--- a
+++ b/python-scripts/runSimulationsCluster.py
@@ -0,0 +1,80 @@
+import numpy as np
+from sklearn.cluster import KMeans
+import numpy as np
+from sklearn.metrics import silhouette_score, davies_bouldin_score
+
+
+datatypes=["equal","heterogeneous"]
+typenums=[5,10,15]
+data_names = ['VAE_FCTAE_EM','AE_FAETC_EM', 'AE_FCTAE_EM', 'DAE_FAETC_EM', 'DAE_FCTAE_EM','SVAE_FCTAE_EM','MMDVAE_EM']
+for datatype in datatypes:
+    for typenum in typenums:
+        for data_name in data_names:
+            encoded_factors=np.loadtxt('./result/simulations/{datatype}/{typenum}/{d}_{typenum}.txt'.format(datatype=datatype,typenum=typenum,d=data_name))
+            savepath='./result/simulations/{datatype}/{typenum}/{d}_cluster_result.txt'.format(datatype=datatype,typenum=typenum,d=data_name)
+            with open(savepath, 'w') as f2:
+                print('method:{d}\n'.format(d=data_name))
+                f2.write('method:{d}\n'.format(d=data_name))
+                for cluster_num in range(2,16,1):
+                    all_silhouette=[]
+                    all_DBI=[]
+                    for i in range(100):
+                        clf = KMeans(n_clusters=cluster_num)
+                        clf.fit(encoded_factors)  # 模型训练
+                        labels = clf.labels_
+                        silhouetteScore = silhouette_score(encoded_factors, labels, metric='euclidean')
+                        all_silhouette.append(silhouetteScore)
+                        davies_bouldinScore = davies_bouldin_score(encoded_factors, labels)
+                        all_DBI.append(davies_bouldinScore)
+                    avg_silhouette=np.mean(all_silhouette)
+                    avg_DBI=np.mean(all_DBI)
+
+                    # print("silhouetteScore:", avg_silhouette)
+                    # print("davies_bouldinScore:", avg_DBI)
+                    print('k:{k}\nsilhouetteScore:{s}\ndavies_bouldinScore:{d}\n'.format(k=cluster_num, s=avg_silhouette,d=avg_DBI))
+                    f2.write('*'*20+'\n')
+                    f2.write('k:{k}\nsilhouetteScore:{s}\ndavies_bouldinScore:{d}\n'.format(k=cluster_num, s=avg_silhouette,d=avg_DBI))
+
+
+#直接拼接
+# files = ['aml', 'breast', 'colon', 'kidney', 'liver', 'lung', 'melanoma', 'ovarian', 'sarcoma','gbm']
+# for f in files:
+#     datapath='./data/cancer_do_cluster/{f}'.format(f=f)
+#     omics1 = np.loadtxt('{}/log_exp_omics.txt'.format(datapath))
+#     omics1 = np.transpose(omics1)
+#     omics1 = normalize(omics1, axis=0, norm='max')
+#     print(omics1.shape)
+#     omics2 = np.loadtxt('{}/log_mirna_omics.txt'.format(datapath))
+#     omics2 = np.transpose(omics2)
+#     omics2 = normalize(omics2, axis=0, norm='max')
+#     print(omics2.shape)
+#     omics3 = np.loadtxt('{}/methy_omics.txt'.format(datapath))
+#     omics3 = np.transpose(omics3)
+#     omics3 = normalize(omics3, axis=0, norm='max')
+#     print(omics3.shape)
+#     omics = np.concatenate((omics1, omics2, omics3), axis=1)
+#     encoded_factors=omics
+#     savepath='./result/cancer_do_cluster/{f}/Contact_cluster_result.txt'.format(f=f)
+#     with open(savepath, 'w') as f2:
+#         print('cancer:{f}\nmethod:直接拼接'.format(f=f))
+#         f2.write('cancer:{f}\nmethod:直接拼接\n'.format(f=f))
+#         for typenum in range(2,7,1):
+#             all_silhouette=[]
+#             all_DBI=[]
+#             for i in range(100):
+#                 clf = KMeans(n_clusters=typenum)
+#                 clf.fit(encoded_factors)  # 模型训练
+#                 labels = clf.labels_
+#                 silhouetteScore = silhouette_score(encoded_factors, labels, metric='euclidean')
+#                 all_silhouette.append(silhouetteScore)
+#                 davies_bouldinScore = davies_bouldin_score(encoded_factors, labels)
+#                 all_DBI.append(davies_bouldinScore)
+#             avg_silhouette=np.mean(all_silhouette)
+#             avg_DBI=np.mean(all_DBI)
+
+#             # print("silhouetteScore:", avg_silhouette)
+#             # print("davies_bouldinScore:", avg_DBI)
+#             print('k:{k}\nsilhouetteScore:{s}\ndavies_bouldinScore:{d}\n'.format(k=typenum, s=avg_silhouette,d=avg_DBI))
+#             f2.write('zly'*20+'\n')
+#             f2.write('k:{k}\nsilhouetteScore:{s}\ndavies_bouldinScore:{d}\n'.format(k=typenum, s=avg_silhouette,d=avg_DBI))            
+            
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