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get_params_class_knnGetParamsClassKnnGetParamsClassKnnget_params_class_knnT_get_params_class_knn🔗

Short description🔗

get_params_class_knnGetParamsClassKnnGetParamsClassKnnget_params_class_knnT_get_params_class_knn — Get parameters of a k-NN classification.

Signature🔗

get_params_class_knn( class_knn KNNHandle, string GenParamName, out number GenParamValue )void GetParamsClassKnn( const HTuple& KNNHandle, const HTuple& GenParamName, HTuple* GenParamValue )static void HOperatorSet.GetParamsClassKnn( HTuple KNNHandle, HTuple genParamName, out HTuple genParamValue )def get_params_class_knn( knnhandle: HHandle, gen_param_name: Sequence[str] ) -> Sequence[Union[int, float, str]]

Herror T_get_params_class_knn( const Htuple KNNHandle, const Htuple GenParamName, Htuple* GenParamValue )

HTuple HClassKnn::GetParamsClassKnn( const HTuple& GenParamName ) const

HTuple HClassKnn.GetParamsClassKnn( HTuple genParamName )

Description🔗

get_params_class_knnGetParamsClassKnn gets parameters of the k-NN referred by KNNHandleKNNHandleknnhandle. The possible entries in GenParamNamegenParamNamegen_param_name are:

  • 'method'"method": Retrieve the currently selected method for determining the result of classify_class_knnClassifyClassKnn. The result can be 'classes_distance'"classes_distance", 'classes_frequency'"classes_frequency", 'classes_weighted_frequencies'"classes_weighted_frequencies" or 'neighbors_distance'"neighbors_distance".

  • 'k'"k": The number of nearest neighbors that is considered to determine the results.

  • 'max_num_classes'"max_num_classes": The maximum number of classes that are returned. This parameter is ignored in case the method 'neighbors_distance'"neighbors_distance" is selected.

  • 'num_checks'"num_checks": Defines the maximum number of runs through the trees.

  • 'epsilon'"epsilon": A parameter to lower the accuracy in the tree to gain speed.

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.

GenParamNamegenParamNamegen_param_name (input_control) string-array → (string)HTuple (HString)HTuple (string)Sequence[str]Htuple (char*)

Names of the parameters that can be read from the k-NN classifier.

Default: ['method', 'k']["method", "k"]
List of values: 'epsilon', 'k', 'method', 'num_checks'"epsilon", "k", "method", "num_checks"

GenParamValuegenParamValuegen_param_value (output_control) number-array → (integer / real / string)HTuple (Hlong / double / HString)HTuple (int / long / double / string)Sequence[Union[int, float, str]]Htuple (Hlong / double / char*)

Values of the selected parameters.

Result🔗

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

Combinations with other operators🔗

Combinations

Possible predecessors

train_class_knnTrainClassKnn, read_class_knnReadClassKnn

Possible successors

classify_class_knnClassifyClassKnn

See also

create_class_knnCreateClassKnn, read_class_knnReadClassKnn

References🔗

Marius Muja, David G. Lowe: ``Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration’‘; International Conference on Computer Vision Theory and Applications (VISAPP 09); 2009.

Module🔗

Foundation