def add(self, micro_cluster):
self.n_samples += micro_cluster.n_samples
self.linear_sum_timestamp += micro_cluster.linear_sum_timestamp
self.squared_sum_timestamp += micro_cluster.squared_sum_timestamp
add_dict_values(self.linear_sum,
micro_cluster.linear_sum,
inplace=True)
add_dict_values(self.squared_sum,
micro_cluster.squared_sum,
inplace=True)
def predict_proba_one(self, x):
proba = {c: 0.0 for c in self.classes}
if self._root is not None:
found_nodes = [self._root]
if isinstance(self._root, DTBranch):
found_nodes = self._root.traverse(x, until_leaf=True)
for leaf in found_nodes:
dist = leaf.prediction(x, tree=self)
# Option Tree prediction (of sorts): combine the response of all leaves reached
# by the instance
proba = add_dict_values(proba, dist, inplace=True)
proba = normalize_values_in_dict(proba)
return proba
def predict_proba_one(self, x):
proba = {c: 0.0 for c in self.classes}
if self._tree_root is not None:
found_nodes = self._filter_instance_to_leaves(x, None, -1)
for fn in found_nodes:
# parent_branch == -999 means that the node is the root of an alternate tree.
# In other words, the alternate tree is a single leaf. It is probably not accurate
# enough to be used to predict, so skip it
if fn.parent_branch != -999:
leaf_node = fn.node
if leaf_node is None:
leaf_node = fn.parent
dist = leaf_node.leaf_prediction(x, tree=self)
# Option Tree prediction (of sorts): combine the response of all leaves reached
# by the instance
proba = add_dict_values(proba, dist, inplace=True)
proba = normalize_values_in_dict(proba)
return proba