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Functions127 in github.com/capitalk/treelearn

⨍ Functions 127◇ Types & classes 14
Methodrandom_threshold_subset
(self, x)
treelearn/randomized_tree.py:81
Methodrefit_leaves
(X,Y)
treelearn/viterbi_tree.py:75
Methodrefit_partition
Assumes that 'init_fit' has already been run.
treelearn/viterbi_tree.py:61
Functionslow_find_min_variance_split
(feature_vec, thresholds, ys)
treelearn/tree_helpers.py:215
Functionsorter
(dir1, dir2)
distribute_setup.py:456
Functiontest_all_classifiers
()
treelearn/test_iris.py:22
Functiontest_all_regressors
()
treelearn/test_regression.py:16
Functiontest_big_tree
(n=1000, d = 50, max_thresholds=10)
treelearn/test_randomized_tree.py:35
Functiontest_binary_data
(n = 1000, d = 50)
treelearn/test_randomized_tree.py:42
Functiontest_eval_all_splits
()
treelearn/test_tree_helpers.py:55
Functiontest_eval_split
()
treelearn/test_tree_helpers.py:45
Functiontest_gini
()
treelearn/test_tree_helpers.py:36
Functiontest_majority
()
treelearn/test_tree_helpers.py:22
Functiontest_simple_forest
()
treelearn/test_ensembles.py:49
Functiontest_simple_tree
()
treelearn/test_randomized_tree.py:21
Functiontest_stacked_random_forest
()
treelearn/test_ensembles.py:55
Functiontest_svm_forest
()
treelearn/test_ensembles.py:52
Methodto_str
(self, indent="", feature_names=None)
treelearn/tree_node.py:53
Functiontrain_additive_regression_forest
(X, Y, num_trees=50, bagging_percent = 0.65, feature_subset_percent = 0.5,
treelearn/recipes.py:337
Functiontrain_clustered_ols
Cluster data and then train a linear regressor per cluster
treelearn/recipes.py:215
Functiontrain_clustered_regression_ensemble
( X, Y, num_models=10, k = 20, stacking=False, additive=Fa
treelearn/recipes.py:297
Functiontrain_clustered_svm
(X, Y, k = 20, C = 1, verbose = True)
treelearn/recipes.py:221
Functiontrain_clustered_svm_ensemble
( X, Y, num_models = 10, C = 1, k = 20, stacking= False,
treelearn/recipes.py:251
Functiontrain_sgd_forest
A random forest whose base classifier is a tree of SGD classifiers Parameters ---------- X : numpy array containing input data.
treelearn/recipes.py:180
Functiontrain_sgd_tree
(X, Y, max_depth=3, randomize_alpha=False, model_args = {}, tree_args={})
treelearn/recipes.py:175
Functiontrain_svm_tree
(X, Y, max_depth = 3, randomize_C = False, model_args = {}, tree_args={})
treelearn/recipes.py:117
Functionviolation
(*args)
distribute_setup.py:211
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