def learn_from_instance(self, X, y, weight, ht):
""" Update the node with the provided instance.
Parameters
----------
X: numpy.ndarray of length equal to the number of features.
Instance attributes for updating the node.
y: int
Instance class.
weight: float
Instance weight.
ht: HoeffdingTree
Hoeffding Tree to update.
"""
# Update attribute_observers
try:
self._observed_class_distribution[y] += weight
except KeyError:
self._observed_class_distribution[y] = weight
for i in range(len(X)):
try:
obs = self._attribute_observers[i]
except KeyError:
if i in ht.nominal_attributes:
obs = NominalAttributeClassObserver()
else:
obs = NumericAttributeClassObserverGaussian()
self._attribute_observers[i] = obs
obs.observe_attribute_class(X[i], int(y), weight)
def learn_from_instance(self, X, y, weight, ht):
"""Update the node with the provided instance.
Parameters
----------
X: numpy.ndarray of length equal to the number of features.
Instance attributes for updating the node.
y: int
Instance class.
weight: float
Instance weight.
ht: HoeffdingTreeClassifier
Hoeffding Tree to update.
"""
try:
self._observed_class_distribution[y] += weight
except KeyError:
self._observed_class_distribution[y] = weight
self._observed_class_distribution = dict(
sorted(self._observed_class_distribution.items()))
if self.list_attributes.size == 0:
self.list_attributes = self._sample_features(get_dimensions(X)[1])
for i in self.list_attributes:
try:
obs = self._attribute_observers[i]
except KeyError:
if ht.nominal_attributes is not None and i in ht.nominal_attributes:
obs = NominalAttributeClassObserver()
else:
obs = NumericAttributeClassObserverGaussian()
self._attribute_observers[i] = obs
obs.observe_attribute_class(X[i], int(y), weight)
def _partial_fit(self, X, y, weight):
try:
self._observed_class_distribution[y] += weight
except KeyError:
self._observed_class_distribution[y] = weight
for i in range(len(X)):
try:
obs = self._attribute_observers[i]
except KeyError:
if i in self._nominal_attributes:
obs = NominalAttributeClassObserver()
else:
obs = NumericAttributeClassObserverGaussian()
self._attribute_observers[i] = obs
obs.observe_attribute_class(X[i], int(y), weight)
def learn_from_instance(self, X, y, weight, ht):
if not (ht.leaf_prediction == NAIVE_BAYES_ADAPTIVE):
y = ''.join(str(e) for e in y)
y = int(y, 2)
try:
self._observed_class_distribution[y] += weight
except KeyError:
self._observed_class_distribution[y] = weight
for i in range(len(X)):
try:
obs = self._attribute_observers[i]
except KeyError:
if ht.nominal_attributes is not None and i in ht.nominal_attributes:
obs = NominalAttributeClassObserver()
else:
obs = NumericAttributeClassObserverGaussian()
self._attribute_observers[i] = obs
obs.observe_attribute_class(X[i], int(y), weight)