Operator Reference

select_sub_feature_class_train_dataT_select_sub_feature_class_train_dataSelectSubFeatureClassTrainDataSelectSubFeatureClassTrainDataselect_sub_feature_class_train_data (Operator)

select_sub_feature_class_train_dataT_select_sub_feature_class_train_dataSelectSubFeatureClassTrainDataSelectSubFeatureClassTrainDataselect_sub_feature_class_train_data — Select certain features from training data to create training data containing less features.

Signature

select_sub_feature_class_train_data( : : ClassTrainDataHandle, SubFeatureIndices : SelectedClassTrainDataHandle)

Description

select_sub_feature_class_train_dataselect_sub_feature_class_train_dataSelectSubFeatureClassTrainDataSelectSubFeatureClassTrainDataselect_sub_feature_class_train_data selects certain features from the training data in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle and returns the subset in SelectedClassTrainDataHandleSelectedClassTrainDataHandleSelectedClassTrainDataHandleselectedClassTrainDataHandleselected_class_train_data_handle. The features that should be selected can be chosen by SubFeatureIndicesSubFeatureIndicesSubFeatureIndicessubFeatureIndicessub_feature_indices. If set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data was not called before, the indices refer to the columns. If set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_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_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data was called with names for the feature groups, those names can be used instead of the indices.

Execution Information

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.

Parameters

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle (input_control) class_train_data → (handle)

Handle of the training data.

SubFeatureIndicesSubFeatureIndicesSubFeatureIndicessubFeatureIndicessub_feature_indices (input_control) number-array → (integer / string)

Indices or names to select the subfeatures or columns.

SelectedClassTrainDataHandleSelectedClassTrainDataHandleSelectedClassTrainDataHandleselectedClassTrainDataHandleselected_class_train_data_handle (output_control) class_train_data → (handle)

Handle of the reduced training data.

Example (HDevelop)

* 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, [], [])
* Use the classifier
* ...

Result

If the parameters are valid, the operator select_sub_feature_class_train_dataselect_sub_feature_class_train_dataSelectSubFeatureClassTrainDataSelectSubFeatureClassTrainDataselect_sub_feature_class_train_data returns the value 2 ( H_MSG_TRUE) . If necessary, an exception is raised.

Possible Predecessors

create_class_train_datacreate_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data, add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data, set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data

Possible Successors

add_class_train_data_gmmadd_class_train_data_gmmAddClassTrainDataGmmAddClassTrainDataGmmadd_class_train_data_gmm, add_class_train_data_mlpadd_class_train_data_mlpAddClassTrainDataMlpAddClassTrainDataMlpadd_class_train_data_mlp, add_class_train_data_svmadd_class_train_data_svmAddClassTrainDataSvmAddClassTrainDataSvmadd_class_train_data_svm, add_class_train_data_knnadd_class_train_data_knnAddClassTrainDataKnnAddClassTrainDataKnnadd_class_train_data_knn

Module

Foundation

Operators