I have a numpy array 'arr' of shape (100000,). I need to create a numpy matrix 'res_matrix' of shape 100000X100000 such that
for i in range(res_matrix.shape[0]):
for j in range(res_matrix.shape[1]):
res_matrix[i][j]= arr[i]*arr[j]
Sample input/output
arr=[1 2 4]
Output:
res_matrix:
[[1 2 4]
[2 4 18]
[4 8 16]]
Is there way to do vectorize this operation, to reduce the computation time of calculating 00000X100000 in loop?
There are a few ways you can get an outer multiplication.
arr = np.array([1,2,4])
#Using Multiply outer
print(np.multiply.outer(arr, arr)) #As suggested by Warren
#Using broadcasting
print(arr[:,None] * arr[None,:]) #(3,1) * (1,3)
[[ 1 2 4]
[ 2 4 8]
[ 4 8 16]]
[[ 1 2 4]
[ 2 4 8]
[ 4 8 16]]
Note, the output is still a very large matrix for storing in memory. Depending on what you need it for, I would advise considering something like a generator function. Let me know how you are going to use this matrix and I could suggest more memory efficient methods.
np.multiply.outer(arr, arr)