numpy.random模块用法总结

random模块用于生成随机数,下面看看模块中一些常用函数的用法:

from numpy import random

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

生出size个符合均分布的浮点数,取值范围为[low, high),默认取值范围为[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)

生成一个(d0, d1, ..., dn)维的数组,数组的元素取自[0, 1)上的均分布,若没有参数输入,则生成一个数

>>> 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')

生成size个整数,取值区间为[low, high),若没有输入参数high则取值区间为[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)

生成size个整数,取值区间为[low, high], 若没有输入参数high则取值区间为[1, low],注意这里左右都是闭区间

>>> random.randint(8)

>>> 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(size=None)

产生[0.0, 1.0)之间的浮点数

>>> 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 (抽取不重复)

 numpy.random.bytes(length)

 生成随机字节

>>> random.bytes(1)

b'%'

>>> random.bytes(2)

b'\xd0\xc3'

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

从a(数组)中选取size(维度)大小的随机数,replace=True表示可重复抽取,p是a中每个数出现的概率

若a是整数,则a代表的数组是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)

随机打乱x中的元素。若x是整数,则打乱arange(x),若x是一个数组,则将copy(x)的第一位索引打乱,意思是先复制x,对副本进行打乱处理,打乱只针对数组的第一维

>>> 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)

与permutation类似,随机打乱x中的元素。若x是整数,则打乱arange(x). 但是shuffle会对x进行修改

>>> 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)

设置随机生成算法的初始值

其它符合函数分布的随机数函数

  • numpy.random.beta
  • numpy.random.binomial
  • numpy.random.chisquare
  • numpy.random.dirichlet
  • numpy.random.exponential
  • numpy.random.f
  • numpy.random.gamma
  • numpy.random.geometric
  • numpy.random.gumbel
  • numpy.random.hypergeometric
  • numpy.random.laplace
  • numpy.random.logistic
  • numpy.random.lognormal
  • numpy.random.logseries
  • numpy.random.multinomial
  • numpy.random.multivariate_normal
  • numpy.random.negative_binomial
  • numpy.random.noncentral_chisquare
  • numpy.random.noncentral_f
  • numpy.random.normal
  • numpy.random.pareto
  • numpy.random.poisson
  • numpy.random.power
  • numpy.random.randn
  • numpy.random.rayleigh
  • numpy.random.standard_cauchy
  • numpy.random.standard_exponential
  • numpy.random.standard_gamma
  • numpy.random.standard_normal
  • numpy.random.standard_t
  • numpy.random.triangular
  • numpy.random.vonmises
  • numpy.random.wald
  • numpy.random.weibull
  • numpy.random.zipf

以上是 numpy.random模块用法总结 的全部内容, 来源链接: utcz.com/z/312638.html

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