1 //----------------------------------------------------------------------
2 // File: brute.cpp
3 // Programmer: Sunil Arya and David Mount
4 // Description: Brute-force nearest neighbors
5 // Last modified: 05/03/05 (Version 1.1)
6 //----------------------------------------------------------------------
7 // Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
8 // David Mount. All Rights Reserved.
9 //
10 // This software and related documentation is part of the Approximate
11 // Nearest Neighbor Library (ANN). This software is provided under
12 // the provisions of the Lesser GNU Public License (LGPL). See the
13 // file ../ReadMe.txt for further information.
14 //
15 // The University of Maryland (U.M.) and the authors make no
16 // representations about the suitability or fitness of this software for
17 // any purpose. It is provided "as is" without express or implied
18 // warranty.
19 //----------------------------------------------------------------------
20 // History:
21 // Revision 0.1 03/04/98
22 // Initial release
23 // Revision 1.1 05/03/05
24 // Added fixed-radius kNN search
25 //----------------------------------------------------------------------
26
27 #include <ANN/ANNx.h> // all ANN includes
28 #include "pr_queue_k.h" // k element priority queue
29
30 //----------------------------------------------------------------------
31 // Brute-force search simply stores a pointer to the list of
32 // data points and searches linearly for the nearest neighbor.
33 // The k nearest neighbors are stored in a k-element priority
34 // queue (which is implemented in a pretty dumb way as well).
35 //
36 // If ANN_ALLOW_SELF_MATCH is ANNfalse then data points at distance
37 // zero are not considered.
38 //
39 // Note that the error bound eps is passed in, but it is ignored.
40 // These routines compute exact nearest neighbors (which is needed
41 // for validation purposes in ann_test.cpp).
42 //----------------------------------------------------------------------
43
ANNbruteForce(ANNpointArray pa,int n,int dd)44 ANNbruteForce::ANNbruteForce( // constructor from point array
45 ANNpointArray pa, // point array
46 int n, // number of points
47 int dd) // dimension
48 {
49 dim = dd; n_pts = n; pts = pa;
50 }
51
~ANNbruteForce()52 ANNbruteForce::~ANNbruteForce() { } // destructor (empty)
53
annkSearch(ANNpoint q,int k,ANNidxArray nn_idx,ANNdistArray dd,double eps)54 void ANNbruteForce::annkSearch( // approx k near neighbor search
55 ANNpoint q, // query point
56 int k, // number of near neighbors to return
57 ANNidxArray nn_idx, // nearest neighbor indices (returned)
58 ANNdistArray dd, // dist to near neighbors (returned)
59 double eps) // error bound (ignored)
60 {
61 ANNmin_k mk(k); // construct a k-limited priority queue
62 int i;
63
64 if (k > n_pts) { // too many near neighbors?
65 annError("Requesting more near neighbors than data points", ANNabort);
66 }
67 // run every point through queue
68 for (i = 0; i < n_pts; i++) {
69 // compute distance to point
70 ANNdist sqDist = annDist(dim, pts[i], q);
71 if (ANN_ALLOW_SELF_MATCH || sqDist != 0)
72 mk.insert(sqDist, i);
73 }
74 for (i = 0; i < k; i++) { // extract the k closest points
75 dd[i] = mk.ith_smallest_key(i);
76 nn_idx[i] = mk.ith_smallest_info(i);
77 }
78 }
79
annkFRSearch(ANNpoint q,ANNdist sqRad,int k,ANNidxArray nn_idx,ANNdistArray dd,double eps)80 int ANNbruteForce::annkFRSearch( // approx fixed-radius kNN search
81 ANNpoint q, // query point
82 ANNdist sqRad, // squared radius
83 int k, // number of near neighbors to return
84 ANNidxArray nn_idx, // nearest neighbor array (returned)
85 ANNdistArray dd, // dist to near neighbors (returned)
86 double eps) // error bound
87 {
88 ANNmin_k mk(k); // construct a k-limited priority queue
89 int i;
90 int pts_in_range = 0; // number of points in query range
91 // run every point through queue
92 for (i = 0; i < n_pts; i++) {
93 // compute distance to point
94 ANNdist sqDist = annDist(dim, pts[i], q);
95 if (sqDist <= sqRad && // within radius bound
96 (ANN_ALLOW_SELF_MATCH || sqDist != 0)) { // ...and no self match
97 mk.insert(sqDist, i);
98 pts_in_range++;
99 }
100 }
101 for (i = 0; i < k; i++) { // extract the k closest points
102 if (dd != NULL)
103 dd[i] = mk.ith_smallest_key(i);
104 if (nn_idx != NULL)
105 nn_idx[i] = mk.ith_smallest_info(i);
106 }
107
108 return pts_in_range;
109 }
110