Random Sampling

NLCPy random number routines produce pseudo random numbers and create sample from different statistical distributions.

Generator

The Generator provides access to a wide variety of probability distributions, and serves as a replacement for RandomState.

An easy example of Generator is below:

from nlcpy.random import Generator, MT19937
rng = Generator(MT19937(12345))
rng.standard_normal()

And, an easy example of using default_rng is below:

import nlcpy as vp
rng = vp.random.default_rng()
rng.standard_normal()

Available Functions and Methods

The following tables show that nlcpy.random.Generator class methods to generate random numbers.

Construct Generator

nlcpy.random.default_rng

Constructs a new nlcpy.random.Generator with the default BitGenerator (MT19937).

Simple Random Data

nlcpy.random.Generator.bytes	Returns random bytes.
nlcpy.random.Generator.integers	Returns random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).
nlcpy.random.Generator.random	Returns random floats in the half-open interval [0.0, 1.0).

Permutations

nlcpy.random.Generator.permutation	Randomly permutes a sequence, or returns a permuted range.
nlcpy.random.Generator.shuffle	Modifies a sequence in-place by shuffling its contents.

Distributions

nlcpy.random.Generator.binomial	Draws samples from a binomial distribution.
nlcpy.random.Generator.exponential	Draws samples from an exponential distribution.
nlcpy.random.Generator.gamma	Draws samples from a Gamma distribution.
nlcpy.random.Generator.geometric	Draws samples from a geometric distribution.
nlcpy.random.Generator.gumbel	Draws samples from a Gumbel distribution.
nlcpy.random.Generator.logistic	Draws samples from a logistic distribution.
nlcpy.random.Generator.lognormal	Draws samples from a log-normal distribution.
nlcpy.random.Generator.normal	Draws random samples from a normal (Gaussian) distribution.
nlcpy.random.Generator.poisson	Draws samples from a Poisson distribution.
nlcpy.random.Generator.standard_cauchy	Draws samples from a standard Cauchy distribution with mode = 0.
nlcpy.random.Generator.standard_exponential	Draws samples from a standard exponential distribution.
nlcpy.random.Generator.standard_gamma	Draws samples from a standard Gamma distribution.
nlcpy.random.Generator.standard_normal	Draws samples from a standard Normal distribution (mean=0, stdev=1).
nlcpy.random.Generator.uniform	Draws samples from a uniform distribution.
nlcpy.random.Generator.weibull	Draws samples from a Weibull distribution.

RandomState

The RandomState provides access to legacy generators. An easy example of RandomState is below:

# Uses the nlcpy.random.RandomState
from nlcpy import random
random.standard_normal()
# or
rst = random.RandomState()
rst.standard_normal()

Available Functions and Methods

Seeding and State

nlcpy.random.RandomState.get_state	Returns an ndarray representing the internal state of the generator.
nlcpy.random.RandomState.seed	Reseeds a default bit generator(MT19937), which provide a stream of random bits.
nlcpy.random.RandomState.set_state	Sets the internal state of the generator from an ndarray.

Simple Random Data

nlcpy.random.RandomState.bytes	Returns random bytes.
nlcpy.random.RandomState.rand	Random values in a given shape.
nlcpy.random.RandomState.randint	Returns random integers from low (inclusive) to high (exclusive).
nlcpy.random.RandomState.randn	Returns a sample (or samples) from the "standard normal" distribution.
nlcpy.random.RandomState.random	Returns random floats in the half-open interval [0.0, 1.0).
nlcpy.random.RandomState.random_integers	Random integers of type nlcpy.int64/nlcpy.int32 between low and high, inclusive.
nlcpy.random.RandomState.random_sample	Returns random floats in the half-open interval [0.0, 1.0).
nlcpy.random.RandomState.ranf	This is an alias of random_sample.
nlcpy.random.RandomState.sample	This is an alias of random_sample.
nlcpy.random.RandomState.tomaxint	Random integers between 0 and nlcpy.iinfo(nlcpy.int_).max, inclusive.

Permutations

nlcpy.random.RandomState.permutation	Randomly permutes a sequence, or returns a permuted range.
nlcpy.random.RandomState.shuffle	Modifies a sequence in-place by shuffling its contents.

Distributions

nlcpy.random.RandomState.binomial	Draws samples from a binomial distribution.
nlcpy.random.RandomState.exponential	Draws samples from an exponential distribution.
nlcpy.random.RandomState.gamma	Draws samples from a Gamma distribution.
nlcpy.random.RandomState.geometric	Draws samples from a geometric distribution.
nlcpy.random.RandomState.gumbel	Draws samples from a Gumbel distribution.
nlcpy.random.RandomState.logistic	Draws samples from a logistic distribution.
nlcpy.random.RandomState.lognormal	Draws samples from a log-normal distribution.
nlcpy.random.RandomState.normal	Draws random samples from a normal (Gaussian) distribution.
nlcpy.random.RandomState.poisson	Draws samples from a Poisson distribution.
nlcpy.random.RandomState.standard_cauchy	Draws samples from a standard Cauchy distribution with mode = 0.
nlcpy.random.RandomState.standard_exponential	Draws samples from a standard exponential distribution.
nlcpy.random.RandomState.standard_gamma	Draws samples from a standard Gamma distribution.
nlcpy.random.RandomState.standard_normal	Draws samples from a standard Normal distribution (mean=0, stdev=1).
nlcpy.random.RandomState.uniform	Draws samples from a uniform distribution.
nlcpy.random.RandomState.weibull	Draws samples from a Weibull distribution.