Random Generator¶
The Generator
provides access to
a wide range of distributions, and served as a replacement for
RandomState
. The main difference between
the two is that Generator
relies on an additional BitGenerator to
manage state and generate the random bits, which are then transformed into
random values from useful distributions. The default BitGenerator used by
Generator
is PCG64
. The BitGenerator
can be changed by passing an instantized BitGenerator to Generator
.
-
numpy.random.
default_rng
()¶ Construct a new Generator with the default BitGenerator (PCG64).
Parameters: - seed : {None, int, array_like[ints], ISeedSequence, BitGenerator, Generator}, optional
A seed to initialize the BitGenerator. If None, then fresh, unpredictable entropy will be pulled from the OS. If an
int
orarray_like[ints]
is passed, then it will be passed toSeedSequence
to derive the initial BitGenerator state. One may also pass in an implementor of the ISeedSequence interface likeSeedSequence
. Additionally, when passed a BitGenerator, it will be wrapped byGenerator
. If passed aGenerator
, it will be returned unaltered.
Notes
When
seed
is omitted orNone
, a new BitGenerator andGenerator
will be instantiated each time. This function does not manage a default global instance.
-
class
numpy.random.
Generator
(bit_generator)¶ Container for the BitGenerators.
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 toNone
. If size isNone
, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.The function
numpy.random.default_rng
will instantiate aGenerator
with numpy’s default BitGenerator.No Compatibility Guarantee
Generator
does not provide a version compatibility guarantee. In particular, as better algorithms evolve the bit stream may change.Parameters: - bit_generator : BitGenerator
BitGenerator to use as the core generator.
See also
default_rng
- Recommended constructor for
Generator
.
Notes
The Python stdlib module
random
contains pseudo-random number generator with a number of methods that are similar to the ones available inGenerator
. It uses Mersenne Twister, and this bit generator can be accessed usingMT19937
.Generator
, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.Examples
>>> from numpy.random import Generator, PCG64 >>> rg = Generator(PCG64()) >>> rg.standard_normal() -0.203 # random
Accessing the BitGenerator¶
bit_generator |
Gets the bit generator instance used by the generator |
Simple random data¶
integers (low[, high, size, dtype, endpoint]) |
Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive). |
random ([size, dtype, out]) |
Return random floats in the half-open interval [0.0, 1.0). |
choice () |
choice(a, size=None, replace=True, p=None, axis=0): |
bytes (length) |
Return random bytes. |
Permutations¶
shuffle (x) |
Modify a sequence in-place by shuffling its contents. |
permutation (x) |
Randomly permute a sequence, or return a permuted range. |
Distributions¶
beta (a, b[, size]) |
Draw samples from a Beta distribution. |
binomial (n, p[, size]) |
Draw samples from a binomial distribution. |
chisquare (df[, size]) |
Draw samples from a chi-square distribution. |
dirichlet (alpha[, size]) |
Draw samples from the Dirichlet distribution. |
exponential ([scale, size]) |
Draw samples from an exponential distribution. |
f (dfnum, dfden[, size]) |
Draw samples from an F distribution. |
gamma (shape[, scale, size]) |
Draw samples from a Gamma distribution. |
geometric (p[, size]) |
Draw samples from the geometric distribution. |
gumbel ([loc, scale, size]) |
Draw samples from a Gumbel distribution. |
hypergeometric (ngood, nbad, nsample[, size]) |
Draw samples from a Hypergeometric distribution. |
laplace ([loc, scale, size]) |
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). |
logistic ([loc, scale, size]) |
Draw samples from a logistic distribution. |
lognormal ([mean, sigma, size]) |
Draw samples from a log-normal distribution. |
logseries (p[, size]) |
Draw samples from a logarithmic series distribution. |
multinomial (n, pvals[, size]) |
Draw samples from a multinomial distribution. |
multivariate_normal (mean, cov[, size, …]) |
Draw random samples from a multivariate normal distribution. |
negative_binomial (n, p[, size]) |
Draw samples from a negative binomial distribution. |
noncentral_chisquare (df, nonc[, size]) |
Draw samples from a noncentral chi-square distribution. |
noncentral_f (dfnum, dfden, nonc[, size]) |
Draw samples from the noncentral F distribution. |
normal ([loc, scale, size]) |
Draw random samples from a normal (Gaussian) distribution. |
pareto (a[, size]) |
Draw samples from a Pareto II or Lomax distribution with specified shape. |
poisson ([lam, size]) |
Draw samples from a Poisson distribution. |
power (a[, size]) |
Draws samples in [0, 1] from a power distribution with positive exponent a - 1. |
rayleigh ([scale, size]) |
Draw samples from a Rayleigh distribution. |
standard_cauchy ([size]) |
Draw samples from a standard Cauchy distribution with mode = 0. |
standard_exponential ([size, dtype, method, out]) |
Draw samples from the standard exponential distribution. |
standard_gamma (shape[, size, dtype, out]) |
Draw samples from a standard Gamma distribution. |
standard_normal ([size, dtype, out]) |
Draw samples from a standard Normal distribution (mean=0, stdev=1). |
standard_t (df[, size]) |
Draw samples from a standard Student’s t distribution with df degrees of freedom. |
triangular (left, mode, right[, size]) |
Draw samples from the triangular distribution over the interval [left, right] . |
uniform ([low, high, size]) |
Draw samples from a uniform distribution. |
vonmises (mu, kappa[, size]) |
Draw samples from a von Mises distribution. |
wald (mean, scale[, size]) |
Draw samples from a Wald, or inverse Gaussian, distribution. |
weibull (a[, size]) |
Draw samples from a Weibull distribution. |
zipf (a[, size]) |
Draw samples from a Zipf distribution. |