def _stop_training(self):
super(ShogunSVMClassifier, self)._stop_training()
self.normalizer = _LabelNormalizer(self.labels)
labels = self.normalizer.normalize(self.labels)
# shogun expects float labels
labels = sgFeatures.Labels(labels.astype(float))
features = sgFeatures.RealFeatures(self.data.transpose())
self.classifier.set_train_features(features, labels)
self.classifier.train()
def _stop_training(self):
super(ShogunSVMClassifier, self)._stop_training()
self.normalizer = _LabelNormalizer(self.labels)
labels = self.normalizer.normalize(self.labels)
# shogun expects float labels
labels = sgFeatures.Labels(labels.astype(float))
features = sgFeatures.RealFeatures(self.data.transpose())
self.classifier.set_train_features(features, labels)
self.classifier.train()
def _stop_training(self):
super(LibSVMClassifier, self)._stop_training()
self.normalizer = _LabelNormalizer(self.labels)
labels = self.normalizer.normalize(self.labels.tolist())
features = self.data
# Call svm training method.
prob = libsvmutil.svm_problem(labels, features.tolist())
# Train
self.model = libsvmutil.svm_train(prob, self.parameter)
def _stop_training(self):
super(LibSVMClassifier, self)._stop_training()
self.normalizer = _LabelNormalizer(self.labels)
labels = self.normalizer.normalize(self.labels.tolist())
features = self.data
# Call svm training method.
prob = libsvmutil.svm_problem(labels, features.tolist())
# Train
self.model = libsvmutil.svm_train(prob, self.parameter)