numpy.random模塊用法總結

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出處：https://www.cnblogs.com/JetReily/p/9398148.html

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numpy.random.uniform(low=0.0, high=1.0, size=None)

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 生出size個符合均分佈的浮點數，取值範圍爲[low, high)，默認取值範圍爲[0, 1.0)

>>> random.uniform()
0.3999807403689315
>>> random.uniform(size=1)
array([0.55950578])
>>> random.uniform(5, 6)
5.293682668235986
>>> random.uniform(5, 6, size=(2,3))
array([[5.82416021, 5.68916836, 5.89708586],
       [5.63843125, 5.22963754, 5.4319899 ]])

 

numpy.random.rand(d0, d1, ..., dn)

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 生成一個(d0, d1, ..., dn)維的數組，數組的元素取自[0, 1)上的均分佈，若沒有參數輸入，則生成一個數

>>> random.rand()
0.4378166124207712
>>> random.rand(1)
array([0.69845956])
>>> random.rand(3,2)
array([[0.15725424, 0.45786148],
       [0.63133098, 0.81789056],
       [0.40032941, 0.19108526]])
>>> random.rand(3,2,1)
array([[[0.00404447],
        [0.3837963 ]],

       [[0.32518355],
        [0.82482599]],

       [[0.79603205],
        [0.19087375]]])

 

numpy.random.randint(low, high=None, size=None, dtype='I')

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生成size個整數，取值區間爲[low, high)，若沒有輸入參數high則取值區間爲[0, low)

>>> random.randint(8)
5
>>> random.randint(8, size=1)
array([1])
>>> random.randint(8, size=(2,2,3))
array([[[4, 7, 0],
        [1, 4, 1]],

       [[2, 2, 5],
        [7, 6, 4]]])
>>> random.randint(8, size=(2,2,3), dtype='int64')
array([[[5, 5, 6],
        [2, 7, 2]],

       [[2, 7, 6],
        [4, 7, 7]]], dtype=int64)

 

numpy.random.random_integers(low, high=None, size=None)

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 生成size個整數，取值區間爲[low, high], 若沒有輸入參數high則取值區間爲[1, low]，注意這裏左右都是閉區間

>>> random.random_integers(5)
1
>>> random.random_integers(5, size=1)
array([2])
>>> random.random_integers(4, 5, size=(2,2))
array([[5, 4],
       [4, 4]])

 

numpy.random.random(size=None)

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 產生[0.0, 1.0)之間的浮點數

>>> random.random(5)
array([0.94128141, 0.98725499, 0.48435957, 0.90948135, 0.40570882])
>>> random.random()
0.49761416226728084

 相同用法：

• numpy.random.random_sample
• numpy.random.ranf
• numpy.random.sample (抽取不重複)

 

numpy.random.bytes(length)

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 生成隨機字節

>>> random.bytes(1)
b'%'
>>> random.bytes(2)
b'\xd0\xc3'

 

numpy.random.choice(a, size=None, replace=True, p=None)

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 從a（數組）中選取size（維度）大小的隨機數，replace=True表示可重複抽取，p是a中每個數出現的概率

 若a是整數，則a代表的數組是arange(a)

>>> random.choice(5)
3
>>> random.choice([0.2, 0.4])
0.2
>>> random.choice([0.2, 0.4], p=[1, 0])
0.2
>>> random.choice([0.2, 0.4], p=[0, 1])
0.4
>>> random.choice(5, 5)
array([1, 2, 4, 2, 4])
>>> random.choice(5, 5, False)
array([2, 0, 1, 4, 3])
>>> random.choice(100, (2, 3, 5), False)
array([[[43, 81, 48,  2,  8],
        [33, 79, 30, 24, 83],
        [ 3, 82, 97, 49, 98]],

       [[32, 12, 15,  0, 96],
        [19, 61,  6, 42, 60],
        [ 7, 93, 20, 18, 58]]])

 

numpy.random.permutation(x)

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 隨機打亂x中的元素。若x是整數，則打亂arange(x)，若x是一個數組，則將copy(x)的第一位索引打亂，意思是先複製x，對副本進行打亂處理，打亂只針對數組的第一維

>>> random.permutation(5)
array([1, 2, 3, 0, 4])
>>> random.permutation(5)
array([1, 4, 3, 2, 0])
>>> random.permutation([[1,2,3],[4,5,6]])
array([[1, 2, 3],
       [4, 5, 6]])
>>> random.permutation([[1,2,3],[4,5,6]])
array([[4, 5, 6],
       [1, 2, 3]])

 

numpy.random.shuffle(x)

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 與permutation類似，隨機打亂x中的元素。若x是整數，則打亂arange(x). 但是shuffle會對x進行修改

>>> a = arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> random.permutation(a)
array([1, 4, 3, 2, 0])
>>> a
array([0, 1, 2, 3, 4])
>>> random.shuffle(a)
>>> a
array([4, 1, 3, 2, 0])

 

numpy.random.seed(seed=None)

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 設置隨機生成算法的初始值