VP-tree searcher. More...

#include <Vptree.hpp>

Inheritance diagram for knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >:

Collaboration diagram for knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >:

Public Member Functions
void	search (Index_ i, Index_ k, std::vector< Index_ > output_indices, std::vector< Float_ > output_distances)

void	search (const Float_ query, Index_ k, std::vector< Index_ > output_indices, std::vector< Float_ > *output_distances)

bool	can_search_all () const

Index_	search_all (Index_ i, Float_ d, std::vector< Index_ > output_indices, std::vector< Float_ > output_distances)

Index_	search_all (const Float_ query, Float_ d, std::vector< Index_ > output_indices, std::vector< Float_ > *output_distances)

Public Member Functions inherited from knncolle::Searcher< Index_, Float_ >

Detailed Description

template<class Distance_, typename Dim_, typename Index_, typename Store_, typename Float_>
class knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >

VP-tree searcher.

Instances of this class are usually constructed using VptreePrebuilt::initialize().

Template Parameters

Distance_	A distance calculation class satisfying the `MockDistance` contract.
Dim_	Integer type for the number of dimensions.
Index_	Integer type for the indices.
Store_	Floating point type for the stored data.
Float_	Floating point type for the query data and output distances.

Member Function Documentation

◆ can_search_all()

template<class Distance_ , typename Dim_ , typename Index_ , typename Store_ , typename Float_ >

bool knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >::can_search_all ( ) const

inlinevirtual

It is expected that the return value of this method should be constant throughout the lifetime of any Searcher instance.

Returns: Whether this instance has an implementation of the search_all() method.

Reimplemented from knncolle::Searcher< Index_, Float_ >.

◆ search() [1/2]

template<class Distance_ , typename Dim_ , typename Index_ , typename Store_ , typename Float_ >

void knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >::search	(	const Float_ *	query,
		Index_	k,
		std::vector< Index_ > *	output_indices,
		std::vector< Float_ > *	output_distances )

inlinevirtual

Find the nearest neighbors of a new observation.

Parameters

	query	Pointer to an array of length equal to `Prebuilt::num_dimensions()`, containing the coordinates of the query point.
	k	The number of neighbors to identify. This should be a non-negative integer that is no greater than the number of observations in `Prebuilt::num_observations()`.
[out]	output_indices	Pointer to a vector, to be filled with the identities of the nearest neighbors in order of increasing distance. On output, the length of the vector should be equal to `k`. All entries should be unique and less than `Prebuilt::num_observations()`. Optionally NULL, in which case no indices are returned.
[out]	output_distances	Pointer to a vector, to be filled with the distances of the nearest neighbors. This corresponds to the indices reported in `output_indices`. Optionally NULL, in which case no distances are returned.

Implements knncolle::Searcher< Index_, Float_ >.

◆ search() [2/2]

template<class Distance_ , typename Dim_ , typename Index_ , typename Store_ , typename Float_ >

void knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >::search	(	Index_	i,
		Index_	k,
		std::vector< Index_ > *	output_indices,
		std::vector< Float_ > *	output_distances )

inlinevirtual

Find the nearest neighbors of the i-th observation in the dataset.

Parameters

	i	The index of the observation of interest. This should be non-negative and less than the total number of observations in `Prebuilt::num_observations()`.
	k	The number of neighbors to identify. This should be a non-negative integer that is less than the total number of observations in `Prebuilt::num_observations()`. (Except if `Prebuilt::num_observations() == 0`, in which case the only valid choice for `k` is also zero.) Users can call `cap_k()` to easily cap `k` based on `Prebuilt::num_observations()`.
[out]	output_indices	Pointer to a vector, to be filled with the identities of the nearest neighbors in order of increasing distance. On output, the length of the vector should be equal to `k`. All entries should be unique and less than `Prebuilt::num_observations()`. This vector is guaranteed to not contain `i` itself. Optionally NULL, in which case no indices are returned.
[out]	output_distances	Pointer to a vector, to be filled with the distances of the nearest neighbors. This corresponds to the indices reported in `output_indices`. Optionally NULL, in which case no distances are returned.

Implements knncolle::Searcher< Index_, Float_ >.

◆ search_all() [1/2]

template<class Distance_ , typename Dim_ , typename Index_ , typename Store_ , typename Float_ >

Index_ knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >::search_all	(	const Float_ *	query,
		Float_	distance,
		std::vector< Index_ > *	output_indices,
		std::vector< Float_ > *	output_distances )

inlinevirtual

Find all neighbors of a new observation within a certain distance. This method is optional and may not be implemented by subclasses, so applications should check can_search_all() before attempting a call. A run-time exception is thrown if this method is called on a subclass that does not implement it.

Parameters

	query	Pointer to an array of length equal to `Prebuilt::num_dimensions()`, containing the coordinates of the query point.
	distance	The distance in which to consider neighbors.
[out]	output_indices	Pointer to a vector, to be filled with the identities of the nearest neighbors in order of increasing distance. Optionally NULL, in which case no indices are returned.
[out]	output_distances	Pointer to a vector, to be filled with the distances of the nearest neighbors. This corresponds to the indices reported in `output_indices`. Optionally NULL, in which case no distances are returned.

Returns: Number of neighbors within distance of query.

Reimplemented from knncolle::Searcher< Index_, Float_ >.

◆ search_all() [2/2]

template<class Distance_ , typename Dim_ , typename Index_ , typename Store_ , typename Float_ >

Index_ knncolle::VptreeSearcher< Distance_, Dim_, Index_, Store_, Float_ >::search_all	(	Index_	i,
		Float_	distance,
		std::vector< Index_ > *	output_indices,
		std::vector< Float_ > *	output_distances )

inlinevirtual

Find all neighbors of the i-th observation within a certain distance. This method is optional and may not be implemented by subclasses, so applications should check can_search_all() before attempting a call. A run-time exception is thrown if this method is called on a subclass that does not implement it.

Parameters

	i	The index of the observation of interest. This should be non-negative and less than the total number of observations in `Prebuilt::num_observations()`.
	distance	The distance in which to consider neighbors.
[out]	output_indices	Pointer to a vector, to be filled with the identities of the nearest neighbors in order of increasing distance. This vector is guaranteed to not contain `i` itself. Optionally NULL, in which case no indices are returned.
[out]	output_distances	Pointer to a vector, to be filled with the distances of the nearest neighbors. This corresponds to the indices reported in `output_indices`. Optionally NULL, in which case no distances are returned.

Returns: Number of neighbors within distance of i.

Reimplemented from knncolle::Searcher< Index_, Float_ >.

The documentation for this class was generated from the following file:

knncolle/Vptree.hpp

Public Member Functions

Detailed Description

Member Function Documentation

◆ can_search_all()

◆ search() [1/2]

◆ search() [2/2]

◆ search_all() [1/2]

◆ search_all() [2/2]