numpy.random用法

最近发现numpy的random用法有很多，不注意很容易混淆，今天参考几个博客内容整理了一下。

numpy.random.randint

low、high、size三个参数。默认high是None,如果只有low，那范围就是[0,low)。如果有high，范围就是[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])

>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])

numpy.random.randn()与rand()的区别

numpy中有一些常用的用来产生随机数的函数，randn()和rand()就属于这其中。
numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值。
numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。
代码：

import numpy as np arr1 = np.random.randn(2,4)
print(arr1)
print('******************************************************************')
arr2 = np.random.rand(2,4)
print(arr2)

结果

[[-1.03021018 0.5197033 0.52117459 -0.70102661]

[ 0.98268569 1.21940697 -1.095241 -0.38161758]]

******************************************************************

[[ 0.19947349 0.05282713 0.56704222 0.45479972]

[ 0.28827103 0.1643551 0.30486786 0.56386943]]

随机抽样 (numpy.random)

参考：https://blog.csdn.net/vicdd/article/details/52667709

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 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()) <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) ‘ 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.
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.

random VS numpy.random

参考链接：https://www.cnblogs.com/ymjyqsx/p/7395685.html

https://blog.csdn.net/u010758410/article/details/71799142

https://blog.csdn.net/kancy110/article/details/69665164