ClusterHierarchical.h

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// $Maintainer: Mathias Walzer $
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#ifndef OPENMS_COMPARISON_CLUSTERING_CLUSTERHIERARCHICAL_H
#define OPENMS_COMPARISON_CLUSTERING_CLUSTERHIERARCHICAL_H

#include <OpenMS/KERNEL/StandardTypes.h>
#include <OpenMS/DATASTRUCTURES/DistanceMatrix.h>
#include <OpenMS/COMPARISON/CLUSTERING/ClusterFunctor.h>
#include <OpenMS/COMPARISON/CLUSTERING/ClusterAnalyzer.h>
#include <OpenMS/COMPARISON/SPECTRA/PeakSpectrumCompareFunctor.h>
#include <OpenMS/COMPARISON/SPECTRA/BinnedSpectrum.h>
#include <OpenMS/COMPARISON/SPECTRA/BinnedSpectrumCompareFunctor.h>
#include <OpenMS/CONCEPT/Exception.h>

#include <vector>

namespace OpenMS
{

  class OPENMS_DLLAPI ClusterHierarchical
  {
private:

    double threshold_;

public:
    ClusterHierarchical() :
      threshold_(1.0)
    {
    }

    ClusterHierarchical(const ClusterHierarchical & source) :
      threshold_(source.threshold_)
    {
    }

    virtual ~ClusterHierarchical()
    {
    }

    template <typename Data, typename SimilarityComparator>
    void cluster(std::vector<Data> & data, const SimilarityComparator & comparator, const ClusterFunctor & clusterer, std::vector<BinaryTreeNode> & cluster_tree, DistanceMatrix<float> & original_distance)
    {
      if (original_distance.dimensionsize() != data.size())
      {
        //create distancematrix for data with comparator
        original_distance.clear();
        original_distance.resize(data.size(), 1);
        for (Size i = 0; i < data.size(); i++)
        {
          for (Size j = 0; j < i; j++)
          {
            //distance value is 1-similarity value, since similarity is in range of [0,1]
            original_distance.setValueQuick(i, j, 1 - comparator(data[i], data[j]));
          }
        }
      }

      //~ std::cout << "done" << std::endl; //maybe progress handler?
      // create clustering with ClusterMethod, DistanceMatrix and Data
      clusterer(original_distance, cluster_tree, threshold_);
    }

    void cluster(std::vector<PeakSpectrum> & data, const BinnedSpectrumCompareFunctor & comparator, double sz, UInt sp, const ClusterFunctor & clusterer, std::vector<BinaryTreeNode> & cluster_tree, DistanceMatrix<float> & original_distance)
    {

      std::vector<BinnedSpectrum> binned_data;
      binned_data.reserve(data.size());

      //transform each PeakSpectrum to a corresponding BinnedSpectrum with given settings of size and spread
      for (Size i = 0; i < data.size(); i++)
      {
        //double sz(2), UInt sp(1);
        binned_data.push_back(BinnedSpectrum(sz, sp, data[i]));
      }

      //create distancematrix for data with comparator
      original_distance.clear();
      original_distance.resize(data.size(), 1);

      for (Size i = 0; i < binned_data.size(); i++)
      {
        for (Size j = 0; j < i; j++)
        {
          //distance value is 1-similarity value, since similarity is in range of [0,1]
          original_distance.setValue(i, j, 1 - comparator(binned_data[i], binned_data[j]));
        }
      }

      // create Clustering with ClusterMethod, DistanceMatrix and Data
      clusterer(original_distance, cluster_tree, threshold_);
    }

    double getThreshold()
    {
      return threshold_;
    }

    void setThreshold(double x)
    {
      threshold_ = x;
    }

  };

  class OPENMS_DLLAPI UnnormalizedComparator :
    public Exception::BaseException
  {
public:
    UnnormalizedComparator(const char * file, int line, const char * function, const char * message
                             = "Clustering with unnormalized similarity measurement requested, normalized is mandatory") throw();
    virtual ~UnnormalizedComparator() throw();
  };

}
#endif //OPENMS_COMPARISON_CLUSTERING_CLUSTERHIERARCHICAL_H