Always, *always*, or just with high enough probability that you don't realistically have to worry about it failing. If the latter, I wonder if you could do something with random projections. Off the top of my head, I wonder if something like the sum of ranks when ordered under a set of random projection directions might be stable for a large enough set of directions. It's been a while since my Randomized Algorithms class, though.
Ray Jones On Sun, Oct 27, 2013 at 6:41 PM, Freddie Witherden <[email protected]>wrote: > On 27/10/13 21:05, Jonathan March wrote: > > If an "almost always works" solution is good enough, then sort on the > > distance to some fixed random point that is in the vicinity of your N > > points. > > I had considered this. Unfortunately I need a solution which really > does always work. > > The only pure-Python solution I can envision -- at the moment anyway -- > is to do some cleverness with the output of np.unique to identify > similar values and replace them with an arbitrarily chosen one. This > should permit the output to be passed to np.lexsort without issue. > > Regards, Freddie. > > > > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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