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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]
laser_interface
Author(s): Hai Nguyen and Travis Deyle. Advisor: Prof. Charlie Kemp, Lab: Healthcare Robotics Lab at Georgia Tech
autogenerated on Wed Nov 27 2013 11:45:51