numpy.random.RandomState

class numpy.random.RandomState

Container for the Mersenne Twister pseudo-random number generator.

RandomState提供多种方法，用于生成从各种概率分布中抽取随机数。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.

兼容性保证 固定种子并使用相同参数的“RandomState”方法的固定顺序调用将始终产生相同的结果，直到四舍五入错误，除非值不正确。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

随机种子，初始化伪随机数生成器。Can be an integer, an array (or other sequence) of integers of any length, or None (the default). 当seed为None时，如果/dev/urandom（在Windows上，则为类似的文件）可用，则RandomState将尝试从它读取，否则从时钟设置种子。

注

Python stdlib模块“random”也包含Mersenne Twister伪随机数生成器，其中有许多方法与RandomState中提供的方法类似。RandomState除了具有NumPy感知之外，还具有一个优点是提供更多数量的概率分布以供选择。

Methods

beta(a, b[, size])	从Beta分布抽取样本。
binomial(n, p[, size])	从二项分布抽取样本。
bytes(length)	Return random bytes.
chisquare(df[, size])	从卡方分布抽取样本。
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()	返回一个元组，表示生成器的内部状态。
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 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 half-open interval [0.0, 1.0).
rayleigh([scale, size])	Draw samples from a Rayleigh distribution.
seed([seed])	种子生成器。
set_state(state)	从一个元组设置发生器的内部状态。
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 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])	Draw samples from a Weibull distribution.
zipf(a[, size])	Draw samples from a Zipf distribution.