## Copyright (C) 1995-2011 Kurt Hornik
##
## This file is part of Octave.
##
## Octave 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; either version 3 of the License, or (at
## your option) any later version.
##
## Octave 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.
##
## You should have received a copy of the GNU General Public License
## along with Octave; see the file COPYING.  If not, see
## <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {Function File} {} cov (@var{x})
## @deftypefnx {Function File} {} cov (@var{x}, @var{opt})
## @deftypefnx {Function File} {} cov (@var{x}, @var{y})
## @deftypefnx {Function File} {} cov (@var{x}, @var{y}, @var{opt})
## Compute the covariance matrix.
##
## If each row of @var{x} and @var{y} is an observation, and each column is
## a variable, then the @w{(@var{i}, @var{j})-th} entry of
## @code{cov (@var{x}, @var{y})} is the covariance between the @var{i}-th
## variable in @var{x} and the @var{j}-th variable in @var{y}.
## @tex
## $$
## \sigma_{ij} = {1 \over N-1} \sum_{i=1}^N (x_i - \bar{x})(y_i - \bar{y})
## $$
## where $\bar{x}$ and $\bar{y}$ are the mean values of $x$ and $y$.
## @end tex
## @ifnottex
##
## @example
## cov (x) = 1/N-1 * SUM_i (x(i) - mean(x)) * (y(i) - mean(y))
## @end example
##
## @end ifnottex
##
## If called with one argument, compute @code{cov (@var{x}, @var{x})}, the
## covariance between the columns of @var{x}.
##
## The argument @var{opt} determines the type of normalization to use.  
## Valid values are
##
## @table @asis
## @item 0:
##   normalize with @math{N-1}, provides the best unbiased estimator of the
## covariance [default]
##
## @item 1:
##   normalize with @math{N}, this provides the second moment around the mean
## @end table
## @seealso{corrcoef, cor}
## @end deftypefn

## Author: KH <Kurt.Hornik@wu-wien.ac.at>
## Description: Compute covariances

function c = cov (x, y = [], opt = 0)

  if (nargin < 1 || nargin > 3)
    print_usage ();
  endif

  if (! (isnumeric (x) && isnumeric (y)))
    error ("cov: X and Y must be numeric matrices or vectors");
  endif

  if (ndims (x) != 2 || ndims (y) != 2)
    error ("cov: X and Y must be 2-D matrices or vectors");
  endif

  if (nargin == 2 && isscalar(y))
    opt = y;
  endif

  if (opt != 0 && opt != 1)
    error ("cov: normalization OPT must be 0 or 1");
  endif

  if (isscalar (x))
    c = 0;
    return;
  endif

  if (rows (x) == 1)
    x = x';
  endif
  n = rows (x);
  
  if (nargin == 1 || isscalar(y))
    x = center (x, 1);
    c = conj (x' * x / (n - 1 + opt));
  else
    if (rows (y) == 1)
      y = y';
    endif
    if (rows (y) != n)
      error ("cov: X and Y must have the same number of observations");
    endif
    x = center (x, 1);
    y = center (y, 1);
    c = conj (x' * y / (n - 1 + opt));
  endif

endfunction

%!test
%! x = rand (10);
%! cx1 = cov (x);
%! cx2 = cov (x, x);
%! assert(size (cx1) == [10, 10] && size (cx2) == [10, 10] && norm(cx1-cx2) < 1e1*eps);

%!test
%! x = [1:5];
%! c = cov (x);
%! assert(isscalar (c));
%! assert(c, 2.5);

%!test
%! x = [1:5];
%! c = cov (x, 0);
%! assert(c, 2.5);
%! c = cov (x, 1);
%! assert(c, 2);

%!assert(cov (5), 0);

%% Test input validation
%!error cov ();
%!error cov (1, 2, 3, 4);
%!error cov ([true, true]);
%!error cov ([1, 2], [true, true]);
%!error cov (ones (2,2,2));
%!error cov (ones (2,2), ones (2,2,2));
%!error cov (1, 3);
%!error cov (ones (2,2), ones (3,2));