18-Aug-2024 12:47:22

nearest_neighbor_test():
  MATLAB/Octave version 6.4.0
  Test nearest_neighbor().

test01():
  A basic nearest neighbor search.
  P: candidate points:
   1.3156e-01   2.4241e-02   1.2472e-02   5.7386e-01   6.6704e-01
   6.1577e-01   3.1399e-04   9.8416e-01   9.6129e-01   7.9953e-03
  X(I): Nearest neighbors
   0.099829   0.141943   0.109231   0.757952   0.767414
   0.475449   0.108038   0.970322   0.924831   0.106198
  Graphics saved as "test01.png"

test02():
  Find nearest neighbors from a subset of full list.

  Nearest X to X[2,4,10].
I =

   13   17   14

  Nearest X to any X.
I =

 Columns 1 through 16:

    3   13    2   17    6    5   20   19    2   14    1    9    5   16    5   18

 Columns 17 through 20:

    4   16   16    7


test03():
  Find four nearest neighbors.

  Index of four nearest neighbors to 3 points
I =

   46    4   27
   41    3   38
   21   39   41
   27    8   21

  Graphics saved as "test03.png"

test04():
  Find all neighbors within a specific radius.
I =

   29   47   50
   40    3   21
   18    8   49
   19    0   23
    0    0   22
    0    0   11
    0    0   41
    0    0   28
    0    0    2

  Graphics saved as "test04.png"

test05():
  Find up to 5 neighbors within a specific radius.
  Graphics saved as "test05.png"

test06():
  Delaunay decision for large data, small pointset.

  Use a large data set, small number of points.
  Spatial dimension d = 3
  Number of candidate points np = 10
  Number of data points nx = 5000
  (DelaunayMode off) : 0.2 seconds
  (DelaunayMode on)  : 1.4 seconds
  (DelaunayMode auto): 0.2 seconds


test07():
  Delaunay decision for small data, large pointset.

  Use a small data set, lots of candidate points.
  Spatial dimension d = 3
  Number of candidate points np = 10000
  Number of data points nx = 500
  (Delaunay mode off): 21 seconds
  (DelaunayMode on)  : 0.15 seconds
  (DelaunayMode auto): 0.15 seconds

nearest_neighbor_test():
  Normal end of execution.
18-Aug-2024 12:47:50