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 vote205 views1 answer
Python get names of input arguments
Python
0 vote184 views1 answer
Is enumerate python lazy
Python
1 vote201 views1 answer
Wheter tomorrow
Python
2 votes191 views1 answer
Tf.reduce_sum()
Python
0 vote186 views2 answers
Mountain array leetcode
Python
63 votes254 views6 answers
Index in python
Python
0 vote183 views1 answer
Wget download file python magic
Python
0 vote192 views1 answer
Exit a pygame program
Python
0 vote190 views1 answer
Switch case python 3.10
Python
0 vote192 views1 answer
Ecrire un code permettant de changer les valeurs de la colonne avec ces deux labels python.
Python