def t_predict(features, labels, cameras): log_file = '/driving_log.csv' log_path = './data' skiprows = 1 # load the csv log file print("Camera: ", cameras) print("Log path: ", log_path) print("Log file: ", log_file) column_names = [ 'center', 'left', 'right', 'steering', 'throttle', 'brake', 'speed' ] data_df = pd.read_csv(log_path + '/' + log_file, names=column_names, skiprows=skiprows) data_count = len(data_df) # initialise data extract t_features = [] t_labels = [] row = data_df.iloc[np.random.randint(data_count - 1)] steering = getattr(row, 'steering') image = load_image(log_path, getattr(row, cameras[0])) t_features.append(image) t_labels.append(steering) clf = GaussianNB() clf.fit(features, labels) pred = clf.pred(t_features) print(accuracy_score(pred, t_labels))
### labels_train and labels_test are the corresponding item labels features_train, features_test, labels_train, labels_test = preprocess() ######################################################### ### your code goes here ### # Defining Classifier - Gaussian Naive Bayes clf = GaussianNB() # Fitting the training data set # training the test data set labels t0 = time() clf.fit(features_train,lables_train) print("training naive bayes:", round(time()-t0, 3), "s") #predicting the test dataset labels using the trained statistics t0 = time() pred = clf.pred(features_test) print("predicting naive bayes:", round(time()-t0, 3), "s") accuracy = clf.score(features_test,labels_test) print(accuracy) #########################################################