Affinity propagation python

from sklearn.cluster import AffinityPropagation
import numpy as np

X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
clustering = AffinityPropagation(affinity = 'euclidean', random_state=5).fit(X)

labels = clustering.labels_ # label to each element
centers = clustering.cluster_centers_ # center of each cluster

# if you need a distance different from euclidean
# calculate your custom, pairwise distance among vectors 
# and store them into a matrix M. 
# Note: cluster_centers are no longer available

clustering = AffinityPropagation(affinity='precomputed', random_state=5).fit(M)

Source: scikit-learn.org

Similar codes

0 vote287 views2 answers
Rotatelist in python
Python
1 vote307 views1 answer
Get only first 10 columns pandas
Python
0 vote295 views2 answers
Turtle keep window open
Python
8 votes298 views3 answers
Save plot in python
Python
2 votes306 views3 answers
.first() in django
Python
0 vote303 views1 answer
Sphinx, where to write the glossary of a sofware project
Python
12 votes303 views3 answers
Matplotlib legend
Python
2 votes645 views4 answers
Python how to count items in array
Python
43 votes286 views1 answer
Abc list
Python
0 vote368 views1 answer
Diamond shape in python
Python