CODEWITHSUNDEEP
pythonnumpy
YoungMath
YoungMath

Reputation: 191

Custom multiplication and numpy

Suppose I have my own multiplication between two Python objects a and b, let's call it my_multiplication(a, b).

How can I perform a matrix multiplication using numpy where my_multiplication is performed instead of the usual *? Is that even possible?

Addendum: Would I still benefit from numpy's speed then?

Upvotes: 1

Views: 206

Answers (2)

maverick
maverick

Reputation: 325

Try the numpy.dot or the x.dot(y). See the documentation here

Example

import numpy as np
x = np.arange(12).reshape((3,4))
y = np.arange(4)
print(x,"\n\n",y,"\n")
print (np.dot(x,y))

[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]] 

 [0 1 2 3] 

[14 38 62]

Upvotes: 0

Akshay Sehgal
Akshay Sehgal

Reputation: 19307

You can use np.vectorise on your function to get your custom multiplication function use all the usual numpy features such as broadcasting.

def my_multiplication(a, b):
    #your code that works on multiplying 2 numbers
    return c
v_my_multiplication = np.vectorize(my_multiplication)
v_my_multiplication([1, 2, 3], [1, 6])

#Will now work for np.array instead of just 2 numbers and utilize the broadcasting and vectorized implementation benefits that numpy has to offer.

Upvotes: 1

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