kmeans.py

import math

from pyinclude.draw import *
from pyinclude.point import *

K = 3
POINTS = 100
RANGE = 640


if __name__ == "__main__":
    points = [ Point() for i in range(POINTS) ]
    centroids = [ Centroid() for i in range(K) ]

    maxIterations = 10

    # set random point positions
    #   generate K initial points and then position the rest of the 
    #   points around the initial K
    assert( len(points) >= len(centroids) )
    for p in points[:len(centroids)]:
        p.randomise( RANGE )

    for i in range( len(centroids), len(points) ):
        points[i].randomise( RANGE, points[i%len(centroids)] )
 
    # set random starting positions for the centroids
    for c in centroids:
        c.randomise( RANGE )
        print( c )

    # clustering iterations
    for i in range( maxIterations ):
        # kmeans

        # display centroid positions
        print( f"Iteration {i}" )
        for c in centroids:
            print( c )

        draw( points, centroids, RANGE, RANGE, f"{i:0{int(math.log(maxIterations,10)+1)}d}_clusters.bmp" )