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.
"""
try:
self._observed_class_distribution[0] += weight
self._observed_class_distribution[1] += y * weight
self._observed_class_distribution[2] += y * y * weight
except KeyError:
self._observed_class_distribution[0] = weight
self._observed_class_distribution[1] = y * weight
self._observed_class_distribution[2] = y * y * weight
for i, x in enumerate(X.tolist()):
try:
obs = self._attribute_observers[i]
except KeyError:
if ht.nominal_attributes is not None and i in ht.nominal_attributes:
obs = NominalAttributeRegressionObserver()
else:
obs = NumericAttributeRegressionObserverMultiTarget()
self._attribute_observers[i] = obs
obs.observe_attribute_class(x, y, weight)
def learn_from_instance(self, X, y, weight, rht):
"""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: numpy.ndarray of length equal to the number of targets.
Instance targets.
weight: float
Instance weight.
rht: RegressionHoeffdingTree
Regression Hoeffding Tree to update.
"""
if self.perceptron_weight is None:
self.perceptron_weight = {}
# Creates matrix of perceptron random weights
_, rows = get_dimensions(y)
_, cols = get_dimensions(X)
self.perceptron_weight[0] = \
self.random_state.uniform(-1.0, 1.0, (rows, cols + 1))
# Cascade Stacking
self.perceptron_weight[1] = \
self.random_state.uniform(-1.0, 1.0, (rows, rows + 1))
self.normalize_perceptron_weights()
try:
self._observed_class_distribution[0] += weight
except KeyError:
self._observed_class_distribution[0] = weight
if rht.learning_ratio_const:
learning_ratio = rht.learning_ratio_perceptron
else:
learning_ratio = rht.learning_ratio_perceptron / \
(1 + self._observed_class_distribution[0] *
rht.learning_ratio_decay)
try:
self._observed_class_distribution[1] += weight * y
self._observed_class_distribution[2] += weight * y * y
except KeyError:
self._observed_class_distribution[1] = weight * y
self._observed_class_distribution[2] = weight * y * y
for i in range(int(weight)):
self.update_weights(X, y, learning_ratio, rht)
for i, x in enumerate(X):
try:
obs = self._attribute_observers[i]
except KeyError:
# Creates targets observers, if not already defined
if rht.nominal_attributes is not None and i in rht.nominal_attributes:
obs = NominalAttributeRegressionObserver()
else:
obs = NumericAttributeRegressionObserverMultiTarget()
self._attribute_observers[i] = obs
obs.observe_attribute_class(x, y, weight)
def learn_from_instance(self, X, y, weight, rht):
"""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.
rht: RegressionHoeffdingTree
Regression Hoeffding Tree to update.
"""
if self.perceptron_weight is None:
self.perceptron_weight = self.random_state.uniform(
-1, 1,
len(X) + 1)
try:
self._observed_class_distribution[0] += weight
except KeyError:
self._observed_class_distribution[0] = weight
if rht.learning_ratio_const:
learning_ratio = rht.learning_ratio_perceptron
else:
learning_ratio = rht.learning_ratio_perceptron / \
(1 + self._observed_class_distribution[0] * rht.learning_ratio_decay)
try:
self._observed_class_distribution[1] += y * weight
self._observed_class_distribution[2] += y * y * weight
except KeyError:
self._observed_class_distribution[1] = y * weight
self._observed_class_distribution[2] = y * y * weight
for i in range(int(weight)):
self.update_weights(X, y, learning_ratio, rht)
for i in range(len(X)):
try:
obs = self._attribute_observers[i]
except KeyError:
if i in rht.nominal_attributes:
obs = NominalAttributeRegressionObserver()
else:
obs = NumericAttributeRegressionObserver()
self._attribute_observers[i] = obs
obs.observe_attribute_class(X[i], y, weight)