KDTree.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Tree for nearest neighbor search in low dimensions.
 * 
 * 
 *
 * \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_ALGORITHMS_NEARESTNEIGHBORS_KDTREE_H
#define SHARK_ALGORITHMS_NEARESTNEIGHBORS_KDTREE_H
 
 
#include <shark/Models/Trees/BinaryTree.h>
#include <shark/Data/DataView.h>
#include <shark/LinAlg/Base.h>
#include <shark/Core/Math.h>
namespace shark {
 
 
///
/// \brief KD-tree, a binary space-partitioning tree
///
/// \par
/// KD-tree data structure for efficient nearest
/// neighbor searches in low-dimensional spaces.
/// Low-dimensional means << 10 dimensions.
///
/// \par
/// The tree is constructed from data by splitting
/// the dimension with largest extent (in the data
/// covered, not space represented by the cell),
/// recursively. An approximate median is used as
/// the cutting point, where the maximal number of
/// points used to estimate the median can be
/// controlled with the template parameter
/// MedianAccuracy.
///
/// \par
/// The bucket size for the tree is aleays one,
/// such that there is a unique point in each leaf
/// cell.
///
/// \ingroup space_trees
template <class InputT>
class KDTree : public BinaryTree<InputT>
{
    typedef KDTree<InputT> self_type;
    typedef BinaryTree<InputT> base_type;
public:
 
    /// Construct the tree from data.
    /// It is assumed that the container exceeds
    /// the lifetime of the KDTree (which holds
    /// only references to the points), and that
    /// the memory locations of the points remain
    /// unchanged.
    KDTree(Data<InputT> const& dataset, TreeConstruction tc = TreeConstruction())
    : base_type(dataset.numberOfElements())
    , m_cutDim(0xffffffff){
        typedef DataView<Data<RealVector> const> PointSet;
        PointSet points(dataset);
        //create a list to the iterator elements as temporary storage
        std::vector<typename boost::range_iterator<PointSet>::type> elements(m_size);
        boost::iota(elements,boost::begin(points));
 
        buildTree(tc,elements);
 
        //after the creation of the trees, the iterators in the array are sorted in the order, 
        //they are referenced by the nodes.so we can create the indexList using the indizes of the iterators
        for(std::size_t i = 0; i != m_size; ++i){
            mp_indexList[i] = elements[i].index();
        }
    }
 
 
    /// lower bound on the box-shaped
    /// space represented by this cell
    double lower(std::size_t dim) const{
        self_type* parent = static_cast<self_type*>(mep_parent);
        if (parent == NULL) return -1e100;
 
        if (parent->m_cutDim == dim && parent->mp_right == this)
            return parent->threshold();
        else
            return parent->lower(dim);
    }
 
    /// upper bound on the box-shaped
    /// space represented by this cell
    double upper(std::size_t dim) const{
        self_type* parent = static_cast<self_type*>(mep_parent);
        if (parent == NULL) return +1e100;
 
        if (parent->m_cutDim == dim && parent->mp_left == this) 
            return parent->threshold();
        else 
            return parent->upper(dim);
    }
 
    /// \par
    /// Compute the squared Euclidean distance of
    /// this cell to the given reference point, or
    /// alternatively a lower bound on this value.
    ///
    /// \par
    /// In the case of the kd-tree the computation
    /// is exact, however, only a lower bound is
    /// required in general, and other space
    /// partitioning trees used in the future may
    /// only be able to provide a lower bound, at
    /// least with reasonable computational efforts.
    double squaredDistanceLowerBound(InputT const& reference) const
    {
        double ret = 0.0;
        for (std::size_t d = 0; d != reference.size(); d++)
        {
            double v = reference(d);
            double l = lower(d);
            double u = upper(d);
            if (v < l){
                ret += sqr(l-v);
            }
            else if (v > u){
                ret += sqr(v-u);
            }
        }
        return ret;
    }
 
#if 0
    // debug code, please ignore
    void print(unsigned int ident = 0) const
    {
        if (this->isLeaf())
        {
            for (unsigned int j=0; j<m_size; j++)
            {
                for (unsigned int i=0; i<ident; i++) printf("  ");
                printf("index: %d\n", (int)this->index(j));
            }
        }
        else
        {
            for (unsigned int i=0; i<ident; i++) printf("  ");
            printf("x[%d] < %g\n", (int)m_cutDim, this->threshold());
            for (unsigned int i=0; i<ident; i++) printf("  ");
            printf("[%d]\n", (int)mp_left->size());
            ((self_type*)mp_left)->print(ident + 1);
            for (unsigned int i=0; i<ident; i++) printf("  ");
            printf("[%d]\n", (int)mp_right->size());
            ((self_type*)mp_right)->print(ident + 1);
        }
    }
#endif
 
protected:
    using base_type::mep_parent;
    using base_type::mp_left;
    using base_type::mp_right;
    using base_type::mp_indexList;
    using base_type::m_size;
    using base_type::m_nodes;
 
    /// (internal) construction of a non-root node
    KDTree(KDTree* parent, std::size_t* list, std::size_t size)
    : base_type(parent, list, size)
    , m_cutDim(0xffffffff)
    { }
 
    /// (internal) construction method:
    /// median-cuts of the dimension with widest spread
    template<class Range>
    void buildTree(TreeConstruction tc, Range& points){
        typedef typename boost::range_iterator<Range>::type iterator;
 
        iterator begin = boost::begin(points);
        iterator end = boost::end(points);
 
        if (tc.maxDepth() == 0 || m_size <= tc.maxBucketSize()){
            m_nodes = 1; 
            return; 
        }
 
        m_cutDim = calculateCuttingDimension(points);
        if (m_cutDim == (*begin)->size()){
            m_nodes = 1; 
            return; 
        }
 
        // calculate the distance of the boundary for every point in the list
        std::vector<double> distance(m_size);
        iterator point = begin;
        for(std::size_t i = 0; i != m_size; ++i,++point){
            distance[i] = (**point)[m_cutDim];
        }
 
        // split the list into sub-cells
        iterator split = this->splitList(distance,points);
        std::size_t leftSize = split-begin;
 
        // create sub-nodes
        mp_left = new KDTree(this, mp_indexList, leftSize);
        mp_right = new KDTree(this, mp_indexList + leftSize, m_size - leftSize);
 
        // recurse
        boost::iterator_range<iterator> left(begin,split);
        boost::iterator_range<iterator> right(split,end);
        ((KDTree*)mp_left)->buildTree(tc.nextDepthLevel(), left);
        ((KDTree*)mp_right)->buildTree(tc.nextDepthLevel(), right);
        m_nodes = 1 + mp_left->nodes() + mp_right->nodes();
    }
 
    ///\brief Calculate the dimension which should be cut by this node
    ///
    ///The KD-Tree calculates the Axis-Aligned-Bounding-Box surrounding the points
    ///and splits the longest dimension
    template<class Range>
    std::size_t calculateCuttingDimension(Range const& points)const{
        typedef typename boost::range_iterator<Range const>::type iterator;
 
        iterator begin = boost::begin(points);
        // iterator end = boost::end(points);
 
        // calculate bounding box of the data
        InputT L = **begin;
        InputT U = **begin;
        std::size_t dim = L.size();
        iterator point = begin;
        ++point;
        for (std::size_t i=1; i != m_size; ++i,++point){
            for (std::size_t d = 0; d != dim; d++){
                double v = (**point)[d];
                if (v < L[d]) L[d] = v;
                if (v > U[d]) U[d] = v;
            }
        }
 
        // find the longest edge of the bounding box
        std::size_t cutDim = 0;
        double extent = U[0] - L[0];
        for (std::size_t d = 1; d != dim; d++)
        {
            double e = U[d] - L[d];
            if (e > extent)
            {
                extent = e;
                cutDim = d;
            }
        }
        if(extent == 0)
            return dim;
        return cutDim;
    }
 
    /// Function describing the separating hyperplane
    /// as its zero set. It is guaranteed that the
    /// gradient has norm one, thus the absolute value
    /// of the function indicates distance from the
    /// hyperplane.
    double funct(InputT const& reference) const{
        return reference[m_cutDim];
    }
 
    /// split/cut dimension of this node
    std::size_t m_cutDim;
};
 
 
}
#endif