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Diffstat (limited to 'geom_bottleneck/bottleneck/include/ANN')
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/ANN.h | 906 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/ANNperf.h | 225 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/ANNx.h | 127 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/bd_tree.h | 102 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_fix_rad_search.h | 46 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_pr_search.h | 51 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_search.h | 50 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_split.h | 123 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_tree.h | 253 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/kd_util.h | 126 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/pr_queue.h | 127 | ||||
| -rw-r--r-- | geom_bottleneck/bottleneck/include/ANN/pr_queue_k.h | 120 |
12 files changed, 2256 insertions, 0 deletions
diff --git a/geom_bottleneck/bottleneck/include/ANN/ANN.h b/geom_bottleneck/bottleneck/include/ANN/ANN.h new file mode 100644 index 0000000..cd48d8e --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/ANN.h @@ -0,0 +1,906 @@ +//---------------------------------------------------------------------- +// 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 <iostream> // I/O streams +#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" + +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 + + ANNkd_tree( // build from dump file + std::istream& in); // input stream for dump file + + ~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; } + + 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 + + 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 + + ANNbd_tree( // build from dump file + std::istream& in); // input stream for dump file +}; + +//---------------------------------------------------------------------- +// 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 diff --git a/geom_bottleneck/bottleneck/include/ANN/ANNperf.h b/geom_bottleneck/bottleneck/include/ANN/ANNperf.h new file mode 100644 index 0000000..d242266 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/ANNperf.h @@ -0,0 +1,225 @@ +//---------------------------------------------------------------------- +// File: ANNperf.h +// Programmer: Sunil Arya and David Mount +// Last modified: 03/04/98 (Release 0.1) +// Description: Include file for ANN performance stats +// +// Some of the code for statistics gathering has been adapted +// from the SmplStat.h package in the g++ library. +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 ANN_ prefix to avoid name conflicts. +//---------------------------------------------------------------------- + +#ifndef ANNperf_H +#define ANNperf_H + +//---------------------------------------------------------------------- +// basic includes +//---------------------------------------------------------------------- + +#include <ANN/ANN.h> // basic ANN includes + +namespace geom_bt { +//---------------------------------------------------------------------- +// kd-tree stats object +// This object is used for collecting information about a kd-tree +// or bd-tree. +//---------------------------------------------------------------------- + +class ANNkdStats { // stats on kd-tree +public: + int dim; // dimension of space + int n_pts; // no. of points + int bkt_size; // bucket size + int n_lf; // no. of leaves (including trivial) + int n_tl; // no. of trivial leaves (no points) + int n_spl; // no. of splitting nodes + int n_shr; // no. of shrinking nodes (for bd-trees) + int depth; // depth of tree + float sum_ar; // sum of leaf aspect ratios + float avg_ar; // average leaf aspect ratio + // + // reset stats + void reset(int d=0, int n=0, int bs=0) + { + dim = d; n_pts = n; bkt_size = bs; + n_lf = n_tl = n_spl = n_shr = depth = 0; + sum_ar = avg_ar = 0.0; + } + + ANNkdStats() // basic constructor + { reset(); } + + void merge(const ANNkdStats &st); // merge stats from child +}; + +//---------------------------------------------------------------------- +// ANNsampStat +// A sample stat collects numeric (double) samples and returns some +// simple statistics. Its main functions are: +// +// reset() Reset to no samples. +// += x Include sample x. +// samples() Return number of samples. +// mean() Return mean of samples. +// stdDev() Return standard deviation +// min() Return minimum of samples. +// max() Return maximum of samples. +//---------------------------------------------------------------------- +class DLL_API ANNsampStat { + int n; // number of samples + double sum; // sum + double sum2; // sum of squares + double minVal, maxVal; // min and max +public : + void reset() // reset everything + { + n = 0; + sum = sum2 = 0; + minVal = ANN_DBL_MAX; + maxVal = -ANN_DBL_MAX; + } + + ANNsampStat() { reset(); } // constructor + + void operator+=(double x) // add sample + { + n++; sum += x; sum2 += x*x; + if (x < minVal) minVal = x; + if (x > maxVal) maxVal = x; + } + + int samples() { return n; } // number of samples + + double mean() { return sum/n; } // mean + + // standard deviation + double stdDev() { return sqrt((sum2 - (sum*sum)/n)/(n-1));} + + double min() { return minVal; } // minimum + double max() { return maxVal; } // maximum +}; + +//---------------------------------------------------------------------- +// Operation count updates +//---------------------------------------------------------------------- + +#ifdef ANN_PERF + #define ANN_FLOP(n) {ann_Nfloat_ops += (n);} + #define ANN_LEAF(n) {ann_Nvisit_lfs += (n);} + #define ANN_SPL(n) {ann_Nvisit_spl += (n);} + #define ANN_SHR(n) {ann_Nvisit_shr += (n);} + #define ANN_PTS(n) {ann_Nvisit_pts += (n);} + #define ANN_COORD(n) {ann_Ncoord_hts += (n);} +#else + #define ANN_FLOP(n) + #define ANN_LEAF(n) + #define ANN_SPL(n) + #define ANN_SHR(n) + #define ANN_PTS(n) + #define ANN_COORD(n) +#endif + +//---------------------------------------------------------------------- +// Performance statistics +// The following data and routines are used for computing performance +// statistics for nearest neighbor searching. Because these routines +// can slow the code down, they can be activated and deactiviated by +// defining the ANN_PERF variable, by compiling with the option: +// -DANN_PERF +//---------------------------------------------------------------------- + +//---------------------------------------------------------------------- +// Global counters for performance measurement +// +// visit_lfs The number of leaf nodes visited in the +// tree. +// +// visit_spl The number of splitting nodes visited in the +// tree. +// +// visit_shr The number of shrinking nodes visited in the +// tree. +// +// visit_pts The number of points visited in all the +// leaf nodes visited. Equivalently, this +// is the number of points for which distance +// calculations are performed. +// +// coord_hts The number of times a coordinate of a +// data point is accessed. This is generally +// less than visit_pts*d if partial distance +// calculation is used. This count is low +// in the sense that if a coordinate is hit +// many times in the same routine we may +// count it only once. +// +// float_ops The number of floating point operations. +// This includes all operations in the heap +// as well as distance calculations to boxes. +// +// average_err The average error of each query (the +// error of the reported point to the true +// nearest neighbor). For k nearest neighbors +// the error is computed k times. +// +// rank_err The rank error of each query (the difference +// in the rank of the reported point and its +// true rank). +// +// data_pts The number of data points. This is not +// a counter, but used in stats computation. +//---------------------------------------------------------------------- + +extern int ann_Ndata_pts; // number of data points +extern int ann_Nvisit_lfs; // number of leaf nodes visited +extern int ann_Nvisit_spl; // number of splitting nodes visited +extern int ann_Nvisit_shr; // number of shrinking nodes visited +extern int ann_Nvisit_pts; // visited points for one query +extern int ann_Ncoord_hts; // coordinate hits for one query +extern int ann_Nfloat_ops; // floating ops for one query +extern ANNsampStat ann_visit_lfs; // stats on leaf nodes visits +extern ANNsampStat ann_visit_spl; // stats on splitting nodes visits +extern ANNsampStat ann_visit_shr; // stats on shrinking nodes visits +extern ANNsampStat ann_visit_nds; // stats on total nodes visits +extern ANNsampStat ann_visit_pts; // stats on points visited +extern ANNsampStat ann_coord_hts; // stats on coordinate hits +extern ANNsampStat ann_float_ops; // stats on floating ops +//---------------------------------------------------------------------- +// The following need to be part of the public interface, because +// they are accessed outside the DLL in ann_test.cpp. +//---------------------------------------------------------------------- +DLL_API extern ANNsampStat ann_average_err; // average error +DLL_API extern ANNsampStat ann_rank_err; // rank error + +//---------------------------------------------------------------------- +// Declaration of externally accessible routines for statistics +//---------------------------------------------------------------------- + +DLL_API void annResetStats(int data_size); // reset stats for a set of queries + +DLL_API void annResetCounts(); // reset counts for one queries + +DLL_API void annUpdateStats(); // update stats with current counts + +DLL_API void annPrintStats(ANNbool validate); // print statistics for a run + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/ANNx.h b/geom_bottleneck/bottleneck/include/ANN/ANNx.h new file mode 100644 index 0000000..0c9e190 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/ANNx.h @@ -0,0 +1,127 @@ +//---------------------------------------------------------------------- +// File: ANNx.h +// Programmer: Sunil Arya and David Mount +// Description: Internal include file for ANN +// Last modified: 01/27/10 (Version 1.1.2) +// +// These declarations are of use in manipulating some of +// the internal data objects appearing in ANN, but are not +// needed for applications just using the nearest neighbor +// search. +// +// Typical users of ANN should not need to access this file. +//---------------------------------------------------------------------- +// 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 +// Changed LO, HI, IN, OUT to ANN_LO, ANN_HI, etc. +// Revision 1.1.2 01/27/10 +// Fixed minor compilation bugs for new versions of gcc +//---------------------------------------------------------------------- + +#ifndef ANNx_H +#define ANNx_H + +#include <iomanip> // I/O manipulators +#include <ANN/ANN.h> // ANN includes + +namespace geom_bt { + +//---------------------------------------------------------------------- +// Global constants and types +//---------------------------------------------------------------------- +enum {ANN_LO=0, ANN_HI=1}; // splitting indices +enum {ANN_IN=0, ANN_OUT=1}; // shrinking indices + // what to do in case of error +enum ANNerr {ANNwarn = 0, ANNabort = 1}; + +//---------------------------------------------------------------------- +// Maximum number of points to visit +// We have an option for terminating the search early if the +// number of points visited exceeds some threshold. If the +// threshold is 0 (its default) this means there is no limit +// and the algorithm applies its normal termination condition. +//---------------------------------------------------------------------- + +extern int ANNmaxPtsVisited; // maximum number of pts visited +extern int ANNptsVisited; // number of pts visited in search + +//---------------------------------------------------------------------- +// Global function declarations +//---------------------------------------------------------------------- + +void annError( // ANN error routine + const char* msg, // error message + ANNerr level); // level of error + +void annPrintPt( // print a point + ANNpoint pt, // the point + int dim, // the dimension + std::ostream &out); // output stream + +void annAssignRect( // assign one rect to another + int dim, // dimension (both must be same) + ANNorthRect &dest, // destination (modified) + const ANNorthRect &source); // source + +//---------------------------------------------------------------------- +// Orthogonal (axis aligned) halfspace +// An orthogonal halfspace is represented by an integer cutting +// dimension cd, coordinate cutting value, cv, and side, sd, which is +// either +1 or -1. Our convention is that point q lies in the (closed) +// halfspace if (q[cd] - cv)*sd >= 0. +//---------------------------------------------------------------------- + +class ANNorthHalfSpace { +public: + int cd; // cutting dimension + ANNcoord cv; // cutting value + int sd; // which side +// + ANNorthHalfSpace() // default constructor + { cd = 0; cv = 0; sd = 0; } + + ANNorthHalfSpace( // basic constructor + int cdd, // dimension of space + ANNcoord cvv, // cutting value + int sdd) // side + { cd = cdd; cv = cvv; sd = sdd; } + + ANNbool in(ANNpoint q) const // is q inside halfspace? + { return (ANNbool) ((q[cd] - cv)*sd >= 0); } + + ANNbool out(ANNpoint q) const // is q outside halfspace? + { return (ANNbool) ((q[cd] - cv)*sd < 0); } + + ANNdist dist(ANNpoint q) const // (squared) distance from q + { return (ANNdist) ANN_POW(q[cd] - cv); } + + void setLowerBound(int d, ANNpoint p)// set to lower bound at p[i] + { cd = d; cv = p[d]; sd = +1; } + + void setUpperBound(int d, ANNpoint p)// set to upper bound at p[i] + { cd = d; cv = p[d]; sd = -1; } + + void project(ANNpoint &q) // project q (modified) onto halfspace + { if (out(q)) q[cd] = cv; } +}; + + // array of halfspaces +typedef ANNorthHalfSpace *ANNorthHSArray; + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/bd_tree.h b/geom_bottleneck/bottleneck/include/ANN/bd_tree.h new file mode 100644 index 0000000..0791429 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/bd_tree.h @@ -0,0 +1,102 @@ +//---------------------------------------------------------------------- +// File: bd_tree.h +// Programmer: David Mount +// Description: Declarations for standard bd-tree routines +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +// Changed IN, OUT to ANN_IN, ANN_OUT +//---------------------------------------------------------------------- + +#ifndef ANN_bd_tree_H +#define ANN_bd_tree_H + +#include <ANN/ANNx.h> // all ANN includes +#include "kd_tree.h" // kd-tree includes + +namespace geom_bt { +//---------------------------------------------------------------------- +// bd-tree shrinking node. +// The main addition in the bd-tree is the shrinking node, which +// is declared here. +// +// Shrinking nodes are defined by list of orthogonal halfspaces. +// These halfspaces define a (possibly unbounded) orthogonal +// rectangle. There are two children, in and out. Points that +// lie within this rectangle are stored in the in-child, and the +// other points are stored in the out-child. +// +// We use a list of orthogonal halfspaces rather than an +// orthogonal rectangle object because typically the number of +// sides of the shrinking box will be much smaller than the +// worst case bound of 2*dim. +// +// BEWARE: Note that constructor just copies the pointer to the +// bounding array, but the destructor deallocates it. This is +// rather poor practice, but happens to be convenient. The list +// is allocated in the bd-tree building procedure rbd_tree() just +// prior to construction, and is used for no other purposes. +// +// WARNING: In the near neighbor searching code it is assumed that +// the list of bounding halfspaces is irredundant, meaning that there +// are no two distinct halfspaces in the list with the same outward +// pointing normals. +//---------------------------------------------------------------------- + +class ANNbd_shrink : public ANNkd_node // splitting node of a kd-tree +{ + int n_bnds; // number of bounding halfspaces + ANNorthHSArray bnds; // list of bounding halfspaces + ANNkd_ptr child[2]; // in and out children +public: + ANNbd_shrink( // constructor + int nb, // number of bounding halfspaces + ANNorthHSArray bds, // list of bounding halfspaces + ANNkd_ptr ic=NULL, ANNkd_ptr oc=NULL) // children + { + n_bnds = nb; // cutting dimension + bnds = bds; // assign bounds + child[ANN_IN] = ic; // set children + child[ANN_OUT] = oc; + } + + ~ANNbd_shrink() // destructor + { + if (child[ANN_IN]!= NULL && child[ANN_IN]!= KD_TRIVIAL) + delete child[ANN_IN]; + if (child[ANN_OUT]!= NULL&& child[ANN_OUT]!= KD_TRIVIAL) + delete child[ANN_OUT]; + if (bnds != NULL) + delete [] bnds; // delete bounds + } + + virtual void getStats( // get tree statistics + int dim, // dimension of space + ANNkdStats &st, // statistics + ANNorthRect &bnd_box); // bounding box + virtual void print(int level, ostream &out);// print node + virtual void dump(ostream &out); // dump node + + virtual void ann_search(ANNdist); // standard search + virtual void ann_pri_search(ANNdist); // priority search + virtual void ann_FR_search(ANNdist); // fixed-radius search +}; + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_fix_rad_search.h b/geom_bottleneck/bottleneck/include/ANN/kd_fix_rad_search.h new file mode 100644 index 0000000..36f9528 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_fix_rad_search.h @@ -0,0 +1,46 @@ +//---------------------------------------------------------------------- +// File: kd_fix_rad_search.h +// Programmer: Sunil Arya and David Mount +// Description: Standard kd-tree fixed-radius kNN search +// Last modified: 05/03/05 (Version 1.1) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 1.1 05/03/05 +// Initial release +//---------------------------------------------------------------------- + +#ifndef ANN_kd_fix_rad_search_H +#define ANN_kd_fix_rad_search_H + +#include "kd_tree.h" // kd-tree declarations +#include "kd_util.h" // kd-tree utilities +#include "pr_queue_k.h" // k-element priority queue + +#include <ANN/ANNperf.h> // performance evaluation + +namespace geom_bt { +//---------------------------------------------------------------------- +// Global variables +// These are active for the life of each call to +// annRangeSearch(). They are set to save the number of +// variables that need to be passed among the various search +// procedures. +//---------------------------------------------------------------------- + +extern ANNpoint ANNkdFRQ; // query point (static copy) + +} +#endif
\ No newline at end of file diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_pr_search.h b/geom_bottleneck/bottleneck/include/ANN/kd_pr_search.h new file mode 100644 index 0000000..1f4c4fc --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_pr_search.h @@ -0,0 +1,51 @@ +//---------------------------------------------------------------------- +// File: kd_pr_search.h +// Programmer: Sunil Arya and David Mount +// Description: Priority kd-tree search +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef ANN_kd_pr_search_H +#define ANN_kd_pr_search_H + +#include "kd_tree.h" // kd-tree declarations +#include "kd_util.h" // kd-tree utilities +#include "pr_queue.h" // priority queue declarations +#include "pr_queue_k.h" // k-element priority queue + +#include <ANN/ANNperf.h> // performance evaluation + +namespace geom_bt { +//---------------------------------------------------------------------- +// Global variables +// Active for the life of each call to Appx_Near_Neigh() or +// Appx_k_Near_Neigh(). +//---------------------------------------------------------------------- + +extern double ANNprEps; // the error bound +extern int ANNprDim; // dimension of space +extern ANNpoint ANNprQ; // query point +extern double ANNprMaxErr; // max tolerable squared error +extern ANNpointArray ANNprPts; // the points +extern ANNpr_queue *ANNprBoxPQ; // priority queue for boxes +extern ANNmin_k *ANNprPointMK; // set of k closest points + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_search.h b/geom_bottleneck/bottleneck/include/ANN/kd_search.h new file mode 100644 index 0000000..7491779 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_search.h @@ -0,0 +1,50 @@ +//---------------------------------------------------------------------- +// File: kd_search.h +// Programmer: Sunil Arya and David Mount +// Description: Standard kd-tree search +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef ANN_kd_search_H +#define ANN_kd_search_H + +#include "kd_tree.h" // kd-tree declarations +#include "kd_util.h" // kd-tree utilities +#include "pr_queue_k.h" // k-element priority queue + +#include <ANN/ANNperf.h> // performance evaluation + +namespace geom_bt { +//---------------------------------------------------------------------- +// More global variables +// These are active for the life of each call to annkSearch(). They +// are set to save the number of variables that need to be passed +// among the various search procedures. +//---------------------------------------------------------------------- + +extern int ANNkdDim; // dimension of space (static copy) +extern ANNpoint ANNkdQ; // query point (static copy) +extern double ANNkdMaxErr; // max tolerable squared error +extern ANNpointArray ANNkdPts; // the points (static copy) +extern ANNmin_k *ANNkdPointMK; // set of k closest points +extern int ANNptsVisited; // number of points visited + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_split.h b/geom_bottleneck/bottleneck/include/ANN/kd_split.h new file mode 100644 index 0000000..62533a1 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_split.h @@ -0,0 +1,123 @@ +//---------------------------------------------------------------------- +// File: kd_split.h +// Programmer: Sunil Arya and David Mount +// Description: Methods for splitting kd-trees +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef ANN_KD_SPLIT_H +#define ANN_KD_SPLIT_H + +#include "kd_tree.h" // kd-tree definitions + +namespace geom_bt { +//---------------------------------------------------------------------- +// External entry points +// These are all splitting procedures for kd-trees. +//---------------------------------------------------------------------- + +void kd_split( // standard (optimized) kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +void midpt_split( // midpoint kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +void sl_midpt_split( // sliding midpoint kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +void fair_split( // fair-split kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +void sl_fair_split( // sliding fair-split kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +//////////////////////////////////////////////////////////////////////////////// +// +void kd_split_wd( // standard (optimized) kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo, // num of points on low side (returned) + int &cut_pt_idx); // index of cutting point (returned) + +void midpt_split_wd( // midpoint kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo, // num of points on low side (returned) + int &cut_pt_idx); // index of cutting point (returned) + +void sl_midpt_split_wd( // sliding midpoint kd-splitter + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo, // num of points on low side (returned) + int &cut_pt_idx); // index of cutting point (returned) + + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_tree.h b/geom_bottleneck/bottleneck/include/ANN/kd_tree.h new file mode 100644 index 0000000..5fb362d --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_tree.h @@ -0,0 +1,253 @@ +//---------------------------------------------------------------------- +// File: kd_tree.h +// Programmer: Sunil Arya and David Mount +// Description: Declarations for standard kd-tree routines +// Last modified: 05/03/05 (Version 1.1) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 1.1 05/03/05 +// Added fixed radius kNN search +// -------------------------------------------------------------------- +// 2015 - modified by A. Nigmetov to support deletion of points +//---------------------------------------------------------------------- + +#ifndef ANN_kd_tree_H +#define ANN_kd_tree_H + +#include <utility> // for std::pair +#include <ANN/ANNx.h> // all ANN includes + +using namespace std; // make std:: available + +namespace geom_bt { +//---------------------------------------------------------------------- +// Generic kd-tree node +// +// Nodes in kd-trees are of two types, splitting nodes which contain +// splitting information (a splitting hyperplane orthogonal to one +// of the coordinate axes) and leaf nodes which contain point +// information (an array of points stored in a bucket). This is +// handled by making a generic class kd_node, which is essentially an +// empty shell, and then deriving the leaf and splitting nodes from +// this. +//---------------------------------------------------------------------- +//class ANNkd_node; +class ANNkd_split; + +//typedef std::pair<ANNidx, ANNkd_node*> ANNreplaceSearchRes; + +class ANNkd_node{ // generic kd-tree node (empty shell) +protected: + int actual_num_points; // + ANNkd_split* parent; +public: + ANNkd_split* getParent() const { return parent; } + void setParent(ANNkd_split* par) { parent = par; } + int getNumPoints() const { return actual_num_points; } + void setNumPoints(int n) { assert(n >=0 ); actual_num_points = n; } + void decNumPoints() { assert(actual_num_points > 0); actual_num_points--; } + virtual ~ANNkd_node() {} // virtual distroyer + + virtual void ann_search(ANNdist) = 0; // tree search + virtual void ann_pri_search(ANNdist) = 0; // priority search + virtual void ann_FR_search(ANNdist) = 0; // fixed-radius search + + virtual void getStats( // get tree statistics + int dim, // dimension of space + ANNkdStats &st, // statistics + ANNorthRect &bnd_box) = 0; // bounding box + // print node + virtual void print(int level, ostream &out) = 0; + virtual void dump(ostream &out) = 0; // dump node + + friend class ANNkd_tree; // allow kd-tree to access us + + //////////////////////////////////////////////////////////////////////// + // deletion + virtual void delete_point(const int point_idx) {} + // range search + virtual void range_search(const ANNorthRect& region, // query region + int ANNkdDim, // dimension of points, + ANNpointArray ANNkdPts, // array of points + ANNorthRect& bnd_box, // bounding box of the current node, + // comes precomputed from the caller + std::vector<size_t>& pointIdices) {} // indices of points are returned in this vector + virtual void range_search_add(std::vector<size_t>& pointIdices) {} // add all points to pointIdices +}; + + + +//---------------------------------------------------------------------- +// kd-splitting function: +// kd_splitter is a pointer to a splitting routine for preprocessing. +// Different splitting procedures result in different strategies +// for building the tree. +//---------------------------------------------------------------------- +typedef void (*ANNkd_splitter)( // splitting routine for kd-trees + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices (permuted on return) + const ANNorthRect &bnds, // bounding rectangle for cell + int n, // number of points + int dim, // dimension of space + int &cut_dim, // cutting dimension (returned) + ANNcoord &cut_val, // cutting value (returned) + int &n_lo); // num of points on low side (returned) + +//---------------------------------------------------------------------- +// Leaf kd-tree node +// Leaf nodes of the kd-tree store the set of points associated +// with this bucket, stored as an array of point indices. These +// are indices in the array points, which resides with the +// root of the kd-tree. We also store the number of points +// that reside in this bucket. +//---------------------------------------------------------------------- + +class ANNkd_leaf: public ANNkd_node // leaf node for kd-tree +{ + int n_pts; + ANNidxArray bkt; // bucket of points +public: + ANNkd_leaf( // constructor + int n, // number of points + ANNidxArray b) : // bucket + n_pts(n), + bkt(b) + { + setNumPoints(n); + parent = NULL; + } + + ~ANNkd_leaf() { } // destructor (none) + + virtual void getStats( // get tree statistics + int dim, // dimension of space + ANNkdStats &st, // statistics + ANNorthRect &bnd_box); // bounding box + virtual void print(int level, ostream &out);// print node + virtual void dump(ostream &out); // dump node + + virtual void ann_search(ANNdist); // standard search + virtual void ann_pri_search(ANNdist); // priority search + virtual void ann_FR_search(ANNdist); // fixed-radius search + // deletion + void delete_point(const int point_idx, const bool killYourself); + // range search + virtual void range_search(const ANNorthRect& region, // query region + int ANNkdDim, // dimension of points, + ANNpointArray ANNkdPts, // array of points + ANNorthRect& bnd_box, // bounding box of the current node, + // comes precomputed from the caller + std::vector<size_t>& pointIdices); // indices of points are returned in this vector + virtual void range_search_add(std::vector<size_t>& pointIdices); // add all points to pointIdices +}; + +//---------------------------------------------------------------------- +// KD_TRIVIAL is a special pointer to an empty leaf node. Since +// some splitting rules generate many (more than 50%) trivial +// leaves, we use this one shared node to save space. +// +// The pointer is initialized to NULL, but whenever a kd-tree is +// created, we allocate this node, if it has not already been +// allocated. This node is *never* deallocated, so it produces +// a small memory leak. +//---------------------------------------------------------------------- + +extern ANNkd_leaf *KD_TRIVIAL; // trivial (empty) leaf node + +//---------------------------------------------------------------------- +// kd-tree splitting node. +// Splitting nodes contain a cutting dimension and a cutting value. +// These indicate the axis-parellel plane which subdivide the +// box for this node. The extent of the bounding box along the +// cutting dimension is maintained (this is used to speed up point +// to box distance calculations) [we do not store the entire bounding +// box since this may be wasteful of space in high dimensions]. +// We also store pointers to the 2 children. +//---------------------------------------------------------------------- + +class ANNkd_split : public ANNkd_node // splitting node of a kd-tree +{ + int cut_dim; // dim orthogonal to cutting plane + ANNcoord cut_val; // location of cutting plane + ANNcoord cd_bnds[2]; // lower and upper bounds of + // rectangle along cut_dim + ANNkd_ptr child[2]; // left and right children +public: + ANNkd_split( // constructor + int cd, // cutting dimension + ANNcoord cv, // cutting value + ANNcoord lv, ANNcoord hv, // low and high values + ANNkd_ptr lc=NULL, ANNkd_ptr hc=NULL) // children + { + cut_dim = cd; // cutting dimension + cut_val = cv; // cutting value + cd_bnds[ANN_LO] = lv; // lower bound for rectangle + cd_bnds[ANN_HI] = hv; // upper bound for rectangle + child[ANN_LO] = lc; // left child + child[ANN_HI] = hc; // right child + parent = NULL; + } + + + ~ANNkd_split() // destructor + { + if (child[ANN_LO]!= NULL && child[ANN_LO]!= KD_TRIVIAL) + delete child[ANN_LO]; + if (child[ANN_HI]!= NULL && child[ANN_HI]!= KD_TRIVIAL) + delete child[ANN_HI]; + } + + virtual void getStats( // get tree statistics + int dim, // dimension of space + ANNkdStats &st, // statistics + ANNorthRect &bnd_box); // bounding box + virtual void print(int level, ostream &out);// print node + virtual void dump(ostream &out); // dump node + + virtual void ann_search(ANNdist); // standard search + virtual void ann_pri_search(ANNdist); // priority search + virtual void ann_FR_search(ANNdist); // fixed-radius search + + /////// + void delete_leaf(ANNkd_leaf* childToDelete); // set the leaf to KD_TRIVIAL + // range search + virtual void range_search(const ANNorthRect& region, // query region + int ANNkdDim, // dimension of points, + ANNpointArray ANNkdPts, // array of points + ANNorthRect& bnd_box, // bounding box of the current node, + // comes precomputed from the caller + std::vector<size_t>& pointIdices); // indices of points are returned in this vector + virtual void range_search_add(std::vector<size_t>& pointIdices); // add all points to pointIdices +}; + +//---------------------------------------------------------------------- +// External entry points +//---------------------------------------------------------------------- + +ANNkd_ptr rkd_tree( // recursive construction of kd-tree + ANNpointArray pa, // point array (unaltered) + ANNidxArray pidx, // point indices to store in subtree + int n, // number of points + int dim, // dimension of space + int bsp, // bucket space + ANNorthRect &bnd_box, // bounding box for current node + ANNkd_splitter splitter, // splitting routine + vector<ANNkd_leaf*>* ppointToLeafVec); + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/kd_util.h b/geom_bottleneck/bottleneck/include/ANN/kd_util.h new file mode 100644 index 0000000..fa9f554 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/kd_util.h @@ -0,0 +1,126 @@ +//---------------------------------------------------------------------- +// File: kd_util.h +// Programmer: Sunil Arya and David Mount +// Description: Common utilities for kd- trees +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef ANN_kd_util_H +#define ANN_kd_util_H + +#include "kd_tree.h" // kd-tree declarations + +namespace geom_bt { +//---------------------------------------------------------------------- +// externally accessible functions +//---------------------------------------------------------------------- + +double annAspectRatio( // compute aspect ratio of box + int dim, // dimension + const ANNorthRect &bnd_box); // bounding cube + +void annEnclRect( // compute smallest enclosing rectangle + ANNpointArray pa, // point array + ANNidxArray pidx, // point indices + int n, // number of points + int dim, // dimension + ANNorthRect &bnds); // bounding cube (returned) + +void annEnclCube( // compute smallest enclosing cube + ANNpointArray pa, // point array + ANNidxArray pidx, // point indices + int n, // number of points + int dim, // dimension + ANNorthRect &bnds); // bounding cube (returned) + +ANNdist annBoxDistance( // compute distance from point to box + const ANNpoint q, // the point + const ANNpoint lo, // low point of box + const ANNpoint hi, // high point of box + int dim); // dimension of space + +ANNcoord annSpread( // compute point spread along dimension + ANNpointArray pa, // point array + ANNidxArray pidx, // point indices + int n, // number of points + int d); // dimension to check + +void annMinMax( // compute min and max coordinates along dim + ANNpointArray pa, // point array + ANNidxArray pidx, // point indices + int n, // number of points + int d, // dimension to check + ANNcoord& min, // minimum value (returned) + ANNcoord& max); // maximum value (returned) + +int annMaxSpread( // compute dimension of max spread + ANNpointArray pa, // point array + ANNidxArray pidx, // point indices + int n, // number of points + int dim); // dimension of space + +void annMedianSplit( // split points along median value + ANNpointArray pa, // points to split + ANNidxArray pidx, // point indices + int n, // number of points + int d, // dimension along which to split + ANNcoord &cv, // cutting value + int n_lo); // split into n_lo and n-n_lo + +void annPlaneSplit( // split points by a plane + ANNpointArray pa, // points to split + ANNidxArray pidx, // point indices + int n, // number of points + int d, // dimension along which to split + ANNcoord cv, // cutting value + int &br1, // first break (values < cv) + int &br2); // second break (values == cv) + +void annBoxSplit( // split points by a box + ANNpointArray pa, // points to split + ANNidxArray pidx, // point indices + int n, // number of points + int dim, // dimension of space + ANNorthRect &box, // the box + int &n_in); // number of points inside (returned) + +int annSplitBalance( // determine balance factor of a split + ANNpointArray pa, // points to split + ANNidxArray pidx, // point indices + int n, // number of points + int d, // dimension along which to split + ANNcoord cv); // cutting value + +void annBox2Bnds( // convert inner box to bounds + const ANNorthRect &inner_box, // inner box + const ANNorthRect &bnd_box, // enclosing box + int dim, // dimension of space + int &n_bnds, // number of bounds (returned) + ANNorthHSArray &bnds); // bounds array (returned) + +void annBnds2Box( // convert bounds to inner box + const ANNorthRect &bnd_box, // enclosing box + int dim, // dimension of space + int n_bnds, // number of bounds + ANNorthHSArray bnds, // bounds array + ANNorthRect &inner_box); // inner box (returned) + +} +#endif diff --git a/geom_bottleneck/bottleneck/include/ANN/pr_queue.h b/geom_bottleneck/bottleneck/include/ANN/pr_queue.h new file mode 100644 index 0000000..f938a73 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/pr_queue.h @@ -0,0 +1,127 @@ +//---------------------------------------------------------------------- +// File: pr_queue.h +// Programmer: Sunil Arya and David Mount +// Description: Include file for priority queue and related +// structures. +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef PR_QUEUE_H +#define PR_QUEUE_H + +#include <ANN/ANNx.h> // all ANN includes +#include <ANN/ANNperf.h> // performance evaluation + +namespace geom_bt { +//---------------------------------------------------------------------- +// Basic types. +//---------------------------------------------------------------------- +typedef void *PQinfo; // info field is generic pointer +typedef ANNdist PQkey; // key field is distance + +//---------------------------------------------------------------------- +// Priority queue +// A priority queue is a list of items, along with associated +// priorities. The basic operations are insert and extract_minimum. +// +// The priority queue is maintained using a standard binary heap. +// (Implementation note: Indexing is performed from [1..max] rather +// than the C standard of [0..max-1]. This simplifies parent/child +// computations.) User information consists of a void pointer, +// and the user is responsible for casting this quantity into whatever +// useful form is desired. +// +// Because the priority queue is so central to the efficiency of +// query processing, all the code is inline. +//---------------------------------------------------------------------- + +class ANNpr_queue { + + struct pq_node { // node in priority queue + PQkey key; // key value + PQinfo info; // info field + }; + int n; // number of items in queue + int max_size; // maximum queue size + pq_node *pq; // the priority queue (array of nodes) + +public: + ANNpr_queue(int max) // constructor (given max size) + { + n = 0; // initially empty + max_size = max; // maximum number of items + pq = new pq_node[max+1]; // queue is array [1..max] of nodes + } + + ~ANNpr_queue() // destructor + { delete [] pq; } + + ANNbool empty() // is queue empty? + { if (n==0) return ANNtrue; else return ANNfalse; } + + ANNbool non_empty() // is queue nonempty? + { if (n==0) return ANNfalse; else return ANNtrue; } + + void reset() // make existing queue empty + { n = 0; } + + inline void insert( // insert item (inlined for speed) + PQkey kv, // key value + PQinfo inf) // item info + { + if (++n > max_size) annError("Priority queue overflow.", ANNabort); + register int r = n; + while (r > 1) { // sift up new item + register int p = r/2; + ANN_FLOP(1) // increment floating ops + if (pq[p].key <= kv) // in proper order + break; + pq[r] = pq[p]; // else swap with parent + r = p; + } + pq[r].key = kv; // insert new item at final location + pq[r].info = inf; + } + + inline void extr_min( // extract minimum (inlined for speed) + PQkey &kv, // key (returned) + PQinfo &inf) // item info (returned) + { + kv = pq[1].key; // key of min item + inf = pq[1].info; // information of min item + register PQkey kn = pq[n--].key;// last item in queue + register int p = 1; // p points to item out of position + register int r = p<<1; // left child of p + while (r <= n) { // while r is still within the heap + ANN_FLOP(2) // increment floating ops + // set r to smaller child of p + if (r < n && pq[r].key > pq[r+1].key) r++; + if (kn <= pq[r].key) // in proper order + break; + pq[p] = pq[r]; // else swap with child + p = r; // advance pointers + r = p<<1; + } + pq[p] = pq[n+1]; // insert last item in proper place + } +}; + +} +#endif
\ No newline at end of file diff --git a/geom_bottleneck/bottleneck/include/ANN/pr_queue_k.h b/geom_bottleneck/bottleneck/include/ANN/pr_queue_k.h new file mode 100644 index 0000000..133a766 --- /dev/null +++ b/geom_bottleneck/bottleneck/include/ANN/pr_queue_k.h @@ -0,0 +1,120 @@ +//---------------------------------------------------------------------- +// File: pr_queue_k.h +// Programmer: Sunil Arya and David Mount +// Description: Include file for priority queue with k items. +// Last modified: 01/04/05 (Version 1.0) +//---------------------------------------------------------------------- +// Copyright (c) 1997-2005 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 +//---------------------------------------------------------------------- + +#ifndef PR_QUEUE_K_H +#define PR_QUEUE_K_H + +#include <ANN/ANNx.h> // all ANN includes +#include <ANN/ANNperf.h> // performance evaluation + +namespace geom_bt { +//---------------------------------------------------------------------- +// Basic types +//---------------------------------------------------------------------- +typedef ANNdist PQKkey; // key field is distance +typedef int PQKinfo; // info field is int + +//---------------------------------------------------------------------- +// Constants +// The NULL key value is used to initialize the priority queue, and +// so it should be larger than any valid distance, so that it will +// be replaced as legal distance values are inserted. The NULL +// info value must be a nonvalid array index, we use ANN_NULL_IDX, +// which is guaranteed to be negative. +//---------------------------------------------------------------------- + +const PQKkey PQ_NULL_KEY = ANN_DIST_INF; // nonexistent key value +const PQKinfo PQ_NULL_INFO = ANN_NULL_IDX; // nonexistent info value + +//---------------------------------------------------------------------- +// ANNmin_k +// An ANNmin_k structure is one which maintains the smallest +// k values (of type PQKkey) and associated information (of type +// PQKinfo). The special info and key values PQ_NULL_INFO and +// PQ_NULL_KEY means that thise entry is empty. +// +// It is currently implemented using an array with k items. +// Items are stored in increasing sorted order, and insertions +// are made through standard insertion sort. (This is quite +// inefficient, but current applications call for small values +// of k and relatively few insertions.) +// +// Note that the list contains k+1 entries, but the last entry +// is used as a simple placeholder and is otherwise ignored. +//---------------------------------------------------------------------- + +class ANNmin_k { + struct mk_node { // node in min_k structure + PQKkey key; // key value + PQKinfo info; // info field (user defined) + }; + + int k; // max number of keys to store + int n; // number of keys currently active + mk_node *mk; // the list itself + +public: + ANNmin_k(int max) // constructor (given max size) + { + n = 0; // initially no items + k = max; // maximum number of items + mk = new mk_node[max+1]; // sorted array of keys + } + + ~ANNmin_k() // destructor + { delete [] mk; } + + PQKkey ANNmin_key() // return minimum key + { return (n > 0 ? mk[0].key : PQ_NULL_KEY); } + + PQKkey max_key() // return maximum key + { return (n == k ? mk[k-1].key : PQ_NULL_KEY); } + + PQKkey ith_smallest_key(int i) // ith smallest key (i in [0..n-1]) + { return (i < n ? mk[i].key : PQ_NULL_KEY); } + + PQKinfo ith_smallest_info(int i) // info for ith smallest (i in [0..n-1]) + { return (i < n ? mk[i].info : PQ_NULL_INFO); } + + inline void insert( // insert item (inlined for speed) + PQKkey kv, // key value + PQKinfo inf) // item info + { + register int i; + // slide larger values up + for (i = n; i > 0; i--) { + if (mk[i-1].key > kv) + mk[i] = mk[i-1]; + else + break; + } + mk[i].key = kv; // store element here + mk[i].info = inf; + if (n < k) n++; // increment number of items + ANN_FLOP(k-i+1) // increment floating ops + } +}; + +} +#endif |