – The number of numpy arrays to generate. Default is 1.
num_source_elems (int) – The number of elements in the source list or numpy array. Default is None.
seed (int) – The seed value for the random number generator. Default is None.

Returns:

List of numpy arrays containing the sampled arrays.

Return type:

List[np.ndarray]

Example:

>>> import mrdja.sampling as sampling
>>> import numpy as np
>>> sampling.sampling_np_arrays_from_enumerable(np.array([1,2,3,4,5,6,7,8,9,10]), 3, 2, seed=42)
>>> [array([9, 2, 6]), array([1, 8, 3])]
>>> np.random.seed(42)
>>> random_3d_points = np.random.rand(100, 3)
>>> random_np_arrays_of_points = sampling.sampling_np_arrays_from_enumerable(random_3d_points, cardinality_of_np_arrays=3, number_of_np_arrays=4, seed=42)
>>> random_np_arrays_of_points
>>> [array([[0.85300946, 0.29444889, 0.38509773],
>>> [0.72821635, 0.36778313, 0.63230583],
>>> [0.54873379, 0.6918952 , 0.65196126]]),
>>> array([[0.32320293, 0.51879062, 0.70301896],
>>> [0.11986537, 0.33761517, 0.9429097 ],
>>> [0.18657006, 0.892559  , 0.53934224]]),
>>> array([[0.14092422, 0.80219698, 0.07455064],
>>> [0.94045858, 0.95392858, 0.91486439],
>>> [0.60754485, 0.17052412, 0.06505159]]),
>>> array([[0.37454012, 0.95071431, 0.73199394],
>>> [0.59789998, 0.92187424, 0.0884925 ],
>>> [0.11959425, 0.71324479, 0.76078505]])]