python - Matlab 与 Python : Reshape

Question

于是我找到了this :

When converting MATLAB code it might be necessary to first reshape a matrix to a linear sequence, perform some indexing operations and then reshape back. As reshape (usually) produces views onto the same storage, it should be possible to do this fairly efficiently.

Note that the scan order used by reshape in Numpy defaults to the 'C' order, whereas MATLAB uses the Fortran order. If you are simply converting to a linear sequence and back this doesn't matter. But if you are converting reshapes from MATLAB code which relies on the scan order, then this MATLAB code:

z = reshape(x,3,4);

should become

z = x.reshape(3,4,order='F').copy()

in Numpy.

当我在 MATLAB 中这样做时，我有一个名为 mafs 的多维 16*2 数组:

mafs2 = reshape(mafs,[4,4,2]) 

我得到的东西与我在 python 中所做的不同:

mafs2 = reshape(mafs,(4,4,2))

甚至

mafs2 = mafs.reshape((4,4,2),order='F').copy()

对此有任何帮助吗？谢谢大家。

Answer 1

例子:

MATLAB:

>> mafs = [(1:16)' (17:32)']
mafs =
     1    17
     2    18
     3    19
     4    20
     5    21
     6    22
     7    23
     8    24
     9    25
    10    26
    11    27
    12    28
    13    29
    14    30
    15    31
    16    32

>> reshape(mafs,[4 4 2])
ans(:,:,1) =
     1     5     9    13
     2     6    10    14
     3     7    11    15
     4     8    12    16
ans(:,:,2) =
    17    21    25    29
    18    22    26    30
    19    23    27    31
    20    24    28    32

Python:

>>> import numpy as np
>>> mafs = np.c_[np.arange(1,17), np.arange(17,33)]
>>> mafs.shape
(16, 2)
>>> mafs[:,0]
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])
>>> mafs[:,1]
array([17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32])

>>> r = np.reshape(mafs, (4,4,2), order="F")
>>> r.shape
(4, 4, 2)
>>> r[:,:,0]
array([[ 1,  5,  9, 13],
       [ 2,  6, 10, 14],
       [ 3,  7, 11, 15],
       [ 4,  8, 12, 16]])
>>> r[:,:,1]
array([[17, 21, 25, 29],
       [18, 22, 26, 30],
       [19, 23, 27, 31],
       [20, 24, 28, 32]])