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18-Aug-2024 11:29:08

nearest_neighbor_test():
  MATLAB/Octave version 9.11.0.2358333 (R2021b) Update 7
  Test nearest_neighbor().

test01():
  A basic nearest neighbor search.
  P: candidate points:
    0.8147    0.1270    0.6324    0.2785    0.9575
    0.9058    0.9134    0.0975    0.5469    0.9649

  X(I): Nearest neighbors
    0.8491    0.1576    0.6557    0.3112    0.8909
    0.9340    0.9706    0.0357    0.5285    0.9593

  Graphics saved as "test01.png"

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

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

I =

     4     2    14

  Nearest X to any X.

I =

  Columns 1 through 13

    12     4     5     2     3     9    17    13    14    14     8     1     8

  Columns 14 through 20

    10    18    20     7    19    18    16


test03():
  Find four nearest neighbors.

  Index of four nearest neighbors to 3 points

I =

    34    44     9
    25     1    29
     2    22    14
    45    26    50

  Graphics saved as "test03.png"

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

I =

     6    13    21
    31    19    30
    11    39    34
    45     5     0
     3    35     0
     0    48     0
     0    26     0

  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) : 0.0081 seconds
  (DelaunayMode on)  : 0.51 seconds
  (DelaunayMode auto): 0.006 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): 0.37 seconds
  (DelaunayMode on)  : 0.031 seconds
  (DelaunayMode auto): 0.031 seconds

nearest_neighbor_test():
  Normal end of execution.
18-Aug-2024 11:29:19