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 vote181 views2 answers
How to get current user info in odoo 8 in a controller
Python
1 vote171 views1 answer
Show all numpy array
Python
0 vote178 views1 answer
How to add an symbol to a certain part of a list python
Python
1 vote249 views2 answers
How to find 1 st digit in python
Python
0 vote170 views1 answer
Customise django admin edit model button in field
Python
0 vote172 views1 answer
Python get response headers
Python
14 votes165 views6 answers
Permutations in python
Python
1 vote188 views1 answer
How to input 2-d array in python
Python
1 vote174 views1 answer
Sparse categorical cross entropy python
Python
2 votes175 views1 answer
How to print palindrome in 100 between 250 in python
Python