[EMAIL PROTECTED] wrote:
I have a large data file of upto 1 million x,y,z coordinates of
points. I want to identify which points are within 0.01 mm from each
other. I can compare the distance from each point to every other
point , but this takes 1 million * 1 million operations, or forever!
Any quick way to do it, perhaps by inserting just the integer portion
of the coordinates into an array, and checking if the integer has
already been defined before inserting a new point?
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There is a whole field of Math/CS research on problems like this called
Computational Geometry. It provides many algorithms for many geometric
problems, including things like this.
In particular, to categorize a list of points and find the
NearestNeighbor, I'd suggest a KD-tree. I believe this would turn your
problem from O(N^2) to O(N log n) or so.
Happy google-ing and good luck.
Gary Herron
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