15-May-2025 17:32:21

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

test01():
  A basic nearest neighbor search.
  P: candidate points:
   0.3160   0.9517   0.8848   0.2143   0.6280
   0.5245   0.4320   0.1901   0.7174   0.8759
  X(I): Nearest neighbors
   0.3356   0.9237   0.9448   0.2320   0.7360
   0.5535   0.3886   0.1972   0.7047   0.8715
  Graphics saved as "test01.png"

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

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

   18    3    1

  Nearest X to any X.
I =

 Columns 1 through 16:

   10   18    4    3   16   11    6   13   15    1    6   17   15   19    6    5

 Columns 17 through 20:

   20    2   14   17


test03():
  Find four nearest neighbors.

  Index of four nearest neighbors to 3 points
I =

   48   35    4
   32   14   39
   13   23   38
   31   17   18

  Graphics saved as "test03.png"

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

    5   15    3
   48   11    8
   42    0   49
   33    0   41
    0    0   14
    0    0   35

  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): 0.19 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)  : 0.14 seconds
  (DelaunayMode auto): 0.14 seconds

nearest_neighbor_test():
  Normal end of execution.
15-May-2025 17:32:49