removing extra file

FSA04/k2center.py

-import sys
-import numpy as np
-import time
-
-# calculate the cost squared for centers with indices c1, c2
-def calc_cost_sq(data,c1,c2):
-    cost_sq = 0;
-    n = len(data)
-    for i in range(n):
-        ds1 = np.inner(data[i]-data[c1],data[i]-data[c1])
-        ds2 = np.inner(data[i]-data[c2],data[i]-data[c2])
-        min_dist_sq = min([ds1,ds2])
-        cost_sq = max([cost_sq,min_dist_sq])
-    return cost_sq
-
-# load the points
-data = np.loadtxt(sys.stdin,skiprows=1)
-
-# start the timer
-tic = time.process_time()
-
-# compute the minimal cost and an optimal solution
-n = len(data)
-min_cost_sq = float("inf")
-tuples_checked = 0;
-for c1 in range(0,n-1):
-    for c2 in range(c1+1,n):
-        tuples_checked += 1
-        cost_sq = calc_cost_sq(data,c1,c2)
-        if (cost_sq < min_cost_sq):
-            min_cost_sq = cost_sq
-            optimal_centers = np.array([c1,c2])
-
-# stop the timer
-toc = time.process_time()
-elapsed = toc-tic
-
-# print the results
-print ('number of points =',n)
-print ('2-tuples checked =',tuples_checked)
-print ('elapsed time =',round(elapsed,2),'seconds')
-print ('2-tuples checked per second =',int(tuples_checked/elapsed))
-print ('minimum cost =',np.round(np.sqrt(min_cost_sq),2))
-print ('optimal centers =',optimal_centers)