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)