HierarchicalClustering.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Hierarchical Clustering.
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        2011
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
#ifndef SHARK_MODELS_CLUSTERING_HIERARCHICALCLUSTERING_H
#define SHARK_MODELS_CLUSTERING_HIERARCHICALCLUSTERING_H
 
 
#include <shark/Models/Clustering/AbstractClustering.h>
#include <shark/Models/Trees/BinaryTree.h>
 
 
namespace shark {
 
 
///
/// \brief Clusters defined by a binary space partitioning tree.
///
/// \par
/// Binary space-partitioning is a simple and fast way of
/// clustering.
///
/// \par
/// It is not clear how the unfolding of the tree can be
/// expressed as a flat parameter vector. Therefore, the
/// parameter vector of this model is empty.
///
/// \ingroup clustering
template < class InputT>
class HierarchicalClustering : public AbstractClustering<InputT>
{
public:
    typedef AbstractClustering<InputT> base_type;
    typedef BinaryTree<InputT> tree_type;
    typedef typename base_type::BatchInputType BatchInputType;
    typedef typename base_type::BatchOutputType BatchOutputType;
 
    /// \brief Constructor
    ///
    /// \par
    /// Construct a hierarchy of clusters from a binary tree.
    ///
    /// \param  tree  tree object underlying the clustering
    HierarchicalClustering(const tree_type* tree)
    : mep_tree(tree){
        SHARK_RUNTIME_CHECK(tree, "[HierarchicalClustering] Tree must not be NULL");
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "HierarchicalClustering"; }
 
    Shape inputShape() const{
        return Shape();
    }
    
    /// Return the number of clusters.
    std::size_t numberOfClusters() const{
        return (mep_tree->nodes() + 1) / 2;
    }
 
    /// Return the best matching cluster for very pattern in the batch.
    BatchOutputType hardMembership(BatchInputType const& patterns) const{
        std::size_t numPatterns = batchSize(patterns);
        BatchOutputType memberships(numPatterns);
        for(std::size_t i = 0; i != numPatterns; ++i){
            tree_type const* tree = mep_tree;
            memberships(i) = 0;
            while (tree->hasChildren()){
                if (tree->isLeft(row(patterns,i))){
                    tree = tree->left();
                }
                else{
                    memberships(i) += unsigned((tree->left()->nodes() + 1) / 2);
                    tree = tree->right();
                }
            }
        }
        return memberships;
    }
 
    /// from IParameterizable
    RealVector parameterVector() const{
        return RealVector();
    }
 
    /// from IParameterizable
    void setParameterVector(RealVector const& newParameters){
        SHARK_ASSERT(newParameters.size() == 0);
    }
 
    /// from IParameterizable
    std::size_t numberOfParameters() const{
        return 0;
    }
 
protected:
    /// binary tree
    tree_type const* mep_tree;
};
 
 
}
#endif