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 vote110 views1 answer
(Word or Phrase to Phone-Number Generator) python
Python
6 votes114 views2 answers
Update jupyter notebook
Python
24 votes97 views10 answers
Python timer
Python
1 vote115 views1 answer
Difference between loc and iloc
Python
0 vote158 views1 answer
Networkx draw graph with weight
Python
13 votes110 views3 answers
Np.hstack in python
Python
0 vote113 views2 answers
Numpy np sign change in df pandas zero crossing
Python
0 vote99 views1 answer
Calling function whose name is in a variable
Python
82 votes109 views6 answers
How to run requirements.txt in python
Python
1 vote97 views1 answer
Get int64 column pandas
Python