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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.
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发表于 2019-11-25 22:36:13
楼主