from scipy.io import arff
import numpy as np
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import DBSCAN
import hdbscan

n_clusters = 2

data_final = []
x_list = []
y_list = []
z_list = []

silhouette = []
calinski = []
davies = []


data = np.loadtxt('t.data')

for (x, y, z) in data :
    x_list.append(x)
    y_list.append(y)
    z_list.append(z)
    data_final.append([x,y,z])

clustering = DBSCAN(eps=0.25, min_samples=10).fit(data_final)
colors = clustering.labels_


silh = metrics.silhouette_score(data_final, colors, metric='euclidean')
dbsc = metrics.davies_bouldin_score(data_final, colors)
caha = metrics.calinski_harabasz_score(data_final, colors)
    
print("Coefficient de silhouette : ", silh)
print("Indice de Davies Bouldin : ", dbsc)
print("Indice de calinski harabasz : ", caha)

plt.axes(projection='3d').scatter3D(x_list, y_list, z_list, c=colors)

plt.show()