mlpack_neighbor_NeighborSearch (3)

Public Member Functions

NeighborSearch (const typename TreeType::Mat &referenceSet, const typename TreeType::Mat &querySet, const bool naive=false, const bool singleMode=false, const MetricType metric=MetricType())

Initialize the NeighborSearch object, passing both a query and reference dataset. NeighborSearch (const typename TreeType::Mat &referenceSet, const bool naive=false, const bool singleMode=false, const MetricType metric=MetricType())

Initialize the NeighborSearch object, passing only one dataset, which is used as both the query and the reference dataset. NeighborSearch (TreeType *referenceTree, TreeType *queryTree, const typename TreeType::Mat &referenceSet, const typename TreeType::Mat &querySet, const bool singleMode=false, const MetricType metric=MetricType())

Initialize the NeighborSearch object with the given datasets and pre-constructed trees. NeighborSearch (TreeType *referenceTree, const typename TreeType::Mat &referenceSet, const bool singleMode=false, const MetricType metric=MetricType())

Initialize the NeighborSearch object with the given reference dataset and pre-constructed tree. ~NeighborSearch ()

Delete the NeighborSearch object. void Search (const size_t k, arma::Mat< size_t > &resultingNeighbors, arma::mat &distances)

Compute the nearest neighbors and store the output in the given matrices. std::string ToString () const

Private Attributes

bool hasQuerySet

Indicates if a separate query set was passed. MetricType metric

Instantiation of metric. bool naive

Indicates if O(n^2) naive search is being used. std::vector< size_t > oldFromNewQueries

Permutations of query points during tree building. std::vector< size_t > oldFromNewReferences

Permutations of reference points during tree building. TreeType::Mat queryCopy

Copy of query dataset (if we need it, because tree building modifies it). const TreeType::Mat & querySet

Query dataset (may not be given). TreeType * queryTree

Pointer to the root of the query tree (might not exist). TreeType::Mat referenceCopy

Copy of reference dataset (if we need it, because tree building modifies it). const TreeType::Mat & referenceSet

Reference dataset. TreeType * referenceTree

Pointer to the root of the reference tree. bool singleMode

Indicates if single-tree search is being used (opposed to dual-tree). bool treeOwner

If true, this object created the trees and is responsible for them.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >>class mlpack::neighbor::NeighborSearch< SortPolicy, MetricType, TreeType >

The NeighborSearch class is a template class for performing distance-based neighbor searches.

It takes a query dataset and a reference dataset (or just a reference dataset) and, for each point in the query dataset, finds the k neighbors in the reference dataset which have the 'best' distance according to a given sorting policy. A constructor is given which takes only a reference dataset, and if that constructor is used, the given reference dataset is also used as the query dataset.

The template parameters SortPolicy and Metric define the sort function used and the metric (distance function) used. More information on those classes can be found in the NearestNeighborSort class and the kernel::ExampleKernel class.

Template Parameters:

SortPolicy The sort policy for distances; see NearestNeighborSort.

MetricType The metric to use for computation.

TreeType The tree type to use.

Definition at line 63 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::\fBNeighborSearch\fP (const typename TreeType::Mat &referenceSet, const typename TreeType::Mat &querySet, const boolnaive = \fCfalse\fP, const boolsingleMode = \fCfalse\fP, const MetricTypemetric = \fCMetricType()\fP)

Initialize the NeighborSearch object, passing both a query and reference dataset. Optionally, perform the computation in naive mode or single-tree mode, and set the leaf size used for tree-building. An initialized distance metric can be given, for cases where the metric has internal data (i.e. the distance::MahalanobisDistance class).

This method will copy the matrices to internal copies, which are rearranged during tree-building. You can avoid this extra copy by pre-constructing the trees and passing them using a diferent constructor.

Parameters:

referenceSet Set of reference points.

querySet Set of query points.

naive If true, O(n^2) naive search will be used (as opposed to dual-tree search). This overrides singleMode (if it is set to true).

singleMode If true, single-tree search will be used (as opposed to dual-tree search).

leafSize Leaf size for tree construction (ignored if tree is given).

metric An optional instance of the MetricType class.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::\fBNeighborSearch\fP (const typename TreeType::Mat &referenceSet, const boolnaive = \fCfalse\fP, const boolsingleMode = \fCfalse\fP, const MetricTypemetric = \fCMetricType()\fP)

Initialize the NeighborSearch object, passing only one dataset, which is used as both the query and the reference dataset. Optionally, perform the computation in naive mode or single-tree mode, and set the leaf size used for tree-building. An initialized distance metric can be given, for cases where the metric has internal data (i.e. the distance::MahalanobisDistance class).

If naive mode is being used and a pre-built tree is given, it may not work: naive mode operates by building a one-node tree (the root node holds all the points). If that condition is not satisfied with the pre-built tree, then naive mode will not work.

Parameters:

referenceSet Set of reference points.

naive If true, O(n^2) naive search will be used (as opposed to dual-tree search). This overrides singleMode (if it is set to true).

singleMode If true, single-tree search will be used (as opposed to dual-tree search).

leafSize Leaf size for tree construction (ignored if tree is given).

metric An optional instance of the MetricType class.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::\fBNeighborSearch\fP (TreeType *referenceTree, TreeType *queryTree, const typename TreeType::Mat &referenceSet, const typename TreeType::Mat &querySet, const boolsingleMode = \fCfalse\fP, const MetricTypemetric = \fCMetricType()\fP)

Initialize the NeighborSearch object with the given datasets and pre-constructed trees. It is assumed that the points in referenceSet and querySet correspond to the points in referenceTree and queryTree, respectively. Optionally, choose to use single-tree mode. Naive mode is not available as an option for this constructor; instead, to run naive computation, construct a tree with all of the points in one leaf (i.e. leafSize = number of points). Additionally, an instantiated distance metric can be given, for cases where the distance metric holds data.

There is no copying of the data matrices in this constructor (because tree-building is not necessary), so this is the constructor to use when copies absolutely must be avoided.

Note:

Because tree-building (at least with BinarySpaceTree) modifies the ordering of a matrix, be sure you pass the modified matrix to this object! In addition, mapping the points of the matrix back to their original indices is not done when this constructor is used.

Parameters:

referenceTree Pre-built tree for reference points.

queryTree Pre-built tree for query points.

referenceSet Set of reference points corresponding to referenceTree.

querySet Set of query points corresponding to queryTree.

singleMode Whether single-tree computation should be used (as opposed to dual-tree computation).

metric Instantiated distance metric.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::\fBNeighborSearch\fP (TreeType *referenceTree, const typename TreeType::Mat &referenceSet, const boolsingleMode = \fCfalse\fP, const MetricTypemetric = \fCMetricType()\fP)

Initialize the NeighborSearch object with the given reference dataset and pre-constructed tree. It is assumed that the points in referenceSet correspond to the points in referenceTree. Optionally, choose to use single-tree mode. Naive mode is not available as an option for this constructor; instead, to run naive computation, construct a tree with all the points in one leaf (i.e. leafSize = number of points). Additionally, an instantiated distance metric can be given, for the case where the distance metric holds data.

There is no copying of the data matrices in this constructor (because tree-building is not necessary), so this is the constructor to use when copies absolutely must be avoided.

Note:

Because tree-building (at least with BinarySpaceTree) modifies the ordering of a matrix, be sure you pass the modified matrix to this object! In addition, mapping the points of the matrix back to their original indices is not done when this constructor is used.

Parameters:

referenceTree Pre-built tree for reference points.

referenceSet Set of reference points corresponding to referenceTree.

singleMode Whether single-tree computation should be used (as opposed to dual-tree computation).

metric Instantiated distance metric.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::~\fBNeighborSearch\fP ()

Delete the NeighborSearch object. The tree is the only member we are responsible for deleting. The others will take care of themselves.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> void \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::Search (const size_tk, arma::Mat< size_t > &resultingNeighbors, arma::mat &distances)

Compute the nearest neighbors and store the output in the given matrices. The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.

Parameters:

k Number of neighbors to search for.

resultingNeighbors Matrix storing lists of neighbors for each query point.

distances Matrix storing distances of neighbors for each query point.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> std::string \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::ToString () const

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> bool \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::hasQuerySet\fC [private]\fP

Indicates if a separate query set was passed.

Definition at line 232 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> MetricType \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::metric\fC [private]\fP

Instantiation of metric.

Definition at line 240 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> bool \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::naive\fC [private]\fP

Indicates if O(n^2) naive search is being used.

Definition at line 235 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> std::vector<size_t> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::oldFromNewQueries\fC [private]\fP

Permutations of query points during tree building.

Definition at line 245 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> std::vector<size_t> \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::oldFromNewReferences\fC [private]\fP

Permutations of reference points during tree building.

Definition at line 243 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> TreeType::Mat \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::queryCopy\fC [private]\fP

Copy of query dataset (if we need it, because tree building modifies it).

Definition at line 217 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> const TreeType::Mat& \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::querySet\fC [private]\fP

Query dataset (may not be given).

Definition at line 222 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> TreeType* \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::queryTree\fC [private]\fP

Pointer to the root of the query tree (might not exist).

Definition at line 227 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> TreeType::Mat \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::referenceCopy\fC [private]\fP

Copy of reference dataset (if we need it, because tree building modifies it).

Definition at line 215 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> const TreeType::Mat& \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::referenceSet\fC [private]\fP

Reference dataset.

Definition at line 220 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> TreeType* \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::referenceTree\fC [private]\fP

Pointer to the root of the reference tree.

Definition at line 225 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> bool \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::singleMode\fC [private]\fP

Indicates if single-tree search is being used (opposed to dual-tree).

Definition at line 237 of file neighbor_search.hpp.

template<typename SortPolicy = NearestNeighborSort, typename MetricType = mlpack::metric::SquaredEuclideanDistance, typename TreeType = tree::BinarySpaceTree<bound::HRectBound<2>, NeighborSearchStat<SortPolicy> >> bool \fBmlpack::neighbor::NeighborSearch\fP< SortPolicy, MetricType, TreeType >::treeOwner\fC [private]\fP

If true, this object created the trees and is responsible for them.

Definition at line 230 of file neighbor_search.hpp.

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