select_sub_feature_class_train_data — Select certain features from training data to create training data containing less features.
select_sub_feature_class_train_data selects certain features from the training data in ClassTrainDataHandle and returns the subset in SelectedClassTrainDataHandle. The features that should be selected can be chosen by SubFeatureIndices. If set_feature_lengths_class_train_data was not called before, the indices refer to the columns. If set_feature_lengths_class_train_data was called before, the grouping defined there is relevant for the meaning of the indices. The entry n in the list selects then the n-th feature group. If set_feature_lengths_class_train_data was called with names for the feature groups, those names can be used instead of the indices.
Handle of the training data.
Indices or names to select the subfeatures or columns.
Handle of the reduced training data.
* Find out which of the two features distinguishes two Classes NameFeature1 := 'Good Feature' NameFeature2 := 'Bad Feature' LengthFeature1 := 3 LengthFeature2 := 2 * Create training data create_class_train_data (LengthFeature1+LengthFeature2,\ ClassTrainDataHandle) * Define the features which are in the training data set_feature_lengths_class_train_data (ClassTrainDataHandle, [LengthFeature1,\ LengthFeature2], [NameFeature1, NameFeature2]) * Add training data * |Feat1| |Feat2| add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1, 2,1 ], 0) add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2, 2,1 ], 1) add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1, 3,4 ], 0) add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2, 3,4 ], 1) * Add more data * ... * Select one of the features select_sub_feature_class_train_data (ClassTrainDataHandle, NameFeature1, \ SelectedClassTrainDataHandle) * Add training data to a classifier create_class_knn (LengthFeature1, KNNHandle) add_class_train_data_knn (KNNHandle, SelectedClassTrainDataHandle) train_class_knn (KNNHandle, , ) clear_class_train_data (ClassTrainDataHandle) * Use the classifier * ... clear_class_knn (KNNHandle)
If the parameters are valid, the operator select_sub_feature_class_train_data returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
create_class_train_data, add_sample_class_train_data, set_feature_lengths_class_train_data
add_class_train_data_gmm, add_class_train_data_mlp, add_class_train_data_svm, add_class_train_data_knn