Algorithm::KNN::XS - A class interface to perform a fast k neareast neighbor search using libANN via XS.
use Algorithm::KNN::XS; # define data points in a N dimensional space my @points = ( [7.45, 2], [16.56, 32.1], ); # create a new knn object my $knn = Algorithm::KNN::XS->new(points => \@points); # perform a k nearest neighbor search at the given query point, the result # contains all neighbors and their distance to the query point. my $result = $knn->annkSearch(query_point => [4.23, 2.45]);
A class interface todo a fast k nearest neighbor search using libANN via XS.
The XS Module automatically creates the namespace Algorithm::KNN::XS::LibANNInterface.
More information about libANN can be found with the following links.
libANN main page: http://www.cs.umd.edu/~mount/ANN/ libANN manual: http://www.cs.umd.edu/~mount/ANN/Files/1.1.2/ANNmanual_1.1.pdf
If you want to use another Minkowski distance metric than "Euclidean" you must compile another libANN library with the correct options before building this Module. Possible other distances are "Manhatten" and "Max".
Algorithm::KNN::XS->new ( ... )
This class method instantiates a new kd or bd tree from a points array or a string of a dumped tree.
If points and a dump string is given, the dump string will be ignored.
The method will croak if something goes wrong.
libANN will do a exit(1) which is not catchable via eval if a wrong dump string is given, so make sure that the dump is valid before using that parameter.
my $knn = Algorithm::KNN::XS->new( points => [ [3.453, 2.324], [6.874, 4.874], ], dump => '', bd_tree => 1, bucket_size => 1, split_rule => 'ANN_KD_SUGGEST', shrink_rule => 'ANN_BD_SUGGEST', );
points
The number of points provided in each element also sets the dimension of the tree that is going to be created. Each item must have the same amount of points.
Either this or the dump parameter is mandatory.
dump
A String of a tree that got dumped via the method Dump(points => 1) of this module.
You can use a dump of a kd tree to create a bd tree but not the other way around.
libANN will do a exit(1) which is not catchable via eval if you try to input a string that was dumped without the "points" parameter set to 1 or if the string is not a valid dump.
Either this or the points parameter is mandatory.
bd_tree
If set to 1 a bd tree will be created, otherwise a kd tree is used.
bucket_size
Must be greater 1 and influences the split and shrinking rule behaviour.
Please refer the libANN manual for further information.
The default value is 1.
split_rule
A String which sets the splitting rule. Possible values are: ANN_KD_SUGGEST, ANN_KD_STD, ANN_KD_MIDPT, ANN_KD_FAIR, ANN_KD_SL_MIDPT and ANN_KD_SL_FAIR.
Please refer the libANN manual for the meaning of these values but for the most people ANN_KD_SUGGEST should work well.
The default value is ANN_KD_SUGGEST.
shrink_rule
A String which sets the shrinking rule. Possible values are: ANN_BD_SUGGEST, ANN_BD_NONE, ANN_BD_SIMPLE, ANN_BD_CENTROID.
Please refer the libANN manual for the meaning of these values but for the most people ANN_BD_SUGGEST should work well.
This parameter only affects bd trees.
The default value is ANN_BD_SUGGEST.
$knn->tree()
This class method returns the current tree object and can be used to access the XS methods directly.
my $tree = $knn->tree();
$knn->set_annMaxPtsVisit( ... )
This class method sets the maximum number of points that the search methods are going to process before they abort. They can return more points than the set value because the abort condition is only checked before processing a leaf node.
$knn->set_annMaxPtsVisit( max_points => 5, );
max_points
Must be 0 or greater. The value 0 means no limit.
The default value is 0.
$knn->annkSearch( ... )
This class method performs a k nearest neighbor search.
my %result = $knn->annkSearch( query_point => [], limit_neighbors => 0, epsilon => 0, );
query_point
A list of points which must be the same dimension as the tree.
limit_neighbors
Determines how many neighbors should be returned. The value 0 means no limit.
epsilon
The relative error bound for approximate nearest neighbor searching. Please refer the libANN manual for more information.
Return value
Following structure is returned:
my $result = [ # 'distance' is the squared distance to the given query point # (the query point found itself in this case) { 'distance' => '0', 'point' => [ '1.1345', '2.657' ] }, { 'distance' => '15.00963986', 'point' => [ '4.023', '5.2389' ] } ];
$knn->annkPriSearch( ... )
This class method performs a k nearest neighbor priority search as described by Arya and Mount, please refer the libANN manual for further information.
my %result = $knn->annkPriSearch( query_point => [], limit_neighbors => 0, epsilon => 0, );
$knn->annkFRSearch( ... )
This class method performs a fixed radius k nearest neighbor search.
my %result = $knn->annkFRSearch( query_point => [], limit_neighbors => 0, epsilon => 0, radius => 10, );
radius
The radius in which the search should take place.
Defaults to 0.
Following Structure is returned:
$knn->annCntNeighbours( ... )
This class method performs a fixed radius k nearest neighbor search and returns the number of neighbors.
my $neighbors = $knn->annCntNeighbours( query_point => [], epsilon => 0, radius => 10, );
Number of found neighbors.
$knn->theDim()
Returns the dimension that the points in the current tree have.
my $dimension = $knn->theDim();
Dimension of the tree points.
$knn->nPoints()
Returns the number of points in the current tree.
my $n_points = $knn->nPoints();
Number of points in the tree.
$knn->Print( ... )
Prints the current tree. Normally useful for debugging.
my $output = $knn->Print( points => 1, );
If set to 1 all points will be printed at the top of the output.
A string of the printed tree.
$knn->Dump( ... )
Dumps the current tree. The dump can be passed into the new method to initialize a new tree or to visualize it via the external programm ann2fig.
my $output = $knn->Dump( points => 1, );
If set to 1 all points will be printed at the top of the output. The new methods only accepts a tree that got dumped with points set to 1.
A string of the dumped tree.
$knn->getStats()
Returns statistics about the current tree.
my $stats = $knn->getStats();
A hash reference of the following format:
my $stats = { dimension => 2, n_points => 7, bucket_size => 1, leaves => 7, trvial_leaves => 0, splitting_nodes => 6, shrinking_nodes => 0, depth => 4, avg_leaf_aspect_ratio => 1.61012744903564, };
http://www.cs.umd.edu/~mount/ANN/
http://www.cs.umd.edu/~mount/ANN/Files/1.1.2/ANNmanual_1.1.pdf
Stephan Conrad, <conrad@stephanconrad.de>
Copyright (C) 2011 by Stephan Conrad
This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself, either Perl version 5.6.1 or, at your option, any later version of Perl 5 you may have available.
To install Algorithm::KNN::XS, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Algorithm::KNN::XS
CPAN shell
perl -MCPAN -e shell install Algorithm::KNN::XS
For more information on module installation, please visit the detailed CPAN module installation guide.