Collection of KNN algorithms. More...

Classes
class	BruteforceBuilder
	Perform a brute-force nearest neighbor search. More...

class	Builder
	Interface to build nearest-neighbor search indices. More...

class	DistanceMetric
	Interface for a distance metric. More...

class	EuclideanDistance
	Compute Euclidean distances between two input vectors. More...

class	L2NormalizedBuilder
	Wrapper around a builder with L2 normalization. More...

class	L2NormalizedMatrix
	Wrapper around a matrix with L2 normalization. More...

struct	L2NormalizedPrebuiltTypes
	Template type of a saved L2-normalized index. More...

class	LoadDistanceMetricNotFoundError
	Exception for unknown distance metrics in `load_distance_metric_raw()`. More...

class	LoadPrebuiltNotFoundError
	Exception for unknown search algorithms in `load_prebuilt_raw()`. More...

class	ManhattanDistance
	Compute Manhattan distances between two input vectors. More...

class	Matrix
	Interface for matrix data. More...

class	MatrixExtractor
	Extractor interface for matrix data. More...

class	NeighborQueue
	Helper class to track nearest neighbors. More...

class	Prebuilt
	Interface for prebuilt nearest-neighbor search indices. More...

class	Searcher
	Interface for searching nearest-neighbor search indices. More...

class	SimpleMatrix
	Simple wrapper for an in-memory matrix. More...

class	VptreeBuilder
	Perform a nearest neighbor search based on a vantage point (VP) tree. More...

struct	VptreeOptions
	Options for `VptreeBuilder` construction. More...

Typedefs
template<typename Data_ , typename Distance_ >
using	LoadDistanceMetricFunction = std::function<DistanceMetric<Data_, Distance_>* (const std::filesystem::path&)>

template<typename Index_ , typename Distance_ >
using	NeighborList = std::vector<std::vector<std::pair<Index_, Distance_> > >

template<typename Index_ , typename Data_ , typename Distance_ >
using	LoadPrebuiltFunction = std::function<Prebuilt<Index_, Data_, Distance_>* (const std::filesystem::path&)>

Enumerations
enum class	NumericType : char { UINT8_T , INT8_T , UINT16_T , INT16_T , UINT32_T , INT32_T , UINT64_T , INT64_T , UNSIGNED_CHAR , SIGNED_CHAR , CHAR , UNSIGNED_SHORT , SHORT , UNSIGNED_INT , INT , UNSIGNED_LONG , LONG , UNSIGNED_LONG_LONG , LONG_LONG , SIZE_T , PTRDIFF_T , FLOAT , DOUBLE , UNKNOWN }

Functions
template<typename Data_ , typename Distance_ >
std::unordered_map< std::string, LoadDistanceMetricFunction< Data_, Distance_ > > &	load_distance_metric_registry ()

template<typename Data_ , typename Distance_ >
void	register_load_euclidean_distance ()

template<typename Data_ , typename Distance_ >
void	register_load_manhattan_distance ()

template<typename Data_ , typename Distance_ >
DistanceMetric< Data_, Distance_ > *	load_distance_metric_raw (const std::filesystem::path &dir)

template<typename Task_ , class Run_ >
void	parallelize (int num_workers, Task_ num_tasks, Run_ run_task_range)

template<typename Index_ >
int	cap_k (int k, Index_ num_observations)

template<typename Index_ >
int	cap_k_query (int k, Index_ num_observations)

template<typename Index_ , typename Data_ , typename Distance_ >
NeighborList< Index_, Distance_ >	find_nearest_neighbors (const Prebuilt< Index_, Data_, Distance_ > &index, int k, int num_threads=1)

template<typename Index_ , typename Data_ , typename Distance_ >
std::vector< std::vector< Index_ > >	find_nearest_neighbors_index_only (const Prebuilt< Index_, Data_, Distance_ > &index, int k, int num_threads=1)

template<typename Normalized_ >
std::function< void(const std::filesystem::path &)> &	custom_save_for_l2normalized_normalized ()

template<typename Index_ , typename Data_ , typename Distance_ >
std::unordered_map< std::string, LoadPrebuiltFunction< Index_, Data_, Distance_ > > &	load_prebuilt_registry ()

template<typename Index_ , typename Data_ , typename Distance_ >
Prebuilt< Index_, Data_, Distance_ > *	load_prebuilt_raw (const std::filesystem::path &dir)

template<typename Index_ , typename Data_ , typename Distance_ >
std::unique_ptr< Prebuilt< Index_, Data_, Distance_ > >	load_prebuilt_unique (const std::filesystem::path &dir)

template<typename Index_ , typename Data_ , typename Distance_ >
std::shared_ptr< Prebuilt< Index_, Data_, Distance_ > >	load_prebuilt_shared (const std::filesystem::path &dir)

L2NormalizedPrebuiltTypes	load_l2normalized_prebuilt_types (const std::filesystem::path &dir)

template<typename Index_ , typename Data_ , typename Distance_ , typename Normalized_ >
Prebuilt< Index_, Data_, Distance_ > *	load_l2normalized_prebuilt (const std::filesystem::path &dir)

template<typename Index_ , typename Data_ , typename Distance_ , class DistanceMetric_ = DistanceMetric<Data_, Distance_>>
Prebuilt< Index_, Data_, Distance_ > *	load_bruteforce_prebuilt (const std::filesystem::path &dir)

template<typename Index_ , typename Data_ , typename Distance_ , class DistanceMetric_ = DistanceMetric<Data_, Distance_>>
Prebuilt< Index_, Data_, Distance_ > *	load_vptree_prebuilt (const std::filesystem::path &dir)

template<typename Type_ >
NumericType	get_numeric_type ()

template<typename Index_ >
Index_	count_all_neighbors_without_self (Index_ count)

template<typename Distance_ , typename Index_ >
void	report_all_neighbors (std::vector< std::pair< Distance_, Index_ > > &all_neighbors, std::vector< Index_ > output_indices, std::vector< Distance_ > output_distances, Index_ self)

template<typename Distance_ , typename Index_ >
void	report_all_neighbors (std::vector< std::pair< Distance_, Index_ > > &all_neighbors, std::vector< Index_ > output_indices, std::vector< Distance_ > output_distances)

template<typename Input_ , typename Length_ >
void	quick_save (const std::filesystem::path &path, const Input_ *const contents, const Length_ length)

template<typename Input_ , typename Length_ >
void	quick_load (const std::filesystem::path &path, Input_ *const contents, const Length_ length)

std::string	quick_load_as_string (const std::filesystem::path &path)

Detailed Description

Collection of KNN algorithms.

Typedef Documentation

◆ LoadDistanceMetricFunction

template<typename Data_ , typename Distance_ >

using knncolle::LoadDistanceMetricFunction = std::function<DistanceMetric<Data_, Distance_>* (const std::filesystem::path&)>

Distance loading function. This accepts a path to a directory (see DistanceMetric::save()) and returns a pointer to a DistanceMetric instance.

Template Parameters

Data_	Numeric type for the input data.
Distance_	Numeric type for the output distance, usually floating-point.

◆ LoadPrebuiltFunction

template<typename Index_ , typename Data_ , typename Distance_ >

using knncolle::LoadPrebuiltFunction = std::function<Prebuilt<Index_, Data_, Distance_>* (const std::filesystem::path&)>

Prebuilt loading function. This accepts a directory (see Prebuilt::save()) and returns a pointer to a Prebuilt instance.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Numeric type for the distances, usually floating-point.

◆ NeighborList

template<typename Index_ , typename Distance_ >

using knncolle::NeighborList = std::vector<std::vector<std::pair<Index_, Distance_> > >

List of nearest neighbors for multiple observations. Each entry corresponds to an observation and contains a nested list (i.e., vector) of its neighbors. Each entry of the nested vector is a pair that contains the identity of the neighbor as an observation index (first) and the distance from the observation to the neighbor (second), sorted by increasing distance.

Template Parameters

Index_	Integer type for the indices.
Distance_	Numeric type for the distances, usually floating-point.

Enumeration Type Documentation

◆ NumericType

enum class knncolle::NumericType : char

strong

Standard numeric types, typically returned by get_numeric_type().

Function Documentation

◆ cap_k()

template<typename Index_ >

int knncolle::cap_k	(	int	k,
		Index_	num_observations )

Cap the number of neighbors to use in Searcher::search() with an index i.

Template Parameters

Index_ Integer type for the number of observations.

Parameters

k	Number of nearest neighbors, should be non-negative.
num_observations	Number of observations in the dataset.

Returns: Capped number of neighbors to search for. This is equal to k if it is less than num_observations; otherwise it is equal to num_observations - 1 if num_observations > 0; otherwise it is equal to zero.

◆ cap_k_query()

template<typename Index_ >

int knncolle::cap_k_query	(	int	k,
		Index_	num_observations )

Cap the number of neighbors to use in Searcher::search() with a pointer query.

Template Parameters

Index_ Integer type for the number of observations.

Parameters

k	Number of nearest neighbors, should be non-negative.
num_observations	Number of observations in the dataset.

Returns: Capped number of neighbors to query. This is equal to the smaller of k and num_observations.

◆ count_all_neighbors_without_self()

template<typename Index_ >

Index_ knncolle::count_all_neighbors_without_self ( Index_ count )

Count the number of neighbors from a range-based search, after removing the observation being searched. This is intended for developer use in implementations of Searcher::search_all(), and protects against pathological situations where an observation fails to be detected as its own neighbor.

Parameters

count Number of neighbors within range of the specified observation, including the observation itself.

Returns: count - 1 in most cases, otherwise zero.

◆ custom_save_for_l2normalized_normalized()

template<typename Normalized_ >

std::function< void(const std::filesystem::path &)> & knncolle::custom_save_for_l2normalized_normalized ( )

Define a global function to preserve Normalized_ type information when saving a prebuilt L2-normalized index in Prebuilt::save(). Users should define their own function here to handle an Normalized_ type that is unknown to get_numeric_type(). The action of setting/unsetting the global function is not thread-safe and should be done in a serial section.

The sole argument of the global function is the same dir provided to Prebuilt::save(). If a global function is provided, it is generally expected to write information about Normalized_ to files inside dir. It is recommended that the names of these files should not start with upper-case letters to avoid conflicts with files generated by save().

Template Parameters

Normalized_ Floating-point type for the L2-normalized data.

Returns: Reference to a global function for saving information about Normalized_. By default, no global function is defined. If set, the global function will be called by the Prebuilt::save() method for the L2-normalized Prebuilt subclass.

◆ find_nearest_neighbors()

template<typename Index_ , typename Data_ , typename Distance_ >

NeighborList< Index_, Distance_ > knncolle::find_nearest_neighbors	(	const Prebuilt< Index_, Data_, Distance_ > &	index,
		int	k,
		int	num_threads = 1 )

Find the nearest neighbors within a pre-built index. This is a convenient wrapper around Searcher::search that saves the caller the trouble of writing a loop.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the input data.
Distance_	Numeric type for the distances, usually floating-point.

Parameters

index	A `Prebuilt` index.
k	Number of nearest neighbors. This should be non-negative. Explicitly calling `cap_k()` is not necessary as this is done automatically inside this function.
num_threads	Number of threads to use. The parallelization scheme is defined by `parallelize()`.

Returns: A NeighborList of length equal to the number of observations in index. Each entry contains (up to) the k nearest neighbors for each observation, sorted by increasing distance. The i-th entry is guaranteed to not contain i itself.

◆ find_nearest_neighbors_index_only()

template<typename Index_ , typename Data_ , typename Distance_ >

std::vector< std::vector< Index_ > > knncolle::find_nearest_neighbors_index_only	(	const Prebuilt< Index_, Data_, Distance_ > &	index,
		int	k,
		int	num_threads = 1 )

Find the nearest neighbors within a pre-built search index. Here, only the neighbor indices are returned, not the distances.

Template Parameters

Index_	Integer type for the indices.
Data_	Numeric type for the input and query data.
Distance_	Numeric type for the distances, usually floating-point.

Parameters

index	A `Prebuilt` index.
k	Number of nearest neighbors. This should be non-negative. Explicitly calling `cap_k()` is not necessary as this is done automatically inside this function.
num_threads	Number of threads to use. The parallelization scheme is defined by `parallelize()`.

Returns: A vector of vectors of length equal to the number of observations in index. Each vector contains the indices of (up to) the k nearest neighbors for each observation, sorted by increasing distance. The i-th entry is guaranteed to not contain i itself.

◆ get_numeric_type()

template<typename Type_ >

NumericType knncolle::get_numeric_type ( )

Template Parameters

Type_ Some integer or floating-point type.

Returns: Identity of the numeric type.

This function is intended for developers writing their own Prebuilt::save() methods, where a subclass may have additional template types beyond those required by the Prebuilt template. In such cases, developers can convert the type into a NumericType that can be saved to disk. The corresponding loader function can then read this type information to accurately reconstitute the original Prebuilt object.

◆ load_bruteforce_prebuilt()

template<typename Index_ , typename Data_ , typename Distance_ , class DistanceMetric_ = DistanceMetric<Data_, Distance_>>

Prebuilt< Index_, Data_, Distance_ > * knncolle::load_bruteforce_prebuilt ( const std::filesystem::path & dir )

Load an brute-force search index (i.e., a Prebuilt created by BruteforceBuilder) from its on-disk representation. This is not provided in the registry by default as its depends on the application's choice of DistanceMetric_.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Floating-point type for the distances.
DistanceMetric_	Class implementing the distance metric calculation. This should satisfy the `DistanceMetric` interface.

Parameters

dir	Path to a directory in which a prebuilt brute-force index was saved. Files should have been generated by the `Prebuilt::save()` method of the brute-force `Prebuilt` subclass instance.

Returns: Pointer to a brute-force Prebuilt instance.

◆ load_distance_metric_raw()

template<typename Data_ , typename Distance_ >

DistanceMetric< Data_, Distance_ > * knncolle::load_distance_metric_raw ( const std::filesystem::path & dir )

Load a distance metric from disk into a Distance object.

Template Parameters

Data_	Numeric type for the input data.
Distance_	Numeric type for the output distance, usually floating-point.

Parameters

dir	Path to a directory containing a distance metric that was saved to disk by `DistanceMetric::save()`.

Returns: Pointer to a Distance instance, created from the files at dir. If no loading function is available for the saved distance, a LoadDistanceMetricNotFoundError is thrown.

◆ load_distance_metric_registry()

template<typename Data_ , typename Distance_ >

std::unordered_map< std::string, LoadDistanceMetricFunction< Data_, Distance_ > > & knncolle::load_distance_metric_registry ( )

inline

Template Parameters

Data_	Numeric type for the input data.
Distance_	Numeric type for the output distance, usually floating-point.

Returns: Reference to a global map where the keys are distance metric names (see DistanceMetric::save()) and the values are distance loading functions.

No loading functions are available when the global map is first initialized. Users should call register_load_euclidean_distance() and/or register_load_manhattan_distance() to populate the map with loaders for distances they intend to support.

◆ load_l2normalized_prebuilt()

template<typename Index_ , typename Data_ , typename Distance_ , typename Normalized_ >

Prebuilt< Index_, Data_, Distance_ > * knncolle::load_l2normalized_prebuilt ( const std::filesystem::path & dir )

Load an L2-normalized search index (i.e., a Prebuilt created by L2NormalizedBuilder) from its on-disk representation. This is not provided in the registry by default as its depends on the application's set of the acceptable Normalized_ types. The Normalized_ type in the saved index can be retrived by load_l2normalized_prebuilt_types().

Template Parameters

Index_	Integer type for the indices.
Data_	Numeric type for the input and query data.
Distance_	Numeric type for the distances, usually floating-point.
Normalized_	Floating-point type for the L2-normalized data.

Parameters

dir	Path to a directory in which a prebuilt L2-normalized index was saved. Files should have been generated by the `Prebuilt::save()` method of the L2-normalized `Prebuilt` subclass instance.

Returns: Pointer to an L2-normalized Prebuilt instance.

◆ load_l2normalized_prebuilt_types()

L2NormalizedPrebuiltTypes knncolle::load_l2normalized_prebuilt_types ( const std::filesystem::path & dir )

inline

Parameters

dir	Path to a directory in which a prebuilt L2-normalized index was saved. Files should have been generated by the `Prebuilt::save()` method of the L2-normalized `Prebuilt` subclass instance.

Returns: Template types of the saved instance of a Prebuilt L2-normalized subclass. This is typically used to choose template parameters for load_l2normalized_prebuilt().

◆ load_prebuilt_raw()

template<typename Index_ , typename Data_ , typename Distance_ >

Prebuilt< Index_, Data_, Distance_ > * knncolle::load_prebuilt_raw ( const std::filesystem::path & dir )

Load a neighbor search index from disk into a Prebuilt object. This should be called with the same template parameters as the Prebuilt interface from which Prebuilt::save() was called. It is expected that load_prebuilt_raw() should create an object that is "equivalent" to the object that was saved with save(), i.e., any neighbor search results should be the same across the original and reloaded Prebuilt object.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Numeric type for the distances, usually floating-point.

Parameters

dir	Path to a directory containing a prebuilt index that was saved to disk by `Prebuilt::save()`.

Returns: Pointer to a Prebuilt instance, created from the files at dir. If no loading function can be found for the search algorithm, a LoadPrebuiltNotFoundError is thrown.

◆ load_prebuilt_registry()

template<typename Index_ , typename Data_ , typename Distance_ >

std::unordered_map< std::string, LoadPrebuiltFunction< Index_, Data_, Distance_ > > & knncolle::load_prebuilt_registry ( )

inline

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Numeric type for the distances, usually floating-point.

Returns: Reference to a global map where the keys are algorithm names (see Prebuilt::save()) and the values are the prebuilt loading functions.

No loading functions are available when the global is first initialized. Users should register LoadPrebuiltFunction functions for each algorithm they intend to support. This is facilitated by the load_bruteforce_prebuilt(), load_vptree_prebuilt() and load_l2normalized_prebuilt() helper functions.

◆ load_prebuilt_shared()

template<typename Index_ , typename Data_ , typename Distance_ >

std::shared_ptr< Prebuilt< Index_, Data_, Distance_ > > knncolle::load_prebuilt_shared ( const std::filesystem::path & dir )

Load a neighbor search index from disk into a Prebuilt object. This should be called with the same template parameters as the Prebuilt interface from which Prebuilt::save() was called.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Numeric type for the distances, usually floating-point.

Parameters

dir	Path to a directory containing a prebuilt index that was saved to disk by `Prebuilt::save()`.

Returns: Shared pointer to a Prebuilt instance, created from the files at dir. This uses the return value of load_prebuilt_raw().

◆ load_prebuilt_unique()

template<typename Index_ , typename Data_ , typename Distance_ >

std::unique_ptr< Prebuilt< Index_, Data_, Distance_ > > knncolle::load_prebuilt_unique ( const std::filesystem::path & dir )

Load a neighbor search index from disk into a Prebuilt object. This should be called with the same template parameters as the Prebuilt interface from which Prebuilt::save() was called.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Numeric type for the distances, usually floating-point.

Parameters

dir	Path to a directory containing a prebuilt index that was saved to disk by `Prebuilt::save()`.

Returns: Unique pointer to a Prebuilt instance, created from the files at dir. This uses the return value of load_prebuilt_raw().

◆ load_vptree_prebuilt()

template<typename Index_ , typename Data_ , typename Distance_ , class DistanceMetric_ = DistanceMetric<Data_, Distance_>>

Prebuilt< Index_, Data_, Distance_ > * knncolle::load_vptree_prebuilt ( const std::filesystem::path & dir )

Load a VP-tree search index (i.e., a Prebuilt created by VptreeBuilder) from its on-disk representation. This is not provided in the registry by default as its depends on the application's choice of DistanceMetric_.

Template Parameters

Index_	Integer type for the observation indices.
Data_	Numeric type for the query data.
Distance_	Floating-point type for the distances.
DistanceMetric_	Class implementing the distance metric calculation. This should satisfy the `DistanceMetric` interface.

Parameters

dir	Path to a directory in which a prebuilt VP-tree index was saved. Files should have been generated by the `Prebuilt::save()` method of the VP-tree `Prebuilt` subclass instance.

Returns: Pointer to a VP-tree Prebuilt instance.

◆ parallelize()

template<typename Task_ , class Run_ >

void knncolle::parallelize	(	int	num_workers,
		Task_	num_tasks,
		Run_	run_task_range )

Template Parameters

Task_	Integer type for the number of tasks.
Run_	Function to execute a range of tasks.

Parameters

num_workers	Number of workers.
num_tasks	Number of tasks.
run_task_range	Function to iterate over a range of tasks within a worker.

By default, this is an alias to subpar::parallelize_range(). However, if the KNNCOLLE_CUSTOM_PARALLEL function-like macro is defined, it is called instead. Any user-defined macro should accept the same arguments as subpar::parallelize_range().

◆ quick_load()

template<typename Input_ , typename Length_ >

void knncolle::quick_load	(	const std::filesystem::path &	path,
		Input_ *const	contents,
		const Length_	length )

Read an array from a binary file at path. This is intended for developer use when defining LoadPrebuiltFunction functions for load_prebuilt_registry().

Template Parameters

Input_	Type of values to be read.
Length_	Integer type of the length of the array.

Parameters

path	File path to read from.
contents	Pointer to an array in which to store the contents of `path`. This should have at least `length` addressable elements.
length	Number of elements (not bytes) to be read from `path`. This should be non-negative.

◆ quick_load_as_string()

std::string knncolle::quick_load_as_string ( const std::filesystem::path & path )

inline

Read the contents of a binary file as a string. This is intended for developer use when defining LoadPrebuiltFunction functions for load_prebuilt_registry().

Parameters

path	File path to read from.

Returns: Contents of path as a string.

◆ quick_save()

template<typename Input_ , typename Length_ >

void knncolle::quick_save	(	const std::filesystem::path &	path,
		const Input_ *const	contents,
		const Length_	length )

Saves an array to a binary file at path. This is intended for developer use in implementations of Prebuilt::save().

Template Parameters

Input_	Type of values to be saved.
Length_	Integer type of the length of the array.

Parameters

path	File path to save to. Any directories in the path should already exist.
contents	Pointer to an array of contents to be saved.
length	Length of the array, in terms of the number of elements (not the number of bytes). This should be non-negative.

◆ register_load_euclidean_distance()

template<typename Data_ , typename Distance_ >

void knncolle::register_load_euclidean_distance ( )

Register a loading function for EuclideanDistance using euclidean_distance_save_name.

Template Parameters

Data_	Numeric type for the input data.
Distance_	Numeric type for the output distance, usually floating-point.

◆ register_load_manhattan_distance()

template<typename Data_ , typename Distance_ >

void knncolle::register_load_manhattan_distance ( )

Register a loading function for ManhattanDistance using manhattan_distance_save_name.

Template Parameters

Data_	Numeric type for the input data.
Distance_	Numeric type for the output distance, usually floating-point.

◆ report_all_neighbors() [1/2]

template<typename Distance_ , typename Index_ >

void knncolle::report_all_neighbors	(	std::vector< std::pair< Distance_, Index_ > > &	all_neighbors,
		std::vector< Index_ > *	output_indices,
		std::vector< Distance_ > *	output_distances )

Report the indices and distances of all neighbors in range of an observation of interest. It is assumed that the observation of interest is not detected as its own neighbor, presumably as it does not exist in the original input dataset.

Template Parameters

Distance_	Numeric type for the distances, usually floating-point.
Index_	Integer type for the observation indices.

Parameters

[in]	all_neighbors	Vector of (distance, index) pairs for all neighbors within range of the observation of interest. This may include the observation itself. Note that this will be sorted on output.
[out]	output_indices	Pointer to a vector in which to store the indices of all neighbors in range, sorted by distance. If `NULL`, the indices will not be reported.
[out]	output_distances	Pointer to a vector in which to store the (sorted) distances to all neighbors in range. If `NULL`, the distances will not be reported. Otherwise, on output, this will have the same length as `*output_indices` and contain distances to each of those neighbors.

◆ report_all_neighbors() [2/2]

template<typename Distance_ , typename Index_ >

void knncolle::report_all_neighbors	(	std::vector< std::pair< Distance_, Index_ > > &	all_neighbors,
		std::vector< Index_ > *	output_indices,
		std::vector< Distance_ > *	output_distances,
		Index_	self )

Report the indices and distances of all neighbors in range of an observation of interest. If the observation of interest is detected as its own neighbor, it will be removed from the output vectors.

Template Parameters

Distance_	Numeric type for the distances, usually floating-point.
Index_	Integer type for the observation indices.

Parameters

[in]	all_neighbors	Vector of (distance, index) pairs for all neighbors within range of the observation of interest. This may include the observation itself. Note that this will be sorted on output.
[out]	output_indices	Pointer to a vector in which to store the indices of all neighbors in range, sorted by distance. If `NULL`, the indices will not be reported.
[out]	output_distances	Pointer to a vector in which to store the (sorted) distances to all neighbors in range. If `NULL`, the distances will not be reported. Otherwise, on output, this will have the same length as `*output_indices` and contain distances to each of those neighbors.
	self	Index of the observation of interest, i.e., for which neighbors are to be identified. If present in `all_neighbors`, this will be removed from `output_indices` and `output_distances`.

Classes

Typedefs

Enumerations

Functions

Detailed Description

Typedef Documentation

◆ LoadDistanceMetricFunction

◆ LoadPrebuiltFunction

◆ NeighborList

Enumeration Type Documentation

◆ NumericType

Function Documentation

◆ cap_k()

◆ cap_k_query()

◆ count_all_neighbors_without_self()

◆ custom_save_for_l2normalized_normalized()

◆ find_nearest_neighbors()

◆ find_nearest_neighbors_index_only()

◆ get_numeric_type()

◆ load_bruteforce_prebuilt()

◆ load_distance_metric_raw()

◆ load_distance_metric_registry()

◆ load_l2normalized_prebuilt()

◆ load_l2normalized_prebuilt_types()

◆ load_prebuilt_raw()

◆ load_prebuilt_registry()

◆ load_prebuilt_shared()

◆ load_prebuilt_unique()

◆ load_vptree_prebuilt()

◆ parallelize()

◆ quick_load()

◆ quick_load_as_string()

◆ quick_save()

◆ register_load_euclidean_distance()

◆ register_load_manhattan_distance()

◆ report_all_neighbors() [1/2]

◆ report_all_neighbors() [2/2]