3dpcp/.svn/pristine/c3/c35500f9ef60d61e8a61d81da69d8364768fe5b5.svn-base

110 lines
4 KiB
Text
Raw Normal View History

2012-09-16 12:33:11 +00:00
//----------------------------------------------------------------------
// 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;
}