Operator Reference

HALCON Operator Reference / Classification / K-Nearest Neighbors

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create_class_knnT_create_class_knnCreateClassKnnCreateClassKnncreate_class_knn (Operator)

create_class_knnT_create_class_knnCreateClassKnnCreateClassKnncreate_class_knn — Create a k-nearest neighbors (k-NN) classifier.

Signature

create_class_knn( : : NumDim : KNNHandle)

Herror T_create_class_knn(const Htuple NumDim, Htuple* KNNHandle)

void CreateClassKnn(const HTuple& NumDim, HTuple* KNNHandle)

void HClassKnn::HClassKnn(const HTuple& NumDim)

void HClassKnn::CreateClassKnn(const HTuple& NumDim)

static void HOperatorSet.CreateClassKnn(HTuple numDim, out HTuple KNNHandle)

public HClassKnn(HTuple numDim)

void HClassKnn.CreateClassKnn(HTuple numDim)

def create_class_knn(num_dim: Sequence[int]) -> HHandle

Description

create_class_knncreate_class_knnCreateClassKnnCreateClassKnncreate_class_knn creates a k-nearest neighbors (k-NN) data structure. This can be either used to classify data or to approximately locate nearest neighbors in a NumDimNumDimNumDimnumDimnum_dim-dimensional space.

Most of the operators described in Classification/K-Nearest-Neighbor use the resulting handle KNNHandleKNNHandleKNNHandleKNNHandleknnhandle.

The k-NN classifies by searching approximately the nearest neighbors and returning their classes as result. With the used approximation, the search time is logarithmically to the number of samples and dimensions.

The dimension of the feature vectors is the only parameter that necessarily has to be set in NumDimNumDimNumDimnumDimnum_dim.

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 returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

Parameters

NumDimNumDimNumDimnumDimnum_dim (input_control)  number-array → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of dimensions of the feature.

Default: 10

KNNHandleKNNHandleKNNHandleKNNHandleknnhandle (output_control)  class_knn → HClassKnn, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Handle of the k-NN classifier.

Result

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

Possible Successors

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn, train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn

Alternatives

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmcreate_class_svm, create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpcreate_class_mlp

See also

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnread_class_knn

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

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