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psdlag-agn/src/inc/alglib/alglibmisc.h

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/*************************************************************************
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
{
typedef struct
{
ae_int_t s1;
ae_int_t s2;
ae_int_t magicv;
} hqrndstate;
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;
ae_vector x;
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;
ae_int_t debugcounter;
} kdtree;
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace 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.
*************************************************************************/
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();
};
/*************************************************************************
*************************************************************************/
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();
};
/*************************************************************************
HQRNDState initialization with random values which come from standard
RNG.
-- ALGLIB --
Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
void hqrndrandomize(hqrndstate &state);
/*************************************************************************
HQRNDState initialization with seed values
-- ALGLIB --
Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
void hqrndseed(const ae_int_t s1, const ae_int_t s2, hqrndstate &state);
/*************************************************************************
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
*************************************************************************/
double hqrnduniformr(const hqrndstate &state);
/*************************************************************************
This function generates random integer number in [0, N)
1. State structure must be initialized with HQRNDRandomize() or HQRNDSeed()
2. N can be any positive number except for very large numbers:
* close to 2^31 on 32-bit systems
* close to 2^62 on 64-bit systems
An exception will be generated if N is too large.
-- ALGLIB --
Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
ae_int_t hqrnduniformi(const hqrndstate &state, const ae_int_t n);
/*************************************************************************
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
*************************************************************************/
double hqrndnormal(const hqrndstate &state);
/*************************************************************************
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
*************************************************************************/
void hqrndunit2(const hqrndstate &state, double &x, double &y);
/*************************************************************************
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
*************************************************************************/
void hqrndnormal2(const hqrndstate &state, double &x1, double &x2);
/*************************************************************************
Random number generator: exponential distribution
State structure must be initialized with HQRNDRandomize() or HQRNDSeed().
-- ALGLIB --
Copyright 11.08.2007 by Bochkanov Sergey
*************************************************************************/
double hqrndexponential(const hqrndstate &state, const double lambdav);
/*************************************************************************
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
*************************************************************************/
double hqrnddiscrete(const hqrndstate &state, const real_1d_array &x, const ae_int_t n);
/*************************************************************************
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
*************************************************************************/
double hqrndcontinuous(const hqrndstate &state, const real_1d_array &x, const ae_int_t n);
/*************************************************************************
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(std::string &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);
void kdtreebuild(const real_2d_array &xy, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &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
*************************************************************************/
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);
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);
/*************************************************************************
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
*************************************************************************/
ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch);
ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k);
/*************************************************************************
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
*************************************************************************/
ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r, const bool selfmatch);
ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r);
/*************************************************************************
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
*************************************************************************/
ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const double eps);
ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const double eps);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsx(const kdtree &kdt, real_2d_array &x);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsxy(const kdtree &kdt, real_2d_array &xy);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultstags(const kdtree &kdt, integer_1d_array &tags);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsdistances(const kdtree &kdt, real_1d_array &r);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsxi(const kdtree &kdt, real_2d_array &x);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsxyi(const kdtree &kdt, real_2d_array &xy);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultstagsi(const kdtree &kdt, integer_1d_array &tags);
/*************************************************************************
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
*************************************************************************/
void kdtreequeryresultsdistancesi(const kdtree &kdt, real_1d_array &r);
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
void hqrndrandomize(hqrndstate* state, ae_state *_state);
void hqrndseed(ae_int_t s1,
ae_int_t s2,
hqrndstate* state,
ae_state *_state);
double hqrnduniformr(hqrndstate* state, ae_state *_state);
ae_int_t hqrnduniformi(hqrndstate* state, ae_int_t n, ae_state *_state);
double hqrndnormal(hqrndstate* state, ae_state *_state);
void hqrndunit2(hqrndstate* state, double* x, double* y, ae_state *_state);
void hqrndnormal2(hqrndstate* state,
double* x1,
double* x2,
ae_state *_state);
double hqrndexponential(hqrndstate* state,
double lambdav,
ae_state *_state);
double hqrnddiscrete(hqrndstate* state,
/* Real */ ae_vector* x,
ae_int_t n,
ae_state *_state);
double hqrndcontinuous(hqrndstate* state,
/* Real */ ae_vector* x,
ae_int_t n,
ae_state *_state);
ae_bool _hqrndstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _hqrndstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _hqrndstate_clear(void* _p);
void _hqrndstate_destroy(void* _p);
void kdtreebuild(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t nx,
ae_int_t ny,
ae_int_t normtype,
kdtree* kdt,
ae_state *_state);
void kdtreebuildtagged(/* Real */ ae_matrix* xy,
/* Integer */ ae_vector* tags,
ae_int_t n,
ae_int_t nx,
ae_int_t ny,
ae_int_t normtype,
kdtree* kdt,
ae_state *_state);
ae_int_t kdtreequeryknn(kdtree* kdt,
/* Real */ ae_vector* x,
ae_int_t k,
ae_bool selfmatch,
ae_state *_state);
ae_int_t kdtreequeryrnn(kdtree* kdt,
/* Real */ ae_vector* x,
double r,
ae_bool selfmatch,
ae_state *_state);
ae_int_t kdtreequeryaknn(kdtree* kdt,
/* Real */ ae_vector* x,
ae_int_t k,
ae_bool selfmatch,
double eps,
ae_state *_state);
void kdtreequeryresultsx(kdtree* kdt,
/* Real */ ae_matrix* x,
ae_state *_state);
void kdtreequeryresultsxy(kdtree* kdt,
/* Real */ ae_matrix* xy,
ae_state *_state);
void kdtreequeryresultstags(kdtree* kdt,
/* Integer */ ae_vector* tags,
ae_state *_state);
void kdtreequeryresultsdistances(kdtree* kdt,
/* Real */ ae_vector* r,
ae_state *_state);
void kdtreequeryresultsxi(kdtree* kdt,
/* Real */ ae_matrix* x,
ae_state *_state);
void kdtreequeryresultsxyi(kdtree* kdt,
/* Real */ ae_matrix* xy,
ae_state *_state);
void kdtreequeryresultstagsi(kdtree* kdt,
/* Integer */ ae_vector* tags,
ae_state *_state);
void kdtreequeryresultsdistancesi(kdtree* kdt,
/* Real */ ae_vector* r,
ae_state *_state);
void kdtreealloc(ae_serializer* s, kdtree* tree, ae_state *_state);
void kdtreeserialize(ae_serializer* s, kdtree* tree, ae_state *_state);
void kdtreeunserialize(ae_serializer* s, kdtree* tree, ae_state *_state);
ae_bool _kdtree_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _kdtree_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _kdtree_clear(void* _p);
void _kdtree_destroy(void* _p);
}
#endif
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