nearest_neighbor_graph_ann.cpp

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/*************************************************************************
 * Copyright (c) 2011 AT&T Intellectual Property
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License v1.0
 * which accompanies this distribution, and is available at
 * https://www.eclipse.org/legal/epl-v10.html
 *
 * Contributors: Details at https://graphviz.org
 *************************************************************************/
 
#include "config.h"
 
#ifdef HAVE_ANN
 
#include <ANN/ANN.h>                                    // ANN declarations
#include <mingle/nearest_neighbor_graph_ann.h>
#include <utility>
#include <vector>
 
static const int dim = 4; // dimension
 
static void sortPtsX(int n, ANNpointArray pts){
  /* sort so that edges always go from left to right in x-doordinate */
  for (int i = 0; i < n; i++){
    ANNpoint p = pts[i];
    if (p[0] < p[2] || (p[0] == p[2] && p[1] < p[3])) continue;
    std::swap(p[0], p[2]);
    std::swap(p[1], p[3]);
  }
}
 
static void sortPtsY(int n, ANNpointArray pts){
  /* sort so that edges always go from left to right in x-doordinate */
  for (int i = 0; i < n; i++){
    ANNpoint p = pts[i];
    if (p[1] < p[3] || (p[1] == p[3] && p[0] < p[2])) continue;
    std::swap(p[0], p[2]);
    std::swap(p[1], p[3]);
  }
}
 
void nearest_neighbor_graph_ann(int nPts, int k, const std::vector<double> &x,
                                int &nz0, std::vector<int> &irn,
                                std::vector<int> &jcn,
                                std::vector<double> &val) {
 
  /* Gives a nearest neighbor graph is a list of dim-dimendional points. The connectivity is in irn/jcn, and the distance in val.
     
    nPts: number of points
    dim: dimension
    k: number of neighbors needed
    x: nPts*dim vector. The i-th point is x[i*dim : i*dim + dim - 1]
    nz: number of entries in the connectivity matrix irn/jcn/val
    irn, jcn: the connectivity 
    val: the distance
 
    note that there could be repeates
  */
 
  // error tolerance
  const double eps = 0;
 
  ANNpointArray dataPts = annAllocPts(nPts, dim);                       // allocate data points
  std::vector<ANNidx> nnIdx(k);                                         // allocate near neighbor indices
  std::vector<ANNdist> dists(k);                                        // allocate near neighbor dists
 
  for (int i = 0; i < nPts; i++){
    double *xx =  dataPts[i];
    for (int j = 0; j < dim; j++) xx[j] = x[i*dim + j];
  }
 
  //========= graph when sort based on x ========
  int nz = 0;
  sortPtsX(nPts, dataPts);
  ANNkd_tree kdTree(                                    // build search structure
                          dataPts,                                      // the data points
                          nPts,                                         // number of points
                          dim);                                         // dimension of space
  for (int ip = 0; ip < nPts; ip++){
    kdTree.annkSearch(                                          // search
                       dataPts[ip],                                             // query point
                       k,                                                               // number of near neighbors
                       nnIdx.data(),                                            // nearest neighbors (returned)
                       dists.data(),                                            // distance (returned)
                       eps);                                                    // error bound
 
    for (int i = 0; i < k; i++) {                       // print summary
      if (nnIdx[i] == ip) continue;
      val[nz] = dists[i];
      irn[nz] = ip;
      jcn[nz++] = nnIdx[i];
    }
  }
  
 
  //========= graph when sort based on y ========
  sortPtsY(nPts, dataPts);
  kdTree = ANNkd_tree(                                  // build search structure
                          dataPts,                                      // the data points
                          nPts,                                         // number of points
                          dim);                                         // dimension of space
  for (int ip = 0; ip < nPts; ip++){
    kdTree.annkSearch(                                          // search
                       dataPts[ip],                                             // query point
                       k,                                                               // number of near neighbors
                       nnIdx.data(),                                            // nearest neighbors (returned)
                       dists.data(),                                            // distance (returned)
                       eps);                                                    // error bound
      
    for (int i = 0; i < k; i++) {                       // print summary
      if (nnIdx[i] == ip) continue;
      val[nz] = dists[i];
      irn[nz] = ip;
      jcn[nz++] = nnIdx[i];
    }
  }
    
  nz0 = nz;
 
  annDeallocPts(dataPts);
  annClose();                                                                   // done with ANN
 
 
 
}
 
#endif  /* HAVE_ANN */