如何在 numpy 中获得逐元素矩阵乘法(Hadamard 乘积)? [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]])

我想得到元素乘积，[[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]]

我试过了

print(np.dot(a,b)) 

和

print(a*b)

但两者都给出结果

[[19 22], [43 50]]

这是矩阵乘积，而不是逐元素乘积.如何使用内置函数获取元素级乘积(又名 Hadamard 乘积)?

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

对于 matrix 对象的元素乘法，您可以使用 numpy.multiply:

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]])

然而，你真的应该使用 array 而不是 matrix.matrix 对象与常规 ndarray 有各种可怕的不兼容.使用 ndarrays，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+，您甚至不会失去使用运算符执行矩阵乘法的能力，因为 @ 现在做矩阵乘法:

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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