如何在numpy中获取按元素矩阵乘法(Hadamard积)? [英] How to get element-wise matrix multiplication (Hadamard product) in numpy?
问题描述
我有两个矩阵
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
,我想获得元素级乘积[[1*5,2*6], [3*7,4*8]]
,等于
and I want to get the element-wise product, [[1*5,2*6], [3*7,4*8]]
, equaling
[[5,12], [21,32]]
我尝试过
print(np.dot(a,b))
和
print(a*b)
但都给出结果
[[19 22], [43 50]]
是矩阵乘积,而不是元素乘积.如何使用内置函数获得按元素分类的产品(又名Hadamard产品)?
which is the matrix product, not the element-wise product. How can I get the the element-wise product (aka Hadamard product) using built-in functions?
推荐答案
For elementwise multiplication of matrix
objects, you can use numpy.multiply
:
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
np.multiply(a,b)
结果
array([[ 5, 12],
[21, 32]])
但是,您应该真正使用array
而不是matrix
. matrix
对象与常规ndarray具有各种可怕的不兼容性.使用ndarray,您可以只使用*
进行元素乘法:
However, you should really use array
instead of matrix
. matrix
objects have all sorts of horrible incompatibilities with regular ndarrays. With ndarrays, you can just use *
for elementwise multiplication:
a * b
如果您使用的是Python 3.5+,则甚至不会失去使用运算符执行矩阵乘法的功能,因为
If you're on Python 3.5+, you don't even lose the ability to perform matrix multiplication with an operator, because @
does matrix multiplication now:
a @ b # matrix multiplication
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