Earlier I asked a similar question where the answer used np.dot, taking advantage of the fact that a dot product involves a sum of products. (To my understanding.)
Now I have a similar issue where I don't think dot will apply, because in place of a sum I want to take an element-wise diagonal. If it does, I haven't been able to apply it correctly.
Given a matrix x and array err:
x = np.matrix([[ 0.02984406, -0.00257266],
[-0.00257266, 0.00320312]])
err = np.array([ 7.6363226 , 13.16548267])
My current implementation with loop is:
res = np.array([np.sqrt(np.diagonal(x * err[i])) for i in range(err.shape[0])])
print(res)
[[ 0.47738755 0.15639712]
[ 0.62682649 0.20535487]]
which takes the diagonal of x.dot(i) for each i in err. Could this be vectorized? In other words, can the output of x * err be 3-dimensional, with np.diagonal then yielding a 2d array, with one element for each diagonal?