diff --git a/.DS_Store b/.DS_Store
index 72c945a..3576c0f 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py
index 2f9831f..e5a55e5 100644
--- a/Neural_Network_Analysis.py
+++ b/Neural_Network_Analysis.py
@@ -285,6 +285,47 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
 
     return average_result, session_training_results
 
+# Retrieves data sets for each session as train set and evalutes on the others.
+# the average of networks trained om them
+# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
+# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
+def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
+    session_training_results = []
+    for i in range(nr_sessions):
+
+        X_test_session, X_train_session, y_test_session, y_train_session = prepare_datasets_sessions(X, y, session_lengths, i)
+    
+        # Model:
+        if model_name == 'LSTM':
+            model = LSTM(input_shape=(1, 208))
+
+        elif model_name == 'GRU':
+            model = GRU(input_shape=(1, 208))
+
+        elif model_name == 'CNN_1D':
+            X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
+            X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 208, 1))
+            model = CNN_1D(input_shape=(208, 1))
+
+        elif model_name == 'FFN':
+            model = FFN(input_shape=(1, 208))
+
+        else:
+            raise Exception('Model not found')
+
+        train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs)
+        test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=0)
+        session_training_results.append(test_acc)
+        #if log_to_csv:
+            #prediction_csv_logger(X_test_session, y_test_session, model_name, model, i)
+        del model
+        K.clear_session()
+        #print('Session', i, 'as test data gives accuracy:', test_acc)
+
+    average_result = statistics.mean((session_training_results))
+
+    return average_result, session_training_results
+
 # Takes in test data and logs input data and the prediction from a model
 # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
 # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
@@ -458,31 +499,27 @@ if __name__ == "__main__":
     print('\n')
     '''
 
-    '''
+    #'''
     # ----- Inverse cross-validation ------
     # Trained on one session, tested on three
-    average_GRU = session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS, 
-                                                                        log_to_csv=LOG,
+    average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS, 
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
-    average_LSTM = session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
-                                                                        log_to_csv=LOG,
+    average_LSTM = inverse_session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
-    average_FFN = session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
-                                                                        log_to_csv=LOG,
+    average_FFN = inverse_session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
-    average_CNN = session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
-                                                                        log_to_csv=LOG,
+    average_CNN = inverse_session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
 
     print('\n')
-    print('Crossvalidated GRU:', average_GRU)
-    print('Crossvalidated LSTM:', average_LSTM)
-    print('Crossvalidated FFN:', average_FFN)
-    print('Cross-validated CNN_1D:', average_CNN)
+    print('Cross-validated one-session-train GRU:', average_GRU)
+    print('Cross-validated one-session-train LSTM:', average_LSTM)
+    print('Cross-validated one-session-train FFN:', average_FFN)
+    print('Cross-validated one-session-train CNN_1D:', average_CNN)
     print('\n')
-    '''
+    #'''