How to do matrix multiplication of N x M and 1 x M with Numpy-CodePudding

I want to do matrix multiplication of N x M and 1 x M matrix : Here is my Matrix :

N=(A,[2,9])
M=[[2,3,4],[3,4,6]]

I want to multiply each element of the first row by 2 and the second row by 9 with the M list but I didn't get any idea, I know matrix multiplication. but with NumPy, I don't know.

Note: M is a matrix and N is also a matrix.

CodePudding user response：

For multiplying to matrices - A,B with dimensions mXn and pXq respectively you must have n=p. In the given example you have a matrix M with dimensions 2x3 and another matrix with dimension 2x1 so matrix multiplication isn't possible.

I want to multiply each element of the first row by 2 and the second row by 9 with the M list but I didn't get any idea, I know matrix multiplication. but with NumPy, I don't know.

This can be done using numpy with the following code:

import numpy as np
x = np.array([2,9])
M = np.array([[2,3,4],[3,4,6]])
for i in range(x.shape[0]):
    M[i] *= x[i]

and the output:

array([[ 4,  6,  8],
   [27, 36, 54]])

CodePudding user response：

What you describe is not matrix multiplication, but simple row-wise multiplication. You can do it conveniently in NumPy by using broadcasting. For your example (ignoring A, which was not explained) this means that if you shape your first matrix as a one-column matrix with two rows and multiply it with your second matrix using the normal * operator, the first matrix will be expanded to the same shape as the second one by repeating its columns. Then element-wise multiplication will be performed between the two matrices:

import numpy as np

m1 = np.array([[2], 
               [9]])
m2 = np.array([[2, 3, 4],
               [3, 4, 6]])

m1 * m2

array([[ 4,  6,  8],
       [27, 36, 54]])