#!/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")
sys.path.append('../') import automatic_test import regressors import reference_implementations import numpy as np import matplotlib.pyplot as plt feature_list = [ "lane_distance_entry_exact", "curve_secant_dist", "lane_distance_exit_exact", "maxspeed_entry", "vehicle_speed_entry", "vehicle_speed_exit", "curvature_exit", "oneway_exit", "lane_count_entry", "has_right_of_way", "curvature_entry" ] kitti_samples = automatic_test.load_samples('../data/training_data/samples_23_09_15/samples.pickle') kitti_samples = automatic_test.normalize_features(kitti_samples) cmu_samples = automatic_test.load_samples('../data/training_data/samples_CMU/samples.pickle') cmu_samples = automatic_test.normalize_features(cmu_samples) kitti_train_samples, kitti_test_samples = automatic_test.get_partitioned_samples(kitti_samples, 0.8) rf_algo = regressors.RandomForestAlgorithm(feature_list) automatic_test.test([rf_algo], kitti_train_samples, kitti_test_samples, cross_validation=False) automatic_test.test([rf_algo], kitti_train_samples, cmu_samples, cross_validation=False)