kdtree2.cpp

//
// (c) Matthew B. Kennel, Institute for Nonlinear Science, UCSD (2004)
//
// Licensed under the Academic Free License version 1.1 found in file LICENSE
// with additional provisions in that same file.


#include "kdtree2.hpp"

#include <algorithm> 
#include <iostream>
using namespace std;

namespace kdtree2 {
// utility

inline float squared(const float x) {
  return(x*x);
}

inline void swap(int& a, int&b) {
  int tmp;
  tmp = a;
  a = b;
  b = tmp; 
}

inline void swap(float& a, float&b) {
  float tmp;
  tmp = a;
  a = b;
  b = tmp; 
}


//
//       KDTREERESULT implementation
// 

inline bool operator<(const KDTreeResult& e1, const KDTreeResult& e2) {
  return (e1.dis < e2.dis);
}

//
//       KDTREE2_RESULT_VECTOR implementation
// 
float KDTreeResultVector::max_value() {
  return( (*begin()).dis ); // very first element
}

void KDTreeResultVector::push_element_and_heapify(KDTreeResult& e) {
  push_back(e); // what a vector does.
  push_heap( begin(), end() ); // and now heapify it, with the new elt.
}
float KDTreeResultVector::replace_maxpri_elt_return_new_maxpri(KDTreeResult& e) {
  // remove the maximum priority element on the queue and replace it
  // with 'e', and return its priority.
  //
  // here, it means replacing the first element [0] with e, and re heapifying.
  
  pop_heap( begin(), end() );
  pop_back();
  push_back(e); // insert new
  push_heap(begin(), end() );  // and heapify. 
  return( (*this)[0].dis );
}

//
//        KDTREE2 implementation
//

// constructor
KDTree::KDTree(KDTreeArray& data_in,bool rearrange_in,int dim_in)
  : the_data(data_in),
    N  ( data_in.shape()[0] ),
    dim( data_in.shape()[1] ),
    sort_results(false),
    rearrange(rearrange_in), 
    root(NULL),
    data(NULL),
    ind(N)
{
  //
  // initialize the constant references using this unusual C++
  // feature.
  //
  if (dim_in > 0) 
    dim = dim_in;

  build_tree();

  if (rearrange) {
    // if we have a rearranged tree.
    // allocate the memory for it. 
    printf("rearranging\n"); 
    rearranged_data.resize( boost::extents[N][dim] );
    
    // permute the data for it.
    for (int i=0; i<N; i++) {
      for (int j=0; j<dim; j++) {
	rearranged_data[i][j] = the_data[ind[i]][j];
	// wouldn't F90 be nice here? 
      }
    }
    data = &rearranged_data;
  } else {
    data = &the_data;
  }
}


// destructor
KDTree::~KDTree() {
  delete root;
}

// building routines
void KDTree::build_tree() {
  for (int i=0; i<N; i++) 
	{ ind[i] = i; }
  root = build_tree_for_range(0,N-1,NULL); 
}

KDTreeNode* KDTree::build_tree_for_range(int l, int u, KDTreeNode* parent) {
  // recursive function to build 
  KDTreeNode* node = new KDTreeNode(dim);
  // the newly created node. 

  if (u<l) {
    return(NULL); // no data in this node. 
  }
      

  if ((u-l) <= bucketsize) {
    // create a terminal node. 
	
    // always compute true bounding box for terminal node. 
    for (int i=0;i<dim;i++) {
      spread_in_coordinate(i,l,u,node->box[i]);
    }
    
    node->cut_dim = 0; 
    node->cut_val = 0.0;
    node->l = l;
    node->u = u;
    node->left = node->right = NULL;
    
	
  } else {
    //
    // Compute an APPROXIMATE bounding box for this node.
    // if parent == NULL, then this is the root node, and 
    // we compute for all dimensions.
    // Otherwise, we copy the bounding box from the parent for
    // all coordinates except for the parent's cut dimension.  
    // That, we recompute ourself.
    //
	
    int c =-1;
    float maxspread = 0.0;
    int m; 
	//cout << "dim="<<dim <<endl;
    for (int i=0;i<dim;i++) {
      if ((parent == NULL) || (parent->cut_dim == i)) {
	spread_in_coordinate(i,l,u,node->box[i]);
      } else {
	node->box[i] = parent->box[i];
      }
      float spread = node->box[i].upper - node->box[i].lower; 
	
      if (spread>maxspread) {
	maxspread = spread;
	c=i; 
      }
	//cout << spread <<" -> "<<maxspread << "\t";
    }
//cout<<"c="<< c <<endl;
	
    // 
    // now, c is the identity of which coordinate has the greatest spread
    //
	
    if (c == -1) {
      m = (l+u)/2;
	c=0;
      select_on_coordinate(c,m,l,u);  
    } else {
      float sum; 
      float average;

      if (true) {
	sum = 0.0;
	//cout<< c <<endl;
	for (int k=l; k <= u; k++) {
	  sum += the_data[ind[k]][c];
	}

	average = sum / static_cast<float> (u-l+1);
      } else {
	// average of top and bottom nodes.
	average = (node->box[c].upper + node->box[c].lower)*0.5; 
      }
	
      m = select_on_coordinate_value(c,average,l,u);
    }
	

    // move the indices around to cut on dim 'c'.
    node->cut_dim=c;
    node->l = l;
    node->u = u;

    node->left = build_tree_for_range(l,m,node);
    node->right = build_tree_for_range(m+1,u,node);

    if (node->right == NULL) {
      for (int i=0; i<dim; i++) 
	node->box[i] = node->left->box[i]; 
       node->cut_val = node->left->box[c].upper;
       node->cut_val_left = node->cut_val_right = node->cut_val;
    } else if (node->left == NULL) {
      for (int i=0; i<dim; i++) 
	node->box[i] = node->right->box[i]; 
      node->cut_val =  node->right->box[c].upper;
      node->cut_val_left = node->cut_val_right = node->cut_val;
    } else {
      node->cut_val_right = node->right->box[c].lower;
      node->cut_val_left  = node->left->box[c].upper;
      node->cut_val = (node->cut_val_left + node->cut_val_right) / 2.0; 
      //
      // now recompute true bounding box as union of subtree boxes.
      // This is now faster having built the tree, being logarithmic in
      // N, not linear as would be from naive method.
      //
      for (int i=0; i<dim; i++) {
	node->box[i].upper = std::max(node->left->box[i].upper,
				 node->right->box[i].upper);
	
	node->box[i].lower = std::min(node->left->box[i].lower,
				 node->right->box[i].lower);
      }
    }
  }
  return(node);
}



void KDTree:: spread_in_coordinate(int c, int l, int u, interval& interv)
{
  // return the minimum and maximum of the indexed data between l and u in
  // smin_out and smax_out.
  float smin, smax;
  float lmin, lmax;
  int i; 

  smin = the_data[ind[l]][c];
  smax = smin;
  

  // process two at a time.
  for (i=l+2; i<= u; i+=2) {
    lmin = the_data[ind[i-1]] [c];
    lmax = the_data[ind[i]  ] [c];

    if (lmin > lmax) {
      swap(lmin,lmax); 
      //      float t = lmin;
      //      lmin = lmax;
      //      lmax = t;
    }

    if (smin > lmin) smin = lmin;
    if (smax <lmax) smax = lmax;
  }
  // is there one more element? 
  if (i == u+1) {
    float last = the_data[ind[u]] [c];
    if (smin>last) smin = last;
    if (smax<last) smax = last;
  }
  interv.lower = smin;
  interv.upper = smax;
  //  printf("Spread in coordinate %d=[%f,%f]\n",c,smin,smax);
}


void KDTree::select_on_coordinate(int c, int k, int l, int u) {
  //
  //  Move indices in ind[l..u] so that the elements in [l .. k] 
  //  are less than the [k+1..u] elmeents, viewed across dimension 'c'. 
  // 
  while (l < u) {
    int t = ind[l];
    int m = l;

    for (int i=l+1; i<=u; i++) {
      if ( the_data[ ind[i] ] [c] < the_data[t][c]) {
	m++;
	swap(ind[i],ind[m]); 
      }
    } // for i 
    swap(ind[l],ind[m]);

    if (m <= k) l = m+1;
    if (m >= k) u = m-1;
  } // while loop
}

int KDTree::select_on_coordinate_value(int c, float alpha, int l, int u) {
  //
  //  Move indices in ind[l..u] so that the elements in [l .. return]
  //  are <= alpha, and hence are less than the [return+1..u]
  //  elmeents, viewed across dimension 'c'.
  // 
  int lb = l, ub = u;

  while (lb < ub) {
    if (the_data[ind[lb]][c] <= alpha) {
      lb++; // good where it is.
    } else {
      swap(ind[lb],ind[ub]); 
      ub--;
    }
  }

  // here ub=lb
  if (the_data[ind[lb]][c] <= alpha)
    return(lb);
  else
    return(lb-1);
  
}


// void KDTree::dump_data() {
//   int upper1, upper2;
  
//   upper1 = N;
//   upper2 = dim;

//   printf("Rearrange=%d\n",rearrange);
//   printf("N=%d, dim=%d\n", upper1, upper2);
//   for (int i=0; i<upper1; i++) {
//     printf("the_data[%d][*]=",i); 
//     for (int j=0; j<upper2; j++)
//       printf("%f,",the_data[i][j]); 
//     printf("\n"); 
//   }
//   for (int i=0; i<upper1; i++)
//     printf("Indexes[%d]=%d\n",i,ind[i]); 
//   for (int i=0; i<upper1; i++) {
//     printf("data[%d][*]=",i); 
//     for (int j=0; j<upper2; j++)
//       printf("%f,",(*data)[i][j]); 
//     printf("\n"); 
//   }
// }



//
// search record substructure
//
// one of these is created for each search.
// this holds useful information  to be used
// during the search



static const float infinity = 1.0e38;

class SearchRecord {

private:
  friend class KDTree;
  friend class KDTreeNode;

  std::vector<float>& qv; 
  int dim;
  bool rearrange;
  unsigned int nn; // , nfound;
  float ballsize;
  int centeridx, correltime;

  KDTreeResultVector& result;  // results
  const KDTreeArray* data; 
  const std::vector<int>& ind; 
  // constructor

public:
  SearchRecord(std::vector<float>& qv_in, KDTree& tree_in,
	       KDTreeResultVector& result_in) :  
    qv(qv_in),
    result(result_in),
    data(tree_in.data),
    ind(tree_in.ind) 
  {
    dim = tree_in.dim;
    rearrange = tree_in.rearrange;
    ballsize = infinity; 
    nn = 0; 
  };

};


void KDTree::n_nearest_brute_force(std::vector<float>& qv, int nn, KDTreeResultVector& result) {

  result.clear();

  for (int i=0; i<N; i++) {
    float dis = 0.0;
    KDTreeResult e; 
    for (int j=0; j<dim; j++) {
      dis += squared( the_data[i][j] - qv[j]);
    }
    e.dis = dis;
    e.idx = i;
    result.push_back(e);
  }
  sort(result.begin(), result.end() ); 

}


void KDTree::n_nearest(std::vector<float>& qv, int nn, KDTreeResultVector& result) {
  SearchRecord sr(qv,*this,result);
  std::vector<float> vdiff(dim,0.0); 

  result.clear(); 

  sr.centeridx = -1;
  sr.correltime = 0;
  sr.nn = nn; 

  root->search(sr); 

  if (sort_results) sort(result.begin(), result.end());
  
}
// search for n nearest to a given query vector 'qv'.

  
void KDTree::n_nearest_around_point(int idxin, int correltime, int nn,
				     KDTreeResultVector& result) {
  std::vector<float> qv(dim);  //  query vector

  result.clear(); 

  for (int i=0; i<dim; i++) {
    qv[i] = the_data[idxin][i]; 
  }
  // copy the query vector.
  
  {
    SearchRecord sr(qv, *this, result);
    // construct the search record.
    sr.centeridx = idxin;
    sr.correltime = correltime;
    sr.nn = nn; 
    root->search(sr); 
  }

  if (sort_results) sort(result.begin(), result.end());
    
}


void KDTree::r_nearest(std::vector<float>& qv, float r2, KDTreeResultVector& result) {
// search for all within a ball of a certain radius
  SearchRecord sr(qv,*this,result);
  std::vector<float> vdiff(dim,0.0); 

  result.clear(); 

  sr.centeridx = -1;
  sr.correltime = 0;
  sr.nn = 0; 
  sr.ballsize = r2; 

  root->search(sr); 

  if (sort_results) sort(result.begin(), result.end());
  
}

int KDTree::r_count(std::vector<float>& qv, float r2) {
// search for all within a ball of a certain radius
  {
    KDTreeResultVector result; 
    SearchRecord sr(qv,*this,result);

    sr.centeridx = -1;
    sr.correltime = 0;
    sr.nn = 0; 
    sr.ballsize = r2; 
    
    root->search(sr); 
    return(result.size());
  }

  
}

void KDTree::r_nearest_around_point(int idxin, int correltime, float r2,
				     KDTreeResultVector& result) {
  std::vector<float> qv(dim);  //  query vector

  result.clear(); 

  for (int i=0; i<dim; i++) {
    qv[i] = the_data[idxin][i]; 
  }
  // copy the query vector.
  
  {
    SearchRecord sr(qv, *this, result);
    // construct the search record.
    sr.centeridx = idxin;
    sr.correltime = correltime;
    sr.ballsize = r2; 
    sr.nn = 0; 
    root->search(sr); 
  }

  if (sort_results) sort(result.begin(), result.end());
    
}


int KDTree::r_count_around_point(int idxin, int correltime, float r2) 
{
  std::vector<float> qv(dim);  //  query vector


  for (int i=0; i<dim; i++) {
    qv[i] = the_data[idxin][i]; 
  }
  // copy the query vector.
  
  {
    KDTreeResultVector result; 
    SearchRecord sr(qv, *this, result);
    // construct the search record.
    sr.centeridx = idxin;
    sr.correltime = correltime;
    sr.ballsize = r2; 
    sr.nn = 0; 
    root->search(sr); 
    return(result.size());
  }

  
}


// 
//        KDTREE_NODE implementation
//

// constructor
KDTreeNode::KDTreeNode(int dim) : box(dim) { 
  left = right = NULL; 
  //
  // all other construction is handled for real in the 
  // KDTree building operations.
  // 
}

// destructor
KDTreeNode::~KDTreeNode() {
  if (left != NULL) delete left; 
  if (right != NULL) delete right; 
  // maxbox and minbox 
  // will be automatically deleted in their own destructors. 
}


void KDTreeNode::search(SearchRecord& sr) {
  // the core search routine.
  // This uses true distance to bounding box as the
  // criterion to search the secondary node. 
  //
  // This results in somewhat fewer searches of the secondary nodes
  // than 'search', which uses the vdiff vector,  but as this
  // takes more computational time, the overall performance may not
  // be improved in actual run time. 
  //

  if ( (left == NULL) && (right == NULL)) {
    // we are on a terminal node
    if (sr.nn == 0) {
      process_terminal_node_fixedball(sr);
    } else {
      process_terminal_node(sr);
    }
  } else {
    KDTreeNode *ncloser, *nfarther;

    float extra;
    float qval = sr.qv[cut_dim]; 
    // value of the wall boundary on the cut dimension. 
    if (qval < cut_val) {
      ncloser = left;
      nfarther = right;
      extra = cut_val_right-qval;
    } else {
      ncloser = right;
      nfarther = left;
      extra = qval-cut_val_left; 
    };

    if (ncloser != NULL) ncloser->search(sr);

    if ((nfarther != NULL) && (squared(extra) < sr.ballsize)) {
      // first cut
      if (nfarther->box_in_search_range(sr)) {
	nfarther->search(sr); 
      }      
    }
  }
}


inline float dis_from_bnd(float x, float amin, float amax) {
  if (x > amax) {
    return(x-amax); 
  } else if (x < amin)
    return (amin-x);
  else
    return 0.0;
  
}

inline bool KDTreeNode::box_in_search_range(SearchRecord& sr) {
  //
  // does the bounding box, represented by minbox[*],maxbox[*]
  // have any point which is within 'sr.ballsize' to 'sr.qv'??
  //
 
  int dim = sr.dim;
  float dis2 =0.0; 
  float ballsize = sr.ballsize; 
  for (int i=0; i<dim;i++) {
    dis2 += squared(dis_from_bnd(sr.qv[i],box[i].lower,box[i].upper));
    if (dis2 > ballsize)
      return(false);
  }
  return(true);
}


void KDTreeNode::process_terminal_node(SearchRecord& sr) {
  int centeridx  = sr.centeridx;
  int correltime = sr.correltime;
  unsigned int nn = sr.nn; 
  int dim        = sr.dim;
  float ballsize = sr.ballsize;
  //
  bool rearrange = sr.rearrange; 
  const KDTreeArray& data = *sr.data;

  const bool debug = false;

  if (debug) {
    printf("Processing terminal node %d, %d\n",l,u);
    std::cout << "Query vector = [";
    for (int i=0; i<dim; i++) std::cout << sr.qv[i] << ','; 
    std::cout << "]\n";
    std::cout << "nn = " << nn << '\n';
    check_query_in_bound(sr);
  }

  for (int i=l; i<=u;i++) {
    int indexofi;  // sr.ind[i]; 
    float dis;
    bool early_exit; 

    if (rearrange) {
      early_exit = false;
      dis = 0.0;
      for (int k=0; k<dim; k++) {
	dis += squared(data[i][k] - sr.qv[k]);
	if (dis > ballsize) {
	  early_exit=true; 
	  break;
	}
      }
      if(early_exit) continue; // next iteration of mainloop
      // why do we do things like this?  because if we take an early
      // exit (due to distance being too large) which is common, then
      // we need not read in the actual point index, thus saving main
      // memory bandwidth.  If the distance to point is less than the
      // ballsize, though, then we need the index.
      //
      indexofi = sr.ind[i];
    } else {
      // 
      // but if we are not using the rearranged data, then
      // we must always 
      indexofi = sr.ind[i];
      early_exit = false;
      dis = 0.0;
      for (int k=0; k<dim; k++) {
	dis += squared(data[indexofi][k] - sr.qv[k]);
	if (dis > ballsize) {
	  early_exit= true; 
	  break;
	}
      }
      if(early_exit) continue; // next iteration of mainloop
    } // end if rearrange. 
    
    if (centeridx > 0) {
      // we are doing decorrelation interval
      if (abs(indexofi-centeridx) < correltime) continue; // skip this point. 
    }

    // here the point must be added to the list.
    //
    // two choices for any point.  The list so far is either
    // undersized, or it is not.
    //
    if (sr.result.size() < nn) {
      KDTreeResult e;
      e.idx = indexofi;
      e.dis = dis;
      sr.result.push_element_and_heapify(e); 
      if (debug) std::cout << "unilaterally pushed dis=" << dis;
      if (sr.result.size() == nn) ballsize = sr.result.max_value();
      // Set the ball radius to the largest on the list (maximum priority).
      if (debug) {
	std::cout << " ballsize = " << ballsize << "\n"; 
	std::cout << "sr.result.size() = "  << sr.result.size() << '\n';
      }
    } else {
      //
      // if we get here then the current node, has a squared 
      // distance smaller
      // than the last on the list, and belongs on the list.
      // 
      KDTreeResult e;
      e.idx = indexofi;
      e.dis = dis;
      ballsize = sr.result.replace_maxpri_elt_return_new_maxpri(e); 
      if (debug) {
	std::cout << "Replaced maximum dis with dis=" << dis << 
	  " new ballsize =" << ballsize << '\n';
      }
    }
  } // main loop
  sr.ballsize = ballsize;
}

void KDTreeNode::process_terminal_node_fixedball(SearchRecord& sr) {
  int centeridx  = sr.centeridx;
  int correltime = sr.correltime;
  int dim        = sr.dim;
  float ballsize = sr.ballsize;
  //
  bool rearrange = sr.rearrange; 
  const KDTreeArray& data = *sr.data;

  for (int i=l; i<=u;i++) {
    int indexofi = sr.ind[i]; 
    float dis;
    bool early_exit; 

    if (rearrange) {
      early_exit = false;
      dis = 0.0;
      for (int k=0; k<dim; k++) {
	dis += squared(data[i][k] - sr.qv[k]);
	if (dis > ballsize) {
	  early_exit=true; 
	  break;
	}
      }
      if(early_exit) continue; // next iteration of mainloop
      // why do we do things like this?  because if we take an early
      // exit (due to distance being too large) which is common, then
      // we need not read in the actual point index, thus saving main
      // memory bandwidth.  If the distance to point is less than the
      // ballsize, though, then we need the index.
      //
      indexofi = sr.ind[i];
    } else {
      // 
      // but if we are not using the rearranged data, then
      // we must always 
      indexofi = sr.ind[i];
      early_exit = false;
      dis = 0.0;
      for (int k=0; k<dim; k++) {
	dis += squared(data[indexofi][k] - sr.qv[k]);
	if (dis > ballsize) {
	  early_exit= true; 
	  break;
	}
      }
      if(early_exit) continue; // next iteration of mainloop
    } // end if rearrange. 
    
    if (centeridx > 0) {
      // we are doing decorrelation interval
      if (abs(indexofi-centeridx) < correltime) continue; // skip this point. 
    }

    {
      KDTreeResult e;
      e.idx = indexofi;
      e.dis = dis;
	sr.result.push_back(e);
    }

  }
}
} // namespace kdtee2