get_sample_class_knnGetSampleClassKnnGetSampleClassKnnget_sample_class_knnT_get_sample_class_knn

HALCON ScriptCpp++.NETPythonC

Short description

get_sample_class_knnGetSampleClassKnnGetSampleClassKnnget_sample_class_knnT_get_sample_class_knn — Return a training sample from the training data of a k-nearest neighbors (k-NN) classifier.

Signature

get_sample_class_knn( class_knn KNNHandle, integer IndexSample, out real Features, out integer ClassID )void GetSampleClassKnn( const HTuple& KNNHandle, const HTuple& IndexSample, HTuple* Features, HTuple* ClassID )static void HOperatorSet.GetSampleClassKnn( HTuple KNNHandle, HTuple indexSample, out HTuple features, out HTuple classID )def get_sample_class_knn( knnhandle: HHandle, index_sample: int ) -> Tuple[Sequence[float], Sequence[int]]

Herror T_get_sample_class_knn( const Htuple KNNHandle, const Htuple IndexSample, Htuple* Features, Htuple* ClassID )

HTuple HClassKnn::GetSampleClassKnn( Hlong IndexSample, HTuple* ClassID ) const

HTuple HClassKnn.GetSampleClassKnn( int indexSample, out HTuple classID )

Description

get_sample_class_knnGetSampleClassKnn reads a training sample from the k-nearest neighbors (k-NN) classifier given by KNNHandleKNNHandleknnhandle that was added with add_sample_class_knnAddSampleClassKnn or read_class_knnReadClassKnn. The index of the sample is specified with IndexSampleindexSampleindex_sample. The index is counted from \(0\), i.e., IndexSampleindexSampleindex_sample must be a number between \(0\) and NumSamplesnumSamplesnum_samples \(- 1\), where NumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_knnGetSampleNumClassKnn. The training sample is returned in Featuresfeaturesfeatures and ClassIDclassIDclass_id. Featuresfeaturesfeatures is a feature vector of length NumDimnumDimnum_dim (see create_class_knnCreateClassKnn), while ClassIDclassIDclass_id is the class label, which is a number between \(0\) and the number of classes.

Execution information

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

KNNHandleKNNHandleknnhandle (input_control) class_knn → (handle)HTuple (HHandle)HClassKnn, HTuple (IntPtr)HHandleHtuple (handle)

Handle of the k-NN classifier.

IndexSampleindexSampleindex_sample (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Index of the training sample.

Featuresfeaturesfeatures (output_control) real-array → (real)HTuple (double)HTuple (double)Sequence[float]Htuple (double)

Feature vector of the training sample.

ClassIDclassIDclass_id (output_control) integer-array → (integer)HTuple (Hlong)HTuple (int / long)Sequence[int]Htuple (Hlong)

Class of the training sample.

Result

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

Combinations with other operators

Combinations

Possible predecessors

add_sample_class_train_dataAddSampleClassTrainData

See also

create_class_knnCreateClassKnn

Module

Foundation