def predict(self, X):
"""
Predict the values for the input in X
"""
# split data set
assert self.tree, "The tree must exist, call fit before predict!"
y = np.zeros((X.shape[0], ))
# propagate the values down the leaves
for leaf in self.tree.leaves:
indices = np.array([True] * X.shape[0]) # initialize dummy index
for ancestor in NodeMixin.iter_path_reverse(
leaf): # iteration begins at the leaf node
if ancestor.is_leaf:
left = ancestor.left
else:
left_indices = X[:, ancestor.
split_feature] <= ancestor.split_value
if left:
indices &= left_indices
else:
indices &= ~left_indices
left = ancestor.left
y[indices] = leaf.value
return y
def __init__(self, treeDeep, continuous):
self.__tree = NodeMixin()
self.__treeDeep = treeDeep
self.__continuous = continuous
def test_parent():
"""Parent attribute."""
foo = NodeMixin()
assert foo.parent is None
def __init__(self, treeDeep, continuous):
super().__init__(treeDeep, continuous)
# http://anytree.readthedocs.io/en/latest/
self.__tree = NodeMixin()