Normalizer = MinMaxScaler() Normalizer.fit(fea) fea = Normalizer.transform(fea) # dist = tool.rank_dis_c(fea) # dist = dist - np.diag(np.diag(dist)) print("------ Clustering ------") start = time.time() dist = cdist(fea, fea) groupNumber = len(np.unique(labels)) K = 15 # the number of nearest neighbors for KNN graph a = 10 # a = 10 # cl = AGDL.AGDL(dist, groupNumber, K, v) # cluster = AGDL.AGDL(fea, dist, groupNumber, K, 5, a) # labels_pred = np.zeros(len(labels), dtype='i') # for i in range(len(cluster)): # for j in range(len(cluster[i])): # labels_pred[cluster[i][j]] = i labels_pred = GDL.gdl(dist, groupNumber, K, a, True) end = time.time() print("time =", end - start) print("------ Computing performance measure ------") NMI = measure.NMI(labels, labels_pred) print("NMI =", NMI) ACC = measure.ACC(labels, labels_pred) print("ACC =", ACC)
fea, labels = loadData.load_coil100() # K=25 u=100 v=0.1 print("data_set = COIL100 data.shape =", fea.shape) print("------ Normalizing data ------") # fea = tool.data_Normalized(fea) Normalizer = MinMaxScaler() Normalizer.fit(fea) fea = Normalizer.transform(fea) # u = 100 # dist = tool.rank_dis_c(fea, u) dist = tool.rank_order_dis(fea) group_number = len(np.unique(labels)) K = 25 # the number of nearest neighbors for KNN graph v = 0.1 print("------ Clustering ------") start = time.time() labels_pred = GDL.gdl(dist, group_number, K, v, True) end = time.time() print("time =", end - start) NMI = measure.NMI(labels, labels_pred) print("NMI =", NMI) ACC = measure.ACC(labels, labels_pred) print("ACC =", ACC) precision_score = measure.precision_score(labels, labels_pred) print("precision_score =", precision_score)