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alglibmisc.h
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alglibmisc.h
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/*************************************************************************
ALGLIB 3.17.0 (source code generated 2020-12-27)
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 >>>
*************************************************************************/
#ifndef _alglibmisc_pkg_h
#define _alglibmisc_pkg_h
#include "ap.h"
#include "alglibinternal.h"
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
#if defined(AE_COMPILE_NEARESTNEIGHBOR) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_vector x;
ae_vector boxmin;
ae_vector boxmax;
ae_int_t kneeded;
double rneeded;
ae_bool selfmatch;
double approxf;
ae_int_t kcur;
ae_vector idx;
ae_vector r;
ae_vector buf;
ae_vector curboxmin;
ae_vector curboxmax;
double curdist;
} kdtreerequestbuffer;
typedef struct
{
ae_int_t n;
ae_int_t nx;
ae_int_t ny;
ae_int_t normtype;
ae_matrix xy;
ae_vector tags;
ae_vector boxmin;
ae_vector boxmax;
ae_vector nodes;
ae_vector splits;
kdtreerequestbuffer innerbuf;
ae_int_t debugcounter;
} kdtree;
#endif
#if defined(AE_COMPILE_HQRND) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t s1;
ae_int_t s2;
ae_int_t magicv;
} hqrndstate;
#endif
#if defined(AE_COMPILE_XDEBUG) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t i;
ae_complex c;
ae_vector a;
} xdebugrecord1;
#endif
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{
#if defined(AE_COMPILE_NEARESTNEIGHBOR) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Buffer object which is used to perform nearest neighbor requests in the
multithreaded mode (multiple threads working with same KD-tree object).
This object should be created with KDTreeCreateRequestBuffer().
*************************************************************************/
class _kdtreerequestbuffer_owner
{
public:
_kdtreerequestbuffer_owner();
_kdtreerequestbuffer_owner(const _kdtreerequestbuffer_owner &rhs);
_kdtreerequestbuffer_owner& operator=(const _kdtreerequestbuffer_owner &rhs);
virtual ~_kdtreerequestbuffer_owner();
alglib_impl::kdtreerequestbuffer* c_ptr();
alglib_impl::kdtreerequestbuffer* c_ptr() const;
protected:
alglib_impl::kdtreerequestbuffer *p_struct;
};
class kdtreerequestbuffer : public _kdtreerequestbuffer_owner
{
public:
kdtreerequestbuffer();
kdtreerequestbuffer(const kdtreerequestbuffer &rhs);
kdtreerequestbuffer& operator=(const kdtreerequestbuffer &rhs);
virtual ~kdtreerequestbuffer();
};
/*************************************************************************
KD-tree object.
*************************************************************************/
class _kdtree_owner
{
public:
_kdtree_owner();
_kdtree_owner(const _kdtree_owner &rhs);
_kdtree_owner& operator=(const _kdtree_owner &rhs);
virtual ~_kdtree_owner();
alglib_impl::kdtree* c_ptr();
alglib_impl::kdtree* c_ptr() const;
protected:
alglib_impl::kdtree *p_struct;
};
class kdtree : public _kdtree_owner
{
public:
kdtree();
kdtree(const kdtree &rhs);
kdtree& operator=(const kdtree &rhs);
virtual ~kdtree();
};
#endif
#if defined(AE_COMPILE_HQRND) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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.
*************************************************************************/
class _hqrndstate_owner
{
public:
_hqrndstate_owner();
_hqrndstate_owner(const _hqrndstate_owner &rhs);
_hqrndstate_owner& operator=(const _hqrndstate_owner &rhs);
virtual ~_hqrndstate_owner();
alglib_impl::hqrndstate* c_ptr();
alglib_impl::hqrndstate* c_ptr() const;
protected:
alglib_impl::hqrndstate *p_struct;
};
class hqrndstate : public _hqrndstate_owner
{
public:
hqrndstate();
hqrndstate(const hqrndstate &rhs);
hqrndstate& operator=(const hqrndstate &rhs);
virtual ~hqrndstate();
};
#endif
#if defined(AE_COMPILE_XDEBUG) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
*************************************************************************/
class _xdebugrecord1_owner
{
public:
_xdebugrecord1_owner();
_xdebugrecord1_owner(const _xdebugrecord1_owner &rhs);
_xdebugrecord1_owner& operator=(const _xdebugrecord1_owner &rhs);
virtual ~_xdebugrecord1_owner();
alglib_impl::xdebugrecord1* c_ptr();
alglib_impl::xdebugrecord1* c_ptr() const;
protected:
alglib_impl::xdebugrecord1 *p_struct;
};
class xdebugrecord1 : public _xdebugrecord1_owner
{
public:
xdebugrecord1();
xdebugrecord1(const xdebugrecord1 &rhs);
xdebugrecord1& operator=(const xdebugrecord1 &rhs);
virtual ~xdebugrecord1();
ae_int_t &i;
alglib::complex &c;
real_1d_array a;
};
#endif
#if defined(AE_COMPILE_NEARESTNEIGHBOR) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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.
*************************************************************************/
void kdtreeserialize(kdtree &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void kdtreeunserialize(const std::string &s_in, kdtree &obj);
/*************************************************************************
This function serializes data structure to C++ stream.
Data stream generated by this function is same as string representation
generated by string version of serializer - alphanumeric characters,
dots, underscores, minus signs, which are grouped into words separated by
spaces and CR+LF.
We recommend you to read comments on string version of serializer to find
out more about serialization of AlGLIB objects.
*************************************************************************/
void kdtreeserialize(kdtree &obj, std::ostream &s_out);
/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void kdtreeunserialize(const std::istream &s_in, kdtree &obj);
/*************************************************************************
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
*************************************************************************/
void kdtreebuild(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt, const xparams _xparams = alglib::xdefault);
void kdtreebuild(const real_2d_array &xy, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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
*************************************************************************/
void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt, const xparams _xparams = alglib::xdefault);
void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function creates buffer structure which can be used to perform
parallel KD-tree requests.
KD-tree subpackage provides two sets of request functions - ones which use
internal buffer of KD-tree object (these functions are single-threaded
because they use same buffer, which can not shared between threads), and
ones which use external buffer.
This function is used to initialize external buffer.
INPUT PARAMETERS
KDT - KD-tree which is associated with newly created buffer
OUTPUT PARAMETERS
Buf - external buffer.
IMPORTANT: KD-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use buffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 18.03.2016 by Bochkanov Sergey
*************************************************************************/
void kdtreecreaterequestbuffer(const kdtree &kdt, kdtreerequestbuffer &buf, const xparams _xparams = alglib::xdefault);
/*************************************************************************
K-NN query: K nearest neighbors
IMPORTANT: this function can not be used in multithreaded code because it
uses internal temporary buffer of kd-tree object, which can not
be shared between multiple threads. If you want to perform
parallel requests, use function which uses external request
buffer: KDTreeTsQueryKNN() ("Ts" stands for "thread-safe").
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
*************************************************************************/
ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const xparams _xparams = alglib::xdefault);
/*************************************************************************
K-NN query: K nearest neighbors, using external thread-local buffer.
You can call this function from multiple threads for same kd-tree instance,
assuming that different instances of buffer object are passed to different
threads.
INPUT PARAMETERS
KDT - kd-tree
Buf - request buffer object created for this particular
instance of kd-tree structure with kdtreecreaterequestbuffer()
function.
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 buffer object. You can use following subroutines to
obtain these results (pay attention to "buf" in their names):
* KDTreeTsQueryResultsX() to get X-values
* KDTreeTsQueryResultsXY() to get X- and Y-values
* KDTreeTsQueryResultsTags() to get tag values
* KDTreeTsQueryResultsDistances() to get distances
IMPORTANT: kd-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use biffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 18.03.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreetsqueryknn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreetsqueryknn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const ae_int_t k, const xparams _xparams = alglib::xdefault);
/*************************************************************************
R-NN query: all points within R-sphere centered at X, ordered by distance
between point and X (by ascending).
NOTE: it is also possible to perform undordered queries performed by means
of kdtreequeryrnnu() and kdtreetsqueryrnnu() functions. Such queries
are faster because we do not have to use heap structure for sorting.
IMPORTANT: this function can not be used in multithreaded code because it
uses internal temporary buffer of kd-tree object, which can not
be shared between multiple threads. If you want to perform
parallel requests, use function which uses external request
buffer: kdtreetsqueryrnn() ("Ts" stands for "thread-safe").
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
*************************************************************************/
ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
R-NN query: all points within R-sphere centered at X, no ordering by
distance as undicated by "U" suffix (faster that ordered query, for large
queries - significantly faster).
IMPORTANT: this function can not be used in multithreaded code because it
uses internal temporary buffer of kd-tree object, which can not
be shared between multiple threads. If you want to perform
parallel requests, use function which uses external request
buffer: kdtreetsqueryrnn() ("Ts" stands for "thread-safe").
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
As indicated by "U" suffix, this function returns unordered results.
-- ALGLIB --
Copyright 01.11.2018 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreequeryrnnu(const kdtree &kdt, const real_1d_array &x, const double r, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreequeryrnnu(const kdtree &kdt, const real_1d_array &x, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
R-NN query: all points within R-sphere centered at X, using external
thread-local buffer, sorted by distance between point and X (by ascending)
You can call this function from multiple threads for same kd-tree instance,
assuming that different instances of buffer object are passed to different
threads.
NOTE: it is also possible to perform undordered queries performed by means
of kdtreequeryrnnu() and kdtreetsqueryrnnu() functions. Such queries
are faster because we do not have to use heap structure for sorting.
INPUT PARAMETERS
KDT - KD-tree
Buf - request buffer object created for this particular
instance of kd-tree structure with kdtreecreaterequestbuffer()
function.
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 buffer object. You can use following subroutines to
obtain these results (pay attention to "buf" in their names):
* KDTreeTsQueryResultsX() to get X-values
* KDTreeTsQueryResultsXY() to get X- and Y-values
* KDTreeTsQueryResultsTags() to get tag values
* KDTreeTsQueryResultsDistances() to get distances
IMPORTANT: kd-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use biffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 18.03.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreetsqueryrnn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const double r, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreetsqueryrnn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
R-NN query: all points within R-sphere centered at X, using external
thread-local buffer, no ordering by distance as undicated by "U" suffix
(faster that ordered query, for large queries - significantly faster).
You can call this function from multiple threads for same kd-tree instance,
assuming that different instances of buffer object are passed to different
threads.
INPUT PARAMETERS
KDT - KD-tree
Buf - request buffer object created for this particular
instance of kd-tree structure with kdtreecreaterequestbuffer()
function.
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 buffer object. You can use following subroutines to
obtain these results (pay attention to "buf" in their names):
* KDTreeTsQueryResultsX() to get X-values
* KDTreeTsQueryResultsXY() to get X- and Y-values
* KDTreeTsQueryResultsTags() to get tag values
* KDTreeTsQueryResultsDistances() to get distances
As indicated by "U" suffix, this function returns unordered results.
IMPORTANT: kd-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use biffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 18.03.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreetsqueryrnnu(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const double r, const bool selfmatch, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreetsqueryrnnu(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
K-NN query: approximate K nearest neighbors
IMPORTANT: this function can not be used in multithreaded code because it
uses internal temporary buffer of kd-tree object, which can not
be shared between multiple threads. If you want to perform
parallel requests, use function which uses external request
buffer: KDTreeTsQueryAKNN() ("Ts" stands for "thread-safe").
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
*************************************************************************/
ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const double eps, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const double eps, const xparams _xparams = alglib::xdefault);
/*************************************************************************
K-NN query: approximate K nearest neighbors, using thread-local buffer.
You can call this function from multiple threads for same kd-tree instance,
assuming that different instances of buffer object are passed to different
threads.
INPUT PARAMETERS
KDT - KD-tree
Buf - request buffer object created for this particular
instance of kd-tree structure with kdtreecreaterequestbuffer()
function.
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 buffer object. You can use following subroutines to
obtain these results (pay attention to "buf" in their names):
* KDTreeTsQueryResultsX() to get X-values
* KDTreeTsQueryResultsXY() to get X- and Y-values
* KDTreeTsQueryResultsTags() to get tag values
* KDTreeTsQueryResultsDistances() to get distances
IMPORTANT: kd-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use biffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 18.03.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreetsqueryaknn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const double eps, const xparams _xparams = alglib::xdefault);
ae_int_t kdtreetsqueryaknn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const ae_int_t k, const double eps, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Box query: all points within user-specified box.
IMPORTANT: this function can not be used in multithreaded code because it
uses internal temporary buffer of kd-tree object, which can not
be shared between multiple threads. If you want to perform
parallel requests, use function which uses external request
buffer: KDTreeTsQueryBox() ("Ts" stands for "thread-safe").
INPUT PARAMETERS
KDT - KD-tree
BoxMin - lower bounds, array[0..NX-1].
BoxMax - upper bounds, array[0..NX-1].
RESULT
number of actual neighbors found (in [0,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() returns zeros for this request
NOTE: this particular query returns unordered results, because there is no
meaningful way of ordering points. Furthermore, no 'distance' is
associated with points - it is either INSIDE or OUTSIDE (so request
for distances will return zeros).
-- ALGLIB --
Copyright 14.05.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreequerybox(const kdtree &kdt, const real_1d_array &boxmin, const real_1d_array &boxmax, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Box query: all points within user-specified box, using thread-local buffer.
You can call this function from multiple threads for same kd-tree instance,
assuming that different instances of buffer object are passed to different
threads.
INPUT PARAMETERS
KDT - KD-tree
Buf - request buffer object created for this particular
instance of kd-tree structure with kdtreecreaterequestbuffer()
function.
BoxMin - lower bounds, array[0..NX-1].
BoxMax - upper bounds, array[0..NX-1].
RESULT
number of actual neighbors found (in [0,N]).
This subroutine performs query and stores its result in the internal
structures of the buffer object. You can use following subroutines to
obtain these results (pay attention to "ts" in their names):
* KDTreeTsQueryResultsX() to get X-values
* KDTreeTsQueryResultsXY() to get X- and Y-values
* KDTreeTsQueryResultsTags() to get tag values
* KDTreeTsQueryResultsDistances() returns zeros for this query
NOTE: this particular query returns unordered results, because there is no
meaningful way of ordering points. Furthermore, no 'distance' is
associated with points - it is either INSIDE or OUTSIDE (so request
for distances will return zeros).
IMPORTANT: kd-tree buffer should be used only with KD-tree object which
was used to initialize buffer. Any attempt to use biffer with
different object is dangerous - you may get integrity check
failure (exception) because sizes of internal arrays do not fit
to dimensions of KD-tree structure.
-- ALGLIB --
Copyright 14.05.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t kdtreetsquerybox(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &boxmin, const real_1d_array &boxmax, const xparams _xparams = alglib::xdefault);
/*************************************************************************
X-values from last query.
This function retuns results stored in the internal buffer of kd-tree
object. If you performed buffered requests (ones which use instances of
kdtreerequestbuffer class), you should call buffered version of this
function - kdtreetsqueryresultsx().
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
*************************************************************************/
void kdtreequeryresultsx(const kdtree &kdt, real_2d_array &x, const xparams _xparams = alglib::xdefault);
/*************************************************************************
X- and Y-values from last query
This function retuns results stored in the internal buffer of kd-tree
object. If you performed buffered requests (ones which use instances of
kdtreerequestbuffer class), you should call buffered version of this
function - kdtreetsqueryresultsxy().
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
*************************************************************************/
void kdtreequeryresultsxy(const kdtree &kdt, real_2d_array &xy, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Tags from last query
This function retuns results stored in the internal buffer of kd-tree
object. If you performed buffered requests (ones which use instances of
kdtreerequestbuffer class), you should call buffered version of this
function - kdtreetsqueryresultstags().
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
*************************************************************************/
void kdtreequeryresultstags(const kdtree &kdt, integer_1d_array &tags, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Distances from last query
This function retuns results stored in the internal buffer of kd-tree
object. If you performed buffered requests (ones which use instances of
kdtreerequestbuffer class), you should call buffered version of this
function - kdtreetsqueryresultsdistances().
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
*************************************************************************/
void kdtreequeryresultsdistances(const kdtree &kdt, real_1d_array &r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
X-values from last query associated with kdtreerequestbuffer object.
INPUT PARAMETERS
KDT - KD-tree
Buf - request buffer object created for this particular
instance of kd-tree structure.
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
*************************************************************************/
void kdtreetsqueryresultsx(const kdtree &kdt, const kdtreerequestbuffer &buf, real_2d_array &x, const xparams _xparams = alglib::xdefault);