#mass = gen.strip_data()
sample = mass[:sample_N]
smpl = data_gen(center, sample_N, K)
smpl.mass = sample
s_edges = smpl.get_edges()
s_edges.sort()
mst = m.get_MST(s_edges)
km_obj = k_means(mass, K)
#mass = gen.strip_data()
#convex = ch.convex_hull(result[0])
#dr.draw_MST(mst, mass)
#dr.draw_data(mass)
sample_clusters = m.get_clusters(
sample) # get clusters from hierarchical clustering
centroids = m.get_centroids() # get center of clusters
res = 0
# nesting check
for k in range(K):
convex = ch.convex_hull(sample_clusters[k])
for i in centroids:
if len(convex) != 0:
if ch.is_inside(i, convex):
res += 1
if res <= K: # data is not nested we use k-means algorithm
print("k-means")
result = km_obj._k_means()
dr.draw_clusters(result)
for k in range(K):
convex = ch.convex_hull(result[k])
