classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm (Operator)
Name
classify_class_svmT_classify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm — Classify a feature vector by a support vector machine.
Signature
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