r3[i] = np.array([r3x[i], r3y[i]])
R = np.concatenate((r1, r2, r3), 0)
# plot them
BEFORE = pl.figure(1)
pl.plot(R[:, 0], R[:, 1], 'o')
BEFORE.show()
# apply kmeans clustering 5 times to eliminate local optimum convergence
objective_val = math.inf
times = []
for i in range(5):
start = timeit.default_timer()
g = Kmeans(R, 3, 0.5)
g.compute()
times.append(timeit.default_timer() - start)
if g.objective_hist[-1] < objective_val:
objective_val = g.objective_hist[-1]
clusters = g.clusters
time_of_exec = sum(times) / len(times)
print('Kmeans.compute() in %f seconds, on the average' % (time_of_exec))
# and plot the clusters in different colors
AFTER = pl.figure(2)
x = [item[0] for item in clusters[0]]
y = [item[1] for item in clusters[0]]
pl.plot(x, y, 'co')
x = [item[0] for item in clusters[1]]
