Update numpy array with sparse indices and values

Question

I have 1-dimensional numpy array and want to store sparse updates of it. Say I have array of length 500000 and want to do 100 updates of 100 elements. Updates are either adds or just changing the values (I do not think it matters).

What is the best way to do it using numpy? I wanted to just store two arrays: indices, values_to_add and therefore have two objects: one stores dense matrix and other just keeps indices and values to add, and I can just do something like this with the dense matrix:

dense_matrix[indices] += values_to_add

And if I have multiple updates, I just concat them.

But this numpy syntax doesn't work fine with repeated elements: they are just ignored.

Updating pair when we have an update that repeats index is O(n). I thought of using dict instead of array to store updates, which looks fine from the point of view of complexity, but it doesn't look good numpy style.

What is the most expressive way to achieve this? I know about scipy sparse objects, but (1) I want pure numpy because (2) I want to understand the most efficient way to implement it.

Answer 1

If you have repeated indices you could use at , from the documentation:

Performs unbuffered in place operation on operand 'a' for elements specified by 'indices'. For addition ufunc, this method is equivalent to a[indices] += b, except that results are accumulated for elements that are indexed more than once.

Code

a = np.arange(10)
indices = [0, 2, 2]
np.add.at(a, indices, [-44, -55, -55])
print(a)

Output

[ -44    1 -108    3    4    5    6    7    8    9]