HMLP: High-performance Machine Learning Primitives
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This the main splitter used to build the Spd-Askit tree. First compute the approximate center using subsamples. Then find the two most far away points to do the projection. More...
#include <gofmm.hpp>
Public Member Functions | |
centersplit (SPDMATRIX &K) | |
vector< vector< size_t > > | operator() (vector< size_t > &gids) const |
Public Attributes | |
SPDMATRIX * | Kptr = NULL |
DistanceMetric | metric = ANGLE_DISTANCE |
size_t | n_centroid_samples = 5 |
This the main splitter used to build the Spd-Askit tree. First compute the approximate center using subsamples. Then find the two most far away points to do the projection.
end class Summary
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inline |
Overload the operator ().
all assertions
Collecting column samples of K.
Compute all pairwise distances.
Zero out the temporary buffer.
Accumulate distances to the temporary buffer.
Find the f2c (far most to center) from points owned
Collecting KIP
Compute all pairwise distances.
Find f2f (far most to far most) from owned points
collecting KIQ
Compute all pairwise distances.
SPDMATRIX* hmlp::gofmm::centersplit< SPDMATRIX, N_SPLIT, T >::Kptr = NULL |
Closure
DistanceMetric hmlp::gofmm::centersplit< SPDMATRIX, N_SPLIT, T >::metric = ANGLE_DISTANCE |
(Default) use angle distance from the Gram vector space.
size_t hmlp::gofmm::centersplit< SPDMATRIX, N_SPLIT, T >::n_centroid_samples = 5 |
Number samples to approximate centroid.