# 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)
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)
K = 10 v = 1 z = 0.01 groupNumber = len(np.unique(labels)) ND = dist.shape[0] # build the adjacency graph graphW, NNIndex = tool.gacBuildDigraph(dist, K, v) graphW = np.around(graphW, decimals=4) # from adjacency matrix to probability transition matrix def f(x): return x / np.sum(x) graphW = np.apply_along_axis(f, 1, graphW) initialCluster = tool.gacNNMerge(dist, NNIndex) numClusters = len(initialCluster) cl = tool.gacMerging(graphW, initialCluster, groupNumber, 'path', z) end = time.time() print("time =", end - start) print("------ Computing performance measure ------") NMI = measure.NMI(labels, cl) print("NMI =", NMI) ACC = measure.ACC(labels, cl) print("ACC =", ACC)