【说明】

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 , use: `sigma * np.random.randn(...) + mu` Examples ```>>> np.random.randn() 2.1923875335537315 #random``` Two-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.random_integers(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.random_integers(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 + d2``` Display 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() + a` Examples ```>>> 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

 beta(a, b[, size]) 贝塔分布样本，在 [0, 1]内。 binomial(n, p[, size]) 二项分布的样本。 chisquare(df[, size]) 卡方分布样本。 dirichlet(alpha[, size]) 狄利克雷分布样本。 exponential([scale, size]) 指数分布 f(dfnum, dfden[, size]) F分布样本。 gamma(shape[, scale, size]) 伽马分布 geometric(p[, size]) 几何分布 gumbel([loc, scale, size]) 耿贝尔分布。 hypergeometric(ngood, nbad, nsample[, size]) 超几何分布样本。 laplace([loc, scale, size]) 拉普拉斯或双指数分布样本 logistic([loc, scale, size]) Logistic分布样本 lognormal([mean, sigma, size]) 对数正态分布 logseries(p[, size]) 对数级数分布。 multinomial(n, pvals[, size]) 多项分布 multivariate_normal(mean, cov[, size]) 多元正态分布。 ```>>> mean = [0,0] >>> 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 True``` 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. Return a tuple representing the internal state of the generator. set_state(state) Set the internal state of the generator from a tuple.

numpy的random模块

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