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Diffstat (limited to 'geom_bottleneck/bottleneck/src/brute.cpp')
| -rw-r--r-- | geom_bottleneck/bottleneck/src/brute.cpp | 110 |
1 files changed, 0 insertions, 110 deletions
diff --git a/geom_bottleneck/bottleneck/src/brute.cpp b/geom_bottleneck/bottleneck/src/brute.cpp deleted file mode 100644 index 200bc35..0000000 --- a/geom_bottleneck/bottleneck/src/brute.cpp +++ /dev/null @@ -1,110 +0,0 @@ -//---------------------------------------------------------------------- -// File: brute.cpp -// Programmer: Sunil Arya and David Mount -// Description: Brute-force nearest neighbors -// Last modified: 05/03/05 (Version 1.1) -//---------------------------------------------------------------------- -// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and -// David Mount. All Rights Reserved. -// -// This software and related documentation is part of the Approximate -// Nearest Neighbor Library (ANN). This software is provided under -// the provisions of the Lesser GNU Public License (LGPL). See the -// file ../ReadMe.txt for further information. -// -// The University of Maryland (U.M.) and the authors make no -// representations about the suitability or fitness of this software for -// any purpose. It is provided "as is" without express or implied -// warranty. -//---------------------------------------------------------------------- -// History: -// Revision 0.1 03/04/98 -// Initial release -// Revision 1.1 05/03/05 -// Added fixed-radius kNN search -//---------------------------------------------------------------------- - -#include <ANN/ANNx.h> // all ANN includes -#include "pr_queue_k.h" // k element priority queue - -//---------------------------------------------------------------------- -// Brute-force search simply stores a pointer to the list of -// data points and searches linearly for the nearest neighbor. -// The k nearest neighbors are stored in a k-element priority -// queue (which is implemented in a pretty dumb way as well). -// -// If ANN_ALLOW_SELF_MATCH is ANNfalse then data points at distance -// zero are not considered. -// -// Note that the error bound eps is passed in, but it is ignored. -// These routines compute exact nearest neighbors (which is needed -// for validation purposes in ann_test.cpp). -//---------------------------------------------------------------------- - -ANNbruteForce::ANNbruteForce( // constructor from point array - ANNpointArray pa, // point array - int n, // number of points - int dd) // dimension -{ - dim = dd; n_pts = n; pts = pa; -} - -ANNbruteForce::~ANNbruteForce() { } // destructor (empty) - -void ANNbruteForce::annkSearch( // approx k near neighbor search - ANNpoint q, // query point - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor indices (returned) - ANNdistArray dd, // dist to near neighbors (returned) - double eps) // error bound (ignored) -{ - ANNmin_k mk(k); // construct a k-limited priority queue - int i; - - if (k > n_pts) { // too many near neighbors? - annError("Requesting more near neighbors than data points", ANNabort); - } - // run every point through queue - for (i = 0; i < n_pts; i++) { - // compute distance to point - ANNdist sqDist = annDist(dim, pts[i], q); - if (ANN_ALLOW_SELF_MATCH || sqDist != 0) - mk.insert(sqDist, i); - } - for (i = 0; i < k; i++) { // extract the k closest points - dd[i] = mk.ith_smallest_key(i); - nn_idx[i] = mk.ith_smallest_info(i); - } -} - -int ANNbruteForce::annkFRSearch( // approx fixed-radius kNN search - ANNpoint q, // query point - ANNdist sqRad, // squared radius - int k, // number of near neighbors to return - ANNidxArray nn_idx, // nearest neighbor array (returned) - ANNdistArray dd, // dist to near neighbors (returned) - double eps) // error bound -{ - ANNmin_k mk(k); // construct a k-limited priority queue - int i; - int pts_in_range = 0; // number of points in query range - // run every point through queue - for (i = 0; i < n_pts; i++) { - // compute distance to point - ANNdist sqDist = annDist(dim, pts[i], q); - if (sqDist <= sqRad && // within radius bound - (ANN_ALLOW_SELF_MATCH || sqDist != 0)) { // ...and no self match - mk.insert(sqDist, i); - pts_in_range++; - } - } - for (i = 0; i < k; i++) { // extract the k closest points - if (dd != NULL) - dd[i] = mk.ith_smallest_key(i); - if (nn_idx != NULL) - nn_idx[i] = mk.ith_smallest_info(i); - } - - return pts_in_range; -} --\n}\n |