scikitplot.random#

Random Number Generation (Numpy-Like Generator) [1] [2] [3].

Use default_rng() to create a Generator and call its methods.

See also

References

[1]

Marsaglia, G. (1999). “Random Number Generators.” Journal of Modern Applied Statistical Methods, 2(1), 2-13.

[2]

Jones, D. “Good Practice in (Pseudo) Random Number Generation for Bioinformatics Applications.” https://www0.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf

[3]

Examples

>>> from scikitplot.random import default_rng, kiss_context
>>> rng = default_rng(42)
>>> data = rng.random(1000)

Context manager

>>> with default_rng(42) as rng:
...     data = rng.random(1000)
>>>
>>> with kiss_context(42) as rng:
...     data = rng.random(1000)

User guide. See the Random section for further details.

Random Number Generation (Numpy-Like `Generator`).#

User guide. See the Random section for further details.

`Kiss32Random`	32-bit KISS RNG with complete serialization support.
`Kiss64Random`	Low-level 64-bit KISS RNG with context manager support.
`KissRandom`	Factory function for auto-detecting 32-bit vs 64-bit RNG.
`KissSeedSequence`	Seed sequence compatible with numpy.random.SeedSequence.
`KissBitGenerator`	NumPy-compatible BitGenerator using KISS algorithm with complete serialization.
`KissGenerator`	High-level random number generator using KISS algorithm.
`KissRandomState`	NumPy RandomState-compatible interface with complete serialization.
`default_rng`	Create default KISS random number generator.
`kiss_context`	Context manager for temporary RNG.

User guide. See the Random section for further details.

`choice`	Random sample from array.
`integers`	Random integers in [low, high) or [low, high].
`normal`	Normal distribution (Box-Muller transform).
`permutation`	Randomly permute sequence or return permuted range.
`random`	Random floats in [0, 1).
`shuffle`	Shuffle array in-place (Fisher-Yates algorithm).
`uniform`	Uniform distribution in [low, high).