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.youkuaiyun.com/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 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 (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
|
, use:
):
multiply the output of 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 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
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 |
is the mean and 
the standard deviation. The square of the standard deviation, 
, is called the variance.
and 
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. |
参考链接：https://www.cnblogs.com/ymjyqsx/p/7395685.html
https://blog.youkuaiyun.com/u010758410/article/details/71799142
https://blog.youkuaiyun.com/kancy110/article/details/69665164
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