numpy. reshape, numpy. Understanding of transfer

These two commands are often used when changing the matrix , After a long time, I will return to think about their influence on the result of matrix transformation . Here is an example of ：

1.np.reshape

This command rearranges the matrix elements , See the code ：

a = np.random.randint(1, 10, [1, 6, 2] )
print(a)
print(a.reshape(2, 3, 2))
print(a.reshape(3, 2, 2))
print(a.reshape(3, 2, 2).transpose(1, 0, 2))

The result is ：

#print(a)
[[[9 1]
  [5 8]
  [4 2]
  [7 1]
  [4 9]
  [7 9]]]

#print(a.reshape(2, 3, 2))
[[[9 1]
  [5 8]
  [4 2]]

 [[7 1]
  [4 9]
  [7 9]]]

#print(a.reshape(3, 2, 2))
[[[9 1]
  [5 8]]

 [[4 2]
  [7 1]]

 [[4 9]
  [7 9]]]

#print(a.reshape(3, 2, 2).transpose(1, 0, 2))
[[[9 1]
  [4 2]
  [4 9]]

 [[5 8]
  [7 1]
  [7 9]]]

reshape After that, the original matrix is actually rearranged . Also found here a.reshape(2, 3, 2).reshape(3, 2, 2) And .reshape(3, 2, 2) Consistent result . Here np.transpose Put it in the 2 In the section .

2.np.transpose

This function is used to realize the transpose of matrix . For high-dimensional matrix, this function can also realize dimension exchange , But with reshape Dissimilarity . Here is the transposed exchange dimension , The order of arrangement will not be the same as the original . Let's start with a two-dimensional matrix ：

mm = np.random.randint(1, 10, [2, 3])
print(mm)#[[1 3 3]
 					  #[9 3 8]]
print(mm.transpose(1, 0))#[[1 9]
 													  #[3 3]
 													  #[3 8]]

Here is the transposed exchange dimension . This can be understood again 1 Section a.reshape(3, 2, 2).transpose(1, 0, 2). Transpose occurs only in 0 and 1 In dimension ,2 Dimensions can be viewed as fixed elements . The result shows that ,transpose Yes, it will 0 and 1 Dimension transpose , also ,a.reshape(3, 2, 2).transpose(1, 0, 2) And a.reshape(2, 3, 2) The result is different , The former is based on transpose to change the dimension , The latter still assembles elements from front to back , Pay special attention to .