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diff --git a/geom_bottleneck/bottleneck/include/ANN/ANN.h b/geom_bottleneck/bottleneck/include/ANN/ANN.h deleted file mode 100644 index 004dfe2..0000000 --- a/geom_bottleneck/bottleneck/include/ANN/ANN.h +++ /dev/null @@ -1,917 +0,0 @@ -//---------------------------------------------------------------------- -// File: ANN.h -// Programmer: Sunil Arya and David Mount -// Description: Basic include file for approximate nearest -// neighbor searching. -// Last modified: 01/27/10 (Version 1.1.2) -//---------------------------------------------------------------------- -// Copyright (c) 1997-2010 University of Maryland and Sunil Arya and -// David Mount. All Rights Reserved. -// -// This software and related documentation is part of the Approximate -// Nearest Neighbor Library (ANN). This software is provided under -// the provisions of the Lesser GNU Public License (LGPL). See the -// file ../ReadMe.txt for further information. -// -// The University of Maryland (U.M.) and the authors make no -// representations about the suitability or fitness of this software for -// any purpose. It is provided "as is" without express or implied -// warranty. -//---------------------------------------------------------------------- -// History: -// Revision 0.1 03/04/98 -// Initial release -// Revision 1.0 04/01/05 -// Added copyright and revision information -// Added ANNcoordPrec for coordinate precision. -// Added methods theDim, nPoints, maxPoints, thePoints to ANNpointSet. -// Cleaned up C++ structure for modern compilers -// Revision 1.1 05/03/05 -// Added fixed-radius k-NN searching -// Revision 1.1.2 01/27/10 -// Fixed minor compilation bugs for new versions of gcc -// -------------------------------------------------------------------- -// 2015 - modified by A. Nigmetov to support deletion of points -//---------------------------------------------------------------------- - -//---------------------------------------------------------------------- -// ANN - approximate nearest neighbor searching -// ANN is a library for approximate nearest neighbor searching, -// based on the use of standard and priority search in kd-trees -// and balanced box-decomposition (bbd) trees. Here are some -// references to the main algorithmic techniques used here: -// -// kd-trees: -// Friedman, Bentley, and Finkel, ``An algorithm for finding -// best matches in logarithmic expected time,'' ACM -// Transactions on Mathematical Software, 3(3):209-226, 1977. -// -// Priority search in kd-trees: -// Arya and Mount, ``Algorithms for fast vector quantization,'' -// Proc. of DCC '93: Data Compression Conference, eds. J. A. -// Storer and M. Cohn, IEEE Press, 1993, 381-390. -// -// Approximate nearest neighbor search and bbd-trees: -// Arya, Mount, Netanyahu, Silverman, and Wu, ``An optimal -// algorithm for approximate nearest neighbor searching,'' -// 5th Ann. ACM-SIAM Symposium on Discrete Algorithms, -// 1994, 573-582. -//---------------------------------------------------------------------- - -#ifndef ANN_H -#define ANN_H - -// A. Nigmetov: ANN code is integrated into bottleneck library, -// CMake will take care of correct __declspec, no need to define DLL_API -#define DLL_API -//#ifdef WIN32 - //---------------------------------------------------------------------- - // For Microsoft Visual C++, externally accessible symbols must be - // explicitly indicated with DLL_API, which is somewhat like "extern." - // - // The following ifdef block is the standard way of creating macros - // which make exporting from a DLL simpler. All files within this DLL - // are compiled with the DLL_EXPORTS preprocessor symbol defined on the - // command line. In contrast, projects that use (or import) the DLL - // objects do not define the DLL_EXPORTS symbol. This way any other - // project whose source files include this file see DLL_API functions as - // being imported from a DLL, wheras this DLL sees symbols defined with - // this macro as being exported. - //---------------------------------------------------------------------- - //#ifdef DLL_EXPORTS - // #define DLL_API __declspec(dllexport) - //#else - //#define DLL_API __declspec(dllimport) - //#endif - //---------------------------------------------------------------------- - // DLL_API is ignored for all other systems - //---------------------------------------------------------------------- -//#else - //#define DLL_API -//#endif - -//---------------------------------------------------------------------- -// basic includes -//---------------------------------------------------------------------- - -#include <cstdlib> // standard lib includes -#include <cmath> // math includes -#include <cstring> // C-style strings -#include <vector> -#include <assert.h> - -//---------------------------------------------------------------------- -// Limits -// There are a number of places where we use the maximum double value as -// default initializers (and others may be used, depending on the -// data/distance representation). These can usually be found in limits.h -// (as LONG_MAX, INT_MAX) or in float.h (as DBL_MAX, FLT_MAX). -// -// Not all systems have these files. If you are using such a system, -// you should set the preprocessor symbol ANN_NO_LIMITS_H when -// compiling, and modify the statements below to generate the -// appropriate value. For practical purposes, this does not need to be -// the maximum double value. It is sufficient that it be at least as -// large than the maximum squared distance between between any two -// points. -//---------------------------------------------------------------------- -#ifdef ANN_NO_LIMITS_H // limits.h unavailable - #include <cvalues> // replacement for limits.h - const double ANN_DBL_MAX = MAXDOUBLE; // insert maximum double -#else - #include <climits> - #include <cfloat> - const double ANN_DBL_MAX = DBL_MAX; -#endif - -#define ANNversion "1.1.2" // ANN version and information -#define ANNversionCmt "" -#define ANNcopyright "David M. Mount and Sunil Arya" -#define ANNlatestRev "Jan 27, 2010" - -#include "def_debug_bt.h" - -#ifndef FOR_R_TDA -#include <iostream> // I/O streams -#endif - -namespace geom_bt { -//---------------------------------------------------------------------- -// ANNbool -// This is a simple boolean type. Although ANSI C++ is supposed -// to support the type bool, some compilers do not have it. -//---------------------------------------------------------------------- - - -enum ANNbool {ANNfalse = 0, ANNtrue = 1}; // ANN boolean type (non ANSI C++) - -//---------------------------------------------------------------------- -// ANNcoord, ANNdist -// ANNcoord and ANNdist are the types used for representing -// point coordinates and distances. They can be modified by the -// user, with some care. It is assumed that they are both numeric -// types, and that ANNdist is generally of an equal or higher type -// from ANNcoord. A variable of type ANNdist should be large -// enough to store the sum of squared components of a variable -// of type ANNcoord for the number of dimensions needed in the -// application. For example, the following combinations are -// legal: -// -// ANNcoord ANNdist -// --------- ------------------------------- -// short short, int, long, float, double -// int int, long, float, double -// long long, float, double -// float float, double -// double double -// -// It is the user's responsibility to make sure that overflow does -// not occur in distance calculation. -//---------------------------------------------------------------------- - -typedef double ANNcoord; // coordinate data type -typedef double ANNdist; // distance data type - -//---------------------------------------------------------------------- -// ANNidx -// ANNidx is a point index. When the data structure is built, the -// points are given as an array. Nearest neighbor results are -// returned as an integer index into this array. To make it -// clearer when this is happening, we define the integer type -// ANNidx. Indexing starts from 0. -// -// For fixed-radius near neighbor searching, it is possible that -// there are not k nearest neighbors within the search radius. To -// indicate this, the algorithm returns ANN_NULL_IDX as its result. -// It should be distinguishable from any valid array index. -//---------------------------------------------------------------------- - -typedef int ANNidx; // point index -const ANNidx ANN_NULL_IDX = -1; // a NULL point index - -//---------------------------------------------------------------------- -// Infinite distance: -// The code assumes that there is an "infinite distance" which it -// uses to initialize distances before performing nearest neighbor -// searches. It should be as larger or larger than any legitimate -// nearest neighbor distance. -// -// On most systems, these should be found in the standard include -// file <limits.h> or possibly <float.h>. If you do not have these -// file, some suggested values are listed below, assuming 64-bit -// long, 32-bit int and 16-bit short. -// -// ANNdist ANN_DIST_INF Values (see <limits.h> or <float.h>) -// ------- ------------ ------------------------------------ -// double DBL_MAX 1.79769313486231570e+308 -// float FLT_MAX 3.40282346638528860e+38 -// long LONG_MAX 0x7fffffffffffffff -// int INT_MAX 0x7fffffff -// short SHRT_MAX 0x7fff -//---------------------------------------------------------------------- - -const ANNdist ANN_DIST_INF = ANN_DBL_MAX; - -//---------------------------------------------------------------------- -// Significant digits for tree dumps: -// When floating point coordinates are used, the routine that dumps -// a tree needs to know roughly how many significant digits there -// are in a ANNcoord, so it can output points to full precision. -// This is defined to be ANNcoordPrec. On most systems these -// values can be found in the standard include files <limits.h> or -// <float.h>. For integer types, the value is essentially ignored. -// -// ANNcoord ANNcoordPrec Values (see <limits.h> or <float.h>) -// -------- ------------ ------------------------------------ -// double DBL_DIG 15 -// float FLT_DIG 6 -// long doesn't matter 19 -// int doesn't matter 10 -// short doesn't matter 5 -//---------------------------------------------------------------------- - -#ifdef DBL_DIG // number of sig. bits in ANNcoord - const int ANNcoordPrec = DBL_DIG; -#else - const int ANNcoordPrec = 15; // default precision -#endif - -//---------------------------------------------------------------------- -// Self match? -// In some applications, the nearest neighbor of a point is not -// allowed to be the point itself. This occurs, for example, when -// computing all nearest neighbors in a set. By setting the -// parameter ANN_ALLOW_SELF_MATCH to ANNfalse, the nearest neighbor -// is the closest point whose distance from the query point is -// strictly positive. -//---------------------------------------------------------------------- - -const ANNbool ANN_ALLOW_SELF_MATCH = ANNtrue; - -//---------------------------------------------------------------------- -// Norms and metrics: -// ANN supports any Minkowski norm for defining distance. In -// particular, for any p >= 1, the L_p Minkowski norm defines the -// length of a d-vector (v0, v1, ..., v(d-1)) to be -// -// (|v0|^p + |v1|^p + ... + |v(d-1)|^p)^(1/p), -// -// (where ^ denotes exponentiation, and |.| denotes absolute -// value). The distance between two points is defined to be the -// norm of the vector joining them. Some common distance metrics -// include -// -// Euclidean metric p = 2 -// Manhattan metric p = 1 -// Max metric p = infinity -// -// In the case of the max metric, the norm is computed by taking -// the maxima of the absolute values of the components. ANN is -// highly "coordinate-based" and does not support general distances -// functions (e.g. those obeying just the triangle inequality). It -// also does not support distance functions based on -// inner-products. -// -// For the purpose of computing nearest neighbors, it is not -// necessary to compute the final power (1/p). Thus the only -// component that is used by the program is |v(i)|^p. -// -// ANN parameterizes the distance computation through the following -// macros. (Macros are used rather than procedures for -// efficiency.) Recall that the distance between two points is -// given by the length of the vector joining them, and the length -// or norm of a vector v is given by formula: -// -// |v| = ROOT(POW(v0) # POW(v1) # ... # POW(v(d-1))) -// -// where ROOT, POW are unary functions and # is an associative and -// commutative binary operator mapping the following types: -// -// ** POW: ANNcoord --> ANNdist -// ** #: ANNdist x ANNdist --> ANNdist -// ** ROOT: ANNdist (>0) --> double -// -// For early termination in distance calculation (partial distance -// calculation) we assume that POW and # together are monotonically -// increasing on sequences of arguments, meaning that for all -// v0..vk and y: -// -// POW(v0) #...# POW(vk) <= (POW(v0) #...# POW(vk)) # POW(y). -// -// Incremental Distance Calculation: -// The program uses an optimized method of computing distances for -// kd-trees and bd-trees, called incremental distance calculation. -// It is used when distances are to be updated when only a single -// coordinate of a point has been changed. In order to use this, -// we assume that there is an incremental update function DIFF(x,y) -// for #, such that if: -// -// s = x0 # ... # xi # ... # xk -// -// then if s' is equal to s but with xi replaced by y, that is, -// -// s' = x0 # ... # y # ... # xk -// -// then the length of s' can be computed by: -// -// |s'| = |s| # DIFF(xi,y). -// -// Thus, if # is + then DIFF(xi,y) is (yi-x). For the L_infinity -// norm we make use of the fact that in the program this function -// is only invoked when y > xi, and hence DIFF(xi,y)=y. -// -// Finally, for approximate nearest neighbor queries we assume -// that POW and ROOT are related such that -// -// v*ROOT(x) = ROOT(POW(v)*x) -// -// Here are the values for the various Minkowski norms: -// -// L_p: p even: p odd: -// ------------------------- ------------------------ -// POW(v) = v^p POW(v) = |v|^p -// ROOT(x) = x^(1/p) ROOT(x) = x^(1/p) -// # = + # = + -// DIFF(x,y) = y - x DIFF(x,y) = y - x -// -// L_inf: -// POW(v) = |v| -// ROOT(x) = x -// # = max -// DIFF(x,y) = y -// -// By default the Euclidean norm is assumed. To change the norm, -// uncomment the appropriate set of macros below. -//---------------------------------------------------------------------- - -//---------------------------------------------------------------------- -// Use the following for the Euclidean norm -//---------------------------------------------------------------------- -//#define ANN_POW(v) ((v)*(v)) -//#define ANN_ROOT(x) sqrt(x) -//#define ANN_SUM(x,y) ((x) + (y)) -//#define ANN_DIFF(x,y) ((y) - (x)) - -//---------------------------------------------------------------------- -// Use the following for the L_1 (Manhattan) norm -//---------------------------------------------------------------------- -// #define ANN_POW(v) fabs(v) -// #define ANN_ROOT(x) (x) -// #define ANN_SUM(x,y) ((x) + (y)) -// #define ANN_DIFF(x,y) ((y) - (x)) - -//---------------------------------------------------------------------- -// Use the following for a general L_p norm -//---------------------------------------------------------------------- -// #define ANN_POW(v) pow(fabs(v),p) -// #define ANN_ROOT(x) pow(fabs(x),1/p) -// #define ANN_SUM(x,y) ((x) + (y)) -// #define ANN_DIFF(x,y) ((y) - (x)) - -//---------------------------------------------------------------------- -// Use the following for the L_infinity (Max) norm -//---------------------------------------------------------------------- -#define ANN_POW(v) fabs(v) -#define ANN_ROOT(x) (x) -#define ANN_SUM(x,y) ((x) > (y) ? (x) : (y)) -#define ANN_DIFF(x,y) (y) - -//---------------------------------------------------------------------- -// Array types -// The following array types are of basic interest. A point is -// just a dimensionless array of coordinates, a point array is a -// dimensionless array of points. A distance array is a -// dimensionless array of distances and an index array is a -// dimensionless array of point indices. The latter two are used -// when returning the results of k-nearest neighbor queries. -//---------------------------------------------------------------------- - -typedef ANNcoord* ANNpoint; // a point -typedef ANNpoint* ANNpointArray; // an array of points -typedef ANNdist* ANNdistArray; // an array of distances -typedef ANNidx* ANNidxArray; // an array of point indices - -//---------------------------------------------------------------------- -// Basic point and array utilities: -// The following procedures are useful supplements to ANN's nearest -// neighbor capabilities. -// -// annDist(): -// Computes the (squared) distance between a pair of points. -// Note that this routine is not used internally by ANN for -// computing distance calculations. For reasons of efficiency -// this is done using incremental distance calculation. Thus, -// this routine cannot be modified as a method of changing the -// metric. -// -// Because points (somewhat like strings in C) are stored as -// pointers. Consequently, creating and destroying copies of -// points may require storage allocation. These procedures do -// this. -// -// annAllocPt() and annDeallocPt(): -// Allocate a deallocate storage for a single point, and -// return a pointer to it. The argument to AllocPt() is -// used to initialize all components. -// -// annAllocPts() and annDeallocPts(): -// Allocate and deallocate an array of points as well a -// place to store their coordinates, and initializes the -// points to point to their respective coordinates. It -// allocates point storage in a contiguous block large -// enough to store all the points. It performs no -// initialization. -// -// annCopyPt(): -// Creates a copy of a given point, allocating space for -// the new point. It returns a pointer to the newly -// allocated copy. -//---------------------------------------------------------------------- - -DLL_API ANNdist annDist( - int dim, // dimension of space - ANNpoint p, // points - ANNpoint q); - -DLL_API ANNpoint annAllocPt( - int dim, // dimension - ANNcoord c = 0); // coordinate value (all equal) - -DLL_API ANNpointArray annAllocPts( - int n, // number of points - int dim); // dimension - -DLL_API void annDeallocPt( - ANNpoint &p); // deallocate 1 point - -DLL_API void annDeallocPts( - ANNpointArray &pa); // point array - -DLL_API ANNpoint annCopyPt( - int dim, // dimension - ANNpoint source); // point to copy - - -//---------------------------------------------------------------------- -// Orthogonal (axis aligned) rectangle -// Orthogonal rectangles are represented by two points, one -// for the lower left corner (min coordinates) and the other -// for the upper right corner (max coordinates). -// -// The constructor initializes from either a pair of coordinates, -// pair of points, or another rectangle. Note that all constructors -// allocate new point storage. The destructor deallocates this -// storage. -// -// BEWARE: Orthogonal rectangles should be passed ONLY BY REFERENCE. -// (C++'s default copy constructor will not allocate new point -// storage, then on return the destructor free's storage, and then -// you get into big trouble in the calling procedure.) -//---------------------------------------------------------------------- - -class DLL_API ANNorthRect { -public: - ANNpoint lo; // rectangle lower bounds - ANNpoint hi; // rectangle upper bounds -// - ANNorthRect( // basic constructor - int dd, // dimension of space - ANNcoord l=0, // default is empty - ANNcoord h=0) - { lo = annAllocPt(dd, l); hi = annAllocPt(dd, h); } - - ANNorthRect( // (almost a) copy constructor - int dd, // dimension - const ANNorthRect &r) // rectangle to copy - { lo = annCopyPt(dd, r.lo); hi = annCopyPt(dd, r.hi); } - - ANNorthRect( // construct from points - int dd, // dimension - ANNpoint l, // low point - ANNpoint h) // hight point - { lo = annCopyPt(dd, l); hi = annCopyPt(dd, h); } - - ~ANNorthRect() // destructor - { annDeallocPt(lo); annDeallocPt(hi); } - - ANNbool inside(const int dim, ANNpoint p) const;// is point p inside rectangle? - bool contains(const int dim, const ANNorthRect& r) const; - bool intersects(const int dim, const ANNorthRect& r) const; -}; - - -//---------------------------------------------------------------------- -//Overall structure: ANN supports a number of different data structures -//for approximate and exact nearest neighbor searching. These are: -// -// ANNbruteForce A simple brute-force search structure. -// ANNkd_tree A kd-tree tree search structure. ANNbd_tree -// A bd-tree tree search structure (a kd-tree with shrink -// capabilities). -// -// At a minimum, each of these data structures support k-nearest -// neighbor queries. The nearest neighbor query, annkSearch, -// returns an integer identifier and the distance to the nearest -// neighbor(s) and annRangeSearch returns the nearest points that -// lie within a given query ball. -// -// Each structure is built by invoking the appropriate constructor -// and passing it (at a minimum) the array of points, the total -// number of points and the dimension of the space. Each structure -// is also assumed to support a destructor and member functions -// that return basic information about the point set. -// -// Note that the array of points is not copied by the data -// structure (for reasons of space efficiency), and it is assumed -// to be constant throughout the lifetime of the search structure. -// -// The search algorithm, annkSearch, is given the query point (q), -// and the desired number of nearest neighbors to report (k), and -// the error bound (eps) (whose default value is 0, implying exact -// nearest neighbors). It returns two arrays which are assumed to -// contain at least k elements: one (nn_idx) contains the indices -// (within the point array) of the nearest neighbors and the other -// (dd) contains the squared distances to these nearest neighbors. -// -// The search algorithm, annkFRSearch, is a fixed-radius kNN -// search. In addition to a query point, it is given a (squared) -// radius bound. (This is done for consistency, because the search -// returns distances as squared quantities.) It does two things. -// First, it computes the k nearest neighbors within the radius -// bound, and second, it returns the total number of points lying -// within the radius bound. It is permitted to set k = 0, in which -// case it effectively answers a range counting query. If the -// error bound epsilon is positive, then the search is approximate -// in the sense that it is free to ignore any point that lies -// outside a ball of radius r/(1+epsilon), where r is the given -// (unsquared) radius bound. -// -// The generic object from which all the search structures are -// dervied is given below. It is a virtual object, and is useless -// by itself. -//---------------------------------------------------------------------- - -class DLL_API ANNpointSet { -public: - virtual ~ANNpointSet() {} // virtual distructor - - virtual void annkSearch( // approx k near neighbor search - ANNpoint q, // query point - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor array (modified) - ANNdistArray dd, // dist to near neighbors (modified) - double eps=0.0 // error bound - ) = 0; // pure virtual (defined elsewhere) - - virtual int annkFRSearch( // approx fixed-radius kNN search - ANNpoint q, // query point - ANNdist sqRad, // squared radius - int k = 0, // number of near neighbors to return - ANNidxArray nn_idx = NULL, // nearest neighbor array (modified) - ANNdistArray dd = NULL, // dist to near neighbors (modified) - double eps=0.0 // error bound - ) = 0; // pure virtual (defined elsewhere) - - virtual int theDim() = 0; // return dimension of space - virtual int nPoints() = 0; // return number of points - // return pointer to points - virtual ANNpointArray thePoints() = 0; -}; - -//---------------------------------------------------------------------- -// Brute-force nearest neighbor search: -// The brute-force search structure is very simple but inefficient. -// It has been provided primarily for the sake of comparison with -// and validation of the more complex search structures. -// -// Query processing is the same as described above, but the value -// of epsilon is ignored, since all distance calculations are -// performed exactly. -// -// WARNING: This data structure is very slow, and should not be -// used unless the number of points is very small. -// -// Internal information: -// --------------------- -// This data structure bascially consists of the array of points -// (each a pointer to an array of coordinates). The search is -// performed by a simple linear scan of all the points. -//---------------------------------------------------------------------- - -class DLL_API ANNbruteForce: public ANNpointSet { - int dim; // dimension - int n_pts; // number of points - ANNpointArray pts; // point array -public: - ANNbruteForce( // constructor from point array - ANNpointArray pa, // point array - int n, // number of points - int dd); // dimension - - ~ANNbruteForce(); // destructor - - void annkSearch( // approx k near neighbor search - ANNpoint q, // query point - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor array (modified) - ANNdistArray dd, // dist to near neighbors (modified) - double eps=0.0); // error bound - - int annkFRSearch( // approx fixed-radius kNN search - ANNpoint q, // query point - ANNdist sqRad, // squared radius - int k = 0, // number of near neighbors to return - ANNidxArray nn_idx = NULL, // nearest neighbor array (modified) - ANNdistArray dd = NULL, // dist to near neighbors (modified) - double eps=0.0); // error bound - - int theDim() // return dimension of space - { return dim; } - - int nPoints() // return number of points - { return n_pts; } - - ANNpointArray thePoints() // return pointer to points - { return pts; } -}; - -//---------------------------------------------------------------------- -// kd- and bd-tree splitting and shrinking rules -// kd-trees supports a collection of different splitting rules. -// In addition to the standard kd-tree splitting rule proposed -// by Friedman, Bentley, and Finkel, we have introduced a -// number of other splitting rules, which seem to perform -// as well or better (for the distributions we have tested). -// -// The splitting methods given below allow the user to tailor -// the data structure to the particular data set. They are -// are described in greater details in the kd_split.cc source -// file. The method ANN_KD_SUGGEST is the method chosen (rather -// subjectively) by the implementors as the one giving the -// fastest performance, and is the default splitting method. -// -// As with splitting rules, there are a number of different -// shrinking rules. The shrinking rule ANN_BD_NONE does no -// shrinking (and hence produces a kd-tree tree). The rule -// ANN_BD_SUGGEST uses the implementors favorite rule. -//---------------------------------------------------------------------- - -enum ANNsplitRule { - ANN_KD_STD = 0, // the optimized kd-splitting rule - ANN_KD_MIDPT = 1, // midpoint split - ANN_KD_FAIR = 2, // fair split - ANN_KD_SL_MIDPT = 3, // sliding midpoint splitting method - ANN_KD_SL_FAIR = 4, // sliding fair split method - ANN_KD_SUGGEST = 5, // the authors' suggestion for best - // for kd-trees with deletion - //ANN_KD_STD_WD = 6, - //ANN_KD_MIDPT_WD = 7, - //ANN_KD_SL_MIDPT_WD = 8 - }; -const int ANN_N_SPLIT_RULES = 6; // number of split rules -//const int ANN_N_SPLIT_RULES = 9; // number of split rules - -enum ANNshrinkRule { - ANN_BD_NONE = 0, // no shrinking at all (just kd-tree) - ANN_BD_SIMPLE = 1, // simple splitting - ANN_BD_CENTROID = 2, // centroid splitting - ANN_BD_SUGGEST = 3}; // the authors' suggested choice -const int ANN_N_SHRINK_RULES = 4; // number of shrink rules - -//---------------------------------------------------------------------- -// kd-tree: -// The main search data structure supported by ANN is a kd-tree. -// The main constructor is given a set of points and a choice of -// splitting method to use in building the tree. -// -// Construction: -// ------------- -// The constructor is given the point array, number of points, -// dimension, bucket size (default = 1), and the splitting rule -// (default = ANN_KD_SUGGEST). The point array is not copied, and -// is assumed to be kept constant throughout the lifetime of the -// search structure. There is also a "load" constructor that -// builds a tree from a file description that was created by the -// Dump operation. -// -// Search: -// ------- -// There are two search methods: -// -// Standard search (annkSearch()): -// Searches nodes in tree-traversal order, always visiting -// the closer child first. -// Priority search (annkPriSearch()): -// Searches nodes in order of increasing distance of the -// associated cell from the query point. For many -// distributions the standard search seems to work just -// fine, but priority search is safer for worst-case -// performance. -// -// Printing: -// --------- -// There are two methods provided for printing the tree. Print() -// is used to produce a "human-readable" display of the tree, with -// indenation, which is handy for debugging. Dump() produces a -// format that is suitable reading by another program. There is a -// "load" constructor, which constructs a tree which is assumed to -// have been saved by the Dump() procedure. -// -// Performance and Structure Statistics: -// ------------------------------------- -// The procedure getStats() collects statistics information on the -// tree (its size, height, etc.) See ANNperf.h for information on -// the stats structure it returns. -// -// Internal information: -// --------------------- -// The data structure consists of three major chunks of storage. -// The first (implicit) storage are the points themselves (pts), -// which have been provided by the users as an argument to the -// constructor, or are allocated dynamically if the tree is built -// using the load constructor). These should not be changed during -// the lifetime of the search structure. It is the user's -// responsibility to delete these after the tree is destroyed. -// -// The second is the tree itself (which is dynamically allocated in -// the constructor) and is given as a pointer to its root node -// (root). These nodes are automatically deallocated when the tree -// is deleted. See the file src/kd_tree.h for further information -// on the structure of the tree nodes. -// -// Each leaf of the tree does not contain a pointer directly to a -// point, but rather contains a pointer to a "bucket", which is an -// array consisting of point indices. The third major chunk of -// storage is an array (pidx), which is a large array in which all -// these bucket subarrays reside. (The reason for storing them -// separately is the buckets are typically small, but of varying -// sizes. This was done to avoid fragmentation.) This array is -// also deallocated when the tree is deleted. -// -// In addition to this, the tree consists of a number of other -// pieces of information which are used in searching and for -// subsequent tree operations. These consist of the following: -// -// dim Dimension of space -// n_pts Number of points currently in the tree -// n_max Maximum number of points that are allowed -// in the tree -// bkt_size Maximum bucket size (no. of points per leaf) -// bnd_box_lo Bounding box low point -// bnd_box_hi Bounding box high point -// splitRule Splitting method used -// -//---------------------------------------------------------------------- - -//---------------------------------------------------------------------- -// Some types and objects used by kd-tree functions -// See src/kd_tree.h and src/kd_tree.cpp for definitions -//---------------------------------------------------------------------- -class ANNkdStats; // stats on kd-tree -class ANNkd_node; // generic node in a kd-tree -typedef ANNkd_node* ANNkd_ptr; // pointer to a kd-tree node -class ANNkd_leaf; - -class DLL_API ANNkd_tree: public ANNpointSet { -protected: - int dim; // dimension of space - int n_pts; // number of points in tree - int bkt_size; // bucket size - ANNpointArray pts; // the points - ANNidxArray pidx; // point indices (to pts array) - ANNkd_ptr root; // root of kd-tree - ANNpoint bnd_box_lo; // bounding box low point - ANNpoint bnd_box_hi; // bounding box high point - - void SkeletonTree( // construct skeleton tree - int n, // number of points - int dd, // dimension - int bs, // bucket size - ANNpointArray pa = NULL, // point array (optional) - ANNidxArray pi = NULL); // point indices (optional) - -public: - ANNkd_tree( // build skeleton tree - int n = 0, // number of points - int dd = 0, // dimension - int bs = 1); // bucket size - - ANNkd_tree( // build from point array - ANNpointArray pa, // point array - int n, // number of points - int dd, // dimension - int bs = 1, // bucket size - ANNsplitRule split = ANN_KD_SUGGEST); // splitting method - -#ifndef FOR_R_TDA - ANNkd_tree( // build from dump file - std::istream& in); // input stream for dump file -#endif - - ~ANNkd_tree(); // tree destructor - - void annkSearch( // approx k near neighbor search - ANNpoint q, // query point - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor array (modified) - ANNdistArray dd, // dist to near neighbors (modified) - double eps=0.0); // error bound - - void annkPriSearch( // priority k near neighbor search - ANNpoint q, // query point - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor array (modified) - ANNdistArray dd, // dist to near neighbors (modified) - double eps=0.0); // error bound - - int annkFRSearch( // approx fixed-radius kNN search - ANNpoint q, // the query point - ANNdist sqRad, // squared radius of query ball - int k, // number of neighbors to return - ANNidxArray nn_idx = NULL, // nearest neighbor array (modified) - ANNdistArray dd = NULL, // dist to near neighbors (modified) - double eps=0.0); // error bound - - int theDim() // return dimension of space - { return dim; } - - int nPoints() // return number of points - { return n_pts; } - - ANNpointArray thePoints() // return pointer to points - { return pts; } - -#ifndef FOR_R_TDA - virtual void Print( // print the tree (for debugging) - ANNbool with_pts, // print points as well? - std::ostream& out); // output stream - - virtual void Dump( // dump entire tree - ANNbool with_pts, // print points as well? - std::ostream& out); // output stream -#endif - - virtual void getStats( // compute tree statistics - ANNkdStats& st); // the statistics (modified) - - /////////////////////////////////////////////////////////////// - // for deletion - std::vector<ANNkd_leaf*> pointToLeafVec; - std::vector<bool> isDeleted; // will be used to check implementation; - //TODO remove after testing - void delete_point(const int point_idx); - int actual_num_points; - int getActualNumPoints(void) const { return actual_num_points; } - void range_search(const ANNorthRect& region, std::vector<size_t>& pointIdices); -}; - -//---------------------------------------------------------------------- -// Box decomposition tree (bd-tree) -// The bd-tree is inherited from a kd-tree. The main difference -// in the bd-tree and the kd-tree is a new type of internal node -// called a shrinking node (in the kd-tree there is only one type -// of internal node, a splitting node). The shrinking node -// makes it possible to generate balanced trees in which the -// cells have bounded aspect ratio, by allowing the decomposition -// to zoom in on regions of dense point concentration. Although -// this is a nice idea in theory, few point distributions are so -// densely clustered that this is really needed. -//---------------------------------------------------------------------- - -class DLL_API ANNbd_tree: public ANNkd_tree { -public: - ANNbd_tree( // build skeleton tree - int n, // number of points - int dd, // dimension - int bs = 1) // bucket size - : ANNkd_tree(n, dd, bs) {} // build base kd-tree - - ANNbd_tree( // build from point array - ANNpointArray pa, // point array - int n, // number of points - int dd, // dimension - int bs = 1, // bucket size - ANNsplitRule split = ANN_KD_SUGGEST, // splitting rule - ANNshrinkRule shrink = ANN_BD_SUGGEST); // shrinking rule - -#ifndef FOR_R_TDA - ANNbd_tree( // build from dump file - std::istream& in); // input stream for dump file -#endif -}; - -//---------------------------------------------------------------------- -// Other functions -// annMaxPtsVisit Sets a limit on the maximum number of points -// to visit in the search. -// annClose Can be called when all use of ANN is finished. -// It clears up a minor memory leak. -//---------------------------------------------------------------------- - -DLL_API void annMaxPtsVisit( // max. pts to visit in search - int maxPts); // the limit - -DLL_API void annClose(); // called to end use of ANN - -} -#endif |