Thanks! Avoiding the inner loop is MUCH faster (~20-300 times than the
original). Nevertheless I don't think I can use hypot as it only works
for two dimensions. The general problem I have is:
A = random( [C, K] )
B = random( [N, K] )
C ~ 1-10
N ~ Large (thousands, millions.. i.e. my dataset)
K ~ 2-100 (dimensions of my problem, i.e. not fixed a priori.)
I adapted your proposed version to this for K dimensions:
def d4():
d = zeros([4, 1000], dtype=float)
for i in range(4):
xy = A[i] - B
d[i] = sqrt( sum(xy**2, axis=1) )
return d
Maybe there's another alternative to d4?
Thanks again,
Sebastian.
> def d_2():
> d = zeros([4, 10000], dtype=float)
> for i in range(4):
> xy = A[i] - B
> d[i] = xy[:,0]**2 + xy[:,1]**2
> return d
>
> This is something like 250 times as fast as the naive Python solution;
> another five times faster than the fastest distance computing version
> that I could come up with (using hypot).
>
> -tim
>
>
>
>
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