def update(self, att_val, class_val, weight):
if utils.is_missing_value(att_val):
self._missing_weight += weight
else:
val_dist = self._class_lookup.get(class_val, None)
if val_dist is None:
val_dist = ValueDistribution()
val_dist.add(att_val, weight)
self._class_lookup[class_val] = val_dist
else:
val_dist.add(att_val, weight)
self._total_weight += weight
def update(self, att_val, class_val, weight):
if utils.is_missing_value(att_val):
self._missing_weight += weight
else:
val_dist = self._class_lookup.get(class_val, None)
if val_dist is None:
val_dist = ValueDistribution()
val_dist.add(att_val, weight)
self._class_lookup[class_val] = val_dist
else:
val_dist.add(att_val, weight)
self._total_weight += weight
def update(self, att_val, class_val, weight):
"""Update the statistics with the supplied attribute and class values.
Args:
att_val (float): The value of the attribute.
class_val (str): The value of the class.
weight (float): The weight of this observation.
"""
if not utils.is_missing_value(att_val):
norm = self._class_lookup.get(class_val, None)
if norm is None:
norm = GaussianEstimator()
self._class_lookup[class_val] = norm
self._min_val_observed_per_class[class_val] = att_val
self._max_val_observed_per_class[class_val] = att_val
else:
if att_val < self._min_val_observed_per_class[class_val]:
self._min_val_observed_per_class[class_val] = att_val
if att_val > self._max_val_observed_per_class[class_val]:
self._max_val_observed_per_class[class_val] = att_val
norm.add_value(att_val, weight)
def update(self, att_val, class_val, weight):
"""Update the statistics with the supplied attribute and class values.
Args:
att_val (float): The value of the attribute.
class_val (str): The value of the class.
weight (float): The weight of this observation.
"""
if not utils.is_missing_value(att_val):
norm = self._class_lookup.get(class_val, None)
if norm is None:
norm = GaussianEstimator()
self._class_lookup[class_val] = norm
self._min_val_observed_per_class[class_val] = att_val
self._max_val_observed_per_class[class_val] = att_val
else:
if att_val < self._min_val_observed_per_class[class_val]:
self._min_val_observed_per_class[class_val] = att_val
if att_val > self._max_val_observed_per_class[class_val]:
self._max_val_observed_per_class[class_val] = att_val
norm.add_value(att_val, weight)
