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alglibmisc.cpp
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alglibmisc.cpp
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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 >>>
*************************************************************************/
#ifdef _MSC_VER
#define _CRT_SECURE_NO_WARNINGS
#endif
#include "stdafx.h"
#include "alglibmisc.h"
// disable some irrelevant warnings
#if (AE_COMPILER==AE_MSVC) && !defined(AE_ALL_WARNINGS)
#pragma warning(disable:4100)
#pragma warning(disable:4127)
#pragma warning(disable:4611)
#pragma warning(disable:4702)
#pragma warning(disable:4996)
#endif
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{
#if defined(AE_COMPILE_NEARESTNEIGHBOR) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_HQRND) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_XDEBUG) || !defined(AE_PARTIAL_BUILD)
#endif
#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().
*************************************************************************/
_kdtreerequestbuffer_owner::_kdtreerequestbuffer_owner()
{
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
if( p_struct!=NULL )
{
alglib_impl::_kdtreerequestbuffer_destroy(p_struct);
alglib_impl::ae_free(p_struct);
}
p_struct = NULL;
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
p_struct = NULL;
p_struct = (alglib_impl::kdtreerequestbuffer*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtreerequestbuffer), &_state);
memset(p_struct, 0, sizeof(alglib_impl::kdtreerequestbuffer));
alglib_impl::_kdtreerequestbuffer_init(p_struct, &_state, ae_false);
ae_state_clear(&_state);
}
_kdtreerequestbuffer_owner::_kdtreerequestbuffer_owner(const _kdtreerequestbuffer_owner &rhs)
{
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
if( p_struct!=NULL )
{
alglib_impl::_kdtreerequestbuffer_destroy(p_struct);
alglib_impl::ae_free(p_struct);
}
p_struct = NULL;
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
p_struct = NULL;
alglib_impl::ae_assert(rhs.p_struct!=NULL, "ALGLIB: kdtreerequestbuffer copy constructor failure (source is not initialized)", &_state);
p_struct = (alglib_impl::kdtreerequestbuffer*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtreerequestbuffer), &_state);
memset(p_struct, 0, sizeof(alglib_impl::kdtreerequestbuffer));
alglib_impl::_kdtreerequestbuffer_init_copy(p_struct, const_cast<alglib_impl::kdtreerequestbuffer*>(rhs.p_struct), &_state, ae_false);
ae_state_clear(&_state);
}
_kdtreerequestbuffer_owner& _kdtreerequestbuffer_owner::operator=(const _kdtreerequestbuffer_owner &rhs)
{
if( this==&rhs )
return *this;
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return *this;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
alglib_impl::ae_assert(p_struct!=NULL, "ALGLIB: kdtreerequestbuffer assignment constructor failure (destination is not initialized)", &_state);
alglib_impl::ae_assert(rhs.p_struct!=NULL, "ALGLIB: kdtreerequestbuffer assignment constructor failure (source is not initialized)", &_state);
alglib_impl::_kdtreerequestbuffer_destroy(p_struct);
memset(p_struct, 0, sizeof(alglib_impl::kdtreerequestbuffer));
alglib_impl::_kdtreerequestbuffer_init_copy(p_struct, const_cast<alglib_impl::kdtreerequestbuffer*>(rhs.p_struct), &_state, ae_false);
ae_state_clear(&_state);
return *this;
}
_kdtreerequestbuffer_owner::~_kdtreerequestbuffer_owner()
{
if( p_struct!=NULL )
{
alglib_impl::_kdtreerequestbuffer_destroy(p_struct);
ae_free(p_struct);
}
}
alglib_impl::kdtreerequestbuffer* _kdtreerequestbuffer_owner::c_ptr()
{
return p_struct;
}
alglib_impl::kdtreerequestbuffer* _kdtreerequestbuffer_owner::c_ptr() const
{
return const_cast<alglib_impl::kdtreerequestbuffer*>(p_struct);
}
kdtreerequestbuffer::kdtreerequestbuffer() : _kdtreerequestbuffer_owner()
{
}
kdtreerequestbuffer::kdtreerequestbuffer(const kdtreerequestbuffer &rhs):_kdtreerequestbuffer_owner(rhs)
{
}
kdtreerequestbuffer& kdtreerequestbuffer::operator=(const kdtreerequestbuffer &rhs)
{
if( this==&rhs )
return *this;
_kdtreerequestbuffer_owner::operator=(rhs);
return *this;
}
kdtreerequestbuffer::~kdtreerequestbuffer()
{
}
/*************************************************************************
KD-tree object.
*************************************************************************/
_kdtree_owner::_kdtree_owner()
{
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
if( p_struct!=NULL )
{
alglib_impl::_kdtree_destroy(p_struct);
alglib_impl::ae_free(p_struct);
}
p_struct = NULL;
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
p_struct = NULL;
p_struct = (alglib_impl::kdtree*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtree), &_state);
memset(p_struct, 0, sizeof(alglib_impl::kdtree));
alglib_impl::_kdtree_init(p_struct, &_state, ae_false);
ae_state_clear(&_state);
}
_kdtree_owner::_kdtree_owner(const _kdtree_owner &rhs)
{
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
if( p_struct!=NULL )
{
alglib_impl::_kdtree_destroy(p_struct);
alglib_impl::ae_free(p_struct);
}
p_struct = NULL;
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
p_struct = NULL;
alglib_impl::ae_assert(rhs.p_struct!=NULL, "ALGLIB: kdtree copy constructor failure (source is not initialized)", &_state);
p_struct = (alglib_impl::kdtree*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtree), &_state);
memset(p_struct, 0, sizeof(alglib_impl::kdtree));
alglib_impl::_kdtree_init_copy(p_struct, const_cast<alglib_impl::kdtree*>(rhs.p_struct), &_state, ae_false);
ae_state_clear(&_state);
}
_kdtree_owner& _kdtree_owner::operator=(const _kdtree_owner &rhs)
{
if( this==&rhs )
return *this;
jmp_buf _break_jump;
alglib_impl::ae_state _state;
alglib_impl::ae_state_init(&_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_state.error_msg);
return *this;
#endif
}
alglib_impl::ae_state_set_break_jump(&_state, &_break_jump);
alglib_impl::ae_assert(p_struct!=NULL, "ALGLIB: kdtree assignment constructor failure (destination is not initialized)", &_state);
alglib_impl::ae_assert(rhs.p_struct!=NULL, "ALGLIB: kdtree assignment constructor failure (source is not initialized)", &_state);
alglib_impl::_kdtree_destroy(p_struct);
memset(p_struct, 0, sizeof(alglib_impl::kdtree));
alglib_impl::_kdtree_init_copy(p_struct, const_cast<alglib_impl::kdtree*>(rhs.p_struct), &_state, ae_false);
ae_state_clear(&_state);
return *this;
}
_kdtree_owner::~_kdtree_owner()
{
if( p_struct!=NULL )
{
alglib_impl::_kdtree_destroy(p_struct);
ae_free(p_struct);
}
}
alglib_impl::kdtree* _kdtree_owner::c_ptr()
{
return p_struct;
}
alglib_impl::kdtree* _kdtree_owner::c_ptr() const
{
return const_cast<alglib_impl::kdtree*>(p_struct);
}
kdtree::kdtree() : _kdtree_owner()
{
}
kdtree::kdtree(const kdtree &rhs):_kdtree_owner(rhs)
{
}
kdtree& kdtree::operator=(const kdtree &rhs)
{
if( this==&rhs )
return *this;
_kdtree_owner::operator=(rhs);
return *this;
}
kdtree::~kdtree()
{
}
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state state;
alglib_impl::ae_serializer serializer;
alglib_impl::ae_int_t ssize;
alglib_impl::ae_state_init(&state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&state, &_break_jump);
alglib_impl::ae_serializer_init(&serializer);
alglib_impl::ae_serializer_alloc_start(&serializer);
alglib_impl::kdtreealloc(&serializer, obj.c_ptr(), &state);
ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer);
s_out.clear();
s_out.reserve((size_t)(ssize+1));
alglib_impl::ae_serializer_sstart_str(&serializer, &s_out);
alglib_impl::kdtreeserialize(&serializer, obj.c_ptr(), &state);
alglib_impl::ae_serializer_stop(&serializer, &state);
alglib_impl::ae_assert( s_out.length()<=(size_t)ssize, "ALGLIB: serialization integrity error", &state);
alglib_impl::ae_serializer_clear(&serializer);
alglib_impl::ae_state_clear(&state);
}
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void kdtreeunserialize(const std::string &s_in, kdtree &obj)
{
jmp_buf _break_jump;
alglib_impl::ae_state state;
alglib_impl::ae_serializer serializer;
alglib_impl::ae_state_init(&state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&state, &_break_jump);
alglib_impl::ae_serializer_init(&serializer);
alglib_impl::ae_serializer_ustart_str(&serializer, &s_in);
alglib_impl::kdtreeunserialize(&serializer, obj.c_ptr(), &state);
alglib_impl::ae_serializer_stop(&serializer, &state);
alglib_impl::ae_serializer_clear(&serializer);
alglib_impl::ae_state_clear(&state);
}
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state state;
alglib_impl::ae_serializer serializer;
alglib_impl::ae_state_init(&state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&state, &_break_jump);
alglib_impl::ae_serializer_init(&serializer);
alglib_impl::ae_serializer_alloc_start(&serializer);
alglib_impl::kdtreealloc(&serializer, obj.c_ptr(), &state);
alglib_impl::ae_serializer_get_alloc_size(&serializer); // not actually needed, but we have to ask
alglib_impl::ae_serializer_sstart_stream(&serializer, &s_out);
alglib_impl::kdtreeserialize(&serializer, obj.c_ptr(), &state);
alglib_impl::ae_serializer_stop(&serializer, &state);
alglib_impl::ae_serializer_clear(&serializer);
alglib_impl::ae_state_clear(&state);
}
/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void kdtreeunserialize(const std::istream &s_in, kdtree &obj)
{
jmp_buf _break_jump;
alglib_impl::ae_state state;
alglib_impl::ae_serializer serializer;
alglib_impl::ae_state_init(&state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&state, &_break_jump);
alglib_impl::ae_serializer_init(&serializer);
alglib_impl::ae_serializer_ustart_stream(&serializer, &s_in);
alglib_impl::kdtreeunserialize(&serializer, obj.c_ptr(), &state);
alglib_impl::ae_serializer_stop(&serializer, &state);
alglib_impl::ae_serializer_clear(&serializer);
alglib_impl::ae_state_clear(&state);
}
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_alglib_env_state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::kdtreebuild(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, nx, ny, normtype, const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
/*************************************************************************
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
*************************************************************************/
#if !defined(AE_NO_EXCEPTIONS)
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = xy.rows();
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::kdtreebuild(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, nx, ny, normtype, const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
#endif
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_alglib_env_state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::kdtreebuildtagged(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), const_cast<alglib_impl::ae_vector*>(tags.c_ptr()), n, nx, ny, normtype, const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
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
*************************************************************************/
#if !defined(AE_NO_EXCEPTIONS)
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
if( (xy.rows()!=tags.length()))
_ALGLIB_CPP_EXCEPTION("Error while calling 'kdtreebuildtagged': looks like one of arguments has wrong size");
n = xy.rows();
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::kdtreebuildtagged(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), const_cast<alglib_impl::ae_vector*>(tags.c_ptr()), n, nx, ny, normtype, const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
#endif
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_alglib_env_state.error_msg);
return;
#endif
}
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::kdtreecreaterequestbuffer(const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), const_cast<alglib_impl::kdtreerequestbuffer*>(buf.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_alglib_env_state.error_msg);
return 0;
#endif
}
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::ae_int_t result = alglib_impl::kdtreequeryknn(const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), k, selfmatch, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<ae_int_t*>(&result));
}
/*************************************************************************
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
*************************************************************************/
#if !defined(AE_NO_EXCEPTIONS)
ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const xparams _xparams)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
bool selfmatch;
selfmatch = true;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::ae_int_t result = alglib_impl::kdtreequeryknn(const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), k, selfmatch, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<ae_int_t*>(&result));
}
#endif
/*************************************************************************
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)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
{
#if !defined(AE_NO_EXCEPTIONS)
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
#else
_ALGLIB_SET_ERROR_FLAG(_alglib_env_state.error_msg);
return 0;
#endif
}
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::ae_int_t result = alglib_impl::kdtreetsqueryknn(const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), const_cast<alglib_impl::kdtreerequestbuffer*>(buf.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), k, selfmatch, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<ae_int_t*>(&result));
}
/*************************************************************************
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
*************************************************************************/
#if !defined(AE_NO_EXCEPTIONS)
ae_int_t kdtreetsqueryknn(const kdtree &kdt, const kdtreerequestbuffer &buf, const real_1d_array &x, const ae_int_t k, const xparams _xparams)
{
jmp_buf _break_jump;
alglib_impl::ae_state _alglib_env_state;
bool selfmatch;
selfmatch = true;
alglib_impl::ae_state_init(&_alglib_env_state);
if( setjmp(_break_jump) )
_ALGLIB_CPP_EXCEPTION(_alglib_env_state.error_msg);
ae_state_set_break_jump(&_alglib_env_state, &_break_jump);
if( _xparams.flags!=0x0 )
ae_state_set_flags(&_alglib_env_state, _xparams.flags);
alglib_impl::ae_int_t result = alglib_impl::kdtreetsqueryknn(const_cast<alglib_impl::kdtree*>(kdt.c_ptr()), const_cast<alglib_impl::kdtreerequestbuffer*>(buf.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), k, selfmatch, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<ae_int_t*>(&result));
}
#endif
/*************************************************************************
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