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linear_svm.sg
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linear_svm.sg
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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]
RealFeatures features_train(f_feats_train)
RealFeatures features_test(f_feats_test)
BinaryLabels labels_train(f_labels_train)
BinaryLabels labels_test(f_labels_test)
#![create_features]
#![set_parameters]
real C = 1.0
real epsilon = 0.001
#![set_parameters]
#![create_instance]
LibLinear svm(C, features_train, labels_train)
svm.set_liblinear_solver_type(enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC)
svm.set_epsilon(epsilon)
#![create_instance]
#![train_and_apply]
svm.train()
BinaryLabels labels_predict = svm.apply_binary(features_test)
#![train_and_apply]
#![extract_weights_bias]
RealVector w = svm.get_w()
real b = svm.get_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()