CODEWITHSUNDEEP
pythonnumpy
iamwhoiam
iamwhoiam

Reputation: 287

Numpy: Perform Multiplication-like Addition

I wanted to define my own addition operator that takes an Nx1 vector (call it A) and a 1xN vector (B) such that the element in the i^th row and j^th column is the sum of the i^th element in A and the j^th element in B. An example is illustrated here.enter image description here

I was able to write the following code for the function (and it is correct as far as I know). 

def test_fn(a, b):
    a_len = a.shape[0]
    b_len = b.shape[1]
    prod = np.array([[0]*a_len]*b_len)
    for i in range(a_len):
        for j in range(b_len):
            prod[i, j] = a[i, 0] + b[0, j]
    return prod

However, the vectors I am working with contain thousands of elements, and the function above is quite slow. I was wondering if there was a better way to approach this problem, or if there was a numpy function that could be of use. Any help would be appreciated.

Upvotes: 0

Views: 67

Answers (1)

distant1219
distant1219

Reputation: 96

According to numpy's broadcasting rules, you can use a+b to implement your own defined operator.

The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions.

The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array.

enter image description here

Upvotes: 2

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