def classifier_multiclasslogisticregression_modular( fm_train_real=traindat, fm_test_real=testdat, label_train_multiclass=label_traindat, label_test_multiclass=label_testdat, z=1, epsilon=1e-5): from modshogun import RealFeatures, MulticlassLabels try: from modshogun import MulticlassLogisticRegression except ImportError: print("recompile shogun with Eigen3 support") return feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = MulticlassLabels(label_train_multiclass) classifier = MulticlassLogisticRegression(z, feats_train, labels) classifier.train() label_pred = classifier.apply(feats_test) out = label_pred.get_labels() if label_test_multiclass is not None: from modshogun import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print('Accuracy = %.4f' % acc) return out
def classifier_multiclasslogisticregression_modular( fm_train_real=traindat, fm_test_real=testdat, label_train_multiclass=label_traindat, label_test_multiclass=label_testdat, z=1, epsilon=1e-5, ): from modshogun import RealFeatures, MulticlassLabels feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = MulticlassLabels(label_train_multiclass) classifier = MulticlassLogisticRegression(z, feats_train, labels) classifier.train() label_pred = classifier.apply(feats_test) out = label_pred.get_labels() if label_test_multiclass is not None: from modshogun import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print("Accuracy = %.4f" % acc) return out
def BuildModel(self, data, responses): # Create and train the classifier. model = MulticlassLogisticRegression(self.z, RealFeatures(data.T), MulticlassLabels(responses)) if self.max_iter is not None: model.set_max_iter(self.max_iter); model.train() return model
def BuildModel(self, data, responses): # Create and train the classifier. model = MulticlassLogisticRegression(self.z, RealFeatures(data.T), MulticlassLabels(responses)) model.train() return model