Data analysis -Numpy.random function summary

In doing numpy.random function usage summary, after encountering add.

See the following article:

https://www.numpy.org/devdocs/user/quickstart.html

https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version

1.numpy.random.random(size = None)

. 1  Import numpy AS NP
 2  '' ' 
. 3  np.random.random (size = None)
 . 4  Number specified array size ranks, return [0,1) floats
 5  ' '' 
6  # obtain a single value 
. 7 z_train = NP. random.random ()
 . 8  Print (z_train)
 . 9  
10  # to obtain an array of 1 * 1 
. 11 m_train = np.random.random (1 )
 12 is  Print (m_train)
 13 is  
14  # to obtain an array of 2 * 
15 x_train = np.random. Random (2 )
 16  Print (x_train)
 . 17  
18 is  #4 * 3 array obtained, the attention of a single, unitary size 
. 19 y_train np.random.random = ((3,4- ))
 20 is  Print (y_train)
 21 is  
22 is  # obtain a floating point number between [-1,1) 
23 is p_train = (np.random.random (2) -0.5) * 2
 24  Print (p_train)

 

2.numpy.random.randn()

. 1  Import numpy AS NP
 2  '' ' 
. 3  numpy.random.randn (D0, D1, ..., DN)
 . 4  returns one or a set of samples, with a standard normal distribution, desirably 0 and variance. 1
 . 5  DN is dimension
 6  '' ' 
. 7  
. 8  # return a single value 
. 9  Print (np.random.randn ())
 10  
. 11  # returns an array of 1 * 1 
12 is  Print (np.random.randn (1 ))
 13 is  
14  # return 3 * 3 array 
15  Print (np.random.randn (3,3 ))
 16  
. 17  # returns 3D array 
18 is  Print (np.random.randn (3,3,3))