def spectral_clustering(similarity, concepts=2, euclid=False):
if euclid:
X = similarity_euclidean(similarity)
else:
X = similarity
X[X < 0] = 0
sc = SpectralClusterer(X, kcut=X.shape[0] / 2, mutual=True)
return sc.run(cluster_number=concepts, KMiter=50, sc_type=2)
def spectral(similarity, euclid=False):
if euclid:
similarity = similarity_euclidean(similarity)
else:
similarity[similarity < 0] = 0
sc = SpectralClusterer(similarity, kcut=similarity.shape[0] / 2, mutual=True)
sc.run(cluster_number=2, KMiter=50, sc_type=2)
return (sc.eig_vect[:, 1], sc.eig_vect[:, 2])
