/usr/include/lda.h is in libalglib-dev 2.6.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Copyright (c) 2008, 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 _lda_h
#define _lda_h
#include "ap.h"
#include "ialglib.h"
#include "hblas.h"
#include "reflections.h"
#include "creflections.h"
#include "sblas.h"
#include "ablasf.h"
#include "ablas.h"
#include "ortfac.h"
#include "blas.h"
#include "rotations.h"
#include "hsschur.h"
#include "evd.h"
#include "hqrnd.h"
#include "matgen.h"
#include "trfac.h"
#include "trlinsolve.h"
#include "safesolve.h"
#include "rcond.h"
#include "matinv.h"
/*************************************************************************
Multiclass Fisher LDA
Subroutine finds coefficients of linear combination which optimally separates
training set on classes.
INPUT PARAMETERS:
XY - training set, array[0..NPoints-1,0..NVars].
First NVars columns store values of independent
variables, next column stores number of class (from 0
to NClasses-1) which dataset element belongs to. Fractional
values are rounded to nearest integer.
NPoints - training set size, NPoints>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - linear combination coefficients, array[0..NVars-1]
-- ALGLIB --
Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherlda(const ap::real_2d_array& xy,
int npoints,
int nvars,
int nclasses,
int& info,
ap::real_1d_array& w);
/*************************************************************************
N-dimensional multiclass Fisher LDA
Subroutine finds coefficients of linear combinations which optimally separates
training set on classes. It returns N-dimensional basis whose vector are sorted
by quality of training set separation (in descending order).
INPUT PARAMETERS:
XY - training set, array[0..NPoints-1,0..NVars].
First NVars columns store values of independent
variables, next column stores number of class (from 0
to NClasses-1) which dataset element belongs to. Fractional
values are rounded to nearest integer.
NPoints - training set size, NPoints>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - basis, array[0..NVars-1,0..NVars-1]
columns of matrix stores basis vectors, sorted by
quality of training set separation (in descending order)
-- ALGLIB --
Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherldan(const ap::real_2d_array& xy,
int npoints,
int nvars,
int nclasses,
int& info,
ap::real_2d_array& w);
#endif
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