Classes | |
| class | Dataset |
| class | DecisionTree |
| class | LinearDimReduceDataset |
| class | RFBase |
| class | RFBreiman |
| class | RFRandomInputSubset |
Functions | |
| def | binary_greater_than |
| def | binary_less_than |
| def | create_binary_tests |
| def | evaluate_classifier |
| def | identity |
| def | load_pickle |
| def | min_entropy_split |
| def | mode_exhaustive |
| def | print_separator |
| def | random_subset |
| def | random_subset_split |
| def | split_random_subset |
| def | totally_random_split |
Variables | |
| tuple | dataset = Dataset(inputs, outputs) |
| dictionary | extra_args = {'number_of_learners': (i+1)*10} |
| tuple | inputs = np.float32(iris_array[:, 0:4]) |
| tuple | iris_array = np.matrix(np.loadtxt('iris.data', dtype='|S30', delimiter=',')) |
| list | outputs = iris_array[:, 4] |
| test_iris = False | |
| test_number_trees = False | |
| test_pca = False | |
| test_pickle = True | |
| list | tree_types = [RFBreiman, RFRandomInputSubset] |
| def laser_interface.random_forest.binary_greater_than | ( | attribute, | |
| threshold, | |||
| input_vec | |||
| ) |
Definition at line 132 of file random_forest.py.
| def laser_interface.random_forest.binary_less_than | ( | attribute, | |
| threshold, | |||
| input_vec | |||
| ) |
Definition at line 129 of file random_forest.py.
| def laser_interface.random_forest.create_binary_tests | ( | attribute, | |
| threshold | |||
| ) |
Definition at line 135 of file random_forest.py.
| def laser_interface.random_forest.evaluate_classifier | ( | building_func, | |
| data, | |||
times = 10.0, |
|||
percentage = None, |
|||
extra_args = {}, |
|||
test_pca = False |
|||
| ) |
Evaluate classifier by dividing dataset into training and test set.
@param building_func Function that will build classifier given data and args in extra_args.
@param data Dataset to use for evaluation/training.
@param times The number of bootstrap samples to take.
@param percentage The percentage of data to use for training.
@param extra_args Extra arguments to pass to building_func.
Definition at line 330 of file random_forest.py.
Definition at line 293 of file random_forest.py.
| def laser_interface.random_forest.load_pickle | ( | filename | ) |
Definition at line 466 of file random_forest.py.
| def laser_interface.random_forest.min_entropy_split | ( | dataset | ) |
Find the split that produces subsets with the minimum combined entropy
return splitting attribute & splitting point for that attribute
Definition at line 165 of file random_forest.py.
Finds the mode of a given set
Definition at line 139 of file random_forest.py.
| def laser_interface.random_forest.print_separator | ( | times = 2 | ) |
Definition at line 472 of file random_forest.py.
| def laser_interface.random_forest.random_subset | ( | subset_size, | |
| total_size | |||
| ) |
Definition at line 190 of file random_forest.py.
| def laser_interface.random_forest.random_subset_split | ( | num_subset, | |
| dataset | |||
| ) |
splitter in decision tree
Definition at line 209 of file random_forest.py.
| def laser_interface.random_forest.split_random_subset | ( | subset_size, | |
| total_size | |||
| ) |
Definition at line 199 of file random_forest.py.
| def laser_interface.random_forest.totally_random_split | ( | dataset | ) |
Definition at line 218 of file random_forest.py.
Definition at line 451 of file random_forest.py.
| dictionary laser_interface::random_forest::extra_args = {'number_of_learners': (i+1)*10} |
Definition at line 491 of file random_forest.py.
| tuple laser_interface::random_forest::inputs = np.float32(iris_array[:, 0:4]) |
Definition at line 449 of file random_forest.py.
| tuple laser_interface::random_forest::iris_array = np.matrix(np.loadtxt('iris.data', dtype='|S30', delimiter=',')) |
Definition at line 448 of file random_forest.py.
Definition at line 450 of file random_forest.py.
Definition at line 442 of file random_forest.py.
Definition at line 444 of file random_forest.py.
Definition at line 445 of file random_forest.py.
Definition at line 443 of file random_forest.py.
Definition at line 483 of file random_forest.py.