random_forest.py File Reference

Go to the source code of this file.

Classes
class	laser_interface.random_forest.Dataset
class	laser_interface.random_forest.DecisionTree
class	laser_interface.random_forest.LinearDimReduceDataset
class	laser_interface.random_forest.RFBase
class	laser_interface.random_forest.RFBreiman
class	laser_interface.random_forest.RFRandomInputSubset
Namespaces
namespace	laser_interface::random_forest
Functions
def	laser_interface::random_forest.binary_greater_than
def	laser_interface::random_forest.binary_less_than
def	laser_interface::random_forest.create_binary_tests
def	laser_interface::random_forest.evaluate_classifier
def	laser_interface::random_forest.identity
def	laser_interface::random_forest.load_pickle
def	laser_interface::random_forest.min_entropy_split
def	laser_interface::random_forest.mode_exhaustive
def	laser_interface::random_forest.print_separator
def	laser_interface::random_forest.random_subset
def	laser_interface::random_forest.random_subset_split
def	laser_interface::random_forest.split_random_subset
def	laser_interface::random_forest.totally_random_split
Variables
tuple	laser_interface::random_forest.dataset = Dataset(inputs, outputs)
dictionary	laser_interface::random_forest.extra_args = {'number_of_learners': (i+1)*10}
tuple	laser_interface::random_forest.inputs = np.float32(iris_array[:, 0:4])
tuple	laser_interface::random_forest.iris_array = np.matrix(np.loadtxt('iris.data', dtype='|S30', delimiter=','))
list	laser_interface::random_forest.outputs = iris_array[:, 4]
	laser_interface::random_forest.test_iris = False
	laser_interface::random_forest.test_number_trees = False
	laser_interface::random_forest.test_pca = False
	laser_interface::random_forest.test_pickle = True
list	laser_interface::random_forest.tree_types = [RFBreiman, RFRandomInputSubset]