class numpy.random.mtrand.RandomState

Container for the Mersenne Twister pseudo-random number generator.

RandomState 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. If size is a tuple, then an array with that shape is filled and returned.

Parameters :

seed : int or array_like, optional

Random seed initializing the pseudo-random number generator. Can be an integer, an array (or other sequence) of integers of any length, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.


The Python stdlib module “random” also contains a Mersenne Twister pseudo-random number generator with a number of methods that are similar to the ones available in RandomState. RandomState, besides being NumPy-aware, has the advantage that it provides a much larger number of probability distributions to choose from.


beta(a, b[, size]) The Beta distribution over [0, 1].
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.
dirichlet(alpha[, size]) Draw samples from the Dirichlet distribution.
exponential([scale, size]) Exponential distribution.
f(dfnum, dfden[, size]) Draw samples from a F distribution.
gamma(shape[, scale, size]) Draw samples from a Gamma distribution.
geometric(p[, size]) Draw samples from the geometric distribution.
get_state() Return a tuple representing the internal state of the generator.
gumbel([loc, scale, size]) 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
logistic([loc, scale, size]) Draw samples from a Logistic distribution.
lognormal([mean, sigma, size]) Return samples drawn 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) 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 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]) Return random integers from low (inclusive) to high (exclusive).
randn(d1) Return a sample (or samples) from the “standard normal” distribution.
random_integers(low[, high, size]) Return random integers between low and high, inclusive.
random_sample([size]) Return random floats in the half-open interval [0.0, 1.0).
rayleigh([scale, size]) Draw samples from a Rayleigh distribution.
seed([seed]) Seed the generator.
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]) 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]) Returns samples from a Standard Normal distribution (mean=0, stdev=1).
standard_t(df[, size]) Standard Student’s t distribution with df degrees of freedom.
triangular(left, mode, right[, size]) Draw samples from the triangular distribution.
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]) Weibull distribution.
zipf(a[, size]) Draw samples from a Zipf distribution.

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