[Numpy-discussion] Find n closest values

[Nicolas P. Rougier]

Thanks, I'll try your solution.

Data (L) is not so big actually, it represents pixels on screen and (I) represents line position (for grids). I need to compute this quantity everytime the user zoom in or out.


Nicolas


On 22 Jun 2014, at 19:05, Eelco Hoogendoorn <hoogendoorn.eelco at gmail.com> wrote:

> Well, if the spacing is truly uniform, then of course you don't really need the search, and you can do away with the extra log-n, and there is a purely linear solution:
> 
> def find_closest_direct(start, end, count, A):
>     Q = (A-start)/(end-start)*count
>     mid = ((Q[1:]+Q[:-1]+1)/2).astype(np.int)
>     boundary = np.zeros(count, np.int)
>     boundary[mid] = 1
>     return np.add.accumulate(boundary)
> 
> I expect this to be a bit faster, but nothing dramatic, unless your datasets are huge. It isn't really more or less elegant either, id say. Note that the output isn't 100% identical; youd need to do a little tinkering to figure out the correct/desired rounding behavior.
> 
> 
> On Sun, Jun 22, 2014 at 5:16 PM, Nicolas P. Rougier <Nicolas.Rougier at inria.fr> wrote:
> 
> Thanks for the answer.
> I was secretly hoping for some kind of hardly-known numpy function that would make things faster auto-magically...
> 
> 
> Nicolas
> 
> 
> On 22 Jun 2014, at 10:30, Eelco Hoogendoorn <hoogendoorn.eelco at gmail.com> wrote:
> 
> > Perhaps you could simplify some statements, but at least the algorithmic complexity is fine, and everything is vectorized, so I doubt you will get huge gains.
> >
> > You could take a look at the functions in scipy.spatial, and see how they perform for your problem parameters.
> >
> >
> > On Sun, Jun 22, 2014 at 10:22 AM, Nicolas P. Rougier <Nicolas.Rougier at inria.fr> wrote:
> >
> >
> > Hi,
> >
> > I have an array L with regular spaced values between 0 and width.
> > I have a (sorted) array I with irregular spaced values between 0 and width.
> >
> > I would like to find the closest value in I for any value in L.
> >
> > Currently, I'm using the following script but I wonder if I missed an obvious (and faster) solution:
> >
> >
> > import numpy as np
> >
> > def find_closest(A, target):
> >     idx = A.searchsorted(target)
> >     idx = np.clip(idx, 1, len(A) - 1)
> >     left = A[idx - 1]
> >     right = A[idx]
> >     idx -= target - left < right - target
> >     return idx
> >
> > n, width = 256, 100.0
> >
> > # 10 random sorted values in [0,width]
> > I = np.sort(np.random.randint(0,width,10))
> >
> > # n regular spaced values in [0,width]
> > L = np.linspace(0, width, n)
> >
> > print I[find_closest(I,L)]
> >
> >
> >
> > Nicolas
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