[Numpy-discussion] finding minimum distance using arrays
Robert Kern
robert.kern@gmail....
Wed Apr 23 01:10:15 CDT 2008
On Wed, Apr 23, 2008 at 1:00 AM, wilson <wilson.t.thompson@gmail.com> wrote:
> hi
> i wrote a function to find euclidian distance between two vectors and
> applied it to the rows of a 2d array of floats as below
>
>
> from math import sqrt
> from numpy import array,sum
>
> def distance(vec1, vec2):
> return sqrt(sum([(x-y)**2 for x,y in zip(vec1, vec2)]))
>
> def findmatch(wts,inputwt):
> mindist=99.0
> index=0
> for i in range(len(wts)):
> d=distance(wts[i],inputwt)
> if d == 0.0:
> mindist=d
> index=i
> break
> elif d < mindist:
> mindist=d
> index=i
> return index,mindist
>
> inputwt is a 1 dim array,wts is a 2d array .i want to find the
> distance between inputwt and each row of wts...I used mindist=99.0 as
> an improbable value for distance for the sample float arrays which i
> used to test it...
> i am not sure if this is the proper way to write this function..can
> someone suggest how i can make it better?
import numpy
def findmatch(wts, inputwts):
wts = numpy.asarray(wts)
inputwts = numpy.asarray(wts)
dx = wts - inputwts
distances = (dx * dx).sum(axis=1)
index = distances.argmin()
mindist = distances[index]
return index, mindist
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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