linear_support_vector_machine.sg

CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat")
CSVFile f_feats_test("../../data/classifier_binary_2d_linear_features_test.dat")
CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat")
CSVFile f_labels_test("../../data/classifier_binary_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]

#![set_parameters]
real C = 1.0
real epsilon = 0.001
#![set_parameters]

#![create_instance]
Machine svm = machine("LibLinear", C1=C, C2=C, labels=labels_train, epsilon=epsilon, liblinear_solver_type=enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC, use_bias=True)
#![create_instance]

#![train_and_apply]
svm.train(features_train)
BinaryLabels labels_predict = svm.apply_binary(features_test)
#![train_and_apply]

#![extract_weights_bias]
RealVector w = svm.get_real_vector("w")
real b = svm.get_real("bias")
#![extract_weights_bias]

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

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