4.5 Example session

 
% create a 3-dimensional data set with 100000 points

pointset = rand(100000, 3);

% do the preprocessing for this point set

atria = nn_prepare(pointset, 'euclidian');

% now search for 2 (exact) nearest neighbors, using points 1 to 
% 10 as query points, excluding self-matches

[index, distance] = nn_search(pointset, atria, 1:10, 2, 0)

index =

        5618       96574
       38209       84549
       54991       60397
       38429       59732
        4114       76991
       72121         452
       13678       59332
       26022       16718
       86042       38436
       24830       44434


distance =

    0.0101    0.0175
    0.0078    0.0134
    0.0132    0.0167
    0.0050    0.0223
    0.0087    0.0097
    0.0124    0.0189
    0.0129    0.0168
    0.0046    0.0110
    0.0101    0.0103
    0.0156    0.0177

% now do a range search for radius 0.0224, using points 1 to 10 as
% query points, excluding self-matches

[count, neighbors] = range_search(pointset, atria, 1:10, 0.0224, 0)   

count =

     4
    10
     7
     2
     5
     6
     2
     4
     7
     5


neighbors = 

    [1x4  double]    [1x4  double]
    [1x10 double]    [1x10 double]
    [1x7  double]    [1x7  double]
    [1x2  double]    [1x2  double]
    [1x5  double]    [1x5  double]
    [1x6  double]    [1x6  double]
    [1x2  double]    [1x2  double]
    [1x4  double]    [1x4  double]
    [1x7  double]    [1x7  double]
    [1x5  double]    [1x5  double]

% let's see the indices of the points that are within range to the first query point

neighbors{1,1}

ans =

       56921       97100       96574        5618


% let's see the corresponding distances of the points that are 
% within range to the first query point

neighbors{1,2}

ans =

    0.0176    0.0186    0.0175    0.0101