You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
But it isn't that clear how this would be done efficiently for large A. I think matrix multiplication is about O(n^3) complexity and I doubt the inverse is good either.
The large A that was slowing things down was quite sparse so using scipy.sparse is a potential option.
The text was updated successfully, but these errors were encountered:
This method was taking an hour to compute
inv(np.dot(A.T, A))
of a denseA
matrix of shape (38520, 1560).Some speed up may be gained by avoiding the copies, see http://wiki.scipy.org/PerformanceTips
But it isn't that clear how this would be done efficiently for large A. I think matrix multiplication is about O(n^3) complexity and I doubt the inverse is good either.
The large A that was slowing things down was quite sparse so using scipy.sparse is a potential option.
The text was updated successfully, but these errors were encountered: