--- a
+++ b/python-scripts/runToGetMOG.py
@@ -0,0 +1,183 @@
+import numpy as np
+from sklearn.preprocessing import normalize
+from keras.layers import Input, Dense,concatenate,Dropout,average
+from keras.models import Model
+from keras import backend as K
+from sklearn.metrics import roc_auc_score, f1_score, accuracy_score
+import numpy as np
+from sklearn.model_selection import StratifiedKFold
+from keras.layers import Input, Dense,concatenate,Dropout,average
+from keras.models import Model
+import keras
+from sklearn.metrics import classification_report
+
+    
+    
+
+#cancer数据
+if __name__ == '__main__':
+    # files = ['breast2']
+    files = ['gbm','breast2']
+    for f in files:
+        datapath='./data/cancer_d2d/{f}'.format(f=f)
+        omics1 = np.loadtxt('{}/after_log_exp.txt'.format(datapath),str)
+        omics1 = np.delete(omics1, 0, axis=1)
+        #omics1 = np.transpose(omics1)
+        omics1 = omics1.astype(np.float)
+        omics1 = normalize(omics1, axis=0, norm='max')
+        print(omics1.shape)
+
+        omics2 = np.loadtxt('{}/after_log_mirna.txt'.format(datapath),str)
+        omics2= np.delete(omics2, 0, axis=1)
+        #omics2 = np.transpose(omics2)
+        omics2 = omics2.astype(np.float)
+        omics2 = normalize(omics2, axis=0, norm='max')
+        print(omics2.shape)
+
+        omics3 = np.loadtxt('{}/after_methy.txt'.format(datapath),str)
+        omics3= np.delete(omics3,0,axis=1)
+        #omics3 = np.transpose(omics3)
+        omics3 = omics3.astype(np.float)
+        omics3 = normalize(omics3, axis=0, norm='max')
+        print(omics3.shape)
+
+        labels = np.loadtxt('{datapath}/after_labels.txt'.format(datapath=datapath), str)
+        labels = np.delete(labels, 0, axis=1)
+        labels = labels.astype(np.int)
+        labels = np.squeeze(labels,axis=1)
+        # datapath = 'data/BRCA'
+        # omics1 = np.loadtxt('{}/1_all.csv'.format(datapath),delimiter=',')
+        # #omics1 = np.transpose(omics1)
+        # omics1 = normalize(omics1, axis=0, norm='max')
+
+        # omics2 = np.loadtxt('{}/2_all.csv'.format(datapath),delimiter=',')
+        # #omics2 = np.transpose(omics2)
+        # omics2 = normalize(omics2, axis=0, norm='max')
+
+        # omics3 = np.loadtxt('{}/3_all.csv'.format(datapath),delimiter=',')
+        # #omics3 = np.transpose(omics3)
+        # omics3 = normalize(omics3, axis=0, norm='max')
+        
+        # k折交叉验证
+        all_acc = []
+        all_f1_macro = []
+        all_f1_weighted = []
+        all_auc_macro = []
+        all_auc_weighted = []
+        #omics = np.loadtxt('./result/nmf/mf_em.txt')
+        omics = np.concatenate((omics1, omics2, omics3), axis=1)
+        
+        # labels = np.loadtxt('./data/BRCA/labels_all.csv', delimiter=',')
+        # data=np.concatenate([])
+        kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+        for train_ix, test_ix in kfold.split(omics1, labels):
+            omics_tobuild=[omics1,omics2,omics3]
+            train_X_1=omics1[train_ix]
+            train_X_2=omics2[train_ix]
+            train_X_3=omics3[train_ix]
+
+            test_X_1=omics1[test_ix]
+            test_X_2=omics2[test_ix]
+            test_X_3=omics3[test_ix]
+            
+            train_y, test_y = labels[train_ix], labels[test_ix]
+            
+            np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')
+            np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')
+            np.savetxt('{}/3_tr.csv'.format(datapath), train_X_3, delimiter=',')
+            np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')
+            np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')
+            np.savetxt('{}/3_te.csv'.format(datapath), test_X_3, delimiter=',')
+            np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')
+            np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')
+            break
+
+
+#simulations数据
+# if __name__ == '__main__':
+#     datatypes=["equal","heterogeneous"]
+#     typenums=[5,10,15]
+#     for datatype in datatypes:
+#         for typenum in typenums:
+#             datapath='data/simulations/{}/{}'.format(datatype, typenum)
+    
+#             labels = np.loadtxt('{}/c.txt'.format(datapath))
+                
+
+#             omics1 = np.loadtxt('{}/o1.txt'.format(datapath))
+#             omics1 = np.transpose(omics1)
+#             omics1 = normalize(omics1, axis=0, norm='max')
+
+#             omics2 = np.loadtxt('{}/o2.txt'.format(datapath))
+#             omics2 = np.transpose(omics2)
+#             omics2 = normalize(omics2, axis=0, norm='max')
+
+#             omics3 = np.loadtxt('{}/o3.txt'.format(datapath))
+#             omics3 = np.transpose(omics3)
+#             omics3 = normalize(omics3, axis=0, norm='max')
+
+#             omics = np.concatenate((omics1, omics2, omics3), axis=1)
+
+#             kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+#             for train_ix, test_ix in kfold.split(omics1, labels):
+#                 omics_tobuild=[omics1,omics2,omics3]
+#                 train_X_1=omics1[train_ix]
+#                 train_X_2=omics2[train_ix]
+#                 train_X_3=omics3[train_ix]
+
+#                 test_X_1=omics1[test_ix]
+#                 test_X_2=omics2[test_ix]
+#                 test_X_3=omics3[test_ix]
+                
+#                 train_y, test_y = labels[train_ix], labels[test_ix]
+                
+#                 np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')
+#                 np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')
+#                 np.savetxt('{}/3_tr.csv'.format(datapath), train_X_3, delimiter=',')
+#                 np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')
+#                 np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')
+#                 np.savetxt('{}/3_te.csv'.format(datapath), test_X_3, delimiter=',')
+#                 np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')
+#                 np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')
+#                 break
+
+#single数据
+if __name__ == '__main__':
+    
+    datapath = 'data/single-cell/'
+    resultpath = 'result/single-cell/'
+    labels = np.loadtxt('{}/c.txt'.format(datapath))
+    # groundtruth = list(np.int_(groundtruth))
+
+    omics = np.loadtxt('{}/omics.txt'.format(datapath))
+    omics = np.transpose(omics)
+    omics1=omics[0:206]
+    omics2=omics[206:412]
+    omics1 = normalize(omics1, axis=0, norm='max')
+    omics2 = normalize(omics2, axis=0, norm='max')
+    omics = np.concatenate((omics1, omics2), axis=1)
+    
+
+    kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+    for train_ix, test_ix in kfold.split(omics1, labels):
+        omics_tobuild=[omics1,omics2]
+        train_X_1=omics1[train_ix]
+        train_X_2=omics2[train_ix]
+
+
+        test_X_1=omics1[test_ix]
+        test_X_2=omics2[test_ix]
+
+        
+        train_y, test_y = labels[train_ix], labels[test_ix]
+        
+        np.savetxt('{}/1_tr.csv'.format(datapath), train_X_1, delimiter=',')
+        np.savetxt('{}/2_tr.csv'.format(datapath), train_X_2, delimiter=',')
+        np.savetxt('{}/1_te.csv'.format(datapath), test_X_1, delimiter=',')
+        np.savetxt('{}/2_te.csv'.format(datapath), test_X_2, delimiter=',')
+        np.savetxt('{}/labels_tr.csv'.format(datapath), train_y, delimiter=',')
+        np.savetxt('{}/labels_te.csv'.format(datapath), test_y, delimiter=',')
+        break
+
+
+