Classes | Namespaces | Functions | Variables
random_forest.py File Reference

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Classes

class  ml_lib.random_forest.DecisionTree
 Basic DecisionTree that the random forest is based on. More...
class  ml_lib.random_forest.RFBase
 Base class for random forest classifier. More...
class  ml_lib.random_forest.RFBreiman
 Train a random forest using DecisionTrees on bootstrap samples using splits on random attributes and random values on those attributes. More...
class  ml_lib.random_forest.RFRandomInputSubset
 Train a random forest using DecisionTrees on bootstrap samples where each sample has a random subset of dimensions but the split point is performed using a minimum entropy criteria. More...

Namespaces

namespace  ml_lib::random_forest

Functions

def ml_lib::random_forest.binary_greater_than
def ml_lib::random_forest.binary_less_than
def ml_lib::random_forest.create_binary_tests
def ml_lib::random_forest.evaluate_classifier
 Evaluate classifier by dividing dataset into training and test set.
def ml_lib::random_forest.load_pickle
def ml_lib::random_forest.min_entropy_split
 Find the split that produces subsets with the minimum combined entropy return splitting attribute & splitting point for that attribute.
def ml_lib::random_forest.mode_exhaustive
 Helper functions.
def ml_lib::random_forest.print_separator
def ml_lib::random_forest.random_subset
def ml_lib::random_forest.random_subset_split
 splitter in decision tree
def ml_lib::random_forest.save_pickle
def ml_lib::random_forest.split_random_subset
def ml_lib::random_forest.totally_random_split

Variables

 ml_lib::random_forest.bad = False
tuple ml_lib::random_forest.data = ds.Dataset(train, resp)
tuple ml_lib::random_forest.dataset = Dataset(inputs, outputs)
tuple ml_lib::random_forest.dt = DecisionTree(data)
dictionary ml_lib::random_forest.extra_args = {'number_of_learners': (i+1)*10}
tuple ml_lib::random_forest.inputs = np.float32(iris_array[:, 0:4])
tuple ml_lib::random_forest.iris_array = np.matrix(np.loadtxt('iris.data', dtype='|S30', delimiter=','))
tuple ml_lib::random_forest.new_dt = DecisionTree()
list ml_lib::random_forest.outputs = iris_array[:, 4]
tuple ml_lib::random_forest.packed = dt.pack_tree()
tuple ml_lib::random_forest.pred_new = new_dt.predict(test)
tuple ml_lib::random_forest.pred_old = dt.predict(test)
list ml_lib::random_forest.preds = []
tuple ml_lib::random_forest.resp = np.mat(np.round(np.random.rand(1, 20)))
tuple ml_lib::random_forest.test = np.mat(np.random.rand(4, 1))
 ml_lib::random_forest.test_iris = False
 ml_lib::random_forest.test_new_design = True
 ml_lib::random_forest.test_number_trees = False
 ml_lib::random_forest.test_packing = False
 ml_lib::random_forest.test_pca = False
 ml_lib::random_forest.test_pickle = False
tuple ml_lib::random_forest.train = np.mat(np.random.rand(4, 20))
list ml_lib::random_forest.tree_types = [RFBreiman, RFRandomInputSubset]


ml_lib
Author(s): haidai
autogenerated on Wed Nov 27 2013 11:46:34