numpy.random模块常用函数解析
numpy.random模块中常用函数解析
1. numpy.random.rand(d0, d1, ..., dn)
Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)
按照给定形状产生一个多维数组，每个元素在0到1之间
注意： 这里定义数组形状时，不能采用tuple
import numpy as np np.random.rand(2, 3)
array([[ 0.44590044, 0.36234046, 0.51609462],
[ 0.45733218, 0.80836224, 0.31628453]])
2. numpy.random.randn(d0, d1, ..., dn)
generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate “normal” distribution of mean 0 and variance 1
按照给定形状产生一个多维数组，数组中的元素服从标准正态分布
若要产生服从N(mu, sigma^2)分布的样本， 使用sigma * np.random.randn(...) + mu
例如产生 2 * 4 samples from N(3, 6.25):
2.5 * np.random.randn(2, 4) + 3
array([[ 2.90478558, 6.05670578, 6.21539068, 3.3955507 ],
[ 0.11594363, 3.17433693, 5.35625762, 1.4824643 ]])
3. numpy.random.randint(low, high=None, size=None, dtype='l')
Return random integers from low (inclusive) to high (exclusive).
按照给定的形状和范围产生随机整数
np.random.randint(0, 10, size=(2, 4))
array([[2, 7, 2, 1],
[3, 2, 4, 1]])
4. numpy.random.random_integers(low, high=None, size=None)
Random integers of type np.int between low and high, inclusive.
np.random.random_integers(1, 10, size=(2, 5))
array([[ 3, 3, 8, 4, 5],
[ 2, 7, 8, 10, 2]])
5. numpy.random.random_sample(size=None)
6. numpy.random.random(size=None)
7. numpy.random.ranf(size=None)
8. numpy.random.sample(size=None)
Return random floats in the half-open interval [0.0, 1.0).
以上四种方式都是生成[0,1)之间的浮点数
To sample Unif[a, b), b > a multiply the output of random_sample by (b-a) and add a:
(b - a) * random_sample() + a
1 import numpy as np 2 print('random_sample:\n', np.random.random_sample((2, 3))) 3 print('random:\n', np.random.random((2, 3))) 4 print('ranf:\n', np.random.ranf((2, 3))) 5 print('sample:\n', np.random.sample((2, 3)))
1 random_sample: 2 [[ 0.87996593 0.2706701 0.42158973] 3 [ 0.91952234 0.99470239 0.07363656]] 4 random: 5 [[ 0.44572326 0.23595379 0.1061901 ] 6 [ 0.48362249 0.4270327 0.12281262]] 7 ranf: 8 [[ 0.07180002 0.25542854 0.55630057] 9 [ 0.38181471 0.91512916 0.04020929]] 10 sample: 11 [[ 0.80390231 0.0024602 0.95974309] 12 [ 0.32902852 0.62796713 0.42254831]]
9. numpy.random.choice(a, size = None, replace=True, p=None)
从给定的一维数组中生成随机数
如a是一个int数， 则产生的数组的元素都在np.arange(a)中
如a是一个1-D array-like， 则产生的数组的元素都在a中
1 print('1:\n', np.random.choice(5)) 2 print('2:\n', np.random.choice(5, 2, p=[0.1, 0.4, 0.3, 0.1, 0.1])) 3 print('3:\n', np.random.choice(5, (2, 3))) 4 print('4:\n', np.random.choice([1, 3, 4, 6], (2, 5), p=[0.1, 0.3, 0.1, 0.5]))
1 1: 2 4 3 2: 4 [1 4] 5 3: 6 [[2 1 4] 7 [0 2 3]] 8 4: 9 [[3 6 1 6 1] 10 [3 3 3 3 1]]
10. numpy.random.seed(None)
设置相同的seed，每次生成的随机数相同。如果不设置seed，则每次会生成不同的随机数
1 np.random.seed(2) 2 np.random.rand(2, 3)
1 array([[ 0.4359949 , 0.02592623, 0.54966248], 2 [ 0.43532239, 0.4203678 , 0.33033482]])
1 np.random.seed(2) 2 np.random.rand(2, 3)
1 array([[ 0.4359949 , 0.02592623, 0.54966248], 2 [ 0.43532239, 0.4203678 , 0.33033482]])
1 np.random.rand(2, 3)
1 array([[ 0.20464863, 0.61927097, 0.29965467], 2 [ 0.26682728, 0.62113383, 0.52914209]])
