data-tree-reordering tweaks
my working, but 10x slower algorithm, which I am going to rewrite today.
np now supports up to 3 trees simulatenously, one built on x, one on y, and one on x+y
possible breakage caused by this
some more 3 trees tweaks
a new ccdf algorithm which in the worst case (many irrelevant variables) is the same order as the old one O(n^3) (but a constant factor slower), and in other cases approaches n^2 log(n).
it crashed when I tested it with ordered data
enable it with np.tree = TRUE, disable with np.tree = FALSE for comparison.
major under-the-hood work to accommodate mixing different kernels together in kernel_weighted_sum_np (e.g.var 1 epanechnikov & var 2 gaussian, etc)
the natural use for this for conditional objects, e.g. y - normalized kernel, x - non-normalized kernel
consolidated code
fixed ordered data = crash w/ ccdf routine
fixed some const issues
fixed regression which broke ll gradients
fixes for various regressions introducing affecting trees + adaptive nn
better error handling in np_kernelsum
Merge branch 'master' into npRmpi