Operator Reference

HALCON Operator Reference / Classification / Support Vector Machines

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classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm (Operator)

classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm — Classify a feature vector by a support vector machine.

Signature

classify_class_svm( : : SVMHandle, Features, Num : Class)

Herror T_classify_class_svm(const Htuple SVMHandle, const Htuple Features, const Htuple Num, Htuple* Class)

void ClassifyClassSvm(const HTuple& SVMHandle, const HTuple& Features, const HTuple& Num, HTuple* Class)

HTuple HClassSvm::ClassifyClassSvm(const HTuple& Features, const HTuple& Num) const

static void HOperatorSet.ClassifyClassSvm(HTuple SVMHandle, HTuple features, HTuple num, out HTuple classVal)

HTuple HClassSvm.ClassifyClassSvm(HTuple features, HTuple num)

def classify_class_svm(svmhandle: HHandle, features: Sequence[float], num: Sequence[int]) -> Sequence[int]

def classify_class_svm_s(svmhandle: HHandle, features: Sequence[float], num: Sequence[int]) -> int

Description

classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm computes the best NumNumNumnumnum classes of the feature vector FeaturesFeaturesFeaturesfeaturesfeatures with the SVM SVMHandleSVMHandleSVMHandleSVMHandlesvmhandle and returns them in ClassClassClassclassValclass. If the classifier was created in the ModeModeModemodemode = 'one-versus-one'"one-versus-one""one-versus-one""one-versus-one""one-versus-one", the classes are ordered by the number of votes of the sub-classifiers. If ModeModeModemodemode = 'one-versus-all'"one-versus-all""one-versus-all""one-versus-all""one-versus-all" was used, the classes are ordered by the value of each sub-classifier (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmcreate_class_svm for more details). If the classifier was created in the ModeModeModemodemode = 'novelty-detection'"novelty-detection""novelty-detection""novelty-detection""novelty-detection", it determines whether the feature vector belongs to the same class as the training data (ClassClassClassclassValclass = 1) or is regarded as outlier (ClassClassClassclassValclass = 0). In this case NumNumNumnumnum must be set to 1 as the classifier only determines membership.

Before calling classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm, the SVM must be trained with train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmtrain_class_svm.

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

SVMHandleSVMHandleSVMHandleSVMHandlesvmhandle (input_control)  class_svm → HClassSvm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

SVM handle.

FeaturesFeaturesFeaturesfeaturesfeatures (input_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Feature vector.

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

Number of best classes to determine.

Default: 1

Suggested values: 1, 2, 3, 4, 5

ClassClassClassclassValclass (output_control)  integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Result of classifying the feature vector with the SVM.

Result

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

Possible Predecessors

train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmtrain_class_svm, read_class_svmread_class_svmReadClassSvmReadClassSvmread_class_svm

Alternatives

apply_dl_classifierapply_dl_classifierApplyDlClassifierApplyDlClassifierapply_dl_classifier

See also

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmcreate_class_svm

References

John Shawe-Taylor, Nello Cristianini: “Kernel Methods for Pattern Analysis”; Cambridge University Press, Cambridge; 2004.
Bernhard Schölkopf, Alexander J.Smola: “Learning with Kernels”; MIT Press, London; 1999.

Module

Foundation

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