/usr/include/flann/flann.h is in libflann-dev 1.7.1-4.
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* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef FLANN_H_
#define FLANN_H_
#include "defines.h"
#ifdef __cplusplus
extern "C"
{
using namespace flann;
#endif
struct FLANNParameters
{
enum flann_algorithm_t algorithm; /* the algorithm to use */
/* search time parameters */
int checks; /* how many leafs (features) to check in one search */
float cb_index; /* cluster boundary index. Used when searching the kmeans tree */
float eps; /* eps parameter for eps-knn search */
/* kdtree index parameters */
int trees; /* number of randomized trees to use (for kdtree) */
int leaf_max_size;
/* kmeans index parameters */
int branching; /* branching factor (for kmeans tree) */
int iterations; /* max iterations to perform in one kmeans cluetering (kmeans tree) */
enum flann_centers_init_t centers_init; /* algorithm used for picking the initial cluster centers for kmeans tree */
/* autotuned index parameters */
float target_precision; /* precision desired (used for autotuning, -1 otherwise) */
float build_weight; /* build tree time weighting factor */
float memory_weight; /* index memory weigthing factor */
float sample_fraction; /* what fraction of the dataset to use for autotuning */
/* LSH parameters */
unsigned int table_number_; /** The number of hash tables to use */
unsigned int key_size_; /** The length of the key in the hash tables */
unsigned int multi_probe_level_; /** Number of levels to use in multi-probe LSH, 0 for standard LSH */
/* other parameters */
enum flann_log_level_t log_level; /* determines the verbosity of each flann function */
long random_seed; /* random seed to use */
};
typedef void* FLANN_INDEX; /* deprecated */
typedef void* flann_index_t;
FLANN_EXPORT extern struct FLANNParameters DEFAULT_FLANN_PARAMETERS;
/**
Sets the log level used for all flann functions (unless
specified in FLANNParameters for each call
Params:
level = verbosity level
*/
FLANN_EXPORT void flann_log_verbosity(int level);
/**
* Sets the distance type to use throughout FLANN.
* If distance type specified is MINKOWSKI, the second argument
* specifies which order the minkowski distance should have.
*/
FLANN_EXPORT void flann_set_distance_type(enum flann_distance_t distance_type, int order);
/**
Builds and returns an index. It uses autotuning if the target_precision field of index_params
is between 0 and 1, or the parameters specified if it's -1.
Params:
dataset = pointer to a data set stored in row major order
rows = number of rows (features) in the dataset
cols = number of columns in the dataset (feature dimensionality)
speedup = speedup over linear search, estimated if using autotuning, output parameter
index_params = index related parameters
flann_params = generic flann parameters
Returns: the newly created index or a number <0 for error
*/
FLANN_EXPORT flann_index_t flann_build_index(float* dataset,
int rows,
int cols,
float* speedup,
struct FLANNParameters* flann_params);
FLANN_EXPORT flann_index_t flann_build_index_float(float* dataset,
int rows,
int cols,
float* speedup,
struct FLANNParameters* flann_params);
FLANN_EXPORT flann_index_t flann_build_index_double(double* dataset,
int rows,
int cols,
float* speedup,
struct FLANNParameters* flann_params);
FLANN_EXPORT flann_index_t flann_build_index_byte(unsigned char* dataset,
int rows,
int cols,
float* speedup,
struct FLANNParameters* flann_params);
FLANN_EXPORT flann_index_t flann_build_index_int(int* dataset,
int rows,
int cols,
float* speedup,
struct FLANNParameters* flann_params);
/**
* Saves the index to a file. Only the index is saved into the file, the dataset corresponding to the index is not saved.
*
* @param index_id The index that should be saved
* @param filename The filename the index should be saved to
* @return Returns 0 on success, negative value on error.
*/
FLANN_EXPORT int flann_save_index(flann_index_t index_id,
char* filename);
FLANN_EXPORT int flann_save_index_float(flann_index_t index_id,
char* filename);
FLANN_EXPORT int flann_save_index_double(flann_index_t index_id,
char* filename);
FLANN_EXPORT int flann_save_index_byte(flann_index_t index_id,
char* filename);
FLANN_EXPORT int flann_save_index_int(flann_index_t index_id,
char* filename);
/**
* Loads an index from a file.
*
* @param filename File to load the index from.
* @param dataset The dataset corresponding to the index.
* @param rows Dataset tors
* @param cols Dataset columns
* @return
*/
FLANN_EXPORT flann_index_t flann_load_index(char* filename,
float* dataset,
int rows,
int cols);
FLANN_EXPORT flann_index_t flann_load_index_float(char* filename,
float* dataset,
int rows,
int cols);
FLANN_EXPORT flann_index_t flann_load_index_double(char* filename,
double* dataset,
int rows,
int cols);
FLANN_EXPORT flann_index_t flann_load_index_byte(char* filename,
unsigned char* dataset,
int rows,
int cols);
FLANN_EXPORT flann_index_t flann_load_index_int(char* filename,
int* dataset,
int rows,
int cols);
/**
Builds an index and uses it to find nearest neighbors.
Params:
dataset = pointer to a data set stored in row major order
rows = number of rows (features) in the dataset
cols = number of columns in the dataset (feature dimensionality)
testset = pointer to a query set stored in row major order
trows = number of rows (features) in the query dataset (same dimensionality as features in the dataset)
indices = pointer to matrix for the indices of the nearest neighbors of the testset features in the dataset
(must have trows number of rows and nn number of columns)
nn = how many nearest neighbors to return
flann_params = generic flann parameters
Returns: zero or -1 for error
*/
FLANN_EXPORT int flann_find_nearest_neighbors(float* dataset,
int rows,
int cols,
float* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_float(float* dataset,
int rows,
int cols,
float* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_double(double* dataset,
int rows,
int cols,
double* testset,
int trows,
int* indices,
double* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_byte(unsigned char* dataset,
int rows,
int cols,
unsigned char* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_int(int* dataset,
int rows,
int cols,
int* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
/**
Searches for nearest neighbors using the index provided
Params:
index_id = the index (constructed previously using flann_build_index).
testset = pointer to a query set stored in row major order
trows = number of rows (features) in the query dataset (same dimensionality as features in the dataset)
indices = pointer to matrix for the indices of the nearest neighbors of the testset features in the dataset
(must have trows number of rows and nn number of columns)
dists = pointer to matrix for the distances of the nearest neighbors of the testset features in the dataset
(must have trows number of rows and 1 column)
nn = how many nearest neighbors to return
flann_params = generic flann parameters
Returns: zero or a number <0 for error
*/
FLANN_EXPORT int flann_find_nearest_neighbors_index(flann_index_t index_id,
float* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_index_float(flann_index_t index_id,
float* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_index_double(flann_index_t index_id,
double* testset,
int trows,
int* indices,
double* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_index_byte(flann_index_t index_id,
unsigned char* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_find_nearest_neighbors_index_int(flann_index_t index_id,
int* testset,
int trows,
int* indices,
float* dists,
int nn,
struct FLANNParameters* flann_params);
/**
* Performs an radius search using an already constructed index.
*
* In case of radius search, instead of always returning a predetermined
* number of nearest neighbours (for example the 10 nearest neighbours), the
* search will return all the neighbours found within a search radius
* of the query point.
*
* The check parameter in the FLANNParameters below sets the level of approximation
* for the search by only visiting "checks" number of features in the index
* (the same way as for the KNN search). A lower value for checks will give
* a higher search speedup at the cost of potentially not returning all the
* neighbours in the specified radius.
*
* The cores parameter in the FLANNParameters below sets the number of cores
* that will be used for the radius search, in case Intel TBB is present on
* the system and FLANN is built with multicore support on. Auto core selection
* can be achieved by setting the number of cores to -1.
*/
FLANN_EXPORT int flann_radius_search(flann_index_t index_ptr, /* the index */
float* query, /* query point */
int* indices, /* array for storing the indices found (will be modified) */
float* dists, /* similar, but for storing distances */
int max_nn, /* size of arrays indices and dists */
float radius, /* search radius (squared radius for euclidian metric) */
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_radius_search_float(flann_index_t index_ptr, /* the index */
float* query, /* query point */
int* indices, /* array for storing the indices found (will be modified) */
float* dists, /* similar, but for storing distances */
int max_nn, /* size of arrays indices and dists */
float radius, /* search radius (squared radius for euclidian metric) */
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_radius_search_double(flann_index_t index_ptr, /* the index */
double* query, /* query point */
int* indices, /* array for storing the indices found (will be modified) */
double* dists, /* similar, but for storing distances */
int max_nn, /* size of arrays indices and dists */
float radius, /* search radius (squared radius for euclidian metric) */
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_radius_search_byte(flann_index_t index_ptr, /* the index */
unsigned char* query, /* query point */
int* indices, /* array for storing the indices found (will be modified) */
float* dists, /* similar, but for storing distances */
int max_nn, /* size of arrays indices and dists */
float radius, /* search radius (squared radius for euclidian metric) */
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_radius_search_int(flann_index_t index_ptr, /* the index */
int* query, /* query point */
int* indices, /* array for storing the indices found (will be modified) */
float* dists, /* similar, but for storing distances */
int max_nn, /* size of arrays indices and dists */
float radius, /* search radius (squared radius for euclidian metric) */
struct FLANNParameters* flann_params);
/**
Deletes an index and releases the memory used by it.
Params:
index_id = the index (constructed previously using flann_build_index).
flann_params = generic flann parameters
Returns: zero or a number <0 for error
*/
FLANN_EXPORT int flann_free_index(flann_index_t index_id,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_free_index_float(flann_index_t index_id,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_free_index_double(flann_index_t index_id,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_free_index_byte(flann_index_t index_id,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_free_index_int(flann_index_t index_id,
struct FLANNParameters* flann_params);
/**
Clusters the features in the dataset using a hierarchical kmeans clustering approach.
This is significantly faster than using a flat kmeans clustering for a large number
of clusters.
Params:
dataset = pointer to a data set stored in row major order
rows = number of rows (features) in the dataset
cols = number of columns in the dataset (feature dimensionality)
clusters = number of cluster to compute
result = memory buffer where the output cluster centers are storred
index_params = used to specify the kmeans tree parameters (branching factor, max number of iterations to use)
flann_params = generic flann parameters
Returns: number of clusters computed or a number <0 for error. This number can be different than the number of clusters requested, due to the
way hierarchical clusters are computed. The number of clusters returned will be the highest number of the form
(branch_size-1)*K+1 smaller than the number of clusters requested.
*/
FLANN_EXPORT int flann_compute_cluster_centers(float* dataset,
int rows,
int cols,
int clusters,
float* result,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_compute_cluster_centers_float(float* dataset,
int rows,
int cols,
int clusters,
float* result,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_compute_cluster_centers_double(double* dataset,
int rows,
int cols,
int clusters,
double* result,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_compute_cluster_centers_byte(unsigned char* dataset,
int rows,
int cols,
int clusters,
float* result,
struct FLANNParameters* flann_params);
FLANN_EXPORT int flann_compute_cluster_centers_int(int* dataset,
int rows,
int cols,
int clusters,
float* result,
struct FLANNParameters* flann_params);
#ifdef __cplusplus
}
#include "flann.hpp"
#endif
#endif /*FLANN_H_*/
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