def _new_learning_node(self,
initial_class_observations=None,
perceptron_weight=None):
"""Create a new learning node. The type of learning node depends on
the tree configuration.
"""
if initial_class_observations is None:
initial_class_observations = {}
if self.leaf_prediction == _PERCEPTRON:
return SSTActiveLearningNode(initial_class_observations,
perceptron_weight, self.random_state)
elif self.leaf_prediction == _ADAPTIVE:
return SSTActiveLearningNodeAdaptive(
initial_class_observations,
perceptron_weight,
random_state=self.random_state)
def _new_learning_node(self,
initial_class_observations=None,
parent_node=None,
is_active_node=True):
"""Create a new learning node. The type of learning node depends on
the tree configuration.
"""
if initial_class_observations is None:
initial_class_observations = {}
if is_active_node:
if self.leaf_prediction == self._PERCEPTRON:
return SSTActiveLearningNode(initial_class_observations,
parent_node,
random_state=self.random_state)
elif self.leaf_prediction == self._ADAPTIVE:
new_node = SSTActiveLearningNodeAdaptive(
initial_class_observations,
parent_node,
random_state=self.random_state)
# Resets faded errors
new_node.fMAE_M = np.zeros(self._n_targets, dtype=np.float64)
new_node.fMAE_P = np.zeros(self._n_targets, dtype=np.float64)
new_node.fMAE_SP = np.zeros(self._n_targets, dtype=np.float64)
return new_node
else:
if self.leaf_prediction == self._PERCEPTRON:
return SSTInactiveLearningNode(
initial_class_observations,
parent_node,
random_state=parent_node.random_state)
elif self.leaf_prediction == self._ADAPTIVE:
new_node = SSTInactiveLearningNodeAdaptive(
initial_class_observations,
parent_node,
random_state=parent_node.random_state)
new_node.fMAE_M = parent_node.fMAE_M
new_node.fMAE_P = parent_node.fMAE_P
new_node.fMAE_SP = parent_node.fMAE_SP
return new_node