Name
add_sample_class_train_dataT_add_sample_class_train_dataAddSampleClassTrainDataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainData — Add a training sample to training data.
add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainData adds a training sample to the
training data given by ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle. The
training sample is given by FeaturesFeaturesFeaturesFeaturesFeaturesfeatures and ClassIDClassIDClassIDClassIDClassIDclassID.
FeaturesFeaturesFeaturesFeaturesFeaturesfeatures is the feature vector of the sample, and
consequently must be a real vector of length NumDim, as
specified in create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData. ClassIDClassIDClassIDClassIDClassIDclassID is the
class of the sample. More than one trainings sample can be added at once.
In this case the parameter OrderOrderOrderOrderOrderorder defines in which order
the elements of the feature vectors are passed in FeaturesFeaturesFeaturesFeaturesFeaturesfeatures.
If it is set to 'row'"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""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 OrderOrderOrderOrderOrderorder is
'feature_column'"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_dataSetFeatureLengthsClassTrainDataset_feature_lengths_class_train_dataSetFeatureLengthsClassTrainDataSetFeatureLengthsClassTrainData
to come completely and row-wise before the second feature, and so on.
- 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:
The value of this parameter may not be shared across multiple threads without external synchronization.
Handle of the training data.
The order of the feature vector.
Default value:
'row'
"row"
"row"
"row"
"row"
"row"
List of values: 'column'"column""column""column""column""column", 'feature_column'"feature_column""feature_column""feature_column""feature_column""feature_column", 'row'"row""row""row""row""row"
Feature vector of the training sample.
Class of the training sample.
If the parameters are valid, the operator
add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainData returns the value 2 (H_MSG_TRUE). If necessary,
an exception is raised.
create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData
add_class_train_data_svmadd_class_train_data_svmAddClassTrainDataSvmadd_class_train_data_svmAddClassTrainDataSvmAddClassTrainDataSvm,
add_class_train_data_knnadd_class_train_data_knnAddClassTrainDataKnnadd_class_train_data_knnAddClassTrainDataKnnAddClassTrainDataKnn,
add_class_train_data_gmmadd_class_train_data_gmmAddClassTrainDataGmmadd_class_train_data_gmmAddClassTrainDataGmmAddClassTrainDataGmm,
add_class_train_data_mlpadd_class_train_data_mlpAddClassTrainDataMlpadd_class_train_data_mlpAddClassTrainDataMlpAddClassTrainDataMlp
create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData
Foundation