from numpy import random
# 设定随机数种子
random.seed(134)
# 产生均匀分布的随机数，维度是 3*2
random.rand(3, 2)
# 输出 array([[0.19151945, 0.62210877],
# [0.43772774, 0.78535858],
# [0.77997581, 0.27259261]])
# 产生标准正态分布随机数，维度是3*2
random.randn(3, 2)
# 输出 array([[ 0.85958841, -0.6365235 ],
# [ 0.01569637, -2.24268495],
# [ 1.15003572, 0.99194602]])
# 在[0,1）内产生随机数，维度是3*2
random.random((3, 2))
# 输出 array([[0.37025075, 0.56119619],
# [0.50308317, 0.01376845],
# [0.77282662, 0.88264119]])
# 产生指定区间的随机整数，维度是3*2
random.randint(low=2, high=10, size=(3, 2))
# 输出 array([[9, 5],
# [2, 3],
# [5, 2]])
# 正态分布，loc表示均值，scale表示方差
random.normal(loc=0, scale=1, size=(3, 2))
# 输出 array([[ 0.86371729, -0.12209157],
# [ 0.12471295, -0.32279481],
# [ 0.84167471, 2.39096052]])
# 泊松分布
random.poisson(lam=100, size=(3, 2))
# 输出 array([[ 95, 86],
# [102, 103],
# [ 93, 99]])
# 均匀分布
random.uniform(low=3, high=10, size=(3, 2))
# 输出 array([[5.67622216, 3.3771158 ],
# [6.16153886, 9.87403319],
# [3.8675989 , 3.83566629]])
# 产生beta分布
random.beta(a=3, b=5, size=(3, 2))
# 输出 array([[0.29453428, 0.43079414],
# [0.74318561, 0.21577619],
# [0.11941289, 0.44674076]])
# 二项分布（伯努利分布）
random.binomial(n=4, p=0.8, size=(3, 2))
# 输出 array([[3, 2],
# [4, 3],
# [3, 3]])
# 指数分布
random.exponential(scale=3, size=(3, 2))
# 输出 array([[ 4.78168574, 2.44771329],
# [10.12979518, 0.47753906],
# [ 0.09028607, 2.70341946]])
# F分布
random.f(dfnum=100, dfden=5, size=(3, 2))
# 输出 array([[1.5113714 , 0.77730943],
# [0.83137741, 0.54722053],
# [3.7934629 , 5.6308887 ]])from numpy import random
random.seed(1234)
# 有放回随机采样
samples = [1, 2, 3, 4, 5, 6, 7, 8, 9]
random.choice(samples, size=5, replace=True)
# 输出 array([4, 3, 4, 2, 4])
# 无放回随机采样
random.choice(samples, size=5, replace=False)
# 输出 array([9, 1, 6, 8, 3])
# 打乱样本的顺序，以产生随机数的效果
samples = [1, 2, 3, 4, 5, 6, 7, 8, 9]
random.shuffle(samples)
print(samples)
# 输出 [4, 3, 9, 7, 1, 5, 6, 8, 2]
