def apply(self):
learner = KNNLearner(n_neighbors=self.n_neighbors,
metric=self.metrics[self.metric_index],
preprocessors=self.preprocessors)
learner.name = self.learner_name
classifier = None
if self.data is not None:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def apply(self):
learner = KNNLearner(
n_neighbors=self.n_neighbors, metric=self.metrics[self.metric_index], preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def apply(self):
learner = KNNLearner(
n_neighbors=self.n_neighbors,
metric=self.metrics[self.metric_index],
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)
