function [ p, t ] = cvt_triangle_uniform ( n, sample_num, delaunay_display )

%*****************************************************************************80
%
%% CVT_TRIANGLE_UNIFORM demonstrates how a CVT can be computed and displayed in MATLAB.
%
%  Discussion:
%
%    This simple example carries out an iterative CVT calculation in a
%    triangle, with a uniform density.  The initial placement of the
%    generators is random.
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    30 September 2008
%
%  Author:
%
%    John Burkardt
%
%  Reference:
%
%    Qiang Du, Vance Faber, Max Gunzburger,
%    Centroidal Voronoi Tessellations: Applications and Algorithms,
%    SIAM Review, 
%    Volume 41, 1999, pages 637-676.
%
%  Parameters:
%
%    Input, integer N, the number of generators.
%
%    Input, integer SAMPLE_NUM, the number of sample points.
%
%    Input, logical DELAUNAY_DISPLAY, is TRUE (nonzero) if the Delaunay
%    triangulation is to be displayed.
%
%    Output, real P(N,2), the location of the generators.
%
%    Output, integer T(NT,3), information defining the Delaunay
%    triangulation of the generators.  NT is the number of triangles,
%    which varies depending on the arrangement of the generators.
%

%
%  Here's the triangle we will use.
%
  triangle = [ 0.50, 1.00; ...
               0.00, 0.75; ...
               0.95, 0.00 ]';

  fprintf ( 1, '\n' );
  fprintf ( 1, 'CVT_TRIANGLE_UNIFORM:\n' );
  fprintf ( 1, '  A simple demonstration of a CVT computation\n' );
  fprintf ( 1, '  (Centroidal Voronoi Tessellation)\n' );
  fprintf ( 1, '  in a triangle, with a uniform density.\n' );
  
  if ( nargin < 1 )
    n = 100;
    fprintf ( 1, '\n' );
    fprintf ( 1, 'CVT_TRIANGLE_UNIFORM - Note:\n' );
    fprintf ( 1, '  No value of N was supplied.\n' );
    fprintf ( 1, '  N is the number of generators.\n' );
    fprintf ( 1, '  A default value N = %d will be used.\n', n );
  else
    fprintf ( 1, '\n' );
    fprintf ( 1, '  User specified number of generators = %d\n',  n );
  end

  if ( nargin < 2 )
    sample_num = 1000 * n;
    fprintf ( 1, '\n' );
    fprintf ( 1, 'CVT_TRIANGLE_UNIFORM - Note:\n' );
    fprintf ( 1, '  No value of SAMPLE_NUM was supplied.\n' );
    fprintf ( 1, '  SAMPLE_NUM is the number of sample points.\n' );
    fprintf ( 1, '  A default value SAMPLE_NUM = %d will be used.\n', ...
      sample_num );
  else
    fprintf ( 1, '\n' );
    fprintf ( 1, '  User specified number of sample points = %d\n', ...
      sample_num );
  end

  if ( nargin < 3 )
    delaunay_display = 0;
    fprintf ( 1, '\n' );
    fprintf ( 1, 'CVT_TRIANGLE_UNIFORM - Note:\n' );
    fprintf ( 1, '  No value of DELAUNAY_DISPLAY was supplied.\n' );
    fprintf ( 1, '  DELAUNAY_DISPLAY is TRUE (nonzero) if the\n' );
    fprintf ( 1, '  Delaunay triangulation is also to be displayed.\n' );
    fprintf ( 1, '  A default value DELAUNAY_DISPLAY = %d will be used.\n', ...
      delaunay_display );
  else
    fprintf ( 1, '\n' );
    fprintf ( 1, '  User specified DELAUNAY_DISPLAY = %d\n', ...
      delaunay_display );
  end
%
%  This switch is set to 1 (TRUE) if the ACCUMARRAY command is available.
%  That speeds up the calculation a lot.  If you don't have the ACCUMARRAY
%  command, just set this to 0.
% 
  fprintf ( 1, '\n' );
  fprintf ( 1, 'CVT_TRIANGLE_UNIFORM:\n' );
  fprintf ( 1, '  MATLAB''s ACCUMARRAY command can be used for faster\n' );
  fprintf ( 1, '  computation.  This command is not available in\n' );
  fprintf ( 1, '  some versions of MATLAB.  If ACCUMARRAY is available,\n' );
  fprintf ( 1, '  simply make sure that the ACCUMARAY_AVAILABLE variable\n' );
  fprintf ( 1, '  is set to 1!\n' );
  
  accumarray_available = 1;

  if ( accumarray_available )
    fprintf ( 1, '\n' );
    fprintf ( 1, '  The ACCUMARRAY command will be used.\n' );
  else
    fprintf ( 1, '\n' );
    fprintf ( 1, '  The ACCUMARRAY command will NOT be used.\n' );
  end
%
%  Clear the figure screen, if already open.
%
  clf
%
%  Randomize the initial locations of the generators.
%
  p(1:2,1:n) = triangle_uniform ( triangle, n );
  
  it = 0;
  
  while ( 1 )
%
%  Compute the Delaunay triangle information T for the current nodes.
%
    triangulation = delaunay ( p(1,:), p(2,:) );
%
%  Display the Delaunay triangulation, if requested.
%
    if ( delaunay_display )
      subplot ( 1, 2, 2 )
      trimesh ( triangulation, p(:,1), p(:,2), zeros(n,1) )
      axis ( [ -0.1, 1.1, -0.1, 1.1 ] )
      line ( [ triangle(1,1), triangle(1,2), triangle(1,3), triangle(1,1) ], ...
           [ triangle(2,1), triangle(2,2), triangle(2,3), triangle(2,1) ], ...
        'Color', 'r' );
      title_string = sprintf ( 'Delaunay, step %d', it );
      title ( title_string );
      axis equal
      view ( 2 )
    end
%
%  Display the CVT generators, and the associated Voronoi diagram.
%
    if ( delaunay_display )
      subplot ( 1, 2, 1 )
    end
    
    voronoi ( p(1,:), p(2,:), triangulation );

    axis ( [ -0.1, 1.1, -0.1, 1.1 ] )
    line ( [ triangle(1,1), triangle(1,2), triangle(1,3), triangle(1,1) ], ...
           [ triangle(2,1), triangle(2,2), triangle(2,3), triangle(2,1) ], ...
      'Color', 'r' );
    title_string = sprintf ( 'Voronoi, step %d', it );
    title ( title_string );
    axis equal
    drawnow
%
%  Generate sample points.  
%
%  These sample points implicitly define the geometry of the region.  
%  If the region is not a unit square, then the range of the sample 
%  data must be changed.
%
%  The data is sampled uniformly.  If a nonuniform density is desired,
%  then the sampling must be done in a biased way.
%    
    s = triangle_uniform ( triangle, sample_num );
%
%  For each sample point, find K, the index of the nearest generator.
%  We do this efficiently by using the Delaunay information with
%  Matlab's DSEARCH command, rather than a brute force nearest neighbor
%  computation.
%  
    k(1:sample_num,1) = dsearch ( p(1,:), p(2,:), triangulation, s(1,:), s(2,:) );
%
%  The centroid of the Voronoi region associated with each generator
%  is approximated by the average of the sample points it was closest to.
%
    if ( accumarray_available )

      count(1:n) = accumarray ( k, ones(sample_num,1) );
      centroid(1,1:n) = accumarray ( k, s(1,:) );
      centroid(2,1:n) = accumarray ( k, s(2,:) );

    else

      count(1:n) = 0;
      centroid(1,1:n) = 0.0;
      centroid(2,1:n) = 0.0;

      for i = 1 : sample_num
        j = k(i);
        count(j) = count(j) + 1;
        centroid(1,j) = centroid(1,j) + s(1,i);
        centroid(2,j) = centroid(2,j) + s(2,i);
      end

    end
%
%  Replace the generators by the centroids.
%
    p(1,1:n) = ( centroid(1,1:n) ./ count(1:n) )';
    p(2,1:n) = ( centroid(2,1:n) ./ count(1:n) )';

    string = input ( 'RETURN, or Q to quit: ', 's' );

    if ( string == 'q' | string == 'Q' )
        break
    end

    it = it + 1;
    
  end

  fprintf ( 1, '\n' );
  fprintf ( 1, 'CVT_TRIANGLE_UNIFORM:\n' );
  fprintf ( 1, '  Normal end of execution.\n' );

  return
end
function p = triangle_uniform ( t, n )

%*****************************************************************************80
%
%% TRIANGLE_UNIFORM returns random points in a triangle.
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    30 September 2008
%
%  Author:
%
%    John Burkardt
%
%  Parameters:
%
%    Input, real T(2,3), the vertices of the triangle.
%
%    Input, integer N, the number of points to generate.
%
%    Output, real P(2,N), random points in the triangle.
%
  dim_num = 2;
  alpha = rand ( 1, n );
%
%  Interpret R as a percentage of the triangle's area.
%
%  Imagine a line L, parallel to side 1, so that the area between
%  vertex 1 and line L is R percent of the full triangle's area.
%
%  The line L will intersect sides 2 and 3 at a fraction
%  ALPHA = SQRT ( R ) of the distance from vertex 1 to vertices 2 and 3.
%
  alpha(1:n) = sqrt ( alpha(1:n) );
%
%  Determine the coordinates of the points on sides 2 and 3 intersected
%  by line L.
%
  for dim = 1 : dim_num
    p12(dim,1:n) = ( 1.0 - alpha(1:n) ) * t(dim,1) ...
                         + alpha(1:n)   * t(dim,2);

    p13(dim,1:n) = ( 1.0 - alpha(1:n) ) * t(dim,1) ...
                         + alpha(1:n)   * t(dim,3);
  end
%
%  Now choose, uniformly at random, a point on the line L.
%
  alpha = rand ( 1, n );

  for dim = 1 : dim_num
    p(dim,1:n) = ( 1.0 - alpha(1:n) ) .* p12(dim,1:n) ...
                       + alpha(1:n)   .* p13(dim,1:n);
  end

 return
end