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numpy random 模块

C
 Claroja
发布于 2017/05/08 23:18
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一下方法都要加np.random.前缀
1.简单随机数据

name describe
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 from low (inclusive) to high (exclusive).
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).
random([size]) Return random floats in the half-open interval [0.0, 1.0).
ranf([size]) Return random floats in the half-open interval [0.0, 1.0).
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.

2.生成随机分布

name describe
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 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 with df 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.

3.重排

name describe
shuffle(x) Modify a sequence in-place by shuffling its contents.
permutation(x) Randomly permute a sequence, or return a permuted range.

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