# "curvature_exit", # Curvature of exit way over INT_DIST "vehicle_speed_entry", # Measured vehicle speed on entry way at INT_DIST # "vehicle_speed_exit", # Measured vehicle speed on exit way at INT_DIST # "lane_count_entry", # Total number of lanes in entry way # "lane_count_exit", # Total number of lanes in exit way # "has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection? "curve_secant_dist" ] kitti_samples = automatic_test.load_samples('../data/training_data/samples_kitti/samples.pickle') darmstadt_samples = automatic_test.load_samples('../data/training_data/samples_darmstadt/samples.pickle') samples = kitti_samples + darmstadt_samples random.shuffle(samples) select_label_method(samples, 'y_distances') sub_samples, test_samples = automatic_test.get_partitioned_samples(samples, 0.8) train_sample_sets, validation_sample_sets = automatic_test.get_cross_validation_samples(sub_samples, 4) random_state = random.get_state() algo_args = { 'features': feature_list, 'single_target_variable': False, 'n_jobs': 1 } hyp_intervals = [ ('n_estimators', 1, 200), ('max_leaf_nodes', 5, len(train_sample_sets[0])), ('max_features', 1, len(feature_list)) ] search_results = automatic_test.random_search_hyperparameters(
#!/usr/bin/python #coding:utf-8 # Comparing random forest and Extra Trees algorithm import sys sys.path.append('../') import automatic_test import regressors import reference_implementations from extract_features import _feature_types feature_list = _feature_types rf_algo = regressors.RandomForestAlgorithm(feature_list) et_algo = regressors.ExtraTreesAlgorithm(feature_list) algos = [rf_algo, et_algo] samples = automatic_test.load_samples('../data/training_data/samples_23_09_15/samples.pickle') samples = automatic_test.normalize_features(samples) train_sample_sets, test_sample_sets = automatic_test.get_cross_validation_samples(samples, 0.8, 5) automatic_test.test(algos, train_sample_sets, test_sample_sets, cross_validation=True) # results = automatic_test.predict(algos, test_samples) # automatic_test.show_intersection_plot(results, test_samples, which_samples="best-worst-case")
"maxspeed_entry", # Allowed maximum speed on entry way "maxspeed_exit", # Allowed maximum speed on exit way "lane_distance_entry_lane_center", # Distance of lane center line to curve secant ceter point at 0 degree angle "lane_distance_exit_lane_center", # Distance of lane center line to curve secant ceter point at 180 degree angle "oneway_entry", # Is entry way a oneway street? "oneway_exit", # Is exit way a oneway street? "curvature_entry", # Curvature of entry way over INT_DIST "curvature_exit", # Curvature of exit way over INT_DIST "bicycle_designated_entry", # Is there a designated bicycle way in the entry street? "bicycle_designated_exit", # Is there a designated bicycle way in the exit street? "lane_count_entry", # Total number of lanes in entry way "lane_count_exit", # Total number of lanes in exit way "has_right_of_way", # Does the vehicle with the respective manoeuver have right of way at the intersection? "curve_secant_dist" # Shortest distance from curve secant to intersection center ] rf_algo_radii = regressors.RandomForestAlgorithm(feature_list) rf_algo_distances = regressors.RandomForestAlgorithm(feature_list) samples_radii = automatic_test.load_samples('../data/training_data/samples.pickle') # samples_radii = automatic_test.normalize_features(samples) samples_distances = automatic_test.load_samples('../data/training_data/samples.pickle') # samples_distances = automatic_test.normalize_features(samples_distances) select_label_method(samples_distances, 'y_distances') train_samples_radii, test_samples_radii = automatic_test.get_cross_validation_samples(samples_radii, 0.7, 5) train_samples_distances, test_samples_distances = automatic_test.get_cross_validation_samples(samples_distances, 0.7, 5) automatic_test.test([rf_algo_radii], train_samples_radii, test_samples_radii, cross_validation=True) automatic_test.test([rf_algo_distances], train_samples_distances, test_samples_distances, cross_validation=True) # automatic_test.train([rf_algo_distances], train_samples_distances) # results = automatic_test.predict([rf_algo_distances], test_samples_distances) # automatic_test.show_intersection_plot(results, test_samples_distances, which_samples="all")