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<josef.pktd <at> gmail.com> writes:
On Sun, May 13, 2012 at 1:07 PM, Zachary Pincus <zachary.pincus <at> yale.edu>
Hello all,
A while ago, someone asked on this list about whether it would be simple to modify scipy.stats.kde.gaussian_kde to deal with weighted data: http://mail.scipy.org/pipermail/scipy-user/2008-November/018578.html
Anne and Robert assured the writer that this was pretty simple (modulo bandwidth selection), though I couldn't find any code that the original author may have generated based on
I've got a problem that could (perhaps) be solved neatly with weighed KDE,
so I'd like to give this a go. I assume that at a minimum, to get basic gaussian_kde.evaluate() functionality:
(1) The covariance calculation would need to be replaced by a weighted-
covariance calculation. (Simple enough.)
(2) In evaluate(), the critical part looks like this (and a similar stanza
wrote: that advice. that loops over the points instead):
# if there are more points than data, so loop over data for i in range(self.n): diff = self.dataset[:, i, newaxis] - points tdiff = dot(self.inv_cov, diff) energy = sum(diff*tdiff,axis=0) / 2.0 result = result + exp(-energy)
I assume that, further, the 'diff' values ought to be scaled by the weights, too. Is this all that would need to be done? (For the integration and resampling, obviously, there would be a bit of other work...)
it looks to me that way, scaled according to weight by dataset points
I don't see what the norm_factor should be: self._norm_factor = sqrt(linalg.det(2*pi*self.covariance)) * self.n there should be the weights somewhere in there, maybe just replace self.n by sum(weights) given a constant covariance
sampling doesn't look difficult, if we want biased sampling, then instead of randint, we would need weighted randint (non-uniform)
integration might require more work, or not (I never tried to understand them)
(I don't know if kde in statsmodels has weights on the schedule.)
Josef mostly guessing
Thanks, Zach _______________________________________________ SciPy-User mailing list SciPy-User <at> scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user
Hi, I am facing the same problem as well, but can't figure out how the weighting should be done exactly. Has anybody successfully completed the modification of the code to allow a weighted kde? I am attempting to perform kde on a set of imaging data with X, Y, and an additional "temperature" column. Performing the kde on only the X,Y axes gives a working heatmap showing the spatial distribution of the data points, but I would also like to use them to see the "temperature" profile (the third axis), much like a geographical heatmap showing temperature or rainfall values over a X-Y map. I found another set of code from http://pastebin.com/LNdYCZgw which allows weighted kde, but when I tried it out with my data, it took much longer than the normal kde (>1 hour) when the original code took only a about twenty seconds (despite claims that it was faster). Thanks, Jackson