# "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")
