转载 https://www.cnblogs.com/zuoshoushizi/p/8727773.html

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 , use:sigma np.random.randn(…) + muExamples>>> np.random.randn() 2.1923875335537315 #randomTwo-by-four array of samples from N(3, 6.25):>>> 2.5 np.random.randn(2, 4) + 3 array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random | | randint(low[, high, size]) | 返回随机的整数，位于半开区间 [low, high)。>>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])

np.random.randint(1, size=10) array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])Generate a 2 x 4 array of ints between 0 and 4, inclusive:>>> np.random.randint(5, size=(2, 4)) array([[4, 0, 2, 1], [3, 2, 2, 0]]) | | random_integers(low[, high, size]) | 返回随机的整数，位于闭区间 [low, high]。
Notes
To sample from N evenly spaced floating-point numbers between a and b, use:a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)Examples
>>> np.random.random_integers(5) 4 type(np.random.random_integers(5))

np.random.randomintegers(5, size=(3.,2.)) array([[5, 4], [3, 3], [4, 5]])
Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (_i.e., from the set ):>>> 2.5 (np.random.randomintegers(5, size=(5,)) - 1) / 4. array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])Roll two six sided dice 1000 times and sum the results:>>> d1 = np.random.random_integers(1, 6, 1000) d2 = np.random.random_integers(1, 6, 1000) dsums = d1 + d2Display results as a histogram:>>> import matplotlib.pyplot as plt count, bins, ignored = plt.hist(dsums, 11, normed=True) plt.show() | | random_sample([size]) | 返回随机的浮点数，在半开区间 [0.0, 1.0)。
To sample multiply the output of random_sample by (b-a) and add _a:(b - a) random_sample() + aExamples>>> np.random.random_sample() 0.47108547995356098 type(np.random.random_sample())

np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])Three-by-two array of random numbers from [-5, 0):>>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]]) | | random([size]) | 返回随机的浮点数，在半开区间 [0.0, 1.0)。
（官网例子与random_sample完全一样） | | ranf([size]) | 返回随机的浮点数，在半开区间 [0.0, 1.0)。
（官网例子与random_sample完全一样） | | sample([size]) | 返回随机的浮点数，在半开区间 [0.0, 1.0)。
（官网例子与random_sample完全一样） | | choice(a[, size, replace, p]) | 生成一个随机样本，从一个给定的一维数组
Examples
Generate a uniform random sample from np.arange(5) of size 3:>>> np.random.choice(5, 3) array([0, 3, 4])

This is equivalent to np.random.randint(0,5,3)Generate a non-uniform random sample from np.arange(5) of size 3:>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])

array([3, 3, 0])Generate a uniform random sample from np.arange(5) of size 3 without replacement:>>> np.random.choice(5, 3, replace=False) array([3,1,0])

This is equivalent to np.random.permutation(np.arange(5))[:3]Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])

array([2, 3, 0])Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance:>>> aa_milne_arr = [‘pooh‘, ‘rabbit‘, ‘piglet‘, ‘Christopher‘] np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3]) array([‘pooh‘, ‘pooh‘, ‘pooh‘, ‘Christopher‘, ‘piglet‘], dtype=‘|S11‘) | | bytes(length) | 返回随机字节。>>> np.random.bytes(10) ‘ eh\x85\x022SZ\xbf\xa4‘ #random |

排列

| shuffle(x) | 现场修改序列，改变自身内容。（类似洗牌，打乱顺序）>>> arr = np.arange(10)

np.random.shuffle(arr) arr [1 7 5 2 9 4 3 6 0 8]
This function only shuffles the array along the first index of a multi-dimensional array:>>> arr = np.arange(9).reshape((3, 3)) np.random.shuffle(arr) arr array([[3, 4, 5], [6, 7, 8], [0, 1, 2]]) | | —- | —- | | permutation(x) | 返回一个随机排列>>> np.random.permutation(10) array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])>>> np.random.permutation([1, 4, 9, 12, 15]) array([15, 1, 9, 4, 12])>>> arr = np.arange(9).reshape((3, 3)) np.random.permutation(arr) array([[6, 7, 8], [0, 1, 2], [3, 4, 5]]) |

分布

cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis>>> import matplotlib.pyplot as plt x, y = np.random.multivariate_normal(mean, cov, 5000).T plt.plot(x, y, ‘x‘); plt.axis(‘equal‘); plt.show() | | negative_binomial(n, p[, size]) | 负二项分布 | | noncentral_chisquare(df, nonc[, size]) | 非中心卡方分布 | | noncentral_f(dfnum, dfden, nonc[, size]) | 非中心F分布 | | normal([loc, scale, size]) | 正态(高斯)分布
Notes
The probability density for the Gaussian distribution is

where is the mean and the standard deviation. The square of the standard deviation, , is called the variance.
The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at and [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)) < 0.01 True abs(sigma - np.std(s, ddof=1)) < 0.01 TrueDisplay 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