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 vote108 views1 answer
What does -> none do in python
Python
3 votes122 views2 answers
COLLECTIONS.DEFAULTDICT(set)
Python
0 vote155 views1 answer
Créer fonction python
Python
0 vote104 views1 answer
Write to file in p
Python
2 votes125 views2 answers
Generate a list of numbers upto n
Python
1 vote112 views1 answer
AttributeError: module 'keras.optimizers' has no attribute 'RMSprop'
Python
4 votes108 views2 answers
How to use ternary operater in python
Python
0 vote109 views1 answer
Python - Comment lire une ligne de fichier par ligne
Python
0 vote120 views1 answer
Python datetime remove timezone
Python
0 vote115 views1 answer
Access django server from another machine
Python