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