'mc_min_measure_count': 2,
# 'mc_surrounding_times_s': [2.0, 5.0],
'outlier_threshold_distance': 1.0,
'outlier_threshold_diff': 0.5,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06,
}
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items():
print(file_name)
dataset = DataSet.get_dataset(measure_collections,
dataset=dataset,
use_floats=True)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
# keras.utils.to_categorical(
x_train = np.array(
[x_t for i, x_t in enumerate(dataset.x) if i < len(dataset.x) * 0.8])
y_train = np.array(
[x_t for i, x_t in enumerate(dataset.y_true) if i < len(dataset.x) * 0.8])
#num_classes=len(dataset.class_labels))
x_test = np.array(
[x_t for i, x_t in enumerate(dataset.x) if i >= len(dataset.x) * 0.8])
y_test = np.array(
[x_t for i, x_t in enumerate(dataset.y_true) if i >= len(dataset.x) * 0.8])
#num_classes=len(dataset.class_labels))
'max_measure_value': 10.0,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06
}
camera_folder = scenario_path + '_images_Camera\\'
ground_truth_file = [
os.path.join(camera_folder, f) for f in os.listdir(camera_folder) if
os.path.isfile(os.path.join(camera_folder, f)) and f.startswith('00gt')
][0]
measurements_scenario = Measurement.read(scenario_path,
ground_truth_file,
options=options)
measure_collections_scenario = MeasureCollection.create_measure_collections(
measurements_scenario, options=options)
dataset_scenario = DataSet.get_dataset(measure_collections_scenario)
options['exclude_mcs'] = measure_collections_scenario
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
for file_name, measure_collections in measure_collections_files_dir.items(
):
dataset = DataSet.get_dataset(measure_collections, dataset=dataset)
clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1, random_state=42)
clf.fit(dataset.x, dataset.y_true)
predictions = clf.predict(np.array(dataset_scenario.x)).reshape(1, -1)[0]
base_path, options=options)
parking_space_map_clusters, _ = create_parking_space_map(
measure_collections_files_dir)
measure_collections_files_dir = filter_parking_space_map_mcs(
measure_collections_files_dir, parking_space_map_clusters)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items(
):
print(file_name)
dataset_softmax_10cm = DataSet.get_raw_sensor_dataset_per_10cm(
measure_collections,
dataset=dataset_softmax_10cm,
is_softmax_y=True)
dataset_normal = DataSet.get_dataset(measure_collections,
dataset=dataset_normal)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
start = time.time()
# confusion_m_simp = evaluate_model(simple_dense_model, dataset)
evaluator = DriveByEvaluation()
confusion_m_lstm, predictions = evaluator.evaluate(create_random_forest,
predict,
dataset_normal,
number_of_splits=10,
shuffle=True)
# confusion_m_lstm = evaluator.evaluate(create_random_forest, predict, dataset_normal)
# confusion_m_lstm = evaluator.evaluate(simple_dense_model, predict_softmax, dataset_softmax_10cm)
# confusion_m_conv = evaluate_model(create_conv_model, dataset)
'mc_min_measure_count': 2,
# 'mc_surrounding_times_s': [2.0, 5.0],
'outlier_threshold_distance': 1.0,
'outlier_threshold_diff': 0.5,
# 'replacement_values': {0.01: 10.01},
'min_measurement_value': 0.06,
}
dataset = None
measure_collections_files_dir = MeasureCollection.read_directory(
base_path, options=options)
measure_collections_dir = {}
for file_name, measure_collections in measure_collections_files_dir.items(
):
print(file_name)
dataset = DataSet.get_dataset(measure_collections, dataset=dataset)
measure_collections_dir.update(
MeasureCollection.mc_list_to_dict(measure_collections))
classifiers = {
'DecisionTree_GINI': DecisionTreeClassifier(max_depth=3),
}
for name, clf in classifiers.items():
clf.fit(dataset.x, dataset.y_true)
import pydot
from io import StringIO
#dot_data = StringIO()
tree.export_graphviz(clf, out_file='tree_pruned_2.dot')