quadratic_discriminant_analysis.sg

CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat")
CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")
CSVFile f_labels_train("../../data/classifier_4class_2d_linear_labels_train.dat")
CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")

#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_instance]
Machine qda = machine("QDA", labels=labels_train, m_tolerance=0.0001, m_store_covs=True)
#![create_instance]

#![train_and_apply]
qda.train(features_train)
MulticlassLabels labels_predict = qda.apply_multiclass(features_test)
#![train_and_apply]

#![extract_mean_and_cov]
RealMatrix m = qda.get_real_matrix("m_means")
#![extract_mean_and_cov]

#![evaluate_accuracy]
MulticlassAccuracy eval()
real accuracy = eval.evaluate(labels_predict, labels_test)
#![evaluate_accuracy]

# additional integration testing variables
RealVector output = labels_predict.get_labels()