DSC-Net-L2-COIL100.py 文件源码

def post_proC(C, K, d, alpha):
    # C: coefficient matrix, K: number of clusters, d: dimension of each subspace
    C = 0.5*(C + C.T)
    r = d*K + 1 
    U, S, _ = svds(C,r,v0 = np.ones(C.shape[0]))
    U = U[:,::-1] 
    S = np.sqrt(S[::-1])
    S = np.diag(S)
    U = U.dot(S)
    U = normalize(U, norm='l2', axis = 1)  
    Z = U.dot(U.T)
    Z = Z * (Z>0)
    L = np.abs(Z ** alpha)
    L = L/L.max()
    L = 0.5 * (L + L.T) 
    spectral = cluster.SpectralClustering(n_clusters=K, eigen_solver='arpack', affinity='precomputed',assign_labels='discretize')
    spectral.fit(L)
    grp = spectral.fit_predict(L) + 1 
    return grp, L