FeatureGroupingAlgorithmKD.h

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// Copyright (c) 2002-2023, The OpenMS Team -- EKU Tuebingen, ETH Zurich, and FU Berlin
// SPDX-License-Identifier: BSD-3-Clause
//
// --------------------------------------------------------------------------
// $Maintainer: Johannes Veit $
// $Authors: Johannes Veit $
// --------------------------------------------------------------------------
 
#pragma once
 
#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();
 
    ~FeatureGroupingAlgorithmKD() override;
 
    void group(const std::vector<FeatureMap>& maps, ConsensusMap& out) override;
 
    void group(const std::vector<ConsensusMap>& maps,
                       ConsensusMap& out) override;
 
    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