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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