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?
64 Answers
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 * bIf 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 multiplicationjust do this:
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
a * bimport 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 loopBoth np.multiply and * would yield element wise multiplication known as the Hadamard Product
%timeit is ipython magic
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)
