fix boolvector

doc/cookbook/source/examples/multiclass_classifier/cartree.rst

@@ -0,0 +1,47 @@
+==================================
+Classification And Regression Tree
+==================================
+
+Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.
+
+Decision trees are mostly used as the following two types:
+
+- Classification tree, where the predicted outcome is the class to which the data belongs.
+- Regression tree, where predicted outcome can be considered a real number.
+
+Classification And Regression Tree (CART) algorithm is an umbrella method that can be applied to generate both classification tree and regression tree.
+
+In this example, we showed how to apply CART algorithm to multi-class dataset and predict the labels with classification tree.
+
+-------
+Example
+-------
+
+Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and :sgclass:`CMulticlassLabels` as
+
+.. sgexample:: cartree.sg:create_features
+
+We set the type of each predictive attribute (true for nominal, false for ordinal/continuous)
+
+.. sgexample:: cartree.sg:set_attribute_types
+
+We create an instance of the :sgclass:`CCARTree` classifier by passting it the attribute types and the tree type.
+We can also set the number of subsets used in cross-valiation and whether to use cross-validation pruning.
+
+.. sgexample:: cartree.sg:create_instance
+
+Then we train and apply it to test data, which here gives :sgclass:`CMulticlassLabels`.
+
+.. sgexample:: cartree.sg:train_and_apply
+
+We can evaluate test performance via e.g. :sgclass:`CMulticlassAccuracy`.
+
+.. sgexample:: cartree.sg:evaluate_accuracy
+
+----------
+References
+----------
+
+:wiki:`Decision_tree_learning`
+
+:wiki:`Predictive_analytics#Classification_and_regression_trees_.28CART.29`

examples/meta/src/multiclass_classifier/cartree.sg

@@ -0,0 +1,35 @@
+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]
+
+#![set_attribute_types]
+BoolVector ft(2)
+ft[0] = 0
+ft[1] = 0
+#![set_attribute_types]
+
+#![create_instance]
+CARTree classifier(ft,enum EProblemType.PT_MULTICLASS, 5, True)
+classifier.set_labels(labels_train)
+#![create_instance]
+
+#![train_and_apply]
+classifier.train(features_train)
+MulticlassLabels labels_predict = 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()