numpy矩阵乘法形状 [英] numpy matrix multiplication shapes

问题描述

在矩阵乘法中，假设A是3 x 2矩阵(3行2列)，而B是2 x 4矩阵(2行4列)，则如果矩阵，则C应该具有3行4列.为什么numpy不做这个乘法?当我尝试以下代码时，出现错误:ValueError: operands could not be broadcast together with shapes (3,2) (2,4)

In matrix multiplication, assume that the A is a 3 x 2 matrix (3 rows, 2 columns ) and B is a 2 x 4 matrix (2 rows, 4 columns ), then if a matrix C = A * B, then C should have 3 rows and 4 columns. Why does numpy not do this multiplication? When I try the following code I get an error : ValueError: operands could not be broadcast together with shapes (3,2) (2,4)

a = np.ones((3,2))
b = np.ones((2,4))
print a*b

我尝试换位A和B，并得到相同的答案.为什么?在这种情况下如何进行矩阵乘法?

I try with transposing A and B and alwasy get the same answer. Why? How do I do the matrix multiplication in this case?

推荐答案

用于numpy数组的*运算符是逐元素乘法(类似于相同维度数组的Hadamard乘积)，而不是矩阵乘法.

The * operator for numpy arrays is element wise multiplication (similar to the Hadamard product for arrays of the same dimension), not matrix multiply.

例如:

>>> a
array([[0],
       [1],
       [2]])
>>> b
array([0, 1, 2])
>>> a*b
array([[0, 0, 0],
       [0, 1, 2],
       [0, 2, 4]])

对于与numpy数组相乘的矩阵:

For matrix multiply with numpy arrays:

>>> a = np.ones((3,2))
>>> b = np.ones((2,4))
>>> np.dot(a,b)
array([[ 2.,  2.,  2.,  2.],
       [ 2.,  2.,  2.,  2.],
       [ 2.,  2.,  2.,  2.]])

此外，您可以使用矩阵类:

In addition you can use the matrix class:

>>> a=np.matrix(np.ones((3,2)))
>>> b=np.matrix(np.ones((2,4)))
>>> a*b
matrix([[ 2.,  2.,  2.,  2.],
        [ 2.,  2.,  2.,  2.],
        [ 2.,  2.,  2.,  2.]])

有关广播numpy数组的更多信息，请参见此处，有关以下内容的更多信息可以在此处找到该矩阵类.

More information on broadcasting numpy arrays can be found here, and more information on the matrix class can be found here.

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