/usr/include/corr.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) 2009, 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 _corr_h
#define _corr_h
#include "ap.h"
#include "ialglib.h"
#include "ftbase.h"
#include "fft.h"
#include "conv.h"
/*************************************************************************
1-dimensional complex cross-correlation.
For given Pattern/Signal returns corr(Pattern,Signal) (non-circular).
Correlation is calculated using reduction to convolution. Algorithm with
max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info
about performance).
IMPORTANT:
for historical reasons subroutine accepts its parameters in reversed
order: CorrC1D(Signal, Pattern) = Pattern x Signal (using traditional
definition of cross-correlation, denoting cross-correlation as "x").
INPUT PARAMETERS
Signal - array[0..N-1] - complex function to be transformed,
signal containing pattern
N - problem size
Pattern - array[0..M-1] - complex function to be transformed,
pattern to search withing signal
M - problem size
OUTPUT PARAMETERS
R - cross-correlation, array[0..N+M-2]:
* positive lags are stored in R[0..N-1],
R[i] = sum(conj(pattern[j])*signal[i+j]
* negative lags are stored in R[N..N+M-2],
R[N+M-1-i] = sum(conj(pattern[j])*signal[-i+j]
NOTE:
It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero
on [-K..M-1], you can still use this subroutine, just shift result by K.
-- ALGLIB --
Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void corrc1d(const ap::complex_1d_array& signal,
int n,
const ap::complex_1d_array& pattern,
int m,
ap::complex_1d_array& r);
/*************************************************************************
1-dimensional circular complex cross-correlation.
For given Pattern/Signal returns corr(Pattern,Signal) (circular).
Algorithm has linearithmic complexity for any M/N.
IMPORTANT:
for historical reasons subroutine accepts its parameters in reversed
order: CorrC1DCircular(Signal, Pattern) = Pattern x Signal (using
traditional definition of cross-correlation, denoting cross-correlation
as "x").
INPUT PARAMETERS
Signal - array[0..N-1] - complex function to be transformed,
periodic signal containing pattern
N - problem size
Pattern - array[0..M-1] - complex function to be transformed,
non-periodic pattern to search withing signal
M - problem size
OUTPUT PARAMETERS
R - convolution: A*B. array[0..M-1].
-- ALGLIB --
Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void corrc1dcircular(const ap::complex_1d_array& signal,
int m,
const ap::complex_1d_array& pattern,
int n,
ap::complex_1d_array& c);
/*************************************************************************
1-dimensional real cross-correlation.
For given Pattern/Signal returns corr(Pattern,Signal) (non-circular).
Correlation is calculated using reduction to convolution. Algorithm with
max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info
about performance).
IMPORTANT:
for historical reasons subroutine accepts its parameters in reversed
order: CorrR1D(Signal, Pattern) = Pattern x Signal (using traditional
definition of cross-correlation, denoting cross-correlation as "x").
INPUT PARAMETERS
Signal - array[0..N-1] - real function to be transformed,
signal containing pattern
N - problem size
Pattern - array[0..M-1] - real function to be transformed,
pattern to search withing signal
M - problem size
OUTPUT PARAMETERS
R - cross-correlation, array[0..N+M-2]:
* positive lags are stored in R[0..N-1],
R[i] = sum(pattern[j]*signal[i+j]
* negative lags are stored in R[N..N+M-2],
R[N+M-1-i] = sum(pattern[j]*signal[-i+j]
NOTE:
It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero
on [-K..M-1], you can still use this subroutine, just shift result by K.
-- ALGLIB --
Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void corrr1d(const ap::real_1d_array& signal,
int n,
const ap::real_1d_array& pattern,
int m,
ap::real_1d_array& r);
/*************************************************************************
1-dimensional circular real cross-correlation.
For given Pattern/Signal returns corr(Pattern,Signal) (circular).
Algorithm has linearithmic complexity for any M/N.
IMPORTANT:
for historical reasons subroutine accepts its parameters in reversed
order: CorrR1DCircular(Signal, Pattern) = Pattern x Signal (using
traditional definition of cross-correlation, denoting cross-correlation
as "x").
INPUT PARAMETERS
Signal - array[0..N-1] - real function to be transformed,
periodic signal containing pattern
N - problem size
Pattern - array[0..M-1] - real function to be transformed,
non-periodic pattern to search withing signal
M - problem size
OUTPUT PARAMETERS
R - convolution: A*B. array[0..M-1].
-- ALGLIB --
Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void corrr1dcircular(const ap::real_1d_array& signal,
int m,
const ap::real_1d_array& pattern,
int n,
ap::real_1d_array& c);
#endif
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