cookbook page

doc/cookbook/source/examples/classifier/multiclass_linearmachine.rst

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+==========================
+Multi-class Linear Machine
+==========================
+
+We extend the application of linear machines to multi-class datasets by constructing generic multiclass classifiers with ensembles of binary classifiers.
+
+In this example, we show how to apply :sgclass:`CLibLinear` to multi-class cases with :sgclass:`CLinearMulticlassMachine`.
+
+`See the linear SVM cookbook <http://shogun.ml/cookbook/latest/examples/classifier/linear_svm.html>`_ for the infomration about :sgclass:`CLibLinear` binary classifier.
+
+-------
+Example
+-------
+
+Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and :sgclass:`CMulticlassLabels` as
+
+.. sgexample:: multiclass_linearmachine.sg:create_features
+
+We use :sgclass:`CLibLinear` as base classifier and create an instance of :sgclass:`CLibLinear`.
+
+.. sgexample:: multiclass_linearmachine.sg:create_classifier
+
+In order to run :sgclass:`CLinearMulticlassMachine`, we need to specify an multi-class strategy from :sgclass:`CMulticlassOneVsRestStrategy` and :sgclass:`CMulticlassOneVsOneStrategy`.
+
+.. sgexample:: multiclass_linearmachine.sg:choose_strategy
+
+We create an instance of the :sgclass:`CLinearMulticlassMachine` classifier by passing it the strategy, dataset, binary classifer and the labels.
+
+.. sgexample:: multiclass_linearmachine.sg:create_instance
+
+Then we train and apply it to test data, which here gives :sgclass:`CMulticlassLabels`.
+
+.. sgexample:: multiclass_linearmachine.sg:train_and_apply
+
+We can evaluate test performance via e.g. :sgclass:`CMulticlassAccuracy`.
+
+.. sgexample:: multiclass_linearmachine.sg:evaluate_accuracy
+
+----------
+References
+----------
+
+:wiki:`Multiclass_classification`

examples/meta/src/classifier/multiclass_linearmachine.sg

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+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]
+RealFeatures features_train(f_feats_train)
+RealFeatures features_test(f_feats_test)
+MulticlassLabels labels_train(f_labels_train)
+MulticlassLabels labels_test(f_labels_test)
+#![create_features]
+
+#![create_classifier]
+LibLinear classifier()
+#![create_classifier]
+
+#![choose_strategy]
+MulticlassOneVsOneStrategy strategy()
+#![choose_strategy]
+
+#![create_instance]
+LinearMulticlassMachine mc_classifier(strategy, features_train, classifier, labels_train)
+#![create_instance]
+
+#![train_and_apply]
+mc_classifier.train()
+MulticlassLabels labels_predict = mc_classifier.apply_multiclass(features_test)
+#![train_and_apply]
+
+#![evaluate_accuracy]
+MulticlassAccuracy eval()
+real accuracy = eval.evaluate(labels_predict, labels_test)
+#![evaluate_accuracy]
+
+# integration testing variables
+RealVector output = labels_predict.get_labels()