FLANN - Fast Library for Approximate Nearest Neighbors User Manual
Contents
1. 3 4 Using FLANN from python FLANN can be used from python programs using the python bindings dis tributed with the library The python bindings can be installed on a system using the distutils script provided setup py by running the following com mand in the build python directory python setup py install The python bindings also require the numpy package to be installed To use the python FLANN bindings the package pyflann must be imported see the python example in section E1 This package contains a class called FLANN that handles the nearest neighbor search operations This class con taing the following methods 19 result dists flann_search dataset testset 5 struct checks 128 algorithm def build_index self dataset kwargs This method builds and internally stores an index to be used for future nearest neighbor matchings It erases any previously stored index so in order to work with multiple indexes multiple instances of the FLANN class must be used The dataset argument must be a 2D numpy array or a matrix The rest of the arguments that can be passed to the method are the same as those used in the build_params structure from section 3 3 1 Similar to the MATLAB version the index can be created using manually specified parameters or the parameters can be automatically computed by specifying the target_precision build_weight and memory_weight ar guments The method re2. 2 COMPOSITE 3 SAVED 254 AUTOTUNED 255 3 enum flann_centers_init_t CENTERS_RANDOM 0 CENTERS_GONZALES CENTERS _KMEANSPP 1 2 F The algorithm field is used to manually select the type of index used The centers_init field specifies how to choose the inital cluster centers when performing the hierarchical k means clustering in case the algorithm used is k means CENTERS_RANDOM chooses the initial centers randomly CENTERS_GONZALES chooses the initial centers to be spaced apart from each other by using Gonzales algorithm and CENTERS_KMEANSPP chooses the initial centers using the algorithm proposed in AVO7 The fields checks cb_index trees branching iterations target_precision build_weight memory_weight and sample_fraction have the same mean ing as described in The random_seed field contains the random seed useed to initialize the random number generator The field log_level controls the verbosity of the messages generated by the FLANN library functions It can take the following values enum flann_log_level_t LOG_NONE 0 LOG_FATAL i LOG_ERROR 2 LOG_WARN 3 LOG_INFO 4 11 3 2 2 flann_find_nearest_neighbors_index int flann_find_nearest_neighbors_index FLANN_INDEX index_id float testset int trows int indices float dists int nn int checks struct FLANNParameters flann_params This function searches for the nearest neighbors of the testset points us
3. autotunedindexparams float target_precision 0 9 float build_weight 0 01 float memory_weight 0 float sample_fraction 0 1 33 target_precision Is a number between 0 and 1 specifying the percentage of the approximate nearest neighbor searches that return the exact nearest neighbor Using a higher value for this parameter gives more accurate results but the search takes longer The optimum value usually depends on the application build_weight Specifies the importance of the index build time raported to the nearest neighbor search time In some applications it s accept able for the index build step to take a long time if the subsequent searches in the index can be performed very fast In other applica tions it s required that the index be build as fast as possible even if that leads to slightly longer search times memory_weight Is used to specify the tradeoff between time index build time and search time and memory used by the index A value less than 1 gives more importance to the time spent and a value greater than 1 gives more importance to the memory usage sample fraction Is a number between 0 and 1 indicating what fraction of the dataset to use in the automatic parameter configuration al gorithm Running the algorithm on the full dataset gives the most accurate results but for very large datasets can take longer than de sired In such case using just a fraction of the data helps speeding up this algorithm while
4. o flann_example_cpp lflann_cpp where FLANN_ROOT is the library main directory The following sections describe the public C API 3 1 1 flann Index The FLANN nearest neighbor index class This class is used to abstract different types of nearest neighbor search indexes namespace flann template lt typename Distance gt class Index typedef typename Distance ElementType ElementType typedef typename Distance ResultType DistanceType public Index const Matrix lt ElementType gt amp features const IndexParams amp params Index void buildIndex void knnSearch const Matrix lt ElementType gt amp queries Matrix lt int gt amp indices Matrix lt DistanceType gt amp dists int knn const SearchParams amp params int radiusSearch const Matrix lt ElementType gt amp query Matrix lt int gt amp indices Matrix lt DistanceType gt amp dists float radius const SearchParams amp params void save std string filename int veclen const int size const const IndexParams getIndexParameters flann Index Index Constructs a nearest neighbor search index for a given dataset Index const Matrix lt ElementType gt amp features const IndexParams amp params features Matrix containing the features points to index The size of the ma trix is num_features x dimensionality params Structure containing the index parameters The type of index that will be construct
5. still giving good approximations of the optimum parameters SavedIndexParams This object type is used for loading a previously saved index from the disk struct SavedIndexParams public IndexParams SavedIndexParams std string filename 3 filename The filename in which the index was saved 3 1 2 flann Index buildIndex Constructs the nearest neighbor search index using the parameters provided to the constructor with the exception of saved index type void buildIndex 3 1 3 flann Index knnSearch Performs a K nearest neighbor search for a given query point using the index void Index knnSearch const Matrix lt ElementType gt amp queries Matrix lt int gt amp indices Matrix lt DistanceType gt amp dists int knn const SearchParams amp params query Matrix containing the query points Size of matrix is num_queries x dimentionality indices Matrix that will contain the indices of the K nearest neighbors found size should be at least num_queries x knn dists Matrix that will contain the distances to the K nearest neighbors found size should be at least num_queries x knn The distance values are computed by the distance function used see flann set_distance_type below for example in the case of euclidean distance function this will contain the squared euclidean distances knn Number of nearest neighbors to search for params Search parameters Structure cont
6. there are bindings provided for the following data types unsigned char int float and double For each of the functions below there is a corresponding version for each of the for data types for example for the function flan_index_t flann_build_index float dataset int rows int cols float speedup struct FLANNParameters flann_params there are also the following versions flan_index_t flann_build_index_byte unsigned char dataset int rows int cols float speedup struct FLANNParameters flann_params flan_index_t flann_build_index_int int dataset int rows int cols float speedup struct FLANNParameters flann_params flan_index_t flann_build_index_float float dataset int rows int cols float speedup struct FLANNParameters flann_params flan_index_t flann_build_index_double double dataset int rows int cols float speedup struct FLANNParameters flann_params 3 2 1 flann_build_index flan_index_t flann_build_index float dataset int rows int cols float speedup struct FLANNParameters flann_params This function builds an index and return a reference to it The arguments expected by this function are as follows dataset rows and cols are used to specify the input dataset of points dataset is a pointer to a rows x cols matrix stored in row major order speedup is used to return the approximate speedup over linear search achieved when usi
7. 1 squared euclidean distance MANHATTAN MINKOWSKI HIK HELLINGER cs KL ANaAnNWN chi square kullback leibler divergence 33 order Used in for the MINKOWSKI distance type to choose the order of the Minkowski distance 13 3 2 9 flann_compute_cluster_centers Performs hierarchical clustering of a set of points see 3 1 6 int flann_compute_cluster_centers float dataset int rows int cols int clusters float result struct FLANNParameters flann_params See section 1 1 for an example of how to use the C C bindings 3 3 Using FLANN from MATLAB The FLANN library can be used from MATLAB through the following wrapper functions flann_build_index flann_search flann_save_index flann_load_index flann_set_distance_type and flann_free_index The flann_build_index function creates a search index from the dataset points flann_search uses this index to perform nearest neighbor searches flann_save_index and flann_load_index can be used to save and load an index to from disk flann_set_distance_type is used to set the distance type to be used when building an index and flann_free_index deletes the index and releases the memory it uses The following sections describe in more detail the FLANN matlab wrapper functions and show examples of how they may be used 3 3 1 flann_build_index This function creates a search index from the initial dataset of points index used later fo
8. FLANN Fast Library for Approximate Nearest Neighbors User Manual Marius Muja mariusm cs ubc ca David Lowe loweQ cs ubc ca November 26 2009 1 Introduction We can define the nearest neighbor search NSS problem in the following way given a set of points P p1 po Pn in a metric space X these points must be preprocessed in such a way that given a new query point q X finding the point in P that is nearest to q can be done quickly The problem of nearest neighbor search is one of major importance in a variety of applications such as image recognition data compression pattern recognition and classification machine learning document retrieval systems statistics and data analysis However solving this problem in high dimensional spaces seems to be a very difficult task and there is no algorithm that performs significantly better than the standard brute force search This has lead to an increasing interest in a class of algorithms that perform approximate nearest neighbor searches which have proven to be a good enough approximation in most practical applications and in most cases orders of magnitude faster that the algorithms performing the exact searches FLANN Fast Library for Approximate Nearest Neighbors is a library for performing fast approximate nearest neighbor searches FLANN is written in the C programming language FLANN can be easily used in many contexts through the C MATLAB and Python bindings provided wi
9. S_KMEANSPP picks the initial centers using the algorithm suggested in AV07 cb_index This parameter cluster boundary index influences the way exploration is performed in the hierarchical kmeans tree When cb_index is zero the next kmeans domain to be explored is choosen to be the one with the closest center A value greater then zero also takes into account the size of the domain KDTreeSingleIndexParams When passing an object of this type the index will contain a single kd tree optimized for searching lower dimen sionality data for example 3D point clouds struct KDTreeSingleIndexParams public IndexParams KDTreeSingleIndexParams int max_leaf_size 10 3 max_leaf_size The maximum number of points to have in a leaf for not branching the tree any more CompositeIndexParams When using a parameters object of this type the index created combines the randomized kd trees and the hierarchical k means tree struct CompositeIndexParams public IndexParams CompositeIndexParams int trees 4 int branching 32 int iterations 11 flann_centers_init_t centers_init CENTERS_RANDOM float cb_index 0 2 F AutotunedIndexParams When passing an object of this type the index created is automatically tuned to offer the best performance by choosing the optimal index type randomized kd trees hierarchical kmeans linear and parameters for the dataset provided struct AutotunedIndexParams public IndexParams
10. aining parameters used during search SearchParameters struct SearchParams SearchParams int checks 32 float eps 0 bool sorted true F3 checks specifies the maximum leafs to visit when searching for neigh bours A higher value for this parameter would give better search precision but also take more time For all leafs to be checked use the value CHECKS_UNLIMITED If automatic configuration was used when the index was created the number of checks required to achieve the specified precision was also computed to use that value specify CHECKS _AUTOTUNED eps Search for eps approximate neighbors only used by KDTreeSingleIn dex sorted Used only by radius search specifies if the neighbors returned should be sorted by distance 3 1 4 flann Index radiusSearch Performs a radius nearest neighbor search for a given query point int Index radiusSearch const Matrix lt ElementType gt amp query Matrix lt int gt amp indices Matrix lt DistanceType gt amp dists float radius const SearchParams amp params query The query point indices Vector that will contain the indices of the points found within the search radius in decreasing order of the distance to the query point If the number of neighbors in the search radius is bigger than the size of this vector the ones that don t fit in the vector are ignored dists Vector that will contain the distances to the points found within the search radiu
11. ance_type order 0 This function part of the pyflann module sets the distance type to be used See for possible values of the distance_type See section 1 1 for an example of how to use the Python bindings References AV07 D Arthur and S Vassilvitskii k means the advantages of careful seeding In Proceedings of the eighteenth annual ACM SIAM sympo sium on Discrete algorithms pages 1027 1035 Society for Industrial and Applied Mathematics Philadelphia PA USA 2007 21
12. ann FLANN result dists flann nn dataset testset 5 algorithm kmeans branching 32 iterations 7 checks 16 2 Downloading and compiling FLANN FLANN can be downloaded from the following address http www cs ubc ca mariusm flann After downloading and unpacking the following files and directories should be present bin directory various for scripts and binary files doc directory containg this documentation examples directory containg examples of using FLANN src directory containg the source files test directory containg unit tests for FLANN To compile the flann library the CMakd build system is required Below is an example of how the FLANN library can be compiled on Linux replace x y with the corresponding version number cd flann x y src BUILD_TYPE release INSTALL_PREFIX lt some directory gt make install 3 Using FLANN 3 1 Using FLANN from C The core of the FLANN library is written in C To make use of the full power and flexibility of the templated code one should use the C bindings http www cmake org if possible To use the C bindings the library header file flann hpp must be included and the library libflann_cpp so for linking dynamically or the libflann_cpp _s a for linking statically must be linked in An example of the compile command that must be used will look something like this g flann_example cpp I FLANN_ROOT include L FLANN_ROOT 1ib
13. ation was used when the index was created the number of checks required to achieve the specified precision is also computed In such case the parameters structure returned by the flann_build_index function can be passed directly to the flann_search function The function returns two matrices each of size kx m The first one contains in which each column the indexes in the dataset matrix of the k nearest neighbors of the corresponding point from testset while the second one contains the corresponding distances The second matrix can be omitted when making the call if the distances to the nearest neighbors are not needed For the case where a single search will be performed with each index the flann_search function accepts the dataset instead of the index as first argu ment in which case the index is created searched and then deleted in one step In this case the parameters structure passed to the flann_search function must also contain the fields of the build_params structure that would normally be passed to the flann_build_index function if the index was build separately result dists flann_search dataset testset k parameters 3 3 3 flann_save_index This function saves an index to a file so that it can be reused at a later time without the need to recompute it Only the index will be saved to the file not also the data points for which the index was created so for the index to be reused the data points must be saved separatel
14. ding up this algorithm while still giving good approximations of the optimum parameters Randomized kd trees index in case the algorithm field is kdtree the following fields should also be present trees the number of randomized kd trees to create Hierarchical k means index in case the algorithm type is means the following fields should also be present 15 branching the branching factor to use for the hierarchical kmeans tree cre ation While kdtree is always a binary tree each node in the kmeans tree may have several branches depending on the value of this parameter iterations the maximum number of iterations to use in the kmeans clustering stage when building the kmeans tree A value of 1 used here means that the kmeans clustering should be performed until convergence centers_init the algorithm to use for selecting the initial centers when per forming a kmeans clustering step The possible values are random picks the initial cluster centers randomly gonzales picks the initial centers using the Gonzales algorithm and kmeanspp picks the initial centers using the algorithm suggested in If this parameters is omitted the default value is random cb_index this parameter cluster boundary index influences the way explo ration is performed in the hierarchical kmeans tree When cb_index is zero the next kmeans domain to be explored is choosen to be the one with the closest cente
15. ed depends on the type of this parameter The possible parameter types are LinearIndexParams When passing an object of this type the index will perform a linear brute force search struct LinearIndexParams public IndexParams 3 KDTreeIndexParams When passing an object of this type the index constructed will consist of a set of randomized kd trees which will be searched in parallel struct KDTreeIndexParams public IndexParams KDTreeIndexParams int trees 4 trees The number of parallel kd trees to use Good values are in the range 1 16 K MeansIndexParams When passing an object of this type the index constructed will be a hierarchical k means tree struct KMeansIndexParams public IndexParams KMeansIndexParams int branching 32 int iterations 11 flann_centers_init_t centers_init CENTERS_RANDOM float cb_index 0 2 3 branching The branching factor to use for the hierarchical k means tree iterations The maximum number of iterations to use in the k means clustering stage when building the k means tree If a value of 1 is used here it means that the k means clustering should be iterated until convergence centers_init The algorithm to use for selecting the initial centers when performing a k means clustering step The possible values are CEN TERS_RANDOM picks the initial cluster centers randomly CEN TERS_GONZALES picks the initial centers using Gonzales algo rithm and CENTER
16. file dataset dat float dataset read_points dataset dat amp rows amp cols float testset read_points testset dat amp t_rows amp t_cols points in dataset and testset should have the same dimensionality assert cols t_cols number of nearest neighbors to search int nn 3 allocate memory for the nearest neighbors indices int result int malloc t_rows nn sizeof int allocate memory for the distances float dists float malloc t_rows nn sizeof float index parameters are stored here struct FLANNParameters p DEFAULT_FLANN_PARAMETERS p algorithm AUTOTUNED or KDTREE KMEANS p target_precision 0 9 want 90 target precision compute the 3 nearest neighbors of each point in the testset flann_find_nearest_neighbors dataset rows cols testset t_rows result dists nn amp p free dataset free testset free result free dists return 0 MATLAB create random dataset and test set dataset single rand 128 10000 testset single rand 128 1000 define index and search parameters params algorithm kdtree params trees 8 params checks 64 perform the nearest neighbor search result dists flann_search dataset testset 5 params Python from pyflann import from numpy import from numpy random import dataset rand 10000 128 testset rand 1000 128 fl
17. he memory used by it flann_free_index index 3 3 7 Examples Let s look at a few examples showing how the functions described above are used 3 3 8 Example 1 In this example the index is constructed using automatic parameter estimation requesting 90 as desired precision and using the default values for the build time and memory usage factors The index is then used to search for the nearest neighbors of the points in the testset matrix and finally the index is deleted dataset testset single rand 128 10000 single rand 128 1000 build_params target_precision 0 9 build_params build_weight 0 01 build_params memory_weight 0 index parameters flann_build_index dataset build_params 18 result flann_search index testset 5 parameters flann_free_index index 3 3 9 Example 2 In this example the index constructed with the parameters specified manually dataset testset single rand 128 10000 single rand 128 1000 index flann_build_index dataset struct algorithm kdtree trees 8 result flann_search index testset 5 struct checks 128 flann_free_index index 3 3 10 Example 3 In this example the index creation searching and deletion are all performed in one step dataset testset single rand 128 10000 single rand 128 1000 kmeans branching 64 iterations 5
18. ic int checks number of features to check sets the level of approximation FLANNParameters flann_params 12 This function performs a radius search to single query point The indices of the neighbors found and the distances to them are stored in the indices and dists arrays The max_nn parameter sets the limit of the neighbors that will be returned the size of the indices and dists arrays must be at least max_nn 3 2 5 flann_save_index int flann_save_index flann_index_t index_id char filename This function saves an index to a file The dataset for which the index was built is not saved with the index 3 2 6 flann_load_index flann_index_t flann_load_index char filename float dataset int rows int cols This function loads a previously saved index from a file Since the dataset is not saved with the index it must be provided to this function 3 2 7 flann_free_index int flann_free_index FLANN_INDEX index_id struct FLANNParameters flann_params This function deletes a previously constructed index and frees all the memory used by it 3 2 8 flann_set_distance_type This function chooses the distance function to use when computing distances between data points void flann_set_distance_type enum flann_distance_t distance_type int order distance_type The distance type to use Possible values are enum flann_distance_t EUCLIDEAN
19. ing an index already build and referenced by index_id The testset is a ma trix stored in row major format with trows rows and the same number of columns as the dimensionality of the points used to build the index The func tion computes nn nearest neighbors for each point in the testset and stores them in the indices matrix which is a trows x nn matrix stored in row major format The memory for the result matrix must be allocated before the flann_find_nearest_neighbors_index function is called The distances to the nearest neighbors found are stored in the dists matrix The checks param eter specifies how many tree traversals should be performed during the search 3 2 3 flann_find_nearest_neighbors 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 This function is similar to the flann_find_nearest_neighbors_index func tion but instread of using a previously constructed index it constructs the index does the nearest neighbor search and deletes the index in one step 3 2 4 flann_radius_search int flann_radius_search FLANN_INDEX index_ptr float query query point int indices array for storing the indices float dists similar but for storing distances int max_nn size of arrays indices and dists float radius search radius squared radius for euclidian metr
20. ng the automatic index and parameter configuration see section 3 3 1 flann_params is astructure containing the parameters passed to the function This structure is defined as follows struct FLANNParameters enum flann_algorithm_t algorithm the algorithm to use search parameters int checks how many leafs features to check in one search float cb_index cluster boundary index Used when searching the kmeans tree kdtree index parameters int trees number of randomized trees to use for kdtree 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 10 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 other parameters enum flann_log_level_t log_level determines the verbosity of each flann function long random_seed random seed to use 33 The algorithm and centers_init fields can take the following values enum flann_algorithm_t LINEAR 0 KDTREE 1 KMEANS
21. r A value greater then zero also takes into account the size of the domain Composite index in case the algorithm type is composite the fields from both randomized kd tree index and hierarchical k means index should be present The flann_build_index function returns the newly created index the parameters used for creating the index and if automatic configuration was used an esti mation of the speedup over linear search that is achieved when searching the index Since the parameter estimation step is costly it is possible to save the computed parameters and reuse them the next time an index is created from similar data points coming from the same distribution 3 3 2 flann_search This function performs nearest neighbor searches using the index already cre ated result dists flann_search index testset k parameters The arguments required by this function are index the index returned by the flann_build_index function testset ad xm matrix containing m test points whose k nearest neighbors need to be found k the number of nearest neighbors to be returned for each point from testset 16 parameters structure containing the search parameters Currently it has only one member parameters checks denoting the number of times the tree s in the index should be recursively traversed A higher value for this parameter would give better search precision but also take more time If automatic configur
22. r fast nearest neighbor searches in the dataset index parameters speedup flann_build_index dataset build_params The arguments passed to the flann_build_index function have the following meaning dataset is a dx n matrix containing n d dimensional points build_params is a MATLAB structure containing the parameters passed to the function The build_params is used to specify the type of index to be built and the index parameters These have a big impact on the performance of the new search index nearest neighbor search time and on the time and memory required to build the index The optimum parameter values depend on the dataset characteristics number of dimensions distribution of points in the dataset and on the application domain desired precision for the approximate nearest neighbor searches The build_params argument is a structure that contains one or more of the following fields 14 algorithm the algorithm to use for building the index The possible val ues are linear kdtree kmeans composite or autotuned The linear option does not create any index it uses brute force search in the original dataset points kdtree creates one or more random ized kd trees kmeans creates a hierarchical kmeans clustering tree gt composite is a mix of both kdtree and kmeans trees and the autotuned automatically determines the best index and optimum parameters
23. s radius The search radius params Search parameters The method returns the number of nearest neighbors found 3 1 5 flann Index save Saves the index to a file void Index save std string filename filename The file to save the index to 3 1 6 flann hierarchicalClustering Clusters the given points by constructing a hierarchical k means tree and choos ing a cut in the tree that minimizes the clusters variance template lt typename Distance gt int hierarchicalClustering const Matrix lt typename Distance ElementType gt amp features Matrix lt typename Distance ResultType gt amp centers const KMeansIndexParams amp params Distance d Distance features The points to be clustered centers The centers of the clusters obtained The number of rows in this matrix represents the number of clusters desired However because of the way the cut in the hierarchical tree is choosen the number of clusters computed will be the highest number of the form branching 1 k 1 that s lower than the number of clusters desired where branching is the tree s branching factor see description of the KMeansIndexParams params Parameters used in the construction of the hierarchical k means tree The function returns the number of clusters computed 3 2 Using FLANN from C FLANN can be used in C programs through the C bindings provided with the library Because there is not template support in C
24. th the library 1 1 Quick Start This section contains small examples of how to use the FLANN library from different programming languages C C MATLAB and Python e C file flann_example cpp include lt flann flann hpp gt include lt flann io hdf5 h gt include lt stdio h gt int main int argc char argv int nn 3 ann Matrix lt float gt dataset ann Matrix lt float gt query ann load_from_file dataset dataset hdf5 dataset ann load_from_file query dataset hdf5 query Fh Fh Fh Fh flann Matrix lt int gt indices new int query rows nn query rows nn flann Matrix lt float gt dists new float query rows nn query rows nn construct an randomized kd tree index using 4 kd trees flann Index lt flann L2 lt float gt gt index dataset flann KDTreeIndexParams 4 index buildIndex do a knn search using 128 checks index knnSearch query indices dists nn flann SearchParams 128 flann save_to_file indices result hdf5 result dataset free query free indices free dists free return 0 C file flann_example c include flann h include lt stdio h gt include lt assert h gt Function that reads a dataset float read_points char filename int rows int cols int main int argc char argv int rows cols int t_rows t_cols float speedup read dataset points from
25. turns a dictionary containing the parameters used to con struct the index In case automatic parameter selection is used the dictio nary will also contain the number of checks required to achieve the desired target precision and an estimation of the speedup over linear search that the library will provide def nn_index self testset num neighbors 1 kwargs This method searches for the num_neighbors nearest neighbors of each point in testset using the index computed by build_index Additionally a parameter called checks denoting the number of times the index tree s should be recursivelly searched must be given Example from pyflann import from numpy import from numpy random import rand 10000 128 rand 1000 128 dataset testset flann FLANN params flann build_index dataset target_precision 0 9 log_level ninfo print params result dists flann nn_index testset 5 checks params checks def nn self dataset testset numneighbors 1 kwargs This method builds the index performs the nearest neighbor search and deleted the index all in one step def save_index self filename This method saves the index to a file The dataset form which the index was build is not saved def load_index self filename pts Load the index from a file The dataset for which the index was build must also be provided since is not saved with the index 20 def set_distance_type dist
26. using a cross validation technique Autotuned index in case the algorithm field is autotuned the following fields should also be present target_precision is a number between 0 and 1 specifying the percentage of the approximate nearest neighbor searches that return the exact nearest neighbor Using a higher value for this parameter gives more accurate results but the search takes longer The optimum value usually depends on the application build_weight specifies the importance of the index build time raported to the nearest neighbor search time In some applications it s acceptable for the index build step to take a long time if the subsequent searches in the index can be performed very fast In other applications it s required that the index be build as fast as possible even if that leads to slightly longer search times Default value 0 01 memory_weight is used to specify the tradeoff between time index build time and search time and memory used by the index A value less than 1 gives more importance to the time spent and a value greater than 1 gives more importance to the memory usage sample fraction is a number between 0 and 1 indicating what fraction of the dataset to use in the automatic parameter configuration algorithm Running the algorithm on the full dataset gives the most accurate results but for very large datasets can take longer than desired In such case using just a fraction of the data helps spee
27. y flann_save_index index filename The argumenst required by this function are index the index to be saved created by flann_build_index filename the name of the file in which to save the index 3 3 4 flann_load_index This function loads a previously saved index from a file It needs to be passed as a second parameter the dataset for which the index was created as this is not saved together with the index index flann_load_index filename dataset The argumenst required by this function are filename the file from which to load the index 17 dataset the dataset for which the index was created This function returns the index object 3 3 5 flann_set_distance_type This function chooses the distance function to use when computing distances between data points flann_set_distance_type type order The argumenst required by this function are type the distance type to use Possible values are euclidean manhattan minkowski max_dist Linfinity distance type is not valid for kd tree index type since it s not dimensionwise additive hik histogram intersection kernel gt hellinger cs chi square and k1 Kullback Leibler order only used if distance type is minkowski and represents the order of the minkowski distance 3 3 6 flann_free_index This function must be called to delete an index and release all t
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