[Numpy-discussion] finding minimum distance using arrays

[Christopher Barker]

Nadav Horesh wrote:
> def distance(v1,v2):
>    return sqrt(((v2-v1)**2).sum())

note that Roberts version didn't compute the sqrt, as to find the 
minimum distance, you can just used the squared value.

If you do want the actual distance, then you might want to use 
numpy.hypot, something like (untested):

import numpy as np

def distance(v1, v2):
    diff = v1-v2
    return np.hypot(diff[:,0], diff[:,1])

It should be faster -- less data copying, one loop.

By the way, it would be nice to have a version that would take a NX2 
array, so you could write: np.hypot(v1-v2)

or even np.EuclidDistance(v1, v2)

But maybe it's not worth bloating numpy for...


-Chris



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