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from sklearn.datasets import make_moons

X, y = make_moons(n_samples=200, noise=0.05, random_state=0)

import matplotlib.pyplot as plt

plt.scatter(X[:,0], X[:,1])
plt.show()

from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering

# +
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
km = KMeans(n_clusters=2, random_state=0)
y_km = km.fit_predict(X)
ax1.scatter(X[y_km==0,0],X[y_km==0,1],c='lightblue',edgecolor='black',marker='o',s=40,label='cluster 1')
ax1.scatter(X[y_km==1,0],X[y_km==1,1],c='red',edgecolor='black',marker='s',s=40,label='cluster 2')
ax1.set_title('K-means clustering')

ac = AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='complete')
y_ac = ac.fit_predict(X)
ax2.scatter(X[y_ac==0,0],X[y_ac==0,1],c='lightblue',edgecolor='black',marker='o',s=40,label='cluster 1')
ax2.scatter(X[y_ac==1,0],X[y_ac==1,1],c='red',edgecolor='black',marker='s',s=40,label='cluster 2')
ax2.set_title('Agglomerative clustering')
plt.legend()
plt.show()
# -

from sklearn.cluster import DBSCAN

db = DBSCAN(eps=0.2,min_samples=5,metric='euclidean')
y_db = db.fit_predict(X)
plt.scatter(X[y_db==0,0],X[y_db==0,1],c='lightblue',edgecolor='black',marker='o',s=40,label='cluster 1')
plt.scatter(X[y_db==1,0],X[y_db==1,1],c='red',edgecolor='black',marker='s',s=40,label='cluster 2')
plt.legend()
plt.show()



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