[37] | 1 | //---------------------------------------------------------------------- |
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| 2 | // File: kd_fix_rad_search.cpp |
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| 3 | // Programmer: Sunil Arya and David Mount |
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| 4 | // Description: Standard kd-tree fixed-radius kNN search |
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| 5 | // Last modified: 05/03/05 (Version 1.1) |
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| 6 | //---------------------------------------------------------------------- |
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| 7 | // Copyright (c) 1997-2005 University of Maryland and Sunil Arya and |
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| 8 | // David Mount. All Rights Reserved. |
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| 9 | // |
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| 10 | // This software and related documentation is part of the Approximate |
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| 11 | // Nearest Neighbor Library (ANN). This software is provided under |
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| 12 | // the provisions of the Lesser GNU Public License (LGPL). See the |
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| 13 | // file ../ReadMe.txt for further information. |
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| 14 | // |
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| 15 | // The University of Maryland (U.M.) and the authors make no |
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| 16 | // representations about the suitability or fitness of this software for |
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| 17 | // any purpose. It is provided "as is" without express or implied |
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| 18 | // warranty. |
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| 19 | //---------------------------------------------------------------------- |
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| 20 | // History: |
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| 21 | // Revision 1.1 05/03/05 |
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| 22 | // Initial release |
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| 23 | //---------------------------------------------------------------------- |
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| 24 | |
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| 25 | #include "kd_fix_rad_search.h" // kd fixed-radius search decls |
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| 26 | |
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| 27 | //---------------------------------------------------------------------- |
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| 28 | // Approximate fixed-radius k nearest neighbor search |
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| 29 | // The squared radius is provided, and this procedure finds the |
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| 30 | // k nearest neighbors within the radius, and returns the total |
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| 31 | // number of points lying within the radius. |
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| 32 | // |
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| 33 | // The method used for searching the kd-tree is a variation of the |
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| 34 | // nearest neighbor search used in kd_search.cpp, except that the |
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| 35 | // radius of the search ball is known. We refer the reader to that |
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| 36 | // file for the explanation of the recursive search procedure. |
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| 37 | //---------------------------------------------------------------------- |
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| 38 | |
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| 39 | //---------------------------------------------------------------------- |
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| 40 | // To keep argument lists short, a number of global variables |
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| 41 | // are maintained which are common to all the recursive calls. |
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| 42 | // These are given below. |
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| 43 | //---------------------------------------------------------------------- |
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| 44 | |
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| 45 | int ANNkdFRDim; // dimension of space |
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| 46 | ANNpoint ANNkdFRQ; // query point |
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| 47 | ANNdist ANNkdFRSqRad; // squared radius search bound |
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| 48 | double ANNkdFRMaxErr; // max tolerable squared error |
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| 49 | ANNpointArray ANNkdFRPts; // the points |
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| 50 | ANNmin_k* ANNkdFRPointMK; // set of k closest points |
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| 51 | int ANNkdFRPtsVisited; // total points visited |
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| 52 | int ANNkdFRPtsInRange; // number of points in the range |
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| 53 | |
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| 54 | //---------------------------------------------------------------------- |
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| 55 | // annkFRSearch - fixed radius search for k nearest neighbors |
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| 56 | //---------------------------------------------------------------------- |
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| 57 | |
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| 58 | int ANNkd_tree::annkFRSearch( |
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| 59 | ANNpoint q, // the query point |
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| 60 | ANNdist sqRad, // squared radius search bound |
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| 61 | int k, // number of near neighbors to return |
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| 62 | ANNidxArray nn_idx, // nearest neighbor indices (returned) |
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| 63 | ANNdistArray dd, // the approximate nearest neighbor |
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| 64 | double eps) // the error bound |
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| 65 | { |
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| 66 | ANNkdFRDim = dim; // copy arguments to static equivs |
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| 67 | ANNkdFRQ = q; |
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| 68 | ANNkdFRSqRad = sqRad; |
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| 69 | ANNkdFRPts = pts; |
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| 70 | ANNkdFRPtsVisited = 0; // initialize count of points visited |
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| 71 | ANNkdFRPtsInRange = 0; // ...and points in the range |
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| 72 | |
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| 73 | ANNkdFRMaxErr = ANN_POW(1.0 + eps); |
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| 74 | ANN_FLOP(2) // increment floating op count |
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| 75 | |
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| 76 | ANNkdFRPointMK = new ANNmin_k(k); // create set for closest k points |
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| 77 | // search starting at the root |
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| 78 | root->ann_FR_search(annBoxDistance(q, bnd_box_lo, bnd_box_hi, dim)); |
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| 79 | |
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| 80 | for (int i = 0; i < k; i++) { // extract the k-th closest points |
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| 81 | if (dd != NULL) |
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| 82 | dd[i] = ANNkdFRPointMK->ith_smallest_key(i); |
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| 83 | if (nn_idx != NULL) |
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| 84 | nn_idx[i] = ANNkdFRPointMK->ith_smallest_info(i); |
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| 85 | } |
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| 86 | |
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| 87 | delete ANNkdFRPointMK; // deallocate closest point set |
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| 88 | return ANNkdFRPtsInRange; // return final point count |
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| 89 | } |
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| 90 | |
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| 91 | //---------------------------------------------------------------------- |
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| 92 | // kd_split::ann_FR_search - search a splitting node |
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| 93 | // Note: This routine is similar in structure to the standard kNN |
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| 94 | // search. It visits the subtree that is closer to the query point |
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| 95 | // first. For fixed-radius search, there is no benefit in visiting |
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| 96 | // one subtree before the other, but we maintain the same basic |
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| 97 | // code structure for the sake of uniformity. |
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| 98 | //---------------------------------------------------------------------- |
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| 99 | |
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| 100 | void ANNkd_split::ann_FR_search(ANNdist box_dist) |
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| 101 | { |
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| 102 | // check dist calc term condition |
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| 103 | if (ANNmaxPtsVisited != 0 && ANNkdFRPtsVisited > ANNmaxPtsVisited) return; |
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| 104 | |
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| 105 | // distance to cutting plane |
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| 106 | ANNcoord cut_diff = ANNkdFRQ[cut_dim] - cut_val; |
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| 107 | |
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| 108 | if (cut_diff < 0) { // left of cutting plane |
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| 109 | child[ANN_LO]->ann_FR_search(box_dist);// visit closer child first |
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| 110 | |
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| 111 | ANNcoord box_diff = cd_bnds[ANN_LO] - ANNkdFRQ[cut_dim]; |
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| 112 | if (box_diff < 0) // within bounds - ignore |
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| 113 | box_diff = 0; |
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| 114 | // distance to further box |
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| 115 | box_dist = (ANNdist) ANN_SUM(box_dist, |
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| 116 | ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff))); |
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| 117 | |
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| 118 | // visit further child if in range |
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| 119 | if (box_dist * ANNkdFRMaxErr <= ANNkdFRSqRad) |
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| 120 | child[ANN_HI]->ann_FR_search(box_dist); |
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| 121 | |
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| 122 | } |
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| 123 | else { // right of cutting plane |
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| 124 | child[ANN_HI]->ann_FR_search(box_dist);// visit closer child first |
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| 125 | |
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| 126 | ANNcoord box_diff = ANNkdFRQ[cut_dim] - cd_bnds[ANN_HI]; |
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| 127 | if (box_diff < 0) // within bounds - ignore |
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| 128 | box_diff = 0; |
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| 129 | // distance to further box |
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| 130 | box_dist = (ANNdist) ANN_SUM(box_dist, |
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| 131 | ANN_DIFF(ANN_POW(box_diff), ANN_POW(cut_diff))); |
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| 132 | |
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| 133 | // visit further child if close enough |
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| 134 | if (box_dist * ANNkdFRMaxErr <= ANNkdFRSqRad) |
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| 135 | child[ANN_LO]->ann_FR_search(box_dist); |
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| 136 | |
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| 137 | } |
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| 138 | ANN_FLOP(13) // increment floating ops |
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| 139 | ANN_SPL(1) // one more splitting node visited |
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| 140 | } |
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| 141 | |
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| 142 | //---------------------------------------------------------------------- |
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| 143 | // kd_leaf::ann_FR_search - search points in a leaf node |
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| 144 | // Note: The unreadability of this code is the result of |
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| 145 | // some fine tuning to replace indexing by pointer operations. |
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| 146 | //---------------------------------------------------------------------- |
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| 147 | |
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| 148 | void ANNkd_leaf::ann_FR_search(ANNdist box_dist) |
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| 149 | { |
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| 150 | register ANNdist dist; // distance to data point |
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| 151 | register ANNcoord* pp; // data coordinate pointer |
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| 152 | register ANNcoord* qq; // query coordinate pointer |
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| 153 | register ANNcoord t; |
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| 154 | register int d; |
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| 155 | |
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| 156 | for (int i = 0; i < n_pts; i++) { // check points in bucket |
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| 157 | |
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| 158 | pp = ANNkdFRPts[bkt[i]]; // first coord of next data point |
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| 159 | qq = ANNkdFRQ; // first coord of query point |
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| 160 | dist = 0; |
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| 161 | |
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| 162 | for(d = 0; d < ANNkdFRDim; d++) { |
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| 163 | ANN_COORD(1) // one more coordinate hit |
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| 164 | ANN_FLOP(5) // increment floating ops |
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| 165 | |
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| 166 | t = *(qq++) - *(pp++); // compute length and adv coordinate |
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| 167 | // exceeds dist to k-th smallest? |
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| 168 | if( (dist = ANN_SUM(dist, ANN_POW(t))) > ANNkdFRSqRad) { |
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| 169 | break; |
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| 170 | } |
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| 171 | } |
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| 172 | |
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| 173 | if (d >= ANNkdFRDim && // among the k best? |
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| 174 | (ANN_ALLOW_SELF_MATCH || dist!=0)) { // and no self-match problem |
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| 175 | // add it to the list |
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| 176 | ANNkdFRPointMK->insert(dist, bkt[i]); |
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| 177 | ANNkdFRPtsInRange++; // increment point count |
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| 178 | } |
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| 179 | } |
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| 180 | ANN_LEAF(1) // one more leaf node visited |
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| 181 | ANN_PTS(n_pts) // increment points visited |
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| 182 | ANNkdFRPtsVisited += n_pts; // increment number of points visited |
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| 183 | } |
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