.TH "mlpack::neighbor" 3 "Tue Sep 9 2014" "Version 1.0.10" "MLPACK" \" -*- nroff -*-
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.SH NAME
mlpack::neighbor \- 
.PP
Neighbor-search routines\&.  

.SH SYNOPSIS
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.PP
.SS "Classes"

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.RI "class \fBFurthestNeighborSort\fP"
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.RI "\fIThis class implements the necessary methods for the SortPolicy template parameter of the \fBNeighborSearch\fP class\&. \fP"
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.RI "class \fBLSHSearch\fP"
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.RI "\fIThe \fBLSHSearch\fP class -- This class builds a hash on the reference set and uses this hash to compute the distance-approximate nearest-neighbors of the given queries\&. \fP"
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.RI "class \fBNearestNeighborSort\fP"
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.RI "\fIThis class implements the necessary methods for the SortPolicy template parameter of the \fBNeighborSearch\fP class\&. \fP"
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.RI "class \fBNeighborSearch\fP"
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.RI "\fIThe \fBNeighborSearch\fP class is a template class for performing distance-based neighbor searches\&. \fP"
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.RI "class \fBNeighborSearchRules\fP"
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.RI "class \fBNeighborSearchStat\fP"
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.RI "\fIExtra data for each node in the tree\&. \fP"
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.RI "class \fBNeighborSearchTraversalInfo\fP"
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.RI "\fITraversal information for \fBNeighborSearch\fP\&. \fP"
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.RI "class \fBRASearchRules\fP"
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.SS "Typedefs"

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.RI "typedef \fBNeighborSearch\fP
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< \fBFurthestNeighborSort\fP, 
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\fBmetric::EuclideanDistance\fP > \fBAllkFN\fP"
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.RI "\fIThe AllkFN class is the all-k-furthest-neighbors method\&. \fP"
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.RI "typedef \fBNeighborSearch\fP
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< \fBNearestNeighborSort\fP, 
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\fBmetric::EuclideanDistance\fP > \fBAllkNN\fP"
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.RI "\fIThe AllkNN class is the all-k-nearest-neighbors method\&. \fP"
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.RI "typedef \fBRASearch\fP
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< \fBFurthestNeighborSort\fP > \fBAllkRAFN\fP"
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.RI "\fIThe AllkRAFN class is the all-k-rank-approximate-farthest-neighbors method\&. \fP"
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.RI "typedef \fBRASearch\fP \fBAllkRANN\fP"
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.RI "\fIThe AllkRANN class is the all-k-rank-approximate-nearest-neighbors method\&. \fP"
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.SS "Functions"

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.RI "void \fBUnmap\fP (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, const std::vector< size_t > &queryMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const bool squareRoot=false)"
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.RI "\fIAssuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors\&. \fP"
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.RI "void \fBUnmap\fP (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const bool squareRoot=false)"
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.RI "\fIAssuming that the datasets have been mapped using referenceMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors\&. \fP"
.in -1c
.SH "Detailed Description"
.PP 
Neighbor-search routines\&. 

These include all-nearest-neighbors and all-furthest-neighbors searches\&. 
.SH "Typedef Documentation"
.PP 
.SS "typedef \fBNeighborSearch\fP<\fBFurthestNeighborSort\fP, \fBmetric::EuclideanDistance\fP> \fBmlpack::neighbor::AllkFN\fP"

.PP
The AllkFN class is the all-k-furthest-neighbors method\&. It returns L2 distances (Euclidean distances) for each of the k furthest neighbors\&. 
.PP
Definition at line 48 of file typedef\&.hpp\&.
.SS "typedef \fBNeighborSearch\fP<\fBNearestNeighborSort\fP, \fBmetric::EuclideanDistance\fP> \fBmlpack::neighbor::AllkNN\fP"

.PP
The AllkNN class is the all-k-nearest-neighbors method\&. It returns L2 distances (Euclidean distances) for each of the k nearest neighbors\&. 
.PP
Definition at line 42 of file typedef\&.hpp\&.
.SS "typedef \fBRASearch\fP<\fBFurthestNeighborSort\fP> \fBmlpack::neighbor::AllkRAFN\fP"

.PP
The AllkRAFN class is the all-k-rank-approximate-farthest-neighbors method\&. It returns squared L2 distances (squared Euclidean distances) for each of the k rank-approximate farthest-neighbors\&. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt())\&.
.PP
The approximation is controlled with two parameters (see allkrann_main\&.cpp) which can be specified at search time\&. So the tree building is done only once while the search can be performed multiple times with different approximation levels\&. 
.PP
Definition at line 63 of file ra_typedef\&.hpp\&.
.SS "typedef \fBRASearch\fP \fBmlpack::neighbor::AllkRANN\fP"

.PP
The AllkRANN class is the all-k-rank-approximate-nearest-neighbors method\&. It returns squared L2 distances (squared Euclidean distances) for each of the k rank-approximate nearest-neighbors\&. Squared distances are used because they are slightly faster than non-squared distances (they have one fewer call to sqrt())\&.
.PP
The approximation is controlled with two parameters (see allkrann_main\&.cpp) which can be specified at search time\&. So the tree building is done only once while the search can be performed multiple times with different approximation levels\&. 
.PP
Definition at line 49 of file ra_typedef\&.hpp\&.
.SH "Function Documentation"
.PP 
.SS "void mlpack::neighbor::Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, const std::vector< size_t > &queryMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const boolsquareRoot = \fCfalse\fP)"

.PP
Assuming that the datasets have been mapped using the referenceMap and the queryMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors\&. This is useful for the dual-tree case\&.
.PP
\fBParameters:\fP
.RS 4
\fIneighbors\fP Matrix of neighbors resulting from neighbor search\&. 
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\fIdistances\fP Matrix of distances resulting from neighbor search\&. 
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\fIreferenceMap\fP Mapping of reference set to old points\&. 
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\fIqueryMap\fP Mapping of query set to old points\&. 
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\fIneighborsOut\fP Matrix to store unmapped neighbors into\&. 
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\fIdistancesOut\fP Matrix to store unmapped distances into\&. 
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\fIsquareRoot\fP If true, take the square root of the distances\&. 
.RE
.PP

.SS "void mlpack::neighbor::Unmap (const arma::Mat< size_t > &neighbors, const arma::mat &distances, const std::vector< size_t > &referenceMap, arma::Mat< size_t > &neighborsOut, arma::mat &distancesOut, const boolsquareRoot = \fCfalse\fP)"

.PP
Assuming that the datasets have been mapped using referenceMap (such as during kd-tree construction), unmap the columns of the distances and neighbors matrices into neighborsOut and distancesOut, and also unmap the entries in each row of neighbors\&. This is useful for the single-tree case\&.
.PP
\fBParameters:\fP
.RS 4
\fIneighbors\fP Matrix of neighbors resulting from neighbor search\&. 
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\fIdistances\fP Matrix of distances resulting from neighbor search\&. 
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\fIreferenceMap\fP Mapping of reference set to old points\&. 
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\fIneighborsOut\fP Matrix to store unmapped neighbors into\&. 
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\fIdistancesOut\fP Matrix to store unmapped distances into\&. 
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\fIsquareRoot\fP If true, take the square root of the distances\&. 
.RE
.PP

.SH "Author"
.PP 
Generated automatically by Doxygen for MLPACK from the source code\&.