2

I have an 2D-array a with shape (k,n) and I want to 'multiply' it with an 1D-array b of shape (m,):

a = np.array([[2, 8],
              [4, 7],
              [1, 2],
              [5, 2],
              [7, 4]])

b = np.array([3, 5, 5])

As a result of the 'multiplication' I'm looking for:

array([[[2*3,2*5,2*5],[8*3,8*5,8*5]],
       [[4*3,4*5,4*5],[7*3,7*5,7*5]],
       [[1*3,1*5,1*5], ..... ]],
          ................. ]]])

= array([[[ 6, 10, 10],
          [24, 40, 40]],

         [[12, 20, 20],
          [21, 35, 35]],

         [[ 3,  5,  5],
          [ ........ ]],

             ....... ]]])

I could solve it with a loop of course, but I'm looking for a fast vectorized way of doing it.

1 Answer 1

1

Extend a to a 3D array case by adding a new axis at the end with np.newaxis/None and then do elementwise multiplication with b, bringing in broadcasting for a vectorized solution, like so -

b*a[...,None]

Sample run -

In [19]: a
Out[19]: 
array([[2, 8],
       [4, 7],
       [1, 2],
       [5, 2],
       [7, 4]])

In [20]: b
Out[20]: array([3, 5, 5])

In [21]: b*a[...,None]
Out[21]: 
array([[[ 6, 10, 10],
        [24, 40, 40]],

       [[12, 20, 20],
        [21, 35, 35]],

       [[ 3,  5,  5],
        [ 6, 10, 10]],

       [[15, 25, 25],
        [ 6, 10, 10]],

       [[21, 35, 35],
        [12, 20, 20]]])
Sign up to request clarification or add additional context in comments.

Comments

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.