fasttransforms.h

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/*************************************************************************
ALGLIB 3.11.0 (source code generated 2017-05-11)
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 _fasttransforms_pkg_h
#define _fasttransforms_pkg_h
#include "ap.h"
#include "alglibinternal.h"
 
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES)
//
namespace alglib_impl
{
 
}
 
//
// THIS SECTION CONTAINS C++ INTERFACE
//
namespace alglib
{
 
 
/*************************************************************************
1-dimensional complex FFT.
 
Array size N may be arbitrary number (composite or prime).  Composite  N's
are handled with cache-oblivious variation of  a  Cooley-Tukey  algorithm.
Small prime-factors are transformed using hard coded  codelets (similar to
FFTW codelets, but without low-level  optimization),  large  prime-factors
are handled with Bluestein's algorithm.
 
Fastests transforms are for smooth N's (prime factors are 2, 3,  5  only),
most fast for powers of 2. When N have prime factors  larger  than  these,
but orders of magnitude smaller than N, computations will be about 4 times
slower than for nearby highly composite N's. When N itself is prime, speed
will be 6 times lower.
 
Algorithm has O(N*logN) complexity for any N (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..N-1] - complex function to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    A   -   DFT of a input array, array[0..N-1]
            A_out[j] = SUM(A_in[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1)
 
 
  -- ALGLIB --
     Copyright 29.05.2009 by Bochkanov Sergey
*************************************************************************/
void fftc1d(complex_1d_array &a, const ae_int_t n);
void fftc1d(complex_1d_array &a);
 
 
/*************************************************************************
1-dimensional complex inverse FFT.
 
Array size N may be arbitrary number (composite or prime).  Algorithm  has
O(N*logN) complexity for any N (composite or prime).
 
See FFTC1D() description for more information about algorithm performance.
 
INPUT PARAMETERS
    A   -   array[0..N-1] - complex array to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    A   -   inverse DFT of a input array, array[0..N-1]
            A_out[j] = SUM(A_in[k]/N*exp(+2*pi*sqrt(-1)*j*k/N), k = 0..N-1)
 
 
  -- ALGLIB --
     Copyright 29.05.2009 by Bochkanov Sergey
*************************************************************************/
void fftc1dinv(complex_1d_array &a, const ae_int_t n);
void fftc1dinv(complex_1d_array &a);
 
 
/*************************************************************************
1-dimensional real FFT.
 
Algorithm has O(N*logN) complexity for any N (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..N-1] - real function to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    F   -   DFT of a input array, array[0..N-1]
            F[j] = SUM(A[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1)
 
NOTE:
    F[] satisfies symmetry property F[k] = conj(F[N-k]),  so just one half
of  array  is  usually needed. But for convinience subroutine returns full
complex array (with frequencies above N/2), so its result may be  used  by
other FFT-related subroutines.
 
 
  -- ALGLIB --
     Copyright 01.06.2009 by Bochkanov Sergey
*************************************************************************/
void fftr1d(const real_1d_array &a, const ae_int_t n, complex_1d_array &f);
void fftr1d(const real_1d_array &a, complex_1d_array &f);
 
 
/*************************************************************************
1-dimensional real inverse FFT.
 
Algorithm has O(N*logN) complexity for any N (composite or prime).
 
INPUT PARAMETERS
    F   -   array[0..floor(N/2)] - frequencies from forward real FFT
    N   -   problem size
 
OUTPUT PARAMETERS
    A   -   inverse DFT of a input array, array[0..N-1]
 
NOTE:
    F[] should satisfy symmetry property F[k] = conj(F[N-k]), so just  one
half of frequencies array is needed - elements from 0 to floor(N/2).  F[0]
is ALWAYS real. If N is even F[floor(N/2)] is real too. If N is odd,  then
F[floor(N/2)] has no special properties.
 
Relying on properties noted above, FFTR1DInv subroutine uses only elements
from 0th to floor(N/2)-th. It ignores imaginary part of F[0],  and in case
N is even it ignores imaginary part of F[floor(N/2)] too.
 
When you call this function using full arguments list - "FFTR1DInv(F,N,A)"
- you can pass either either frequencies array with N elements or  reduced
array with roughly N/2 elements - subroutine will  successfully  transform
both.
 
If you call this function using reduced arguments list -  "FFTR1DInv(F,A)"
- you must pass FULL array with N elements (although higher  N/2 are still
not used) because array size is used to automatically determine FFT length
 
 
  -- ALGLIB --
     Copyright 01.06.2009 by Bochkanov Sergey
*************************************************************************/
void fftr1dinv(const complex_1d_array &f, const ae_int_t n, real_1d_array &a);
void fftr1dinv(const complex_1d_array &f, real_1d_array &a);
 
/*************************************************************************
1-dimensional Fast Hartley Transform.
 
Algorithm has O(N*logN) complexity for any N (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..N-1] - real function to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    A   -   FHT of a input array, array[0..N-1],
            A_out[k] = sum(A_in[j]*(cos(2*pi*j*k/N)+sin(2*pi*j*k/N)), j=0..N-1)
 
 
  -- ALGLIB --
     Copyright 04.06.2009 by Bochkanov Sergey
*************************************************************************/
void fhtr1d(real_1d_array &a, const ae_int_t n);
 
 
/*************************************************************************
1-dimensional inverse FHT.
 
Algorithm has O(N*logN) complexity for any N (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..N-1] - complex array to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    A   -   inverse FHT of a input array, array[0..N-1]
 
 
  -- ALGLIB --
     Copyright 29.05.2009 by Bochkanov Sergey
*************************************************************************/
void fhtr1dinv(real_1d_array &a, const ae_int_t n);
 
/*************************************************************************
1-dimensional complex convolution.
 
For given A/B returns conv(A,B) (non-circular). Subroutine can automatically
choose between three implementations: straightforward O(M*N)  formula  for
very small N (or M), overlap-add algorithm for  cases  where  max(M,N)  is
significantly larger than min(M,N), but O(M*N) algorithm is too slow,  and
general FFT-based formula for cases where two previois algorithms are  too
slow.
 
Algorithm has max(M,N)*log(max(M,N)) complexity for any M/N.
 
INPUT PARAMETERS
    A   -   array[0..M-1] - complex function to be transformed
    M   -   problem size
    B   -   array[0..N-1] - complex function to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    R   -   convolution: A*B. array[0..N+M-2].
 
NOTE:
    It is assumed that A is zero at T<0, B is zero too.  If  one  or  both
functions have non-zero values at negative T's, you  can  still  use  this
subroutine - just shift its result correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convc1d(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r);
 
 
/*************************************************************************
1-dimensional complex non-circular deconvolution (inverse of ConvC1D()).
 
Algorithm has M*log(M)) complexity for any M (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..M-1] - convolved signal, A = conv(R, B)
    M   -   convolved signal length
    B   -   array[0..N-1] - response
    N   -   response length, N<=M
 
OUTPUT PARAMETERS
    R   -   deconvolved signal. array[0..M-N].
 
NOTE:
    deconvolution is unstable process and may result in division  by  zero
(if your response function is degenerate, i.e. has zero Fourier coefficient).
 
NOTE:
    It is assumed that A is zero at T<0, B is zero too.  If  one  or  both
functions have non-zero values at negative T's, you  can  still  use  this
subroutine - just shift its result correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convc1dinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r);
 
 
/*************************************************************************
1-dimensional circular complex convolution.
 
For given S/R returns conv(S,R) (circular). Algorithm has linearithmic
complexity for any M/N.
 
IMPORTANT:  normal convolution is commutative,  i.e.   it  is symmetric  -
conv(A,B)=conv(B,A).  Cyclic convolution IS NOT.  One function - S - is  a
signal,  periodic function, and another - R - is a response,  non-periodic
function with limited length.
 
INPUT PARAMETERS
    S   -   array[0..M-1] - complex periodic signal
    M   -   problem size
    B   -   array[0..N-1] - complex non-periodic response
    N   -   problem size
 
OUTPUT PARAMETERS
    R   -   convolution: A*B. array[0..M-1].
 
NOTE:
    It is assumed that B is zero at T<0. If  it  has  non-zero  values  at
negative T's, you can still use this subroutine - just  shift  its  result
correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convc1dcircular(const complex_1d_array &s, const ae_int_t m, const complex_1d_array &r, const ae_int_t n, complex_1d_array &c);
 
 
/*************************************************************************
1-dimensional circular complex deconvolution (inverse of ConvC1DCircular()).
 
Algorithm has M*log(M)) complexity for any M (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..M-1] - convolved periodic signal, A = conv(R, B)
    M   -   convolved signal length
    B   -   array[0..N-1] - non-periodic response
    N   -   response length
 
OUTPUT PARAMETERS
    R   -   deconvolved signal. array[0..M-1].
 
NOTE:
    deconvolution is unstable process and may result in division  by  zero
(if your response function is degenerate, i.e. has zero Fourier coefficient).
 
NOTE:
    It is assumed that B is zero at T<0. If  it  has  non-zero  values  at
negative T's, you can still use this subroutine - just  shift  its  result
correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convc1dcircularinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r);
 
 
/*************************************************************************
1-dimensional real convolution.
 
Analogous to ConvC1D(), see ConvC1D() comments for more details.
 
INPUT PARAMETERS
    A   -   array[0..M-1] - real function to be transformed
    M   -   problem size
    B   -   array[0..N-1] - real function to be transformed
    N   -   problem size
 
OUTPUT PARAMETERS
    R   -   convolution: A*B. array[0..N+M-2].
 
NOTE:
    It is assumed that A is zero at T<0, B is zero too.  If  one  or  both
functions have non-zero values at negative T's, you  can  still  use  this
subroutine - just shift its result correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convr1d(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r);
 
 
/*************************************************************************
1-dimensional real deconvolution (inverse of ConvC1D()).
 
Algorithm has M*log(M)) complexity for any M (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..M-1] - convolved signal, A = conv(R, B)
    M   -   convolved signal length
    B   -   array[0..N-1] - response
    N   -   response length, N<=M
 
OUTPUT PARAMETERS
    R   -   deconvolved signal. array[0..M-N].
 
NOTE:
    deconvolution is unstable process and may result in division  by  zero
(if your response function is degenerate, i.e. has zero Fourier coefficient).
 
NOTE:
    It is assumed that A is zero at T<0, B is zero too.  If  one  or  both
functions have non-zero values at negative T's, you  can  still  use  this
subroutine - just shift its result correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convr1dinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r);
 
 
/*************************************************************************
1-dimensional circular real convolution.
 
Analogous to ConvC1DCircular(), see ConvC1DCircular() comments for more details.
 
INPUT PARAMETERS
    S   -   array[0..M-1] - real signal
    M   -   problem size
    B   -   array[0..N-1] - real response
    N   -   problem size
 
OUTPUT PARAMETERS
    R   -   convolution: A*B. array[0..M-1].
 
NOTE:
    It is assumed that B is zero at T<0. If  it  has  non-zero  values  at
negative T's, you can still use this subroutine - just  shift  its  result
correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convr1dcircular(const real_1d_array &s, const ae_int_t m, const real_1d_array &r, const ae_int_t n, real_1d_array &c);
 
 
/*************************************************************************
1-dimensional complex deconvolution (inverse of ConvC1D()).
 
Algorithm has M*log(M)) complexity for any M (composite or prime).
 
INPUT PARAMETERS
    A   -   array[0..M-1] - convolved signal, A = conv(R, B)
    M   -   convolved signal length
    B   -   array[0..N-1] - response
    N   -   response length
 
OUTPUT PARAMETERS
    R   -   deconvolved signal. array[0..M-N].
 
NOTE:
    deconvolution is unstable process and may result in division  by  zero
(if your response function is degenerate, i.e. has zero Fourier coefficient).
 
NOTE:
    It is assumed that B is zero at T<0. If  it  has  non-zero  values  at
negative T's, you can still use this subroutine - just  shift  its  result
correspondingly.
 
  -- ALGLIB --
     Copyright 21.07.2009 by Bochkanov Sergey
*************************************************************************/
void convr1dcircularinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r);
 
/*************************************************************************
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 complex_1d_array &signal, const ae_int_t n, const complex_1d_array &pattern, const ae_int_t m, 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 complex_1d_array &signal, const ae_int_t m, const complex_1d_array &pattern, const ae_int_t n, 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 real_1d_array &signal, const ae_int_t n, const real_1d_array &pattern, const ae_int_t m, 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 real_1d_array &signal, const ae_int_t m, const real_1d_array &pattern, const ae_int_t n, real_1d_array &c);
}
 
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS)
//
namespace alglib_impl
{
void fftc1d(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state);
void fftc1dinv(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state);
void fftr1d(/* Real    */ ae_vector* a,
     ae_int_t n,
     /* Complex */ ae_vector* f,
     ae_state *_state);
void fftr1dinv(/* Complex */ ae_vector* f,
     ae_int_t n,
     /* Real    */ ae_vector* a,
     ae_state *_state);
void fftr1dinternaleven(/* Real    */ ae_vector* a,
     ae_int_t n,
     /* Real    */ ae_vector* buf,
     fasttransformplan* plan,
     ae_state *_state);
void fftr1dinvinternaleven(/* Real    */ ae_vector* a,
     ae_int_t n,
     /* Real    */ ae_vector* buf,
     fasttransformplan* plan,
     ae_state *_state);
void fhtr1d(/* Real    */ ae_vector* a, ae_int_t n, ae_state *_state);
void fhtr1dinv(/* Real    */ ae_vector* a, ae_int_t n, ae_state *_state);
void convc1d(/* Complex */ ae_vector* a,
     ae_int_t m,
     /* Complex */ ae_vector* b,
     ae_int_t n,
     /* Complex */ ae_vector* r,
     ae_state *_state);
void convc1dinv(/* Complex */ ae_vector* a,
     ae_int_t m,
     /* Complex */ ae_vector* b,
     ae_int_t n,
     /* Complex */ ae_vector* r,
     ae_state *_state);
void convc1dcircular(/* Complex */ ae_vector* s,
     ae_int_t m,
     /* Complex */ ae_vector* r,
     ae_int_t n,
     /* Complex */ ae_vector* c,
     ae_state *_state);
void convc1dcircularinv(/* Complex */ ae_vector* a,
     ae_int_t m,
     /* Complex */ ae_vector* b,
     ae_int_t n,
     /* Complex */ ae_vector* r,
     ae_state *_state);
void convr1d(/* Real    */ ae_vector* a,
     ae_int_t m,
     /* Real    */ ae_vector* b,
     ae_int_t n,
     /* Real    */ ae_vector* r,
     ae_state *_state);
void convr1dinv(/* Real    */ ae_vector* a,
     ae_int_t m,
     /* Real    */ ae_vector* b,
     ae_int_t n,
     /* Real    */ ae_vector* r,
     ae_state *_state);
void convr1dcircular(/* Real    */ ae_vector* s,
     ae_int_t m,
     /* Real    */ ae_vector* r,
     ae_int_t n,
     /* Real    */ ae_vector* c,
     ae_state *_state);
void convr1dcircularinv(/* Real    */ ae_vector* a,
     ae_int_t m,
     /* Real    */ ae_vector* b,
     ae_int_t n,
     /* Real    */ ae_vector* r,
     ae_state *_state);
void convc1dx(/* Complex */ ae_vector* a,
     ae_int_t m,
     /* Complex */ ae_vector* b,
     ae_int_t n,
     ae_bool circular,
     ae_int_t alg,
     ae_int_t q,
     /* Complex */ ae_vector* r,
     ae_state *_state);
void convr1dx(/* Real    */ ae_vector* a,
     ae_int_t m,
     /* Real    */ ae_vector* b,
     ae_int_t n,
     ae_bool circular,
     ae_int_t alg,
     ae_int_t q,
     /* Real    */ ae_vector* r,
     ae_state *_state);
void corrc1d(/* Complex */ ae_vector* signal,
     ae_int_t n,
     /* Complex */ ae_vector* pattern,
     ae_int_t m,
     /* Complex */ ae_vector* r,
     ae_state *_state);
void corrc1dcircular(/* Complex */ ae_vector* signal,
     ae_int_t m,
     /* Complex */ ae_vector* pattern,
     ae_int_t n,
     /* Complex */ ae_vector* c,
     ae_state *_state);
void corrr1d(/* Real    */ ae_vector* signal,
     ae_int_t n,
     /* Real    */ ae_vector* pattern,
     ae_int_t m,
     /* Real    */ ae_vector* r,
     ae_state *_state);
void corrr1dcircular(/* Real    */ ae_vector* signal,
     ae_int_t m,
     /* Real    */ ae_vector* pattern,
     ae_int_t n,
     /* Real    */ ae_vector* c,
     ae_state *_state);
 
}
#endif