# colour.algebra.random Module¶

## Random Numbers Utilities¶

Defines random numbers generator objects:

• random_triplet_generator()
random_triplet_generator(size, limits=array([[0, 1],
[0, 1],
[0, 1]]), random_state=<mtrand.RandomState object at 0x2adc363d0a98>)

Returns a generator yielding random triplets.

Parameters: size (integer) – Generator size. limits (array_like, (3, 2)) – Random values limits on each triplet axis. random_state (RandomState) – Mersenne Twister pseudo-random number generator. Random triplets generator. generator

Notes

The doctest is assuming that np.random.RandomState() definition will return the same sequence no matter which OS or Python version is used. There is however no formal promise about the prng sequence reproducibility of either Python or *Numpy implementations: Laurent. (2012). Reproducibility of python pseudo-random numbers across systems and versions? Retrieved January 20, 2015, from http://stackoverflow.com/questions/8786084/reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions

Examples

>>> from pprint import pprint
>>> prng = np.random.RandomState(4)
>>> pprint(tuple(random_triplet_generator(10, random_state=prng)))
(array([ 0.9670298...,  0.5472322...,  0.9726843...]),
array([ 0.7148159...,  0.6977288...,  0.2160895...]),
array([ 0.9762744...,  0.0062302...,  0.2529823...]),
array([ 0.4347915...,  0.7793829...,  0.1976850...]),
array([ 0.8629932...,  0.9834006...,  0.1638422...]),
array([ 0.5973339...,  0.0089861...,  0.3865712...]),
array([ 0.0441600...,  0.9566529...,  0.4361466...]),
array([ 0.9489773...,  0.7863059...,  0.8662893...]),
array([ 0.1731654...,  0.0749485...,  0.6007427...]),
array([ 0.1679721...,  0.7333801...,  0.4084438...]))