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I am using sklearn affinity propagation algorithm as below.
affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)
I also have a similarity matrix created for the data I am using. Now I want to use my similarity matrix to use in the affinity propagation model.
In sklearn they have different methods for this such as fit, fit_predict, predict. So, I'm not sure what to use.
Is it correct if I use,
affprop.fit(my similarity matrix)
It is correct to use as you indicated.
For example, in below , I use levenshtein for make similarity matrix. then use this matrix for clustring by affinity algorithm.
lev_similarity = -1*np.array([[distance.levenshtein(w1,w2) for w1 in words]
for w2 in words])
af = affprop.fit(lev_similarity)
