import numpy as np
from sklearn.cluster import KMeans
from scipy.sparse import identity
def SpectralClustering(CKSym, n):
# This is direct port of JHU vision lab code. Could probably use sklearn SpectralClustering.
CKSym = CKSym.astype(float)
N, _ = CKSym.shape
MAXiter = 1000 # Maximum number of iterations for KMeans
REPlic = 20 # Number of replications for KMeans
DN = np.diag(np.divide(1, np.sqrt(np.sum(CKSym, axis=0) + np.finfo(float).eps)))
LapN = identity(N).toarray().astype(float) - np.matmul(np.matmul(DN, CKSym), DN)
_, _, vN = np.linalg.svd(LapN)
vN = vN.T
kerN = vN[:, N - n:N]
normN = np.sqrt(np.sum(np.square(kerN), axis=1))
kerNS = np.divide(kerN, normN.reshape(len(normN), 1) + np.finfo(float).eps)
km = KMeans(n_clusters=n, n_init=REPlic, max_iter=MAXiter, n_jobs=-1).fit(kerNS)
return km.labels_
if __name__ == "__main__":
pass
