[37] | 1 | //---------------------------------------------------------------------- |
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| 2 | // File: brute.cpp |
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| 3 | // Programmer: Sunil Arya and David Mount |
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| 4 | // Description: Brute-force nearest neighbors |
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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 0.1 03/04/98 |
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| 22 | // Initial release |
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| 23 | // Revision 1.1 05/03/05 |
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| 24 | // Added fixed-radius kNN search |
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| 25 | //---------------------------------------------------------------------- |
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| 26 | |
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| 27 | #include <ANN/ANNx.h> // all ANN includes |
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| 28 | #include "pr_queue_k.h" // k element priority queue |
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| 29 | |
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| 30 | //---------------------------------------------------------------------- |
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| 31 | // Brute-force search simply stores a pointer to the list of |
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| 32 | // data points and searches linearly for the nearest neighbor. |
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| 33 | // The k nearest neighbors are stored in a k-element priority |
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| 34 | // queue (which is implemented in a pretty dumb way as well). |
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| 35 | // |
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| 36 | // If ANN_ALLOW_SELF_MATCH is ANNfalse then data points at distance |
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| 37 | // zero are not considered. |
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| 38 | // |
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| 39 | // Note that the error bound eps is passed in, but it is ignored. |
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| 40 | // These routines compute exact nearest neighbors (which is needed |
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| 41 | // for validation purposes in ann_test.cpp). |
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| 42 | //---------------------------------------------------------------------- |
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| 43 | |
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| 44 | ANNbruteForce::ANNbruteForce( // constructor from point array |
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| 45 | ANNpointArray pa, // point array |
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| 46 | int n, // number of points |
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| 47 | int dd) // dimension |
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| 48 | { |
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| 49 | dim = dd; n_pts = n; pts = pa; |
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| 50 | } |
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| 51 | |
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| 52 | ANNbruteForce::~ANNbruteForce() { } // destructor (empty) |
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| 53 | |
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| 54 | void ANNbruteForce::annkSearch( // approx k near neighbor search |
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| 55 | ANNpoint q, // query point |
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| 56 | int k, // number of near neighbors to return |
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| 57 | ANNidxArray nn_idx, // nearest neighbor indices (returned) |
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| 58 | ANNdistArray dd, // dist to near neighbors (returned) |
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| 59 | double eps) // error bound (ignored) |
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| 60 | { |
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| 61 | ANNmin_k mk(k); // construct a k-limited priority queue |
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| 62 | int i; |
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| 63 | |
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| 64 | if (k > n_pts) { // too many near neighbors? |
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| 65 | annError("Requesting more near neighbors than data points", ANNabort); |
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| 66 | } |
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| 67 | // run every point through queue |
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| 68 | for (i = 0; i < n_pts; i++) { |
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| 69 | // compute distance to point |
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| 70 | ANNdist sqDist = annDist(dim, pts[i], q); |
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| 71 | if (ANN_ALLOW_SELF_MATCH || sqDist != 0) |
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| 72 | mk.insert(sqDist, i); |
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| 73 | } |
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| 74 | for (i = 0; i < k; i++) { // extract the k closest points |
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| 75 | dd[i] = mk.ith_smallest_key(i); |
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| 76 | nn_idx[i] = mk.ith_smallest_info(i); |
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| 77 | } |
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| 78 | } |
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| 79 | |
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| 80 | int ANNbruteForce::annkFRSearch( // approx fixed-radius kNN search |
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| 81 | ANNpoint q, // query point |
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| 82 | ANNdist sqRad, // squared radius |
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| 83 | int k, // number of near neighbors to return |
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| 84 | ANNidxArray nn_idx, // nearest neighbor array (returned) |
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| 85 | ANNdistArray dd, // dist to near neighbors (returned) |
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| 86 | double eps) // error bound |
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| 87 | { |
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| 88 | ANNmin_k mk(k); // construct a k-limited priority queue |
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| 89 | int i; |
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| 90 | int pts_in_range = 0; // number of points in query range |
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| 91 | // run every point through queue |
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| 92 | for (i = 0; i < n_pts; i++) { |
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| 93 | // compute distance to point |
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| 94 | ANNdist sqDist = annDist(dim, pts[i], q); |
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| 95 | if (sqDist <= sqRad && // within radius bound |
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| 96 | (ANN_ALLOW_SELF_MATCH || sqDist != 0)) { // ...and no self match |
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| 97 | mk.insert(sqDist, i); |
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| 98 | pts_in_range++; |
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| 99 | } |
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| 100 | } |
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| 101 | for (i = 0; i < k; i++) { // extract the k closest points |
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| 102 | if (dd != NULL) |
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| 103 | dd[i] = mk.ith_smallest_key(i); |
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| 104 | if (nn_idx != NULL) |
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| 105 | nn_idx[i] = mk.ith_smallest_info(i); |
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| 106 | } |
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| 107 | |
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| 108 | return pts_in_range; |
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| 109 | } |
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