numpy.random模組常用函數解析

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numpy.random模組中常用函數解析

numpy.random模組官方文檔

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)
按照給定形狀產生一個多維陣列，每個元素在0到1之間
注意： 這裡定義數組形狀時，不能採用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
按照給定形狀產生一個多維陣列，數組中的元素服從標準常態分佈

若要產生服從N(mu, sigma^2)分布的樣本， 使用sigma * np.random.randn(...) + mu

例如產生 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).

按照給定的形狀和範圍產生隨機整數

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).

以上四種方式都是產生[0,1)之間的浮點數

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 np2 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)
從給定的一維數組中產生隨機數

如a是一個int數， 則產生的數組的元素都在np.arange(a)中

如a是一個1-D array-like， 則產生的數組的元素都在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)

設定相同的seed，每次產生的隨機數相同。如果不設定seed，則每次會產生不同的隨機數

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]])

 

numpy.random模組常用函數解析