Lets say I have the following matrix:
A = np.array([
[1,2,3],
[4,5,6],
[7,8,9]])
How can I extract the upper triangle matrix without the diagonal efficiently? The output would be the following array:
B = np.array([2,3,6])
One approach with masking -
def upper_tri_masking(A):
m = A.shape[0]
r = np.arange(m)
mask = r[:,None] < r
return A[mask]
Another with np.triu_indices -
def upper_tri_indexing(A):
m = A.shape[0]
r,c = np.triu_indices(m,1)
return A[r,c]
Sample run -
In [403]: A
Out[403]:
array([[79, 17, 79, 58, 14],
[87, 63, 89, 26, 31],
[69, 34, 90, 24, 96],
[59, 60, 80, 52, 46],
[75, 80, 11, 61, 47]])
In [404]: upper_tri_masking(A)
Out[404]: array([17, 79, 58, 14, 89, 26, 31, 24, 96, 46])
Runtime test -
In [415]: A = np.random.randint(0,9,(5000,5000))
In [416]: %timeit upper_tri_masking(A)
10 loops, best of 3: 64.2 ms per loop
In [417]: %timeit upper_tri_indexing(A)
1 loop, best of 3: 252 ms per loop