183 lines
7.2 KiB
Text
183 lines
7.2 KiB
Text
//----------------------------------------------------------------------
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// File: kd_fix_rad_search.cpp
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// Programmer: Sunil Arya and David Mount
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// Description: Standard kd-tree fixed-radius kNN search
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// Last modified: 05/03/05 (Version 1.1)
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//----------------------------------------------------------------------
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// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
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// David Mount. All Rights Reserved.
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//
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// This software and related documentation is part of the Approximate
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// Nearest Neighbor Library (ANN). This software is provided under
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// the provisions of the Lesser GNU Public License (LGPL). See the
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// file ../ReadMe.txt for further information.
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//
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// The University of Maryland (U.M.) and the authors make no
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// representations about the suitability or fitness of this software for
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// any purpose. It is provided "as is" without express or implied
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// warranty.
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//----------------------------------------------------------------------
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// History:
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// Revision 1.1 05/03/05
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// Initial release
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//----------------------------------------------------------------------
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#include "kd_fix_rad_search.h" // kd fixed-radius search decls
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//----------------------------------------------------------------------
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// Approximate fixed-radius k nearest neighbor search
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// The squared radius is provided, and this procedure finds the
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// k nearest neighbors within the radius, and returns the total
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// number of points lying within the radius.
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//
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// The method used for searching the kd-tree is a variation of the
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// nearest neighbor search used in kd_search.cpp, except that the
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// radius of the search ball is known. We refer the reader to that
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// file for the explanation of the recursive search procedure.
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//----------------------------------------------------------------------
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//----------------------------------------------------------------------
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// To keep argument lists short, a number of global variables
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// are maintained which are common to all the recursive calls.
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// These are given below.
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//----------------------------------------------------------------------
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int ANNkdFRDim; // dimension of space
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ANNpoint ANNkdFRQ; // query point
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ANNdist ANNkdFRSqRad; // squared radius search bound
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double ANNkdFRMaxErr; // max tolerable squared error
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ANNpointArray ANNkdFRPts; // the points
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ANNmin_k* ANNkdFRPointMK; // set of k closest points
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int ANNkdFRPtsVisited; // total points visited
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int ANNkdFRPtsInRange; // number of points in the range
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//----------------------------------------------------------------------
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// annkFRSearch - fixed radius search for k nearest neighbors
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//----------------------------------------------------------------------
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int ANNkd_tree::annkFRSearch(
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ANNpoint q, // the query point
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ANNdist sqRad, // squared radius search bound
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int k, // number of near neighbors to return
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ANNidxArray nn_idx, // nearest neighbor indices (returned)
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ANNdistArray dd, // the approximate nearest neighbor
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double eps) // the error bound
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{
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ANNkdFRDim = dim; // copy arguments to static equivs
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ANNkdFRQ = q;
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ANNkdFRSqRad = sqRad;
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ANNkdFRPts = pts;
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ANNkdFRPtsVisited = 0; // initialize count of points visited
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ANNkdFRPtsInRange = 0; // ...and points in the range
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ANNkdFRMaxErr = ANN_POW(1.0 + eps);
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ANN_FLOP(2) // increment floating op count
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ANNkdFRPointMK = new ANNmin_k(k); // create set for closest k points
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// search starting at the root
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root->ann_FR_search(annBoxDistance(q, bnd_box_lo, bnd_box_hi, dim));
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for (int i = 0; i < k; i++) { // extract the k-th closest points
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if (dd != NULL)
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dd[i] = ANNkdFRPointMK->ith_smallest_key(i);
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if (nn_idx != NULL)
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nn_idx[i] = ANNkdFRPointMK->ith_smallest_info(i);
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}
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delete ANNkdFRPointMK; // deallocate closest point set
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return ANNkdFRPtsInRange; // return final point count
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}
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//----------------------------------------------------------------------
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// kd_split::ann_FR_search - search a splitting node
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// Note: This routine is similar in structure to the standard kNN
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// search. It visits the subtree that is closer to the query point
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// first. For fixed-radius search, there is no benefit in visiting
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// one subtree before the other, but we maintain the same basic
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// code structure for the sake of uniformity.
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//----------------------------------------------------------------------
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void ANNkd_split::ann_FR_search(ANNdist box_dist)
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{
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// check dist calc term condition
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if (ANNmaxPtsVisited != 0 && ANNkdFRPtsVisited > ANNmaxPtsVisited) return;
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// distance to cutting plane
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ANNcoord cut_diff = ANNkdFRQ[cut_dim] - cut_val;
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if (cut_diff < 0) { // left of cutting plane
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child[ANN_LO]->ann_FR_search(box_dist);// visit closer child first
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ANNcoord box_diff = cd_bnds[ANN_LO] - ANNkdFRQ[cut_dim];
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if (box_diff < 0) // within bounds - ignore
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box_diff = 0;
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// distance to further box
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box_dist = (ANNdist) ANN_SUM(box_dist,
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ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff)));
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// visit further child if in range
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if (box_dist * ANNkdFRMaxErr <= ANNkdFRSqRad)
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child[ANN_HI]->ann_FR_search(box_dist);
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}
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else { // right of cutting plane
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child[ANN_HI]->ann_FR_search(box_dist);// visit closer child first
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ANNcoord box_diff = ANNkdFRQ[cut_dim] - cd_bnds[ANN_HI];
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if (box_diff < 0) // within bounds - ignore
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box_diff = 0;
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// distance to further box
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box_dist = (ANNdist) ANN_SUM(box_dist,
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ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff)));
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// visit further child if close enough
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if (box_dist * ANNkdFRMaxErr <= ANNkdFRSqRad)
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child[ANN_LO]->ann_FR_search(box_dist);
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}
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ANN_FLOP(13) // increment floating ops
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ANN_SPL(1) // one more splitting node visited
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}
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//----------------------------------------------------------------------
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// kd_leaf::ann_FR_search - search points in a leaf node
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// Note: The unreadability of this code is the result of
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// some fine tuning to replace indexing by pointer operations.
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//----------------------------------------------------------------------
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void ANNkd_leaf::ann_FR_search(ANNdist box_dist)
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{
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register ANNdist dist; // distance to data point
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register ANNcoord* pp; // data coordinate pointer
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register ANNcoord* qq; // query coordinate pointer
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register ANNcoord t;
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register int d;
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for (int i = 0; i < n_pts; i++) { // check points in bucket
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pp = ANNkdFRPts[bkt[i]]; // first coord of next data point
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qq = ANNkdFRQ; // first coord of query point
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dist = 0;
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for(d = 0; d < ANNkdFRDim; d++) {
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ANN_COORD(1) // one more coordinate hit
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ANN_FLOP(5) // increment floating ops
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t = *(qq++) - *(pp++); // compute length and adv coordinate
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// exceeds dist to k-th smallest?
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if( (dist = ANN_SUM(dist, ANN_POW(t))) > ANNkdFRSqRad) {
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break;
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}
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}
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if (d >= ANNkdFRDim && // among the k best?
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(ANN_ALLOW_SELF_MATCH || dist!=0)) { // and no self-match problem
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// add it to the list
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ANNkdFRPointMK->insert(dist, bkt[i]);
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ANNkdFRPtsInRange++; // increment point count
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}
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}
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ANN_LEAF(1) // one more leaf node visited
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ANN_PTS(n_pts) // increment points visited
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ANNkdFRPtsVisited += n_pts; // increment number of points visited
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}
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