Python
Numpy知识总结
numpy的random模块应该很常用，这里整理一下，
参考文章：
http://www.mamicode.com/info-detail-507676.html
https://docs.scipy.org/doc/numpy/reference/routines.random.html
简单随机数据
numpy.random.rand(d0, d1, ..., dn)
Random values in a given shape.
Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)
生成给定形状的随机值，随机值在[0,1)
import numpy as np
np.random.rand(10)
Out[2]:
array([ 0.41103285, 0.19043225, 0.30385602, 0.19330136, 0.09727556,
0.96518049, 0.29930132, 0.00633969, 0.64269577, 0.79953589])
np.random.rand(2,3)
Out[3]:
array([[ 0.86213038, 0.56657202, 0.83083843],
[ 0.48660386, 0.20508572, 0.4927877 ]])
np.random.rand(2,3,1)
Out[4]:
array([[[ 0.06676746],
[ 0.55548283],
[ 0.04411342]],
[[ 0.18659571],
[ 0.02209355],
[ 0.83529269]]])
numpy.random.randn(d0, d1, ..., dn)
返回指定形状的状态分布样本
For random samples from N(\mu, \sigma^2), use:
sigma * np.random.randn(...) + mu
np.random.randn(2,1)
Out[6]:
array([[-0.29088142],
[ 1.29634911]])
np.random.randn(2,2)
Out[7]:
array([[ 0.24125164, 1.62201226],
[ 0.10129715, -1.62001598]])
Two-by-four array of samples from N(3, 6.25):
2.5 * np.random.randn(2, 4) + 3
Out[8]:
array([[ 5.09295036, 2.2706219 , 3.26392307, 0.86550482],
[ 7.59911261, 5.22543816, 2.0441248 , 1.03322082]])
numpy.random.randint(low, high=None, size=None, dtype='l')
返回随机整数，左闭右开[low,high)
np.random.randint(low=1,high=10,size=8)
Out[10]: array([4, 2, 6, 7, 2, 4, 3, 8])
#high为空的话，直接[0,low)
np.random.randint(10,size=5)
Out[11]: array([1, 3, 7, 9, 9])
np.random.randint(low=1,high=10,size=(2,3))
Out[12]:
array([[8, 3, 6],
[4, 1, 9]])
numpy.random.random_integers(low, high=None, size=None)
返回随机整数，闭区间[low,high]
这个和random.randint类似，已经不推荐使用了
np.random.random_integers(low=1,high=5,size=5)
__main__:1: DeprecationWarning: This function is deprecated. Please call randint(1, 5 + 1) instead
Out[13]: array([3, 2, 2, 4, 5])
numpy.random.random_sample(size=None)
numpy.random.random(size=None)
numpy.random.ranf(size=None)
numpy.random.sample(size=None)
返回随机的浮点值，左闭右开区间[0.0, 1.0)
np.random.random_sample(8)
Out[14]:
array([ 0.70353035, 0.79018004, 0.50390916, 0.46261548, 0.85556642,
0.68129238, 0.07098945, 0.65927063])
np.random.random_sample([2,3])
Out[16]:
array([[ 0.37546444, 0.50352846, 0.3496647 ],
[ 0.02849239, 0.6035842 , 0.32514876]])
排列
numpy.random.shuffle(x)
就地修改序列的顺序，类似于洗牌，打乱顺序
a = np.random.randint(low=1,high=10,size=10)
a
Out[18]: array([6, 2, 5, 5, 2, 2, 4, 9, 7, 8])
np.random.shuffle(a)
a
Out[20]: array([2, 8, 4, 7, 5, 2, 5, 6, 2, 9])
numpy.random.permutation(x)
返回一个随机排列
If x is an integer, randomly permute np.arange(x). If x is an array, make a copy and shuffle the elements randomly.
np.random.permutation(10)
Out[21]: array([8, 9, 7, 0, 6, 1, 2, 5, 3, 4])
np.random.permutation([1, 4, 9, 12, 15])
Out[22]: array([ 4, 12, 1, 9, 15])
