source: proiecte/pmake3d/make3d_original/Make3dSingleImageStanford_version0.1/third_party/ann_1.1.1/src/brute.cpp @ 37

//----------------------------------------------------------------------
// 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;
}