翻译自官网的文档。转自http://www.mamicode.com/info-detail-507676.html
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)) <type ‘int‘> >>> 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 ofrandom_sample by (b-a) and add a: (b - a) * random_sample() + a Examples >>> np.random.random_sample() 0.47108547995356098 >>> type(np.random.random_sample()) <type ‘float‘> >>> 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) ‘ ehx85x022SZxbfxa4‘ #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
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. |