ClusterHierarchical.h

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// Copyright (c) 2002-present, OpenMS Inc. -- EKU Tuebingen, ETH Zurich, and FU Berlin
// SPDX-License-Identifier: BSD-3-Clause
//
//
 
//  You should have received a copy of the GNU Lesser General Public
//  License along with this library; if not, write to the Free Software
//  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
//
// --------------------------------------------------------------------------
// $Maintainer: Mathias Walzer $
// $Authors: $
// --------------------------------------------------------------------------
//
#pragma once
 
#include <OpenMS/ML/CLUSTERING/ClusterAnalyzer.h>
#include <OpenMS/ML/CLUSTERING/ClusterFunctor.h>
#include <OpenMS/KERNEL/BinnedSpectrum.h>
#include <OpenMS/COMPARISON/BinnedSpectrumCompareFunctor.h>
#include <OpenMS/COMPARISON/PeakSpectrumCompareFunctor.h>
#include <OpenMS/CONCEPT/Exception.h>
#include <OpenMS/DATASTRUCTURES/DistanceMatrix.h>
#include <OpenMS/KERNEL/StandardTypes.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 distance matrix for data using 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]));
        }
      }
    }
 
    // 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,
               float offset,
               const ClusterFunctor& clusterer,
               std::vector<BinaryTreeNode>& cluster_tree,
               DistanceMatrix<float>& original_distance) const
  {
    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.emplace_back(data[i], sz, false, sp, offset);
    }
 
    // 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() const
  {
    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();
  ~UnnormalizedComparator() throw() override;
};
 
} // namespace OpenMS