FeatureGroupingAlgorithmKD.h

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// --------------------------------------------------------------------------
//                   OpenMS -- Open-Source Mass Spectrometry
// --------------------------------------------------------------------------
// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
// ETH Zurich, and Freie Universitaet Berlin 2002-2017.
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// $Maintainer: Johannes Veit $
// $Authors: Johannes Veit $
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#ifndef OPENMS_ANALYSIS_MAPMATCHING_FEATUREGROUPINGALGORITHMKD_H
#define OPENMS_ANALYSIS_MAPMATCHING_FEATUREGROUPINGALGORITHMKD_H

#include <OpenMS/ANALYSIS/MAPMATCHING/FeatureGroupingAlgorithm.h>
#include <OpenMS/ANALYSIS/QUANTITATION/KDTreeFeatureMaps.h>
#include <OpenMS/ANALYSIS/MAPMATCHING/FeatureDistance.h>
#include <OpenMS/CONCEPT/ProgressLogger.h>

namespace OpenMS
{


class OPENMS_DLLAPI ClusterProxyKD
{

public:

  ClusterProxyKD() :
    size_(0), // => isValid() returns false
    avg_distance_(0),
    center_index_(0)
  {
  }

  ClusterProxyKD(Size size, double avg_distance, Size center_index) :
    size_(size),
    avg_distance_(avg_distance),
    center_index_(center_index)
  {
  }

  ClusterProxyKD(const ClusterProxyKD& rhs) :
    size_(rhs.size_),
    avg_distance_(rhs.avg_distance_),
    center_index_(rhs.center_index_)
  {
  }

  ~ClusterProxyKD()
  {
  }

  ClusterProxyKD& operator=(const ClusterProxyKD& rhs)
  {
    size_ = rhs.size_;
    avg_distance_ = rhs.avg_distance_;
    center_index_ = rhs.center_index_;

    return *this;
  }

  bool operator<(const ClusterProxyKD& rhs) const
  {
    if (size_ > rhs.size_) return true;
    if (size_ < rhs.size_) return false;

    if (avg_distance_ < rhs.avg_distance_) return true;
    if (avg_distance_ > rhs.avg_distance_) return false;

    // arbitrary, but required for finding unambiguous elements in std::set
    if (center_index_ > rhs.center_index_) return true;
    if (center_index_ < rhs.center_index_) return false;

    // they are equal
    return false;
  }

  bool operator!=(const ClusterProxyKD& rhs) const
  {
    return *this < rhs || rhs < *this;
  }

  bool operator==(const ClusterProxyKD& rhs) const
  {
    return !(*this != rhs);
  }

  Size getSize() const
  {
    return size_;
  }

  bool isValid() const
  {
    return size_;
  }

  double getAvgDistance() const
  {
    return avg_distance_;
  }

  Size getCenterIndex() const
  {
    return center_index_;
  }

private:

  Size size_;

  double avg_distance_;

  Size center_index_;
};


  class OPENMS_DLLAPI FeatureGroupingAlgorithmKD :
    public FeatureGroupingAlgorithm,
    public ProgressLogger
  {

public:

    FeatureGroupingAlgorithmKD();

    virtual ~FeatureGroupingAlgorithmKD();

    virtual void group(const std::vector<FeatureMap>& maps, ConsensusMap& out);

    virtual void group(const std::vector<ConsensusMap>& maps,
                       ConsensusMap& out);

    static FeatureGroupingAlgorithm* create()
    {
      return new FeatureGroupingAlgorithmKD();
    }

    static String getProductName()
    {
      return "unlabeled_kd";
    }

private:

    FeatureGroupingAlgorithmKD(const FeatureGroupingAlgorithmKD&);

    FeatureGroupingAlgorithmKD& operator=(const FeatureGroupingAlgorithmKD&);

    template <typename MapType>
    void group_(const std::vector<MapType>& input_maps, ConsensusMap& out);

    void runClustering_(const KDTreeFeatureMaps& kd_data, ConsensusMap& out);

    void updateClusterProxies_(std::set<ClusterProxyKD>& potential_clusters, std::vector<ClusterProxyKD>& cluster_for_idx, const std::set<Size>& update_these, const std::vector<Int>& assigned, const KDTreeFeatureMaps& kd_data);

    ClusterProxyKD computeBestClusterForCenter_(Size i, std::vector<Size>& cf_indices, const std::vector<Int>& assigned, const KDTreeFeatureMaps& kd_data) const;

    void addConsensusFeature_(const std::vector<Size>& indices, const KDTreeFeatureMaps& kd_data, ConsensusMap& out) const;

    SignedSize progress_;

    double rt_tol_secs_;

    double mz_tol_;

    bool mz_ppm_;

    FeatureDistance feature_distance_;
  };

} // namespace OpenMS

#endif // OPENMS_ANALYSIS_MAPMATCHING_FEATUREGROUPINGALGORITHMKD_H