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# numpy.random.RandomState

class `numpy.random.``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.

  Notes

  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.

  Methods

  `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.
  `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]) | 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, 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]) | 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]) | 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.
  `tomaxint`([size]) | Random integers between 0 and `sys.maxint`, inclusive.
  `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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