Dear Jake, Thank you for your prompt reply. I started with KD-tree but after realising it doesn't support custom metrics (I don't know the reason for this - would be nice feature) I shifted to BallTree and was looking for a 2 metric based categorisation. After looking around, the best I could find at most were brute-force methods written in python (had my own version too) or better optimised ones in C or FORTRAN. The closest one was halotools which again works with euclidean metric. For now, I will try to get my work done with 2 different BallTrees iteratively in bins. If I find a better option will try to post an update.
Regards, Rohin. On Tue, Aug 1, 2017 at 10:55 PM, Jacob Vanderplas <jake...@cs.washington.edu > wrote: > Hi Rohin, > Ah, I see. I don't think a BallTree is the right data structure for an > anisotropic N-point query, because it fundamentally assumes spherical > symmetry of the metric. You may be able to do something like this with a > specialized KD-tree, but scikit-learn doesn't support this, and I don't > imagine that it ever will given the very specialized nature of the > application. > > I'm certain someone has written efficient code for this operation in the > astronomy community, but I don't know of any good Python package to > recommend for this – I'd suggest googling for keywords and seeing where > that gets you. > > Thanks, > Jake > > Jake VanderPlas > Senior Data Science Fellow > Director of Open Software > University of Washington eScience Institute > > On Tue, Aug 1, 2017 at 6:15 AM, Rohin Kumar <yrohinku...@gmail.com> wrote: > >> Since you seem to be from Astrophysics/Cosmology background (I am >> assuming you are jakevdp - the creator of astroML - if you are - I am >> lucky!), I can explain my application scenario. I am trying to calculate >> the anisotropic two-point correlation function something like done in >> rp_pi_tpcf >> <http://halotools.readthedocs.io/en/latest/api/halotools.mock_observables.rp_pi_tpcf.html#halotools.mock_observables.rp_pi_tpcf> >> or s_mu_tpcf >> <http://halotools.readthedocs.io/en/latest/api/halotools.mock_observables.s_mu_tpcf.html#halotools.mock_observables.s_mu_tpcf> >> using pairs (DD,DR,RR) calculated from BallTree.two_point_correlation >> >> In halotools (http://halotools.readthedocs.io/en/latest/function_usage/mo >> ck_observables_functions.html) it is implemented using rectangular >> grids. I could calculate 2pcf with custom metrics using one variable with >> BallTree as done in astroML. I intend to find the anisotropic counter part. >> >> Thanks & Regards, >> Rohin >> >> >> On Tue, Aug 1, 2017 at 5:18 PM, Rohin Kumar <yrohinku...@gmail.com> >> wrote: >> >>> Dear Jake, >>> >>> Thanks for your response. I meant to group/count pairs in boxes (using >>> two arrays simultaneously-hence needing 2 metrics) instead of one distance >>> array as the binning parameter. I don't know if the algorithm supports such >>> a thing. For now, I am proceeding with your suggestion of two ball trees at >>> huge computational cost. I hope I am able to frame my question properly. >>> >>> Thanks & Regards, >>> Rohin. >>> >>> >>> >>> On Mon, Jul 31, 2017 at 8:16 PM, Jacob Vanderplas < >>> jake...@cs.washington.edu> wrote: >>> >>>> On Sun, Jul 30, 2017 at 11:18 AM, Rohin Kumar <yrohinku...@gmail.com> >>>> wrote: >>>> >>>>> *update* >>>>> >>>>> May be it doesn't have to be done at the tree creation level. It could >>>>> be using loops and creating two different balltrees. Something like >>>>> >>>>> tree1=BallTree(X,metric='metric1') #for x-z plane >>>>> tree2=BallTree(X,metric='metric2') #for y-z plane >>>>> >>>>> And then calculate correlation functions in a loop to get >>>>> tpcf(X,r1,r2) using tree1.two_point_correlation(X,r1) and >>>>> tree2.two_point_correlation(X,r2) >>>>> >>>> >>>> Hi Rohin, >>>> It's not exactly clear to me what you wish the tree to do with the two >>>> different metrics, but in any case the ball tree only supports one metric >>>> at a time. If you can construct your desired result from two ball trees >>>> each with its own metric, then that's probably the best way to proceed, >>>> Jake >>>> >>>> >>>>> >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn@python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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