Logo Search packages:      
Sourcecode: madlib version File versions  Download package

kd_pr_search.cpp

//----------------------------------------------------------------------
// File:                kd_pr_search.cpp
// Programmer:          Sunil Arya and David Mount
// Description:         Priority search for kd-trees
// Last modified: 01/04/05 (Version 1.0)
//----------------------------------------------------------------------
// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
// David Mount.  All Rights Reserved.
// 
// This software and related documentation is part of the Approximate
// Nearest Neighbor Library (ANN).  This software is provided under
// the provisions of the Lesser GNU Public License (LGPL).  See the
// file ../ReadMe.txt for further information.
// 
// The University of Maryland (U.M.) and the authors make no
// representations about the suitability or fitness of this software for
// any purpose.  It is provided "as is" without express or implied
// warranty.
//----------------------------------------------------------------------
// History:
//    Revision 0.1  03/04/98
//          Initial release
//----------------------------------------------------------------------

#include "kd_pr_search.h"                       // kd priority search declarations

//----------------------------------------------------------------------
//    Approximate nearest neighbor searching by priority search.
//          The kd-tree is searched for an approximate nearest neighbor.
//          The point is returned through one of the arguments, and the
//          distance returned is the SQUARED distance to this point.
//
//          The method used for searching the kd-tree is called priority
//          search.  (It is described in Arya and Mount, ``Algorithms for
//          fast vector quantization,'' Proc. of DCC '93: Data Compression
//          Conference}, eds. J. A. Storer and M. Cohn, IEEE Press, 1993,
//          381--390.)
//
//          The cell of the kd-tree containing the query point is located,
//          and cells are visited in increasing order of distance from the
//          query point.  This is done by placing each subtree which has
//          NOT been visited in a priority queue, according to the closest
//          distance of the corresponding enclosing rectangle from the
//          query point.  The search stops when the distance to the nearest
//          remaining rectangle exceeds the distance to the nearest point
//          seen by a factor of more than 1/(1+eps). (Implying that any
//          point found subsequently in the search cannot be closer by more
//          than this factor.)
//
//          The main entry point is annkPriSearch() which sets things up and
//          then call the recursive routine ann_pri_search().  This is a
//          recursive routine which performs the processing for one node in
//          the kd-tree.  There are two versions of this virtual procedure,
//          one for splitting nodes and one for leaves. When a splitting node
//          is visited, we determine which child to continue the search on
//          (the closer one), and insert the other child into the priority
//          queue.  When a leaf is visited, we compute the distances to the
//          points in the buckets, and update information on the closest
//          points.
//
//          Some trickery is used to incrementally update the distance from
//          a kd-tree rectangle to the query point.  This comes about from
//          the fact that which each successive split, only one component
//          (along the dimension that is split) of the squared distance to
//          the child rectangle is different from the squared distance to
//          the parent rectangle.
//----------------------------------------------------------------------

//----------------------------------------------------------------------
//          To keep argument lists short, a number of global variables
//          are maintained which are common to all the recursive calls.
//          These are given below.
//----------------------------------------------------------------------

double                  ANNprEps;                     // the error bound
int                     ANNprDim;                     // dimension of space
ANNpoint          ANNprQ;                             // query point
double                  ANNprMaxErr;                  // max tolerable squared error
ANNpointArray     ANNprPts;                     // the points
ANNpr_queue       *ANNprBoxPQ;                  // priority queue for boxes
ANNmin_k          *ANNprPointMK;                // set of k closest points

//----------------------------------------------------------------------
//    annkPriSearch - priority search for k nearest neighbors
//----------------------------------------------------------------------

void ANNkd_tree::annkPriSearch(
      ANNpoint                q,                      // query point
      int                           k,                      // number of near neighbors to return
      ANNidxArray             nn_idx,                 // nearest neighbor indices (returned)
      ANNdistArray            dd,                     // dist to near neighbors (returned)
      double                        eps)              // error bound (ignored)
{
                                                            // max tolerable squared error
      ANNprMaxErr = ANN_POW(1.0 + eps);
      ANN_FLOP(2)                                     // increment floating ops

      ANNprDim = dim;                                 // copy arguments to static equivs
      ANNprQ = q;
      ANNprPts = pts;
      ANNptsVisited = 0;                              // initialize count of points visited

      ANNprPointMK = new ANNmin_k(k);           // create set for closest k points

                                                            // distance to root box
      ANNdist box_dist = annBoxDistance(q,
                        bnd_box_lo, bnd_box_hi, dim);

      ANNprBoxPQ = new ANNpr_queue(n_pts);// create priority queue for boxes
      ANNprBoxPQ->insert(box_dist, root); // insert root in priority queue

      while (ANNprBoxPQ->non_empty() &&
            (!(ANNmaxPtsVisited != 0 && ANNptsVisited > ANNmaxPtsVisited))) {
            ANNkd_ptr np;                             // next box from prior queue

                                                            // extract closest box from queue
            ANNprBoxPQ->extr_min(box_dist, (void *&) np);

            ANN_FLOP(2)                               // increment floating ops
            if (box_dist*ANNprMaxErr >= ANNprPointMK->max_key())
                  break;

            np->ann_pri_search(box_dist); // search this subtree.
      }

      for (int i = 0; i < k; i++) {       // extract the k-th closest points
            dd[i] = ANNprPointMK->ith_smallest_key(i);
            nn_idx[i] = ANNprPointMK->ith_smallest_info(i);
      }

      delete ANNprPointMK;                      // deallocate closest point set
      delete ANNprBoxPQ;                              // deallocate priority queue
}

//----------------------------------------------------------------------
//    kd_split::ann_pri_search - search a splitting node
//----------------------------------------------------------------------

void ANNkd_split::ann_pri_search(ANNdist box_dist)
{
      ANNdist new_dist;                         // distance to child visited later
                                                            // distance to cutting plane
      ANNcoord cut_diff = ANNprQ[cut_dim] - cut_val;

      if (cut_diff < 0) {                             // left of cutting plane
            ANNcoord box_diff = cd_bnds[ANN_LO] - ANNprQ[cut_dim];
            if (box_diff < 0)                   // within bounds - ignore
                  box_diff = 0;
                                                            // distance to further box
            new_dist = (ANNdist) ANN_SUM(box_dist,
                        ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff)));

            if (child[ANN_HI] != KD_TRIVIAL)// enqueue if not trivial
                  ANNprBoxPQ->insert(new_dist, child[ANN_HI]);
                                                            // continue with closer child
            child[ANN_LO]->ann_pri_search(box_dist);
      }
      else {                                                // right of cutting plane
            ANNcoord box_diff = ANNprQ[cut_dim] - cd_bnds[ANN_HI];
            if (box_diff < 0)                   // within bounds - ignore
                  box_diff = 0;
                                                            // distance to further box
            new_dist = (ANNdist) ANN_SUM(box_dist,
                        ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff)));

            if (child[ANN_LO] != KD_TRIVIAL)// enqueue if not trivial
                  ANNprBoxPQ->insert(new_dist, child[ANN_LO]);
                                                            // continue with closer child
            child[ANN_HI]->ann_pri_search(box_dist);
      }
      ANN_SPL(1)                                      // one more splitting node visited
      ANN_FLOP(8)                                     // increment floating ops
}

//----------------------------------------------------------------------
//    kd_leaf::ann_pri_search - search points in a leaf node
//
//          This is virtually identical to the ann_search for standard search.
//----------------------------------------------------------------------

void ANNkd_leaf::ann_pri_search(ANNdist box_dist)
{
      register ANNdist dist;                    // distance to data point
      register ANNcoord* pp;                    // data coordinate pointer
      register ANNcoord* qq;                    // query coordinate pointer
      register ANNdist min_dist;                // distance to k-th closest point
      register ANNcoord t;
      register int d;

      min_dist = ANNprPointMK->max_key(); // k-th smallest distance so far

      for (int i = 0; i < n_pts; i++) {   // check points in bucket

            pp = ANNprPts[bkt[i]];              // first coord of next data point
            qq = ANNprQ;                              // first coord of query point
            dist = 0;

            for(d = 0; d < ANNprDim; d++) {
                  ANN_COORD(1)                        // one more coordinate hit
                  ANN_FLOP(4)                         // increment floating ops

                  t = *(qq++) - *(pp++);        // compute length and adv coordinate
                                                            // exceeds dist to k-th smallest?
                  if( (dist = ANN_SUM(dist, ANN_POW(t))) > min_dist) {
                        break;
                  }
            }

            if (d >= ANNprDim &&                            // among the k best?
               (ANN_ALLOW_SELF_MATCH || dist!=0)) { // and no self-match problem
                                                                        // add it to the list
                  ANNprPointMK->insert(dist, bkt[i]);
                  min_dist = ANNprPointMK->max_key();
            }
      }
      ANN_LEAF(1)                                     // one more leaf node visited
      ANN_PTS(n_pts)                                  // increment points visited
      ANNptsVisited += n_pts;                   // increment number of points visited
}

Generated by  Doxygen 1.6.0   Back to index