pelleg_moore_kmeans_statistic.hpp

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#ifndef MLPACK_METHODS_KMEANS_PELLEG_MOORE_KMEANS_STATISTIC_HPP
#define MLPACK_METHODS_KMEANS_PELLEG_MOORE_KMEANS_STATISTIC_HPP

namespace mlpack {
namespace kmeans {

class PellegMooreKMeansStatistic
{
 public:
  PellegMooreKMeansStatistic() { }

  template<typename TreeType>
  PellegMooreKMeansStatistic(TreeType& node)
  {
    centroid.zeros(node.Dataset().n_rows);

    // Hope it's a depth-first build procedure.  Also, this won't work right for
    // trees that have self-children or stuff like that.
    for (size_t i = 0; i < node.NumChildren(); ++i)
    {
      centroid += node.Child(i).NumDescendants() *
          node.Child(i).Stat().Centroid();
    }

    for (size_t i = 0; i < node.NumPoints(); ++i)
    {
      centroid += node.Dataset().col(node.Point(i));
    }

    if (node.NumDescendants() > 0)
      centroid /= node.NumDescendants();
    else
      centroid.fill(DBL_MAX); // Invalid centroid.  What else can we do?
  }

  const arma::uvec& Blacklist() const { return blacklist; }
  arma::uvec& Blacklist() { return blacklist; }

  const arma::vec& Centroid() const { return centroid; }
  arma::vec& Centroid() { return centroid; }

 private:
  arma::uvec blacklist;
  arma::vec centroid;
};

} // namespace kmeans
} // namespace mlpack

#endif