--- a
+++ b/python-scripts/runSingleDNN.py
@@ -0,0 +1,445 @@
+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
+#训练两个神经网络
+def build_NN_model1(omics,class_num):
+    omics1=omics[0]
+    omics2=omics[1]
+
+    input1_dim=omics1.shape[1]
+    input2_dim = omics2.shape[1]
+
+    # class_num = 4
+
+
+    #omics1
+    input_factor1 = Input(shape=(input1_dim,),name='omics1')
+    # NN
+    omics1_nn = Dense(1000, activation='relu')(input_factor1)
+    omics1_nn = Dropout(0.1)(omics1_nn)
+    # omics1_nn = Dense(500, activation='relu')(omics1_nn)
+    # omics1_nn = Dropout(0.1)(omics1_nn)
+    omics1_nn = Dense(100, activation='relu')(omics1_nn)
+    omics1_nn = Dropout(0.1)(omics1_nn)
+
+
+    # omics2
+    input_factor2 = Input(shape=(input2_dim,), name='omics2')
+    # NN
+    omics2_nn = Dense(1000, activation='relu')(input_factor2)
+    omics2_nn = Dropout(0.1)(omics2_nn)
+    # omics2_nn = Dense(100, activation='relu')(omics2_nn)
+    # omics2_nn = Dropout(0.1)(omics2_nn)
+    omics2_nn = Dense(100, activation='relu')(omics2_nn)
+    omics2_nn = Dropout(0.1)(omics2_nn)
+
+
+
+    mid_concat=concatenate([omics1_nn, omics2_nn])
+    # classifier
+    nn_classifier = Dense(100, activation='relu')(mid_concat)
+    nn_classifier=Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(50, activation='relu')(nn_classifier)
+    nn_classifier = Dropout(0.1)(nn_classifier)
+    # nn_classifier = Dense(50, activation='relu')(nn_classifier)
+    # nn_classifier = Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(10, activation='relu')(nn_classifier)
+    #nn_classifier = Dropout(0.1)(nn_classifier)
+    nn_classifier = Dense(class_num, activation='softmax', name='classifier')(nn_classifier)
+    my_metrics = {
+        'classifier': ['acc']
+    }
+    my_loss = {
+        'classifier': 'categorical_crossentropy', \
+        }
+    adam=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+    zlyNN = Model(inputs=[input_factor1,input_factor2], outputs=nn_classifier)
+    zlyNN.compile(optimizer=adam, loss=my_loss, metrics=my_metrics)
+    return zlyNN
+
+
+
+def build_NN_model2(omics,class_num):
+    
+    input_dim=omics.shape[1]
+    
+    #class_num = 5
+
+
+    #omics1
+    input_factor1 = Input(shape=(input_dim,),name='omics')
+    # NN
+    omics1_nn = Dense(2000, activation='relu')(input_factor1)
+    omics1_nn = Dropout(0.1)(omics1_nn)
+    omics1_nn = Dense(500, activation='relu')(omics1_nn)
+    omics1_nn = Dropout(0.1)(omics1_nn)
+    omics1_nn = Dense(100, activation='relu')(omics1_nn)
+    omics1_nn = Dropout(0.1)(omics1_nn)
+    # omics1_nn1 = Dense(100, activation='relu')(omics1_nn1)
+    # omics1_nn1 = Dropout(0.1)(omics1_nn1)
+    # omics1_nn = Dense(10, activation='relu')(omics1_nn)
+    # omics1_nn = Dropout(0.1)(omics1_nn)
+    # omics1_nn = average([omics1_nn1,omics1_nn])
+    # omics1_nn = Dense(100, activation='relu')(omics1_nn)
+    # omics1_nn = Dropout(0.1)(omics1_nn)
+    nn_classifier = Dense(50, activation='relu')(omics1_nn)
+    # nn_classifier = Dropout(0.1)(nn_classifier)
+    if class_num==2:
+        nn_classifier = Dense(1, activation='sigmoid', name='classifier')(nn_classifier)
+    else:
+        nn_classifier = Dense(class_num, activation='softmax', name='classifier')(nn_classifier)
+    my_metrics_multi = {
+        'classifier': ['acc']
+    }
+    my_loss_multi = {
+        'classifier': 'categorical_crossentropy', \
+        }
+    my_metrics_bi = {
+        'classifier': ['acc']
+    }
+    my_loss_bi = {
+        'classifier': 'binary_crossentropy', \
+        }
+    # compile autoencoder
+    # self.autoencoder.compile(optimizer='adam', loss='mse')
+    zlyNN = Model(inputs=[input_factor1], outputs=nn_classifier) 
+    if class_num==2:
+        zlyNN.compile(optimizer='adam', loss=my_loss_bi, metrics=my_metrics_bi)
+    else:
+        zlyNN.compile(optimizer='adam', loss=my_loss_multi, metrics=my_metrics_multi)
+    return zlyNN
+
+
+
+if __name__ == '__main__':
+    
+
+    # datatypes=["equal","heterogeneous"]
+    # typenums=[5,10,15]
+    # noise_factor=0.5
+    # savepath='./result/simulations/lfnn_res.txt'
+    # with open(savepath, 'w') as f2:
+    #     for datatype in datatypes:
+    #         f2.write(datatype+'\n')
+    #         for typenum in typenums:
+    #             f2.write(str(typenum)+'\n')
+    #             datapath='data/simulations/{}/{}'.format(datatype, typenum)
+    #             resultpath='result/simulations/{}/{}'.format(datatype, typenum)
+    #             labels = np.loadtxt('{}/c.txt'.format(datapath))
+    #             # groundtruth = list(np.int_(groundtruth))
+
+    #             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)
+
+    #             # k折交叉验证
+    #             all_acc = []
+    #             all_f1_macro = []
+    #             all_f1_weighted = []
+
+                
+    #             kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+    #             for train_ix, test_ix in kfold.split(omics, 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]
+    #                 # select rows
+    #                 train_X, test_X = [train_X_1,train_X_2,train_X_3],[test_X_1,test_X_2,test_X_3]
+    #                 #train_X, test_X = (train_X_1,train_X_2,train_X_3),(test_X_1,test_X_2,test_X_3)
+    #                 train_y, test_y = labels[train_ix], labels[test_ix]
+    #                 # summarize train and test composition
+    #                 unique, count = np.unique(train_y, return_counts=True)
+    #                 train_data_count = dict(zip(unique, count))
+    #                 print('train:' + str(train_data_count))
+    #                 unique, count = np.unique(test_y, return_counts=True)
+    #                 test_data_count = dict(zip(unique, count))
+    #                 print('test:' + str(test_data_count))
+
+    #                 class_num=typenum
+    #                 # 多分类的输出
+    #                 train_y = list(np.int_(train_y))
+    #                 # groundtruth = np.int_(groundtruth)
+    #                 y = []
+    #                 num = len(train_y)
+    #                 for i in range(num):
+    #                     tmp = np.zeros(class_num, dtype='uint8')
+    #                     tmp[train_y[i]] = 1
+    #                     y.append(tmp)
+    #                 train_y = np.array(y)
+
+    #                 test_y = list(np.int_(test_y))
+    #                 # groundtruth = np.int_(groundtruth)
+    #                 y = []
+    #                 num = len(test_y)
+    #                 for i in range(num):
+    #                     tmp = np.zeros(class_num, dtype='uint8')
+    #                     tmp[test_y[i]] = 1
+    #                     y.append(tmp)
+    #                 test_y = np.array(y)
+
+    #                 model = build_NN_model1(omics_tobuild,class_num)
+    #                 history = model.fit(train_X, train_y, epochs=50, verbose=2, batch_size=16, shuffle=True,validation_data=(test_X, test_y))
+    #                 y_true = []
+    #                 for i in range(len(test_y)):
+    #                     y_true.append(np.argmax(test_y[i]))
+    #                 predictions = model.predict(test_X)
+    #                 y_pred = []
+    #                 for i in range(len(predictions)):
+    #                     y_pred.append(np.argmax(predictions[i]))
+    #                 acc = accuracy_score(y_true, y_pred)
+    #                 f1_macro = f1_score(y_true, y_pred, average='macro')
+    #                 # f1_micro=f1_score(y_true, y_pred, average='micro')
+    #                 f1_weighted = f1_score(y_true, y_pred, average='weighted')
+    #                 all_acc.append(acc)
+    #                 all_f1_macro.append(f1_macro)
+    #                 all_f1_weighted.append(f1_weighted)
+
+
+    #                 print(classification_report(y_true, y_pred))
+    #                 # print_precison_recall_f1(y_true, y_pred)
+    #             print('caicai' * 20)
+    #             print(
+    #                 'acc:{all_acc}\nf1_macro:{all_f1_macro}\nf1_weighted:{all_f1_weighted}\n'. \
+    #                 format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted))
+    #             avg_acc = np.mean(all_acc)
+    #             avg_f1_macro = np.mean(all_f1_macro)
+    #             avg_f1_weighted = np.mean(all_f1_weighted)
+
+    #             print(
+    #                 'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+    #                 format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+    #             f2.write('acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+    #                 format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+    #         f2.write('*'*20)
+
+
+    
+
+    
+    # groundtruth = np.loadtxt('{}/c.txt'.format(datapath))
+    # groundtruth = list(np.int_(groundtruth))
+
+    
+    # savepath='./result/single-cell/efnn_res.txt'
+    # with open(savepath, 'w') as f2:
+    #     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)
+
+
+    #     # k折交叉验证
+    #     all_acc = []
+    #     all_f1_macro = []
+    #     all_f1_weighted = []
+
+        
+    #     kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+    #     for train_ix, test_ix in kfold.split(omics, labels):
+            
+
+    #         train_X, test_X = omics[train_ix], omics[test_ix]
+    #         train_y, test_y = labels[train_ix], labels[test_ix]
+    #         # summarize train and test composition
+    #         unique, count = np.unique(train_y, return_counts=True)
+    #         train_data_count = dict(zip(unique, count))
+    #         print('train:' + str(train_data_count))
+    #         unique, count = np.unique(test_y, return_counts=True)
+    #         test_data_count = dict(zip(unique, count))
+    #         print('test:' + str(test_data_count))
+
+    #         class_num=3
+    #         # 多分类的输出
+    #         train_y = list(np.int_(train_y))
+    #         # groundtruth = np.int_(groundtruth)
+    #         y = []
+    #         num = len(train_y)
+    #         for i in range(num):
+    #             tmp = np.zeros(class_num, dtype='uint8')
+    #             tmp[train_y[i]] = 1
+    #             y.append(tmp)
+    #         train_y = np.array(y)
+
+    #         test_y = list(np.int_(test_y))
+    #         # groundtruth = np.int_(groundtruth)
+    #         y = []
+    #         num = len(test_y)
+    #         for i in range(num):
+    #             tmp = np.zeros(class_num, dtype='uint8')
+    #             tmp[test_y[i]] = 1
+    #             y.append(tmp)
+    #         test_y = np.array(y)
+
+    #         model = build_NN_model2(omics, class_num)
+    #         history = model.fit(train_X, train_y, epochs=50, verbose=2, batch_size=8, shuffle=True,
+    #                             validation_data=(test_X, test_y))
+    #         y_true = []
+    #         for i in range(len(test_y)):
+    #             y_true.append(np.argmax(test_y[i]))
+    #         predictions = model.predict(test_X)
+    #         y_pred = []
+    #         for i in range(len(predictions)):
+    #             y_pred.append(np.argmax(predictions[i]))
+    #         acc = accuracy_score(y_true, y_pred)
+    #         f1_macro = f1_score(y_true, y_pred, average='macro')
+    #         # f1_micro=f1_score(y_true, y_pred, average='micro')
+    #         f1_weighted = f1_score(y_true, y_pred, average='weighted')
+    #         all_acc.append(acc)
+    #         all_f1_macro.append(f1_macro)
+    #         all_f1_weighted.append(f1_weighted)
+
+
+    #         print(classification_report(y_true, y_pred))
+    #         # print_precison_recall_f1(y_true, y_pred)
+    #     print('caicai' * 20)
+    #     print(
+    #         'acc:{all_acc}\nf1_macro:{all_f1_macro}\nf1_weighted:{all_f1_weighted}\n'. \
+    #         format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted))
+    #     avg_acc = np.mean(all_acc)
+    #     avg_f1_macro = np.mean(all_f1_macro)
+    #     avg_f1_weighted = np.mean(all_f1_weighted)
+
+    #     print(
+    #         'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+    #         format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+    #     f2.write('acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+    #         format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+
+
+    savepath='./result/single-cell/lfnn_res1.txt'
+    with open(savepath, 'w') as f2:
+        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)
+
+
+        # k折交叉验证
+        all_acc = []
+        all_f1_macro = []
+        all_f1_weighted = []
+
+        
+        kfold = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
+        for train_ix, test_ix in kfold.split(omics, 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]
+
+            # select rows
+            train_X, test_X = [train_X_1,train_X_2],[test_X_1,test_X_2]
+            train_y, test_y = labels[train_ix], labels[test_ix]
+            # summarize train and test composition
+            unique, count = np.unique(train_y, return_counts=True)
+            train_data_count = dict(zip(unique, count))
+            print('train:' + str(train_data_count))
+            unique, count = np.unique(test_y, return_counts=True)
+            test_data_count = dict(zip(unique, count))
+            print('test:' + str(test_data_count))
+
+            class_num=3
+            # 多分类的输出
+            train_y = list(np.int_(train_y))
+            # groundtruth = np.int_(groundtruth)
+            y = []
+            num = len(train_y)
+            for i in range(num):
+                tmp = np.zeros(class_num, dtype='uint8')
+                tmp[train_y[i]] = 1
+                y.append(tmp)
+            train_y = np.array(y)
+
+            test_y = list(np.int_(test_y))
+            # groundtruth = np.int_(groundtruth)
+            y = []
+            num = len(test_y)
+            for i in range(num):
+                tmp = np.zeros(class_num, dtype='uint8')
+                tmp[test_y[i]] = 1
+                y.append(tmp)
+            test_y = np.array(y)
+
+            model = build_NN_model1(omics_tobuild,class_num)
+            history = model.fit(train_X, train_y, epochs=50, verbose=2, batch_size=32, shuffle=True,validation_data=(test_X, test_y))
+            y_true = []
+            for i in range(len(test_y)):
+                y_true.append(np.argmax(test_y[i]))
+            predictions = model.predict(test_X)
+            y_pred = []
+            for i in range(len(predictions)):
+                y_pred.append(np.argmax(predictions[i]))
+            acc = accuracy_score(y_true, y_pred)
+            f1_macro = f1_score(y_true, y_pred, average='macro')
+            # f1_micro=f1_score(y_true, y_pred, average='micro')
+            f1_weighted = f1_score(y_true, y_pred, average='weighted')
+            all_acc.append(acc)
+            all_f1_macro.append(f1_macro)
+            all_f1_weighted.append(f1_weighted)
+
+
+            print(classification_report(y_true, y_pred))
+            break
+            # print_precison_recall_f1(y_true, y_pred)
+        print('caicai' * 20)
+        print(
+            'acc:{all_acc}\nf1_macro:{all_f1_macro}\nf1_weighted:{all_f1_weighted}\n'. \
+            format(all_acc=all_acc, all_f1_macro=all_f1_macro, all_f1_weighted=all_f1_weighted))
+        avg_acc = np.mean(all_acc)
+        avg_f1_macro = np.mean(all_f1_macro)
+        avg_f1_weighted = np.mean(all_f1_weighted)
+
+        print(
+            'acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+            format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+        f2.write('acc:{avg_acc}\nf1_macro:{avg_f1_macro}\nf1_weighted:{avg_f1_weighted}\n'. \
+            format(avg_acc=avg_acc, avg_f1_macro=avg_f1_macro, avg_f1_weighted=avg_f1_weighted))
+    
+
+
+    
+