[Numpy-discussion] Find n closest values

[Eelco Hoogendoorn]

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
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion at scipy.org
> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
> >
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion at scipy.org
> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20140622/90c416b6/attachment.html>