Random in Arkouda¶

Pseudo random number generation in Arkouda is modeled after numpy. Just like in numpy the preferred way to access the random functionality in arkouda is via Generator objects. If a Generator is initialized with a seed, the stream of random numbers it produces can be reproduced by a new Generator with the same seed. This reproducibility is not guaranteed across releases.

class arkouda.random.Generator(name_dict=None, seed=None, state=1)[source]¶

Generator exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults to None. If size is None, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned.

Parameters:

seed (int) – Seed to allow for reproducible random number generation.
name_dict (dict) – Dictionary mapping the server side names associated with the generators for each dtype.
state (int) – The current state we are in the random number generation stream. This information makes it so calls to any dtype generator function affects the stream of random numbers for the other generators. This mimics the behavior we see in numpy

Creation¶

To create a Generator use the default_rng constructor

arkouda.random.default_rng(seed=None)[source]¶

Construct a new Generator.

Right now we only support PCG64, since this is what is available in chapel.

Parameters:: seed ({None, int, Generator}, optional) – A seed to initialize the Generator. If None, then the seed will be generated by chapel in an implementation specific manner based on the current time. This behavior is currently unstable and may change in the future. If an int, then the value must be non-negative. If passed a Generator, it will be returned unaltered.
Returns:: The initialized generator object.
Return type:: Generator

Features¶

integers¶

arkouda.random.Generator.integers(self, low, high=None, size=None, dtype=dtype('int64'), endpoint=False)¶

Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).

Return random integers from the “discrete uniform” distribution of the specified dtype. If high is None (the default), then results are from 0 to low.

Parameters:

low (numeric_scalars) – Lowest (signed) integers to be drawn from the distribution (unless high=None, in which case this parameter is 0 and this value is used for high).
high (numeric_scalars) – If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None)
size (numeric_scalars) – Output shape. Default is None, in which case a single value is returned.
dtype (dtype, optional) – Desired dtype of the result. The default value is ak.int64.
endpoint (bool, optional) – If true, sample from the interval [low, high] instead of the default [low, high). Defaults to False

Returns:

Values drawn uniformly from the specified range having the desired dtype, or a single such random int if size not provided.

Return type:

pdarray, numeric_scalar

Examples

>>> rng = ak.random.default_rng()
>>> rng.integers(5, 20, 10)
array([15, 13, 10, 8, 5, 18, 16, 14, 7, 13])  # random
>>> rng.integers(5, size=10)
array([2, 4, 0, 0, 0, 3, 1, 5, 5, 3])  # random

random¶

arkouda.random.Generator.random(self, size=None)¶

Return random floats in the half-open interval [0.0, 1.0).

Results are from the uniform distribution over the stated interval.

Parameters:: size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.
Returns:: Pdarray of random floats (unless size=None, in which case a single float is returned).
Return type:: pdarray

Notes

To sample over [a,b), use uniform or multiply the output of random by (b - a) and add a:

(b - a) * random() + a

standard_normal¶

arkouda.random.Generator.standard_normal(self, size=None)¶

Draw samples from a standard Normal distribution (mean=0, stdev=1).

Parameters:: size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.
Returns:: Pdarray of floats (unless size=None, in which case a single float is returned).
Return type:: pdarray

Notes

For random samples from \(N(\mu, \sigma^2)\), use:

(sigma * standard_normal(size)) + mu

Examples

>>> rng = ak.random.default_rng()
>>> rng.standard_normal()
2.1923875335537315 # random
>>> rng.standard_normal(3)
array([0.8797352989638163, -0.7085325853376141, 0.021728052940979934])  # random

uniform¶

arkouda.random.Generator.uniform(self, low=0.0, high=1.0, size=None)¶

Draw samples from a uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high). In other words, any value within the given interval is equally likely to be drawn by uniform.

Parameters:

low (float, optional) – Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high (float, optional) – Upper boundary of the output interval. All values generated will be less than high. high must be greater than or equal to low. The default value is 1.0.
size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.

Returns:

Pdarray of floats (unless size=None, in which case a single float is returned).

Return type:

pdarray