classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm (Operator)

Name

classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm — Calculate the class of a feature vector by a Gaussian Mixture Model.

Signature

classify_class_gmm( : : GMMHandle, Features, Num : ClassID, ClassProb, Density, KSigmaProb)

Herror T_classify_class_gmm(const Htuple GMMHandle, const Htuple Features, const Htuple Num, Htuple* ClassID, Htuple* ClassProb, Htuple* Density, Htuple* KSigmaProb)

void ClassifyClassGmm(const HTuple& GMMHandle, const HTuple& Features, const HTuple& Num, HTuple* ClassID, HTuple* ClassProb, HTuple* Density, HTuple* KSigmaProb)

HTuple HClassGmm::ClassifyClassGmm(const HTuple& Features, Hlong Num, HTuple* ClassProb, HTuple* Density, HTuple* KSigmaProb) const

static void HOperatorSet.ClassifyClassGmm(HTuple GMMHandle, HTuple features, HTuple num, out HTuple classID, out HTuple classProb, out HTuple density, out HTuple KSigmaProb)

HTuple HClassGmm.ClassifyClassGmm(HTuple features, int num, out HTuple classProb, out HTuple density, out HTuple KSigmaProb)

def classify_class_gmm(gmmhandle: HHandle, features: Sequence[float], num: int) -> Tuple[Sequence[int], Sequence[float], Sequence[float], Sequence[float]]

def classify_class_gmm_s(gmmhandle: HHandle, features: Sequence[float], num: int) -> Tuple[int, Sequence[float], Sequence[float], Sequence[float]]

Description

classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmmclassify_class_gmm computes the best NumNumNumNumnumnum classes of the feature vector FeaturesFeaturesFeaturesFeaturesfeaturesfeatures with the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle and returns the classes in ClassIDClassIDClassIDClassIDclassIDclass_id and the corresponding probabilities of the classes in ClassProbClassProbClassProbClassProbclassProbclass_prob. Before calling classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmmclassify_class_gmm, the GMM must be trained with train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmmtrain_class_gmm.

classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmmclassify_class_gmm corresponds to a call to evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm and an additional step that extracts the best NumNumNumNumnumnum classes. As described with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm, the output values of the GMM can be interpreted as probabilities of the occurrence of the respective classes. However, here the posterior probability ClassProbClassProbClassProbClassProbclassProbclass_prob is further normalized as ClassProbClassProbClassProbClassProbclassProbclass_prob = p(i|x)/p(x) , where p(i|x) and p(x) are specified with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm. In most cases it should be sufficient to use NumNumNumNumnumnum = 1 in order to decide whether the probability of the best class is high enough. In some applications it may be interesting to also take the second best class into account (NumNumNumNumnumnum = 2), particularly if it can be expected that the classes show a significant degree of overlap.

DensityDensityDensityDensitydensitydensity and KSigmaProbKSigmaProbKSigmaProbKSigmaProbKSigmaProbksigma_prob are explained with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm.

Execution Information

Parameters

GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (input_control)  class_gmm HClassGmm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

GMM handle.

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

Feature vector.

NumNumNumNumnumnum (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of best classes to determine.

Default value: 1

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

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

Result of classifying the feature vector with the GMM.

ClassProbClassProbClassProbClassProbclassProbclass_prob (output_control)  real-array HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

A-posteriori probability of the classes.

DensityDensityDensityDensitydensitydensity (output_control)  real-array HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Probability density of the feature vector.

KSigmaProbKSigmaProbKSigmaProbKSigmaProbKSigmaProbksigma_prob (output_control)  real-array HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Normalized k-sigma-probability for the feature vector.

Result

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

Possible Predecessors

train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmmtrain_class_gmm, read_class_gmmread_class_gmmReadClassGmmReadClassGmmReadClassGmmread_class_gmm

Alternatives

evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm

See also

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Mario A.T. Figueiredo: “Unsupervised Learning of Finite Mixture Models”; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 3; March 2002.

Module

Foundation