data = np.loadtxt(dataset) fea = data[:, :-1] labels = data[:, -1] fea = tool.data_Normalized(fea) dist = cdist(fea, fea) p = 2 groupNumber = len(np.unique(labels)) print("------ clustering ------") start = time.time() # 调参 eps min_samples DBSCAN_CLUSTER = DBSCAN(eps=11, min_samples=4, metric='precomputed') DBSCAN_CLUSTER.fit(dist) labels_pred = DBSCAN_CLUSTER.labels_ 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) dd = DBSCAN(eps=10, min_samples=4) dd.fit(fea) labels_pred = dd.labels_ NMI = measure.NMI(labels, labels_pred) print("NMI =", NMI) print('world')
print("data_set = %s data.shape = %s" % (data_set, fea.shape)) print("------ Normalizing data ------") # tool.data_Normalized(fea) Normalizer = MinMaxScaler() Normalizer.fit(fea) fea = Normalizer.transform(fea) print("------ Decomposition ------") #fea,b,c = PCA.pca(fea, 150) pca = PCA(n_components=150) fea = pca.fit_transform(fea) print("fea.shape =", fea.shape) K = 20 u = 1 group_number = len(np.unique(labels)) print("------ Clustering ------") start = time.time() labels_pred = DPC.knnDPC1(fea, group_number, K, u) 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)
# labels = data[:, -1] # print("dataset = %s data.shape = %s" % (dataset, fea.shape)) fea, labels = loadData.load_coil100() print("------ Normalizing data ------") # fea = tool.data_Normalized(fea) Normalizer = MinMaxScaler() Normalizer.fit(fea) fea = Normalizer.transform(fea) print("------ PCA decomposition ------") # fea,b,c = tool.PCA.pca(fea, 150) pca = PCA(n_components=150) fea = pca.fit_transform(fea) print("fea.shape =", fea.shape) K = 5 groupNumber = len(np.unique(labels)) print("------ Clustering ------") start = time.time() cl = knnDPC.knnDPC2(fea, groupNumber, K) end = time.time() print("time =", end - start) print("------ Computing performance measure ------") nmi = measure.NMI(labels, cl) print("nmi =", nmi) acc = measure.ACC(labels, cl) # 计算ACC需要label和labels_pred 在同一个区间[1,20] print("acc =", acc)