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statistics.cpp
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statistics.cpp
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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 >>>
*************************************************************************/
#include "stdafx.h"
#include "statistics.h"
// disable some irrelevant warnings
#if (AE_COMPILER==AE_MSVC)
#pragma warning(disable:4100)
#pragma warning(disable:4127)
#pragma warning(disable:4702)
#pragma warning(disable:4996)
#endif
using namespace std;
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{
/*************************************************************************
Calculation of the distribution moments: mean, variance, skewness, kurtosis.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
OUTPUT PARAMETERS
Mean - mean.
Variance- variance.
Skewness- skewness (if variance<>0; zero otherwise).
Kurtosis- kurtosis (if variance<>0; zero otherwise).
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void samplemoments(const real_1d_array &x, const ae_int_t n, double &mean, double &variance, double &skewness, double &kurtosis)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplemoments(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &mean, &variance, &skewness, &kurtosis, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the distribution moments: mean, variance, skewness, kurtosis.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
OUTPUT PARAMETERS
Mean - mean.
Variance- variance.
Skewness- skewness (if variance<>0; zero otherwise).
Kurtosis- kurtosis (if variance<>0; zero otherwise).
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void samplemoments(const real_1d_array &x, double &mean, double &variance, double &skewness, double &kurtosis)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplemoments(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &mean, &variance, &skewness, &kurtosis, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the mean.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Mean' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplemean(const real_1d_array &x, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplemean(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the mean.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Mean' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplemean(const real_1d_array &x)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplemean(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the variance.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Variance' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplevariance(const real_1d_array &x, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplevariance(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the variance.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Variance' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplevariance(const real_1d_array &x)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplevariance(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the skewness.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Skewness' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double sampleskewness(const real_1d_array &x, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::sampleskewness(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the skewness.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Skewness' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double sampleskewness(const real_1d_array &x)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::sampleskewness(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the kurtosis.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Kurtosis' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplekurtosis(const real_1d_array &x, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplekurtosis(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Calculation of the kurtosis.
INPUT PARAMETERS:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
NOTE:
This function return result which calculated by 'SampleMoments' function
and stored at 'Kurtosis' variable.
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
double samplekurtosis(const real_1d_array &x)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::samplekurtosis(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
ADev
Input parameters:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
Output parameters:
ADev- ADev
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void sampleadev(const real_1d_array &x, const ae_int_t n, double &adev)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::sampleadev(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &adev, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
ADev
Input parameters:
X - sample
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
Output parameters:
ADev- ADev
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void sampleadev(const real_1d_array &x, double &adev)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::sampleadev(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &adev, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Median calculation.
Input parameters:
X - sample (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
Output parameters:
Median
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void samplemedian(const real_1d_array &x, const ae_int_t n, double &median)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplemedian(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &median, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Median calculation.
Input parameters:
X - sample (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
Output parameters:
Median
-- ALGLIB --
Copyright 06.09.2006 by Bochkanov Sergey
*************************************************************************/
void samplemedian(const real_1d_array &x, double &median)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplemedian(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, &median, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Percentile calculation.
Input parameters:
X - sample (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
P - percentile (0<=P<=1)
Output parameters:
V - percentile
-- ALGLIB --
Copyright 01.03.2008 by Bochkanov Sergey
*************************************************************************/
void samplepercentile(const real_1d_array &x, const ae_int_t n, const double p, double &v)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplepercentile(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, p, &v, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Percentile calculation.
Input parameters:
X - sample (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only leading N elements of X are processed
* if not given, automatically determined from size of X
P - percentile (0<=P<=1)
Output parameters:
V - percentile
-- ALGLIB --
Copyright 01.03.2008 by Bochkanov Sergey
*************************************************************************/
void samplepercentile(const real_1d_array &x, const double p, double &v)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::samplepercentile(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, p, &v, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
2-sample covariance
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
covariance (zero for N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
double cov2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::cov2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
2-sample covariance
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
covariance (zero for N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
double cov2(const real_1d_array &x, const real_1d_array &y)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
if( (x.length()!=y.length()))
throw ap_error("Error while calling 'cov2': looks like one of arguments has wrong size");
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::cov2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Pearson product-moment correlation coefficient
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
Pearson product-moment correlation coefficient
(zero for N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
double pearsoncorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::pearsoncorr2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Pearson product-moment correlation coefficient
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
Pearson product-moment correlation coefficient
(zero for N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
double pearsoncorr2(const real_1d_array &x, const real_1d_array &y)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
if( (x.length()!=y.length()))
throw ap_error("Error while calling 'pearsoncorr2': looks like one of arguments has wrong size");
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::pearsoncorr2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Spearman's rank correlation coefficient
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
Spearman's rank correlation coefficient
(zero for N=0 or N=1)
-- ALGLIB --
Copyright 09.04.2007 by Bochkanov Sergey
*************************************************************************/
double spearmancorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::spearmancorr2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Spearman's rank correlation coefficient
Input parameters:
X - sample 1 (array indexes: [0..N-1])
Y - sample 2 (array indexes: [0..N-1])
N - N>=0, sample size:
* if given, only N leading elements of X/Y are processed
* if not given, automatically determined from input sizes
Result:
Spearman's rank correlation coefficient
(zero for N=0 or N=1)
-- ALGLIB --
Copyright 09.04.2007 by Bochkanov Sergey
*************************************************************************/
double spearmancorr2(const real_1d_array &x, const real_1d_array &y)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
if( (x.length()!=y.length()))
throw ap_error("Error while calling 'spearmancorr2': looks like one of arguments has wrong size");
n = x.length();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
double result = alglib_impl::spearmancorr2(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return *(reinterpret_cast<double*>(&result));
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Covariance matrix
SMP EDITION OF ALGLIB:
! This function can utilize multicore capabilities of your system. In
! order to do this you have to call version with "smp_" prefix, which
! indicates that multicore code will be used.
!
! This note is given for users of SMP edition; if you use GPL edition,
! or commercial edition of ALGLIB without SMP support, you still will
! be able to call smp-version of this function, but all computations
! will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
!
! You should remember that starting/stopping worker thread always have
! non-zero cost. Although multicore version is pretty efficient on
! large problems, we do not recommend you to use it on small problems -
! with covariance matrices smaller than 128*128.
INPUT PARAMETERS:
X - array[N,M], sample matrix:
* J-th column corresponds to J-th variable
* I-th row corresponds to I-th observation
N - N>=0, number of observations:
* if given, only leading N rows of X are used
* if not given, automatically determined from input size
M - M>0, number of variables:
* if given, only leading M columns of X are used
* if not given, automatically determined from input size
OUTPUT PARAMETERS:
C - array[M,M], covariance matrix (zero if N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
void covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::covm(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), n, m, const_cast<alglib_impl::ae_matrix*>(c.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
void smp_covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c)
{
alglib_impl::ae_state _alglib_env_state;
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::_pexec_covm(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), n, m, const_cast<alglib_impl::ae_matrix*>(c.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Covariance matrix
SMP EDITION OF ALGLIB:
! This function can utilize multicore capabilities of your system. In
! order to do this you have to call version with "smp_" prefix, which
! indicates that multicore code will be used.
!
! This note is given for users of SMP edition; if you use GPL edition,
! or commercial edition of ALGLIB without SMP support, you still will
! be able to call smp-version of this function, but all computations
! will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
!
! You should remember that starting/stopping worker thread always have
! non-zero cost. Although multicore version is pretty efficient on
! large problems, we do not recommend you to use it on small problems -
! with covariance matrices smaller than 128*128.
INPUT PARAMETERS:
X - array[N,M], sample matrix:
* J-th column corresponds to J-th variable
* I-th row corresponds to I-th observation
N - N>=0, number of observations:
* if given, only leading N rows of X are used
* if not given, automatically determined from input size
M - M>0, number of variables:
* if given, only leading M columns of X are used
* if not given, automatically determined from input size
OUTPUT PARAMETERS:
C - array[M,M], covariance matrix (zero if N=0 or N=1)
-- ALGLIB --
Copyright 28.10.2010 by Bochkanov Sergey
*************************************************************************/
void covm(const real_2d_array &x, real_2d_array &c)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
ae_int_t m;
n = x.rows();
m = x.cols();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::covm(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), n, m, const_cast<alglib_impl::ae_matrix*>(c.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
void smp_covm(const real_2d_array &x, real_2d_array &c)
{
alglib_impl::ae_state _alglib_env_state;
ae_int_t n;
ae_int_t m;
n = x.rows();
m = x.cols();
alglib_impl::ae_state_init(&_alglib_env_state);
try
{
alglib_impl::_pexec_covm(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), n, m, const_cast<alglib_impl::ae_matrix*>(c.c_ptr()), &_alglib_env_state);
alglib_impl::ae_state_clear(&_alglib_env_state);
return;
}
catch(alglib_impl::ae_error_type)
{
throw ap_error(_alglib_env_state.error_msg);
}
}
/*************************************************************************
Pearson product-moment correlation matrix
SMP EDITION OF ALGLIB:
! This function can utilize multicore capabilities of your system. In
! order to do this you have to call version with "smp_" prefix, which
! indicates that multicore code will be used.
!
! This note is given for users of SMP edition; if you use GPL edition,
! or commercial edition of ALGLIB without SMP support, you still will
! be able to call smp-version of this function, but all computations
! will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
!
! You should remember that starting/stopping worker thread always have
! non-zero cost. Although multicore version is pretty efficient on
! large problems, we do not recommend you to use it on small problems -
! with correlation matrices smaller than 128*128.
INPUT PARAMETERS:
X - array[N,M], sample matrix:
* J-th column corresponds to J-th variable
* I-th row corresponds to I-th observation
N - N>=0, number of observations:
* if given, only leading N rows of X are used
* if not given, automatically determined from input size
M - M>0, number of variables:
* if given, only leading M columns of X are used
* if not given, automatically determined from input size