## Copyright (C) 2012  Arno Onken
##
## 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, either version 3 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.
##
## You should have received a copy of the GNU General Public License
## along with this program.  If not, see <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {statistics} {@var{r} =} mnrnd (@var{n}, @var{pk})
## @deftypefnx {statistics} {@var{r} =} mnrnd (@var{n}, @var{pk}, @var{s})
##
## Random arrays from the multinomial distribution.
##
## @subheading Arguments
##
## @itemize @bullet
## @item
## @var{n} is the first parameter of the multinomial distribution. @var{n} can
## be scalar or a vector containing the number of trials of each multinomial
## sample. The elements of @var{n} must be non-negative integers.
##
## @item
## @var{pk} is the second parameter of the multinomial distribution. @var{pk}
## can be a vector with the probabilities of the categories or a matrix with
## each row containing the probabilities of a multinomial sample.  If @var{pk}
## has more than one row and @var{n} is non-scalar, then the number of rows of
## @var{pk} must match the number of elements of @var{n}.
##
## @item
## @var{s} is the number of multinomial samples to be generated. @var{s} must
## be a non-negative integer. If @var{s} is specified, then @var{n} must be
## scalar and @var{pk} must be a vector.
## @end itemize
##
## @subheading Return values
##
## @itemize @bullet
## @item
## @var{r} is a matrix of random samples from the multinomial distribution with
## corresponding parameters @var{n} and @var{pk}. Each row corresponds to one
## multinomial sample. The number of columns, therefore, corresponds to the
## number of columns of @var{pk}. If @var{s} is not specified, then the number
## of rows of @var{r} is the maximum of the number of elements of @var{n} and
## the number of rows of @var{pk}. If a row of @var{pk} does not sum to
## @code{1}, then the corresponding row of @var{r} will contain only @code{NaN}
## values.
## @end itemize
##
## @subheading Examples
##
## @example
## @group
## n = 10;
## pk = [0.2, 0.5, 0.3];
## r = mnrnd (n, pk);
## @end group
##
## @group
## n = 10 * ones (3, 1);
## pk = [0.2, 0.5, 0.3];
## r = mnrnd (n, pk);
## @end group
##
## @group
## n = (1:2)';
## pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
## r = mnrnd (n, pk);
## @end group
## @end example
##
## @subheading References
##
## @enumerate
## @item
## Wendy L. Martinez and Angel R. Martinez. @cite{Computational Statistics
## Handbook with MATLAB}. Appendix E, pages 547-557, Chapman & Hall/CRC, 2001.
##
## @item
## Merran Evans, Nicholas Hastings and Brian Peacock. @cite{Statistical
## Distributions}. pages 134-136, Wiley, New York, third edition, 2000.
## @end enumerate
##
## @seealso{mnpdf}
## @end deftypefn

function r = mnrnd (n, pk, s)

  # Check arguments
  if (nargin == 3)
    if (! isscalar (n) || n < 0 || round (n) != n)
      error ("mnrnd: N must be a non-negative integer.");
    endif
    if (! isvector (pk) || any (pk < 0 | pk > 1))
      error ("mnrnd: PK must be a vector of probabilities.");
    endif
    if (! isscalar (s) || s < 0 || round (s) != s)
      error ("mnrnd: S must be a non-negative integer.");
    endif
  elseif (nargin == 2)
    if (isvector (pk) && size (pk, 1) > 1)
      pk = pk';
    endif
    if (! isvector (n) || any (n < 0 | round (n) != n) || size (n, 2) > 1)
      error ("mnrnd: N must be a non-negative integer column vector.");
    endif
    if (! ismatrix (pk) || isempty (pk) || any (pk < 0 | pk > 1))
      error (strcat (["mnrnd: PK must be a non-empty matrix with"], ...
                     [" rows of probabilities."]));
    endif
    if (! isscalar (n) && size (pk, 1) > 1 && length (n) != size (pk, 1))
      error ("mnrnd: the length of N must match the number of rows of PK.");
    endif
  else
    print_usage ();
  endif

  # Adjust input sizes
  if (nargin == 3)
    n = n * ones (s, 1);
    pk = repmat (pk(:)', s, 1);
  elseif (nargin == 2)
    if (isscalar (n) && size (pk, 1) > 1)
      n = n * ones (size (pk, 1), 1);
    elseif (size (pk, 1) == 1)
      pk = repmat (pk, length (n), 1);
    endif
  endif
  sz = size (pk);

  # Upper bounds of categories
  ub = cumsum (pk, 2);
  # Make sure that the greatest upper bound is 1
  gub = ub(:, end);
  ub(:, end) = 1;
  # Lower bounds of categories
  lb = [zeros(sz(1), 1) ub(:, 1:(end-1))];

  # Draw multinomial samples
  r = zeros (sz);
  for i = 1:sz(1)
    # Draw uniform random numbers
    r_tmp = repmat (rand (n(i), 1), 1, sz(2));
    # Compare the random numbers of r_tmp to the cumulated probabilities of pk
    # and count the number of samples for each category
    r(i, :) =  sum (r_tmp <= repmat (ub(i, :), n(i), 1) & ...
                    r_tmp > repmat (lb(i, :), n(i), 1), 1);
  endfor
  # Set invalid rows to NaN
  k = (abs (gub - 1) > 1e-6);
  r(k, :) = NaN;

endfunction

%!test
%! n = 10;
%! pk = [0.2, 0.5, 0.3];
%! r = mnrnd (n, pk);
%! assert (size (r), size (pk));
%! assert (all (r >= 0));
%! assert (all (round (r) == r));
%! assert (sum (r) == n);

%!test
%! n = 10 * ones (3, 1);
%! pk = [0.2, 0.5, 0.3];
%! r = mnrnd (n, pk);
%! assert (size (r), [length(n), length(pk)]);
%! assert (all (r >= 0));
%! assert (all (round (r) == r));
%! assert (all (sum (r, 2) == n));

%!test
%! n = (1:2)';
%! pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
%! r = mnrnd (n, pk);
%! assert (size (r), size (pk));
%! assert (all (r >= 0));
%! assert (all (round (r) == r));
%! assert (all (sum (r, 2) == n));
