finding euclidean distance,better code?

[Gabriel Genellina]

En Mon, 17 Mar 2008 03:04:16 -0200, devnew at gmail.com <devnew at gmail.com>  
escribi�:

> while trying to write a function that processes some numpy arrays and
> calculate euclidean distance ,i ended up with this code
> (though i used numpy ,i believe my problem has more to do with python
> coding style..so am posting it here)
>
> for image in range(numimgs):
>     distance = abs(input_wk - weights[image, :])
>     if image==0:
>         #copy from distance to mindistance
>         mindistance=distance.copy()
>     if sum(mindistance) > sum(distance):
>         imgindex=image
>         mindistance=distance.copy()
> if max(mindistance) > 0.0:
>     #normalise mindistance
>     mindistance=mindistance/(max(mindistance)+1)
> dist=sum(mindistance)
>
> this gets me the euclidean distance value.

It looks like you're rather computing a distance derived from the 1-norm  
(sum of coordinates; like in a city with square blocks).
I'd save the sum(mindistance) value to avoid recomputing it in every  
iteration.

> I want to know if the way i
> coded it can be improved,made more compact....if someone can give
> suggestions it will be a great help .

The code is pretty legible as it is now. Anyway, using min() and a  
generator:

_, imgindex = min((sum(abs(input_wk - weights[image, :])),image) for image  
in xrange(numimgs))
mindistance = abs(input_wk - weights[imgindex, :])
# normalize and sum again


-- 
Gabriel Genellina