classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm (Operator)
名称
classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm — 通过高斯混合模型计算特征向量的类。
签名
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]]
描述
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.
执行信息
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
参数
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.
默认值：
1
建议值：
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.
结果
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.
可能的前置算子
train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmmtrain_class_gmm,
read_class_gmmread_class_gmmReadClassGmmReadClassGmmReadClassGmmread_class_gmm
替代算子
evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm
另见
create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm
参考文献
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.
模块
Foundation