矩阵乘法导致MATLAB和NUMPY(?)中的值不同 [英] Matrix multiplication resulting in different values in MATLAB and NUMPY(?)

问题描述

这是矩阵

>> x = [2 7 5 9 2; 8 3 1 6 10; 4 7 3 10 1; 6 7 10 1 8;2 8 2 5 9]

Matlab给了我

>> mtimes(x',x)
ans =

   124   124    94   122   154
   124   220   145   198   179
    94   145   139   101   121
   122   198   101   243   141
   154   179   121   141   250

但是，python(numpy)中的相同操作(对相同数据)产生不同的结果.我不明白为什么?

However, the same operation(on same data) in python(numpy) produces different result. I'm unable to understand why?

import numpy as np
a = [[2, 7, 5, 9, 2],[8,3,1,6,10],[4,7,3,10,1],[6,7,10,1,8],[2,8,2,5,9]]
x = np.array(a)
print 'A : ',type(x),'\n',x,'\n\n'
# print np.transpose(A)
X = np.multiply(np.transpose(x),x)
print "A'*A",type(X),'\n',X

产生

A :  <type 'numpy.ndarray'> 
[[ 2  7  5  9  2]
 [ 8  3  1  6 10]
 [ 4  7  3 10  1]
 [ 6  7 10  1  8]
 [ 2  8  2  5  9]] 


A'*A <type 'numpy.ndarray'> 
[[  4  56  20  54   4]
 [ 56   9   7  42  80]
 [ 20   7   9 100   2]
 [ 54  42 100   1  40]
 [  4  80   2  40  81]]

推荐答案

Numpy

Numpy documentation states that the operator you apply performs element-wise multiplication.

但是，MATLAB中的 mtimes 会进行矩阵乘法

However, mtimes in MATLAB does matrix multiplication.

要进行验证，MATLAB逐元素乘法的语法会产生与numpy中相同的结果:

To verify, MATLAB syntax for element-wise multiplication produces the same result you see in numpy:

disp(x.'.*x)

     4    56    20    54     4
    56     9     7    42    80
    20     7     9   100     2
    54    42   100     1    40
     4    80     2    40    81

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