numpy.random.RandomState¶

class
numpy.random.
RandomState
¶ Container for the Mersenne Twister pseudorandom number generator.
RandomState
exposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distributionspecific 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 1D array filled with generated values is returned. If size is a tuple, then an array with that shape is filled and returned.Compatibility Guarantee A fixed seed and a fixed series of calls to ‘RandomState’ methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect. Incorrect values will be fixed and the NumPy version in which the fix was made will be noted in the relevant docstring. Extension of existing parameter ranges and the addition of new parameters is allowed as long the previous behavior remains unchanged.
Parameters: seed : {None, int, array_like}, optional
Random seed used to initialize the pseudorandom number generator. Can be any integer between 0 and 2**32  1 inclusive, an array (or other sequence) of such integers, or
None
(the default). Ifseed
isNone
, thenRandomState
will try to read data from/dev/urandom
(or the Windows analogue) if available or seed from the clock otherwise.Notes
The Python stdlib module “random” also contains a Mersenne Twister pseudorandom number generator with a number of methods that are similar to the ones available in
RandomState
.RandomState
, besides being NumPyaware, has the advantage that it provides a much larger number of probability distributions to choose from.Methods
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 chisquare distribution. choice
(a[, size, replace, p])Generates a random sample from a given 1D 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
()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 lognormal 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 chisquare 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 from low (inclusive) to high (exclusive). randn
(d0, d1, ..., dn)Return a sample (or samples) from the “standard normal” distribution. random_integers
(low[, high, size])Random integers of type np.int between low and high, inclusive. random_sample
([size])Return random floats in the halfopen 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 inplace 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 with df degrees of freedom. tomaxint
([size])Random integers between 0 and sys.maxint
, inclusive.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.