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Distributed K-Nearest-Neighbors using MPI and Vantage Point Trees
For large values of m, n, block the query Y and compute the distance matrix one block at a time, to avoid storing large intermediate distance matrices.
Investigate better suited routines for calculating all-to-all euclidean distance matrix. Report speedups.
Need to fix the case that X and Y have some common points. In that case, y should reject adding itself to its kNN.
Originally posted by @georgegito in #1 (comment)
Should be addressed by #6
This is a followup issue for discussion on the benchmarks and plots to be created for the report. Refer to #22 for the initial discussion.
On each process, after receiving the new serialized tree, the kNN heap is missing, as it is a member of the VPT object of the previous process and kNN update fails.
In the current state, the vp-tree is created by having at least b
points linked to every leaf. When, for example a leaf has 100 points, kNN search must validate the query point against all 100.
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