随机抽样 (numpy.random)

简单的随机数据

rand(d0, d1, ..., dn)	随机值 >>> np.random.rand(3,2)array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random
randn(d0, d1, ..., dn)	返回一个样本，具有标准正态分布。 Notes For random samples from [R217]).   Examples Draw samples from the distribution: >>> mu, sigma = 0, 0.1 # mean and standard deviation>>> s = np.random.normal(mu, sigma, 1000) Verify the mean and the variance: >>> abs(mu - np.mean(s)) < 0.01True>>> abs(sigma - np.std(s, ddof=1)) < 0.01True Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt>>> count, bins, ignored = plt.hist(s, 30, normed=True)>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),... linewidth=2, color=‘r‘)>>> plt.show()
pareto(a[, size])	帕累托（Lomax）分布
poisson([lam, size])	泊松分布
power(a[, size])	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh([scale, size])	Rayleigh 分布
standard_cauchy([size])	标准柯西分布
standard_exponential([size])	标准的指数分布
standard_gamma(shape[, size])	标准伽马分布
standard_normal([size])	标准正态分布 (mean=0, stdev=1).
standard_t(df[, size])	Standard Student’s t distribution with df degrees of freedom.
triangular(left, mode, right[, size])	三角形分布
uniform([low, high, size])	均匀分布
vonmises(mu, kappa[, size])	von Mises分布
wald(mean, scale[, size])	瓦尔德（逆高斯）分布
weibull(a[, size])	Weibull 分布
zipf(a[, size])	齐普夫分布

随机数生成器

RandomState	Container for the Mersenne Twister pseudo-random number generator.
seed([seed])	Seed the generator.
get_state()	Return a tuple representing the internal state of the generator.
set_state(state)	Set the internal state of the generator from a tuple.