add_sample_class_train_dataT_add_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data (Operator)

Name

add_sample_class_train_dataT_add_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data — Add a training sample to training data.

Signature

add_sample_class_train_data( : : ClassTrainDataHandle, Order, Features, ClassID : )

Description

add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data adds a training sample to the training data given by ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle. The training sample is given by FeaturesFeaturesFeaturesfeaturesfeatures and ClassIDClassIDClassIDclassIDclass_id. FeaturesFeaturesFeaturesfeaturesfeatures is the feature vector of the sample, and consequently must be a real vector of length NumDimNumDimNumDimnumDimnum_dim, as specified in create_class_train_datacreate_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data. ClassIDClassIDClassIDclassIDclass_id is the class of the sample. More than one training sample can be added at once. In this case the parameter OrderOrderOrderorderorder defines in which order the elements of the feature vectors are passed in FeaturesFeaturesFeaturesfeaturesfeatures. If it is set to 'row'"row""row""row""row", the first training sample comes first, the second comes second, and so on. If it is set to 'column'"column""column""column""column", the first dimension of all feature vectors comes first, and then the second dimension of all feature vectors, and so on. The third possible mode for OrderOrderOrderorderorder is 'feature_column'"feature_column""feature_column""feature_column""feature_column". This mode expects features which were grouped before with set_feature_lengths_class_train_dataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_data to come completely and row-wise before the second feature, and so on.

Execution Information

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

This operator modifies the state of the following input parameter:

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle (input_control, state is modified) class_train_data → (handle)

Handle of the training data.

OrderOrderOrderorderorder (input_control) string → (string)

The order of the feature vector.

Default: 'row' "row" "row" "row" "row"

List of values: 'column'"column""column""column""column", 'feature_column'"feature_column""feature_column""feature_column""feature_column", 'row'"row""row""row""row"

FeaturesFeaturesFeaturesfeaturesfeatures (input_control) number-array → (real)

Feature vector of the training sample.

ClassIDClassIDClassIDclassIDclass_id (input_control) integer-array → (integer)

Class of the training sample.

Result

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