问题：如何在numpy中获得按元素矩阵乘法（Hadamard积）？
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
[[5,12], [21,32]]
我努力了
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，而不是元素乘积。如何使用内置函数获取按元素分类的产品（又名Hadamard产品）？
I have two matrices
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
and I want to get the element-wise product, [[1*5,2*6], [3*7,4*8]], equaling
[[5,12], [21,32]]
I have tried
print(np.dot(a,b))
and
print(a*b)
but both give the result
[[19 22], [43 50]]
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?
回答 0
对于matrix对象的元素乘法，可以使用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ndarrays时，您可以仅使用*元素级乘法：
a * b
如果您使用的是Python 3.5+，则您甚至都不会失去使用运算符执行矩阵乘法的能力，因为@矩阵乘法现在可以：
a @ b # matrix multiplication
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)
Result
array([[ 5, 12],
[21, 32]])
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
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
回答 1
只是这样做：
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
a * b
just do this:
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
a * b
回答 2
import numpy as np
x = np.array([[1,2,3], [4,5,6]])
y = np.array([[-1, 2, 0], [-2, 5, 1]])
x*y
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit x*y
1000000 loops, best of 3: 421 ns per loop
np.multiply(x,y)
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit np.multiply(x, y)
1000000 loops, best of 3: 457 ns per loop
两者np.multiply和*都会产生元素明智的乘法，称为Hadamard积
%timeit 是ipython的魔力
import numpy as np
x = np.array([[1,2,3], [4,5,6]])
y = np.array([[-1, 2, 0], [-2, 5, 1]])
x*y
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit x*y
1000000 loops, best of 3: 421 ns per loop
np.multiply(x,y)
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit np.multiply(x, y)
1000000 loops, best of 3: 457 ns per loop
Both np.multiply and * would yield element wise multiplication known as the Hadamard Product
%timeit is ipython magic
回答 3
试试这个：
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
#This would result a 'numpy.ndarray'
result = np.array(a) * np.array(b)
在此，np.array(a)返回类型为2D的2D数组，ndarray并且ndarray将导致元素相乘。因此结果将是：
result = [[5, 12], [21, 32]]
如果您想获取矩阵，请执行以下操作：
result = np.mat(result)
Try this:
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
#This would result a 'numpy.ndarray'
result = np.array(a) * np.array(b)
Here, np.array(a) returns a 2D array of type ndarray and multiplication of two ndarray would result element wise multiplication. So the result would be:
result = [[5, 12], [21, 32]]
If you wanna get a matrix, the do it with this:
result = np.mat(result)
