/************************************************************************* Copyright (c) Sergey Bochkanov (ALGLIB project). >>> SOURCE LICENSE >>> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation (www.fsf.org); either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A copy of the GNU General Public License is available at http://www.fsf.org/licensing/licenses >>> END OF LICENSE >>> *************************************************************************/ #pragma warning disable 162 #pragma warning disable 219 using System; public partial class alglib { /************************************************************************* Portable high quality random number generator state. Initialized with HQRNDRandomize() or HQRNDSeed(). Fields: S1, S2 - seed values V - precomputed value MagicV - 'magic' value used to determine whether State structure was correctly initialized. *************************************************************************/ public class hqrndstate { // // Public declarations // public hqrndstate() { _innerobj = new hqrnd.hqrndstate(); } // // Although some of declarations below are public, you should not use them // They are intended for internal use only // private hqrnd.hqrndstate _innerobj; public hqrnd.hqrndstate innerobj { get { return _innerobj; } } public hqrndstate(hqrnd.hqrndstate obj) { _innerobj = obj; } } /************************************************************************* HQRNDState initialization with random values which come from standard RNG. -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndrandomize(out hqrndstate state) { state = new hqrndstate(); hqrnd.hqrndrandomize(state.innerobj); return; } /************************************************************************* HQRNDState initialization with seed values -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndseed(int s1, int s2, out hqrndstate state) { state = new hqrndstate(); hqrnd.hqrndseed(s1, s2, state.innerobj); return; } /************************************************************************* This function generates random real number in (0,1), not including interval boundaries State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static double hqrnduniformr(hqrndstate state) { double result = hqrnd.hqrnduniformr(state.innerobj); return result; } /************************************************************************* This function generates random integer number in [0, N) 1. N must be less than HQRNDMax-1. 2. State structure must be initialized with HQRNDRandomize() or HQRNDSeed() -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static int hqrnduniformi(hqrndstate state, int n) { int result = hqrnd.hqrnduniformi(state.innerobj, n); return result; } /************************************************************************* Random number generator: normal numbers This function generates one random number from normal distribution. Its performance is equal to that of HQRNDNormal2() State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static double hqrndnormal(hqrndstate state) { double result = hqrnd.hqrndnormal(state.innerobj); return result; } /************************************************************************* Random number generator: random X and Y such that X^2+Y^2=1 State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndunit2(hqrndstate state, out double x, out double y) { x = 0; y = 0; hqrnd.hqrndunit2(state.innerobj, ref x, ref y); return; } /************************************************************************* Random number generator: normal numbers This function generates two independent random numbers from normal distribution. Its performance is equal to that of HQRNDNormal() State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndnormal2(hqrndstate state, out double x1, out double x2) { x1 = 0; x2 = 0; hqrnd.hqrndnormal2(state.innerobj, ref x1, ref x2); return; } /************************************************************************* Random number generator: exponential distribution State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 11.08.2007 by Bochkanov Sergey *************************************************************************/ public static double hqrndexponential(hqrndstate state, double lambdav) { double result = hqrnd.hqrndexponential(state.innerobj, lambdav); return result; } /************************************************************************* This function generates random number from discrete distribution given by finite sample X. INPUT PARAMETERS State - high quality random number generator, must be initialized with HQRNDRandomize() or HQRNDSeed(). X - finite sample N - number of elements to use, N>=1 RESULT this function returns one of the X[i] for random i=0..N-1 -- ALGLIB -- Copyright 08.11.2011 by Bochkanov Sergey *************************************************************************/ public static double hqrnddiscrete(hqrndstate state, double[] x, int n) { double result = hqrnd.hqrnddiscrete(state.innerobj, x, n); return result; } /************************************************************************* This function generates random number from continuous distribution given by finite sample X. INPUT PARAMETERS State - high quality random number generator, must be initialized with HQRNDRandomize() or HQRNDSeed(). X - finite sample, array[N] (can be larger, in this case only leading N elements are used). THIS ARRAY MUST BE SORTED BY ASCENDING. N - number of elements to use, N>=1 RESULT this function returns random number from continuous distribution which tries to approximate X as mush as possible. min(X)<=Result<=max(X). -- ALGLIB -- Copyright 08.11.2011 by Bochkanov Sergey *************************************************************************/ public static double hqrndcontinuous(hqrndstate state, double[] x, int n) { double result = hqrnd.hqrndcontinuous(state.innerobj, x, n); return result; } } public partial class alglib { /************************************************************************* *************************************************************************/ public class kdtree { // // Public declarations // public kdtree() { _innerobj = new nearestneighbor.kdtree(); } // // Although some of declarations below are public, you should not use them // They are intended for internal use only // private nearestneighbor.kdtree _innerobj; public nearestneighbor.kdtree innerobj { get { return _innerobj; } } public kdtree(nearestneighbor.kdtree obj) { _innerobj = obj; } } /************************************************************************* This function serializes data structure to string. Important properties of s_out: * it contains alphanumeric characters, dots, underscores, minus signs * these symbols are grouped into words, which are separated by spaces and Windows-style (CR+LF) newlines * although serializer uses spaces and CR+LF as separators, you can replace any separator character by arbitrary combination of spaces, tabs, Windows or Unix newlines. It allows flexible reformatting of the string in case you want to include it into text or XML file. But you should not insert separators into the middle of the "words" nor you should change case of letters. * s_out can be freely moved between 32-bit and 64-bit systems, little and big endian machines, and so on. You can serialize structure on 32-bit machine and unserialize it on 64-bit one (or vice versa), or serialize it on SPARC and unserialize on x86. You can also serialize it in C# version of ALGLIB and unserialize in C++ one, and vice versa. *************************************************************************/ public static void kdtreeserialize(kdtree obj, out string s_out) { alglib.serializer s = new alglib.serializer(); s.alloc_start(); nearestneighbor.kdtreealloc(s, obj.innerobj); s.sstart_str(); nearestneighbor.kdtreeserialize(s, obj.innerobj); s.stop(); s_out = s.get_string(); } /************************************************************************* This function unserializes data structure from string. *************************************************************************/ public static void kdtreeunserialize(string s_in, out kdtree obj) { alglib.serializer s = new alglib.serializer(); obj = new kdtree(); s.ustart_str(s_in); nearestneighbor.kdtreeunserialize(s, obj.innerobj); s.stop(); } /************************************************************************* KD-tree creation This subroutine creates KD-tree from set of X-values and optional Y-values INPUT PARAMETERS XY - dataset, array[0..N-1,0..NX+NY-1]. one row corresponds to one point. first NX columns contain X-values, next NY (NY may be zero) columns may contain associated Y-values N - number of points, N>=0. NX - space dimension, NX>=1. NY - number of optional Y-values, NY>=0. NormType- norm type: * 0 denotes infinity-norm * 1 denotes 1-norm * 2 denotes 2-norm (Euclidean norm) OUTPUT PARAMETERS KDT - KD-tree NOTES 1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory requirements. 2. Although KD-trees may be used with any combination of N and NX, they are more efficient than brute-force search only when N >> 4^NX. So they are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another inefficient case, because simple binary search (without additional structures) is much more efficient in such tasks than KD-trees. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreebuild(double[,] xy, int n, int nx, int ny, int normtype, out kdtree kdt) { kdt = new kdtree(); nearestneighbor.kdtreebuild(xy, n, nx, ny, normtype, kdt.innerobj); return; } public static void kdtreebuild(double[,] xy, int nx, int ny, int normtype, out kdtree kdt) { int n; kdt = new kdtree(); n = ap.rows(xy); nearestneighbor.kdtreebuild(xy, n, nx, ny, normtype, kdt.innerobj); return; } /************************************************************************* KD-tree creation This subroutine creates KD-tree from set of X-values, integer tags and optional Y-values INPUT PARAMETERS XY - dataset, array[0..N-1,0..NX+NY-1]. one row corresponds to one point. first NX columns contain X-values, next NY (NY may be zero) columns may contain associated Y-values Tags - tags, array[0..N-1], contains integer tags associated with points. N - number of points, N>=0 NX - space dimension, NX>=1. NY - number of optional Y-values, NY>=0. NormType- norm type: * 0 denotes infinity-norm * 1 denotes 1-norm * 2 denotes 2-norm (Euclidean norm) OUTPUT PARAMETERS KDT - KD-tree NOTES 1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory requirements. 2. Although KD-trees may be used with any combination of N and NX, they are more efficient than brute-force search only when N >> 4^NX. So they are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another inefficient case, because simple binary search (without additional structures) is much more efficient in such tasks than KD-trees. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreebuildtagged(double[,] xy, int[] tags, int n, int nx, int ny, int normtype, out kdtree kdt) { kdt = new kdtree(); nearestneighbor.kdtreebuildtagged(xy, tags, n, nx, ny, normtype, kdt.innerobj); return; } public static void kdtreebuildtagged(double[,] xy, int[] tags, int nx, int ny, int normtype, out kdtree kdt) { int n; if( (ap.rows(xy)!=ap.len(tags))) throw new alglibexception("Error while calling 'kdtreebuildtagged': looks like one of arguments has wrong size"); kdt = new kdtree(); n = ap.rows(xy); nearestneighbor.kdtreebuildtagged(xy, tags, n, nx, ny, normtype, kdt.innerobj); return; } /************************************************************************* K-NN query: K nearest neighbors INPUT PARAMETERS KDT - KD-tree X - point, array[0..NX-1]. K - number of neighbors to return, K>=1 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True RESULT number of actual neighbors found (either K or N, if K>N). This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain these results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryknn(kdtree kdt, double[] x, int k, bool selfmatch) { int result = nearestneighbor.kdtreequeryknn(kdt.innerobj, x, k, selfmatch); return result; } public static int kdtreequeryknn(kdtree kdt, double[] x, int k) { bool selfmatch; selfmatch = true; int result = nearestneighbor.kdtreequeryknn(kdt.innerobj, x, k, selfmatch); return result; } /************************************************************************* R-NN query: all points within R-sphere centered at X INPUT PARAMETERS KDT - KD-tree X - point, array[0..NX-1]. R - radius of sphere (in corresponding norm), R>0 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True RESULT number of neighbors found, >=0 This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain actual results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryrnn(kdtree kdt, double[] x, double r, bool selfmatch) { int result = nearestneighbor.kdtreequeryrnn(kdt.innerobj, x, r, selfmatch); return result; } public static int kdtreequeryrnn(kdtree kdt, double[] x, double r) { bool selfmatch; selfmatch = true; int result = nearestneighbor.kdtreequeryrnn(kdt.innerobj, x, r, selfmatch); return result; } /************************************************************************* K-NN query: approximate K nearest neighbors INPUT PARAMETERS KDT - KD-tree X - point, array[0..NX-1]. K - number of neighbors to return, K>=1 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True Eps - approximation factor, Eps>=0. eps-approximate nearest neighbor is a neighbor whose distance from X is at most (1+eps) times distance of true nearest neighbor. RESULT number of actual neighbors found (either K or N, if K>N). NOTES significant performance gain may be achieved only when Eps is is on the order of magnitude of 1 or larger. This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain these results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryaknn(kdtree kdt, double[] x, int k, bool selfmatch, double eps) { int result = nearestneighbor.kdtreequeryaknn(kdt.innerobj, x, k, selfmatch, eps); return result; } public static int kdtreequeryaknn(kdtree kdt, double[] x, int k, double eps) { bool selfmatch; selfmatch = true; int result = nearestneighbor.kdtreequeryaknn(kdt.innerobj, x, k, selfmatch, eps); return result; } /************************************************************************* X-values from last query INPUT PARAMETERS KDT - KD-tree X - possibly pre-allocated buffer. If X is too small to store result, it is resized. If size(X) is enough to store result, it is left unchanged. OUTPUT PARAMETERS X - rows are filled with X-values NOTES 1. points are ordered by distance from the query point (first = closest) 2. if XY is larger than required to store result, only leading part will be overwritten; trailing part will be left unchanged. So if on input XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get XY = [[1,2],[C,D]]. This is done purposely to increase performance; if you want function to resize array according to result size, use function with same name and suffix 'I'. SEE ALSO * KDTreeQueryResultsXY() X- and Y-values * KDTreeQueryResultsTags() tag values * KDTreeQueryResultsDistances() distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsx(kdtree kdt, ref double[,] x) { nearestneighbor.kdtreequeryresultsx(kdt.innerobj, ref x); return; } /************************************************************************* X- and Y-values from last query INPUT PARAMETERS KDT - KD-tree XY - possibly pre-allocated buffer. If XY is too small to store result, it is resized. If size(XY) is enough to store result, it is left unchanged. OUTPUT PARAMETERS XY - rows are filled with points: first NX columns with X-values, next NY columns - with Y-values. NOTES 1. points are ordered by distance from the query point (first = closest) 2. if XY is larger than required to store result, only leading part will be overwritten; trailing part will be left unchanged. So if on input XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get XY = [[1,2],[C,D]]. This is done purposely to increase performance; if you want function to resize array according to result size, use function with same name and suffix 'I'. SEE ALSO * KDTreeQueryResultsX() X-values * KDTreeQueryResultsTags() tag values * KDTreeQueryResultsDistances() distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsxy(kdtree kdt, ref double[,] xy) { nearestneighbor.kdtreequeryresultsxy(kdt.innerobj, ref xy); return; } /************************************************************************* Tags from last query INPUT PARAMETERS KDT - KD-tree Tags - possibly pre-allocated buffer. If X is too small to store result, it is resized. If size(X) is enough to store result, it is left unchanged. OUTPUT PARAMETERS Tags - filled with tags associated with points, or, when no tags were supplied, with zeros NOTES 1. points are ordered by distance from the query point (first = closest) 2. if XY is larger than required to store result, only leading part will be overwritten; trailing part will be left unchanged. So if on input XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get XY = [[1,2],[C,D]]. This is done purposely to increase performance; if you want function to resize array according to result size, use function with same name and suffix 'I'. SEE ALSO * KDTreeQueryResultsX() X-values * KDTreeQueryResultsXY() X- and Y-values * KDTreeQueryResultsDistances() distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultstags(kdtree kdt, ref int[] tags) { nearestneighbor.kdtreequeryresultstags(kdt.innerobj, ref tags); return; } /************************************************************************* Distances from last query INPUT PARAMETERS KDT - KD-tree R - possibly pre-allocated buffer. If X is too small to store result, it is resized. If size(X) is enough to store result, it is left unchanged. OUTPUT PARAMETERS R - filled with distances (in corresponding norm) NOTES 1. points are ordered by distance from the query point (first = closest) 2. if XY is larger than required to store result, only leading part will be overwritten; trailing part will be left unchanged. So if on input XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get XY = [[1,2],[C,D]]. This is done purposely to increase performance; if you want function to resize array according to result size, use function with same name and suffix 'I'. SEE ALSO * KDTreeQueryResultsX() X-values * KDTreeQueryResultsXY() X- and Y-values * KDTreeQueryResultsTags() tag values -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsdistances(kdtree kdt, ref double[] r) { nearestneighbor.kdtreequeryresultsdistances(kdt.innerobj, ref r); return; } /************************************************************************* X-values from last query; 'interactive' variant for languages like Python which support constructs like "X = KDTreeQueryResultsXI(KDT)" and interactive mode of interpreter. This function allocates new array on each call, so it is significantly slower than its 'non-interactive' counterpart, but it is more convenient when you call it from command line. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsxi(kdtree kdt, out double[,] x) { x = new double[0,0]; nearestneighbor.kdtreequeryresultsxi(kdt.innerobj, ref x); return; } /************************************************************************* XY-values from last query; 'interactive' variant for languages like Python which support constructs like "XY = KDTreeQueryResultsXYI(KDT)" and interactive mode of interpreter. This function allocates new array on each call, so it is significantly slower than its 'non-interactive' counterpart, but it is more convenient when you call it from command line. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsxyi(kdtree kdt, out double[,] xy) { xy = new double[0,0]; nearestneighbor.kdtreequeryresultsxyi(kdt.innerobj, ref xy); return; } /************************************************************************* Tags from last query; 'interactive' variant for languages like Python which support constructs like "Tags = KDTreeQueryResultsTagsI(KDT)" and interactive mode of interpreter. This function allocates new array on each call, so it is significantly slower than its 'non-interactive' counterpart, but it is more convenient when you call it from command line. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultstagsi(kdtree kdt, out int[] tags) { tags = new int[0]; nearestneighbor.kdtreequeryresultstagsi(kdt.innerobj, ref tags); return; } /************************************************************************* Distances from last query; 'interactive' variant for languages like Python which support constructs like "R = KDTreeQueryResultsDistancesI(KDT)" and interactive mode of interpreter. This function allocates new array on each call, so it is significantly slower than its 'non-interactive' counterpart, but it is more convenient when you call it from command line. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsdistancesi(kdtree kdt, out double[] r) { r = new double[0]; nearestneighbor.kdtreequeryresultsdistancesi(kdt.innerobj, ref r); return; } } public partial class alglib { public class hqrnd { /************************************************************************* Portable high quality random number generator state. Initialized with HQRNDRandomize() or HQRNDSeed(). Fields: S1, S2 - seed values V - precomputed value MagicV - 'magic' value used to determine whether State structure was correctly initialized. *************************************************************************/ public class hqrndstate { public int s1; public int s2; public double v; public int magicv; }; public const int hqrndmax = 2147483563; public const int hqrndm1 = 2147483563; public const int hqrndm2 = 2147483399; public const int hqrndmagic = 1634357784; /************************************************************************* HQRNDState initialization with random values which come from standard RNG. -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndrandomize(hqrndstate state) { int s0 = 0; int s1 = 0; s0 = math.randominteger(hqrndm1); s1 = math.randominteger(hqrndm2); hqrndseed(s0, s1, state); } /************************************************************************* HQRNDState initialization with seed values -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndseed(int s1, int s2, hqrndstate state) { state.s1 = s1%(hqrndm1-1)+1; state.s2 = s2%(hqrndm2-1)+1; state.v = (double)1/(double)hqrndmax; state.magicv = hqrndmagic; } /************************************************************************* This function generates random real number in (0,1), not including interval boundaries State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static double hqrnduniformr(hqrndstate state) { double result = 0; result = state.v*hqrndintegerbase(state); return result; } /************************************************************************* This function generates random integer number in [0, N) 1. N must be less than HQRNDMax-1. 2. State structure must be initialized with HQRNDRandomize() or HQRNDSeed() -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static int hqrnduniformi(hqrndstate state, int n) { int result = 0; int mx = 0; // // Correct handling of N's close to RNDBaseMax // (avoiding skewed distributions for RNDBaseMax<>K*N) // alglib.ap.assert(n>0, "HQRNDUniformI: N<=0!"); alglib.ap.assert(n=RNDBaseMax-1!"); mx = hqrndmax-1-(hqrndmax-1)%n; do { result = hqrndintegerbase(state)-1; } while( result>=mx ); result = result%n; return result; } /************************************************************************* Random number generator: normal numbers This function generates one random number from normal distribution. Its performance is equal to that of HQRNDNormal2() State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static double hqrndnormal(hqrndstate state) { double result = 0; double v1 = 0; double v2 = 0; hqrndnormal2(state, ref v1, ref v2); result = v1; return result; } /************************************************************************* Random number generator: random X and Y such that X^2+Y^2=1 State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndunit2(hqrndstate state, ref double x, ref double y) { double v = 0; double mx = 0; double mn = 0; x = 0; y = 0; do { hqrndnormal2(state, ref x, ref y); } while( !((double)(x)!=(double)(0) || (double)(y)!=(double)(0)) ); mx = Math.Max(Math.Abs(x), Math.Abs(y)); mn = Math.Min(Math.Abs(x), Math.Abs(y)); v = mx*Math.Sqrt(1+math.sqr(mn/mx)); x = x/v; y = y/v; } /************************************************************************* Random number generator: normal numbers This function generates two independent random numbers from normal distribution. Its performance is equal to that of HQRNDNormal() State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 02.12.2009 by Bochkanov Sergey *************************************************************************/ public static void hqrndnormal2(hqrndstate state, ref double x1, ref double x2) { double u = 0; double v = 0; double s = 0; x1 = 0; x2 = 0; while( true ) { u = 2*hqrnduniformr(state)-1; v = 2*hqrnduniformr(state)-1; s = math.sqr(u)+math.sqr(v); if( (double)(s)>(double)(0) && (double)(s)<(double)(1) ) { // // two Sqrt's instead of one to // avoid overflow when S is too small // s = Math.Sqrt(-(2*Math.Log(s)))/Math.Sqrt(s); x1 = u*s; x2 = v*s; return; } } } /************************************************************************* Random number generator: exponential distribution State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). -- ALGLIB -- Copyright 11.08.2007 by Bochkanov Sergey *************************************************************************/ public static double hqrndexponential(hqrndstate state, double lambdav) { double result = 0; alglib.ap.assert((double)(lambdav)>(double)(0), "HQRNDExponential: LambdaV<=0!"); result = -(Math.Log(hqrnduniformr(state))/lambdav); return result; } /************************************************************************* This function generates random number from discrete distribution given by finite sample X. INPUT PARAMETERS State - high quality random number generator, must be initialized with HQRNDRandomize() or HQRNDSeed(). X - finite sample N - number of elements to use, N>=1 RESULT this function returns one of the X[i] for random i=0..N-1 -- ALGLIB -- Copyright 08.11.2011 by Bochkanov Sergey *************************************************************************/ public static double hqrnddiscrete(hqrndstate state, double[] x, int n) { double result = 0; alglib.ap.assert(n>0, "HQRNDDiscrete: N<=0"); alglib.ap.assert(n<=alglib.ap.len(x), "HQRNDDiscrete: Length(X)=1 RESULT this function returns random number from continuous distribution which tries to approximate X as mush as possible. min(X)<=Result<=max(X). -- ALGLIB -- Copyright 08.11.2011 by Bochkanov Sergey *************************************************************************/ public static double hqrndcontinuous(hqrndstate state, double[] x, int n) { double result = 0; double mx = 0; double mn = 0; int i = 0; alglib.ap.assert(n>0, "HQRNDContinuous: N<=0"); alglib.ap.assert(n<=alglib.ap.len(x), "HQRNDContinuous: Length(X)=(double)(mn), "HQRNDDiscrete: X is not sorted by ascending"); if( (double)(mx)!=(double)(mn) ) { result = (mx-mn)*hqrnduniformr(state)+mn; } else { result = mn; } return result; } /************************************************************************* L'Ecuyer, Efficient and portable combined random number generators *************************************************************************/ private static int hqrndintegerbase(hqrndstate state) { int result = 0; int k = 0; alglib.ap.assert(state.magicv==hqrndmagic, "HQRNDIntegerBase: State is not correctly initialized!"); k = state.s1/53668; state.s1 = 40014*(state.s1-k*53668)-k*12211; if( state.s1<0 ) { state.s1 = state.s1+2147483563; } k = state.s2/52774; state.s2 = 40692*(state.s2-k*52774)-k*3791; if( state.s2<0 ) { state.s2 = state.s2+2147483399; } // // Result // result = state.s1-state.s2; if( result<1 ) { result = result+2147483562; } return result; } } public class nearestneighbor { public class kdtree { public int n; public int nx; public int ny; public int normtype; public double[,] xy; public int[] tags; public double[] boxmin; public double[] boxmax; public int[] nodes; public double[] splits; public double[] x; public int kneeded; public double rneeded; public bool selfmatch; public double approxf; public int kcur; public int[] idx; public double[] r; public double[] buf; public double[] curboxmin; public double[] curboxmax; public double curdist; public int debugcounter; public kdtree() { xy = new double[0,0]; tags = new int[0]; boxmin = new double[0]; boxmax = new double[0]; nodes = new int[0]; splits = new double[0]; x = new double[0]; idx = new int[0]; r = new double[0]; buf = new double[0]; curboxmin = new double[0]; curboxmax = new double[0]; } }; public const int splitnodesize = 6; public const int kdtreefirstversion = 0; /************************************************************************* KD-tree creation This subroutine creates KD-tree from set of X-values and optional Y-values INPUT PARAMETERS XY - dataset, array[0..N-1,0..NX+NY-1]. one row corresponds to one point. first NX columns contain X-values, next NY (NY may be zero) columns may contain associated Y-values N - number of points, N>=0. NX - space dimension, NX>=1. NY - number of optional Y-values, NY>=0. NormType- norm type: * 0 denotes infinity-norm * 1 denotes 1-norm * 2 denotes 2-norm (Euclidean norm) OUTPUT PARAMETERS KDT - KD-tree NOTES 1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory requirements. 2. Although KD-trees may be used with any combination of N and NX, they are more efficient than brute-force search only when N >> 4^NX. So they are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another inefficient case, because simple binary search (without additional structures) is much more efficient in such tasks than KD-trees. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreebuild(double[,] xy, int n, int nx, int ny, int normtype, kdtree kdt) { int[] tags = new int[0]; int i = 0; alglib.ap.assert(n>=0, "KDTreeBuild: N<0"); alglib.ap.assert(nx>=1, "KDTreeBuild: NX<1"); alglib.ap.assert(ny>=0, "KDTreeBuild: NY<0"); alglib.ap.assert(normtype>=0 && normtype<=2, "KDTreeBuild: incorrect NormType"); alglib.ap.assert(alglib.ap.rows(xy)>=n, "KDTreeBuild: rows(X)=nx+ny || n==0, "KDTreeBuild: cols(X)0 ) { tags = new int[n]; for(i=0; i<=n-1; i++) { tags[i] = 0; } } kdtreebuildtagged(xy, tags, n, nx, ny, normtype, kdt); } /************************************************************************* KD-tree creation This subroutine creates KD-tree from set of X-values, integer tags and optional Y-values INPUT PARAMETERS XY - dataset, array[0..N-1,0..NX+NY-1]. one row corresponds to one point. first NX columns contain X-values, next NY (NY may be zero) columns may contain associated Y-values Tags - tags, array[0..N-1], contains integer tags associated with points. N - number of points, N>=0 NX - space dimension, NX>=1. NY - number of optional Y-values, NY>=0. NormType- norm type: * 0 denotes infinity-norm * 1 denotes 1-norm * 2 denotes 2-norm (Euclidean norm) OUTPUT PARAMETERS KDT - KD-tree NOTES 1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory requirements. 2. Although KD-trees may be used with any combination of N and NX, they are more efficient than brute-force search only when N >> 4^NX. So they are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another inefficient case, because simple binary search (without additional structures) is much more efficient in such tasks than KD-trees. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreebuildtagged(double[,] xy, int[] tags, int n, int nx, int ny, int normtype, kdtree kdt) { int i = 0; int j = 0; int maxnodes = 0; int nodesoffs = 0; int splitsoffs = 0; int i_ = 0; int i1_ = 0; alglib.ap.assert(n>=0, "KDTreeBuildTagged: N<0"); alglib.ap.assert(nx>=1, "KDTreeBuildTagged: NX<1"); alglib.ap.assert(ny>=0, "KDTreeBuildTagged: NY<0"); alglib.ap.assert(normtype>=0 && normtype<=2, "KDTreeBuildTagged: incorrect NormType"); alglib.ap.assert(alglib.ap.rows(xy)>=n, "KDTreeBuildTagged: rows(X)=nx+ny || n==0, "KDTreeBuildTagged: cols(X) quick exit // if( n==0 ) { return; } // // Allocate // kdtreeallocdatasetindependent(kdt, nx, ny); kdtreeallocdatasetdependent(kdt, n, nx, ny); // // Initial fill // for(i=0; i<=n-1; i++) { for(i_=0; i_<=nx-1;i_++) { kdt.xy[i,i_] = xy[i,i_]; } i1_ = (0) - (nx); for(i_=nx; i_<=2*nx+ny-1;i_++) { kdt.xy[i,i_] = xy[i,i_+i1_]; } kdt.tags[i] = tags[i]; } // // Determine bounding box // for(i_=0; i_<=nx-1;i_++) { kdt.boxmin[i_] = kdt.xy[0,i_]; } for(i_=0; i_<=nx-1;i_++) { kdt.boxmax[i_] = kdt.xy[0,i_]; } for(i=1; i<=n-1; i++) { for(j=0; j<=nx-1; j++) { kdt.boxmin[j] = Math.Min(kdt.boxmin[j], kdt.xy[i,j]); kdt.boxmax[j] = Math.Max(kdt.boxmax[j], kdt.xy[i,j]); } } // // prepare tree structure // * MaxNodes=N because we guarantee no trivial splits, i.e. // every split will generate two non-empty boxes // maxnodes = n; kdt.nodes = new int[splitnodesize*2*maxnodes]; kdt.splits = new double[2*maxnodes]; nodesoffs = 0; splitsoffs = 0; for(i_=0; i_<=nx-1;i_++) { kdt.curboxmin[i_] = kdt.boxmin[i_]; } for(i_=0; i_<=nx-1;i_++) { kdt.curboxmax[i_] = kdt.boxmax[i_]; } kdtreegeneratetreerec(kdt, ref nodesoffs, ref splitsoffs, 0, n, 8); } /************************************************************************* K-NN query: K nearest neighbors INPUT PARAMETERS KDT - KD-tree X - point, array[0..NX-1]. K - number of neighbors to return, K>=1 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True RESULT number of actual neighbors found (either K or N, if K>N). This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain these results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryknn(kdtree kdt, double[] x, int k, bool selfmatch) { int result = 0; alglib.ap.assert(k>=1, "KDTreeQueryKNN: K<1!"); alglib.ap.assert(alglib.ap.len(x)>=kdt.nx, "KDTreeQueryKNN: Length(X)0 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True RESULT number of neighbors found, >=0 This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain actual results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryrnn(kdtree kdt, double[] x, double r, bool selfmatch) { int result = 0; int i = 0; int j = 0; alglib.ap.assert((double)(r)>(double)(0), "KDTreeQueryRNN: incorrect R!"); alglib.ap.assert(alglib.ap.len(x)>=kdt.nx, "KDTreeQueryRNN: Length(X)=2; i--) { tsort.tagheappopi(ref kdt.r, ref kdt.idx, ref j); } return result; } /************************************************************************* K-NN query: approximate K nearest neighbors INPUT PARAMETERS KDT - KD-tree X - point, array[0..NX-1]. K - number of neighbors to return, K>=1 SelfMatch - whether self-matches are allowed: * if True, nearest neighbor may be the point itself (if it exists in original dataset) * if False, then only points with non-zero distance are returned * if not given, considered True Eps - approximation factor, Eps>=0. eps-approximate nearest neighbor is a neighbor whose distance from X is at most (1+eps) times distance of true nearest neighbor. RESULT number of actual neighbors found (either K or N, if K>N). NOTES significant performance gain may be achieved only when Eps is is on the order of magnitude of 1 or larger. This subroutine performs query and stores its result in the internal structures of the KD-tree. You can use following subroutines to obtain these results: * KDTreeQueryResultsX() to get X-values * KDTreeQueryResultsXY() to get X- and Y-values * KDTreeQueryResultsTags() to get tag values * KDTreeQueryResultsDistances() to get distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static int kdtreequeryaknn(kdtree kdt, double[] x, int k, bool selfmatch, double eps) { int result = 0; int i = 0; int j = 0; alglib.ap.assert(k>0, "KDTreeQueryAKNN: incorrect K!"); alglib.ap.assert((double)(eps)>=(double)(0), "KDTreeQueryAKNN: incorrect Eps!"); alglib.ap.assert(alglib.ap.len(x)>=kdt.nx, "KDTreeQueryAKNN: Length(X)=2; i--) { tsort.tagheappopi(ref kdt.r, ref kdt.idx, ref j); } return result; } /************************************************************************* X-values from last query INPUT PARAMETERS KDT - KD-tree X - possibly pre-allocated buffer. If X is too small to store result, it is resized. If size(X) is enough to store result, it is left unchanged. OUTPUT PARAMETERS X - rows are filled with X-values NOTES 1. points are ordered by distance from the query point (first = closest) 2. if XY is larger than required to store result, only leading part will be overwritten; trailing part will be left unchanged. So if on input XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get XY = [[1,2],[C,D]]. This is done purposely to increase performance; if you want function to resize array according to result size, use function with same name and suffix 'I'. SEE ALSO * KDTreeQueryResultsXY() X- and Y-values * KDTreeQueryResultsTags() tag values * KDTreeQueryResultsDistances() distances -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ public static void kdtreequeryresultsx(kdtree kdt, ref double[,] x) { int i = 0; int k = 0; int i_ = 0; int i1_ = 0; if( kdt.kcur==0 ) { return; } if( alglib.ap.rows(x)0, "KDTreeSplit: internal error"); // // split XY/Tags in two parts: // * [ILeft,IRight] is non-processed part of XY/Tags // // After cycle is done, we have Ileft=IRight. We deal with // this element separately. // // After this, [I1,ILeft) contains left part, and [ILeft,I2) // contains right part. // ileft = i1; iright = i2-1; while( ileft0, "KDTreeGenerateTreeRec: internal error"); alglib.ap.assert(i2>i1, "KDTreeGenerateTreeRec: internal error"); // // Generate leaf if needed // if( i2-i1<=maxleafsize ) { kdt.nodes[nodesoffs+0] = i2-i1; kdt.nodes[nodesoffs+1] = i1; nodesoffs = nodesoffs+2; return; } // // Load values for easier access // nx = kdt.nx; ny = kdt.ny; // // select dimension to split: // * D is a dimension number // d = 0; ds = kdt.curboxmax[0]-kdt.curboxmin[0]; for(i=1; i<=nx-1; i++) { v = kdt.curboxmax[i]-kdt.curboxmin[i]; if( (double)(v)>(double)(ds) ) { ds = v; d = i; } } // // Select split position S using sliding midpoint rule, // rearrange points into [I1,I3) and [I3,I2) // s = kdt.curboxmin[d]+0.5*ds; i1_ = (i1) - (0); for(i_=0; i_<=i2-i1-1;i_++) { kdt.buf[i_] = kdt.xy[i_+i1_,d]; } n = i2-i1; cntless = 0; cntgreater = 0; minv = kdt.buf[0]; maxv = kdt.buf[0]; minidx = i1; maxidx = i1; for(i=0; i<=n-1; i++) { v = kdt.buf[i]; if( (double)(v)<(double)(minv) ) { minv = v; minidx = i1+i; } if( (double)(v)>(double)(maxv) ) { maxv = v; maxidx = i1+i; } if( (double)(v)<(double)(s) ) { cntless = cntless+1; } if( (double)(v)>(double)(s) ) { cntgreater = cntgreater+1; } } if( cntless>0 && cntgreater>0 ) { // // normal midpoint split // kdtreesplit(kdt, i1, i2, d, s, ref i3); } else { // // sliding midpoint // if( cntless==0 ) { // // 1. move split to MinV, // 2. place one point to the left bin (move to I1), // others - to the right bin // s = minv; if( minidx!=i1 ) { for(i=0; i<=2*kdt.nx+kdt.ny-1; i++) { v = kdt.xy[minidx,i]; kdt.xy[minidx,i] = kdt.xy[i1,i]; kdt.xy[i1,i] = v; } j = kdt.tags[minidx]; kdt.tags[minidx] = kdt.tags[i1]; kdt.tags[i1] = j; } i3 = i1+1; } else { // // 1. move split to MaxV, // 2. place one point to the right bin (move to I2-1), // others - to the left bin // s = maxv; if( maxidx!=i2-1 ) { for(i=0; i<=2*kdt.nx+kdt.ny-1; i++) { v = kdt.xy[maxidx,i]; kdt.xy[maxidx,i] = kdt.xy[i2-1,i]; kdt.xy[i2-1,i] = v; } j = kdt.tags[maxidx]; kdt.tags[maxidx] = kdt.tags[i2-1]; kdt.tags[i2-1] = j; } i3 = i2-1; } } // // Generate 'split' node // kdt.nodes[nodesoffs+0] = 0; kdt.nodes[nodesoffs+1] = d; kdt.nodes[nodesoffs+2] = splitsoffs; kdt.splits[splitsoffs+0] = s; oldoffs = nodesoffs; nodesoffs = nodesoffs+splitnodesize; splitsoffs = splitsoffs+1; // // Recirsive generation: // * update CurBox // * call subroutine // * restore CurBox // kdt.nodes[oldoffs+3] = nodesoffs; v = kdt.curboxmax[d]; kdt.curboxmax[d] = s; kdtreegeneratetreerec(kdt, ref nodesoffs, ref splitsoffs, i1, i3, maxleafsize); kdt.curboxmax[d] = v; kdt.nodes[oldoffs+4] = nodesoffs; v = kdt.curboxmin[d]; kdt.curboxmin[d] = s; kdtreegeneratetreerec(kdt, ref nodesoffs, ref splitsoffs, i3, i2, maxleafsize); kdt.curboxmin[d] = v; } /************************************************************************* Recursive subroutine for NN queries. -- ALGLIB -- Copyright 28.02.2010 by Bochkanov Sergey *************************************************************************/ private static void kdtreequerynnrec(kdtree kdt, int offs) { double ptdist = 0; int i = 0; int j = 0; int nx = 0; int i1 = 0; int i2 = 0; int d = 0; double s = 0; double v = 0; double t1 = 0; int childbestoffs = 0; int childworstoffs = 0; int childoffs = 0; double prevdist = 0; bool todive = new bool(); bool bestisleft = new bool(); bool updatemin = new bool(); alglib.ap.assert(kdt.n>0, "KDTreeQueryNNRec: internal error"); // // Leaf node. // Process points. // if( kdt.nodes[offs]>0 ) { i1 = kdt.nodes[offs+1]; i2 = i1+kdt.nodes[offs]; for(i=i1; i<=i2-1; i++) { // // Calculate distance // ptdist = 0; nx = kdt.nx; if( kdt.normtype==0 ) { for(j=0; j<=nx-1; j++) { ptdist = Math.Max(ptdist, Math.Abs(kdt.xy[i,j]-kdt.x[j])); } } if( kdt.normtype==1 ) { for(j=0; j<=nx-1; j++) { ptdist = ptdist+Math.Abs(kdt.xy[i,j]-kdt.x[j]); } } if( kdt.normtype==2 ) { for(j=0; j<=nx-1; j++) { ptdist = ptdist+math.sqr(kdt.xy[i,j]-kdt.x[j]); } } // // Skip points with zero distance if self-matches are turned off // if( (double)(ptdist)==(double)(0) && !kdt.selfmatch ) { continue; } // // We CAN'T process point if R-criterion isn't satisfied, // i.e. (RNeeded<>0) AND (PtDist>R). // if( (double)(kdt.rneeded)==(double)(0) || (double)(ptdist)<=(double)(kdt.rneeded) ) { // // R-criterion is satisfied, we must either: // * replace worst point, if (KNeeded<>0) AND (KCur=KNeeded) // (or skip, if worst point is better) // * add point without replacement otherwise // if( kdt.kcur=(double)(s) ) { if( kdt.normtype==0 ) { kdt.curdist = Math.Max(kdt.curdist, t1-s); } if( kdt.normtype==1 ) { kdt.curdist = kdt.curdist-Math.Max(t1-v, 0)+t1-s; } if( kdt.normtype==2 ) { kdt.curdist = kdt.curdist-math.sqr(Math.Max(t1-v, 0))+math.sqr(t1-s); } } kdt.curboxmax[d] = s; } // // Decide: to dive into cell or not to dive // if( (double)(kdt.rneeded)!=(double)(0) && (double)(kdt.curdist)>(double)(kdt.rneeded) ) { todive = false; } else { if( kdt.kcur0, "KDTreeInitBox: internal error"); // // calculate distance from point to current bounding box // kdt.curdist = 0; if( kdt.normtype==0 ) { for(i=0; i<=kdt.nx-1; i++) { vx = x[i]; vmin = kdt.boxmin[i]; vmax = kdt.boxmax[i]; kdt.x[i] = vx; kdt.curboxmin[i] = vmin; kdt.curboxmax[i] = vmax; if( (double)(vx)<(double)(vmin) ) { kdt.curdist = Math.Max(kdt.curdist, vmin-vx); } else { if( (double)(vx)>(double)(vmax) ) { kdt.curdist = Math.Max(kdt.curdist, vx-vmax); } } } } if( kdt.normtype==1 ) { for(i=0; i<=kdt.nx-1; i++) { vx = x[i]; vmin = kdt.boxmin[i]; vmax = kdt.boxmax[i]; kdt.x[i] = vx; kdt.curboxmin[i] = vmin; kdt.curboxmax[i] = vmax; if( (double)(vx)<(double)(vmin) ) { kdt.curdist = kdt.curdist+vmin-vx; } else { if( (double)(vx)>(double)(vmax) ) { kdt.curdist = kdt.curdist+vx-vmax; } } } } if( kdt.normtype==2 ) { for(i=0; i<=kdt.nx-1; i++) { vx = x[i]; vmin = kdt.boxmin[i]; vmax = kdt.boxmax[i]; kdt.x[i] = vx; kdt.curboxmin[i] = vmin; kdt.curboxmax[i] = vmax; if( (double)(vx)<(double)(vmin) ) { kdt.curdist = kdt.curdist+math.sqr(vmin-vx); } else { if( (double)(vx)>(double)(vmax) ) { kdt.curdist = kdt.curdist+math.sqr(vx-vmax); } } } } } /************************************************************************* This function allocates all dataset-independent array fields of KDTree, i.e. such array fields that their dimensions do not depend on dataset size. This function do not sets KDT.NX or KDT.NY - it just allocates arrays -- ALGLIB -- Copyright 14.03.2011 by Bochkanov Sergey *************************************************************************/ private static void kdtreeallocdatasetindependent(kdtree kdt, int nx, int ny) { alglib.ap.assert(kdt.n>0, "KDTreeAllocDatasetIndependent: internal error"); kdt.x = new double[nx]; kdt.boxmin = new double[nx]; kdt.boxmax = new double[nx]; kdt.curboxmin = new double[nx]; kdt.curboxmax = new double[nx]; } /************************************************************************* This function allocates all dataset-dependent array fields of KDTree, i.e. such array fields that their dimensions depend on dataset size. This function do not sets KDT.N, KDT.NX or KDT.NY - it just allocates arrays. -- ALGLIB -- Copyright 14.03.2011 by Bochkanov Sergey *************************************************************************/ private static void kdtreeallocdatasetdependent(kdtree kdt, int n, int nx, int ny) { alglib.ap.assert(n>0, "KDTreeAllocDatasetDependent: internal error"); kdt.xy = new double[n, 2*nx+ny]; kdt.tags = new int[n]; kdt.idx = new int[n]; kdt.r = new double[n]; kdt.x = new double[nx]; kdt.buf = new double[Math.Max(n, nx)]; kdt.nodes = new int[splitnodesize*2*n]; kdt.splits = new double[2*n]; } /************************************************************************* This function allocates temporaries. This function do not sets KDT.N, KDT.NX or KDT.NY - it just allocates arrays. -- ALGLIB -- Copyright 14.03.2011 by Bochkanov Sergey *************************************************************************/ private static void kdtreealloctemporaries(kdtree kdt, int n, int nx, int ny) { alglib.ap.assert(n>0, "KDTreeAllocTemporaries: internal error"); kdt.x = new double[nx]; kdt.idx = new int[n]; kdt.r = new double[n]; kdt.buf = new double[Math.Max(n, nx)]; kdt.curboxmin = new double[nx]; kdt.curboxmax = new double[nx]; } } }