def initialize(self):
"""
Initialize the widget's state.
"""
learner = NaiveBayesLearner()
learner.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", None)
def initialize(self):
"""
Initialize the widget's state.
"""
learner = NaiveBayesLearner()
learner.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", None)
def apply(self):
learner = NaiveBayesLearner(preprocessors=self.preprocessors)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def apply(self):
learner = NaiveBayesLearner(
preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
