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Add svmlin & svmsgd meta example #4404

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Oct 31, 2018
Merged

Add svmlin & svmsgd meta example #4404

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9b98061
Add svmlin & svmsgd meta example
vinx13 Oct 11, 2018
ce4e008
Update integration data
vinx13 Oct 12, 2018
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2 changes: 1 addition & 1 deletion data
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Submodule data updated 3 files
+2 −2 testsuite/meta/binary/svmlin.dat
+2 −2 testsuite/meta/binary/svmsgd.dat
+2 −2 testsuite/meta/evaluation/cross_validation_pipeline.dat
19 changes: 19 additions & 0 deletions examples/meta/src/binary/svmlin.sg
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File f_feats_train = csv_file("../../data/classifier_binary_2d_linear_features_train.dat")
File f_feats_test = csv_file("../../data/classifier_binary_2d_linear_features_test.dat")
File f_labels_train = csv_file("../../data/classifier_binary_2d_linear_labels_train.dat")
File f_labels_test = csv_file("../../data/classifier_binary_2d_linear_labels_test.dat")

Features feats_train = features(f_feats_train)
Features feats_test = features(f_feats_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)

Machine svm = machine("SVMLin", C1=0.9, C2=0.9, epsilon=0.00001, labels=labels_train)
svm.train(feats_train)

RealVector weights = svm.get_real_vector("w")
real bias = svm.get_real("bias")

Labels labels_predict = svm.apply(feats_test)
Evaluation eval = evaluation("AccuracyMeasure")
real accuracy = eval.evaluate(labels_predict, labels_test)
19 changes: 19 additions & 0 deletions examples/meta/src/binary/svmsgd.sg
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File f_feats_train = csv_file("../../data/classifier_binary_2d_linear_features_train.dat")
File f_feats_test = csv_file("../../data/classifier_binary_2d_linear_features_test.dat")
File f_labels_train = csv_file("../../data/classifier_binary_2d_linear_labels_train.dat")
File f_labels_test = csv_file("../../data/classifier_binary_2d_linear_labels_test.dat")

Features feats_train = features(f_feats_train)
Features feats_test = features(f_feats_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)

Machine svm = machine("SVMSGD", C1=0.9, C2=0.9, epochs=5, labels=labels_train)
svm.train(feats_train)

RealVector weights = svm.get_real_vector("w")
real bias = svm.get_real("bias")

Labels labels_predict = svm.apply(feats_test)
Evaluation eval = evaluation("AccuracyMeasure")
real accuracy = eval.evaluate(labels_predict, labels_test)