% computes a clustering solution total cost
function cost = compute_cost_pc(points, centroids)
    cost  = 0;
  [m n] = size(points);
  [NC c] = size(centroids);
  assigned_centroid = zeros(m,1);
  cost_clustere = zeros(NC,1);
  for i = 1:m
    dist_minima = 10000;
    for j = 1:NC
      dist = norm(points(i,:) - centroids(j,:));
      if dist < dist_minima
        dist_minima = dist;
        centroid_asociat = j;
      endif
    endfor
    assigned_centroid(i) = centroid_asociat;
  endfor
  for j = 1:NC
    cost_clustere(j) = 0;
    for i = 1:m
      if assigned_centroid(i) == j
        dist = norm(points(i,:) - centroids(j,:));
        cost_clustere(j) = cost_clustere(j) + dist;
      endif
    endfor
    cost = cost + cost_clustere(j);
  endfor
end