Legacy random generation#

TheRandomState provides access tolegacy generators. This generator is considered frozen and will haveno further improvements. It is guaranteed to produce the same valuesas the final point release of NumPy v1.16. These all depend on Box-Mullernormals or inverse CDF exponentials or gammas. This class should only be usedif it is essential to have randoms that are identical to whatwould have been produced by previous versions of NumPy.

RandomState adds additional informationto the state which is required when using Box-Muller normals since theseare produced in pairs. It is important to useRandomState.get_state, and not the underlying bit generatorsstate, when accessing the state so that these extra values are saved.

Although we provide theMT19937 BitGenerator for use independent ofRandomState, note that its default seeding usesSeedSequencerather than the legacy seeding algorithm.RandomState will use thelegacy seeding algorithm. The methods to use the legacy seeding algorithm arecurrently private as the main reason to use them is just to implementRandomState. However, one can reset the state ofMT19937using the state of theRandomState:

fromnumpy.randomimportMT19937fromnumpy.randomimportRandomStaters=RandomState(12345)mt19937=MT19937()mt19937.state=rs.get_state()rs2=RandomState(mt19937)# Same outputrs.standard_normal()rs2.standard_normal()rs.random()rs2.random()rs.standard_exponential()rs2.standard_exponential()
classnumpy.random.RandomState(seed=None)#

Container for the slow Mersenne Twister pseudo-random number generator.Consider using a different BitGenerator with the Generator containerinstead.

RandomState andGenerator expose a number of methods for generatingrandom numbers drawn from a variety of probability distributions. Inaddition to the distribution-specific arguments, each method takes akeyword argumentsize that defaults toNone. Ifsize isNone,then a single value is generated and returned. Ifsize is an integer,then a 1-D array filled with generated values is returned. Ifsize is atuple, then an array with that shape is filled and returned.

Compatibility Guarantee

A fixed bit generator using a fixed seed and a fixed series of calls to‘RandomState’ methods using the same parameters will always produce thesame results up to roundoff error except when the values were incorrect.RandomState is effectively frozen and will only receive updates thatare required by changes in the internals of Numpy. More substantialchanges, including algorithmic improvements, are reserved forGenerator.

Parameters:
seed{None, int, array_like, BitGenerator}, optional

Random seed used to initialize the pseudo-random number generator oran instantized BitGenerator. If an integer or array, used as a seed forthe MT19937 BitGenerator. Values can be any integer between 0 and2**32 - 1 inclusive, an array (or other sequence) of such integers,orNone (the default). Ifseed isNone, then theMT19937BitGenerator is initialized by reading data from/dev/urandom(or the Windows analogue) if available or seed from the clockotherwise.

Notes

The Python stdlib module “random” also contains a Mersenne Twisterpseudo-random number generator with a number of methods that are similarto the ones available inRandomState.RandomState, besides beingNumPy-aware, has the advantage that it provides a much larger numberof probability distributions to choose from.

Seeding and state#

get_state([legacy])

Return a tuple representing the internal state of the generator.

set_state(state)

Set the internal state of the generator from a tuple.

seed([seed])

Reseed a legacy MT19937 BitGenerator

Simple random data#

rand(d0, d1, ..., dn)

Random values in a given shape.

randn(d0, d1, ..., dn)

Return a sample (or samples) from the "standard normal" distribution.

randint(low[, high, size, dtype])

Return random integers fromlow (inclusive) tohigh (exclusive).

random_integers(low[, high, size])

Random integers of typenumpy.int_ betweenlow andhigh, inclusive.

random_sample([size])

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

choice(a[, size, replace, p])

Generates a random sample from a given 1-D array

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])

Draw samples from the standard exponential distribution.

standard_gamma(shape[, size])

Draw samples from a standard Gamma distribution.

standard_normal([size])

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

standard_t(df[, size])

Draw samples from a standard Student's t distribution withdf 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.

Functions innumpy.random#

Many of the RandomState methods above are exported as functions innumpy.random This usage is discouraged, as it is implemented via a globalRandomState instance which is not advised on two counts:

  • It uses global state, which means results will change as the code changes

  • It uses aRandomState rather than the more modernGenerator.

For backward compatible legacy reasons, we will not change this.

beta(a, b[, size])

Draw samples from a Beta distribution.

binomial(n, p[, size])

Draw samples from a binomial distribution.

bytes(length)

Return random bytes.

chisquare(df[, size])

Draw samples from a chi-square distribution.

choice(a[, size, replace, p])

Generates a random sample from a given 1-D array

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.

get_state([legacy])

Return a tuple representing the internal state of the generator.

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.

permutation(x)

Randomly permute a sequence, or return a permuted range.

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.

rand(d0, d1, ..., dn)

Random values in a given shape.

randint(low[, high, size, dtype])

Return random integers fromlow (inclusive) tohigh (exclusive).

randn(d0, d1, ..., dn)

Return a sample (or samples) from the "standard normal" distribution.

random([size])

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

random_integers(low[, high, size])

Random integers of typenumpy.int_ betweenlow andhigh, inclusive.

random_sample([size])

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

ranf(*args, **kwargs)

This is an alias ofrandom_sample.

rayleigh([scale, size])

Draw samples from a Rayleigh distribution.

sample(*args, **kwargs)

This is an alias ofrandom_sample.

seed([seed])

Reseed the singleton RandomState instance.

set_state(state)

Set the internal state of the generator from a tuple.

shuffle(x)

Modify a sequence in-place by shuffling its contents.

standard_cauchy([size])

Draw samples from a standard Cauchy distribution with mode = 0.

standard_exponential([size])

Draw samples from the standard exponential distribution.

standard_gamma(shape[, size])

Draw samples from a standard Gamma distribution.

standard_normal([size])

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

standard_t(df[, size])

Draw samples from a standard Student's t distribution withdf 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.