get_sample_class_gmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmmT_get_sample_class_gmm

HALCON ScriptCpp++.NETPythonC

Short description

get_sample_class_gmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmmT_get_sample_class_gmm — Return a training sample from the training data of a Gaussian Mixture Models (GMM).

Signature

get_sample_class_gmm( class_gmm GMMHandle, integer NumSample, out real Features, out number ClassID )void GetSampleClassGmm( const HTuple& GMMHandle, const HTuple& NumSample, HTuple* Features, HTuple* ClassID )static void HOperatorSet.GetSampleClassGmm( HTuple GMMHandle, HTuple numSample, out HTuple features, out HTuple classID )def get_sample_class_gmm( gmmhandle: HHandle, num_sample: int ) -> Tuple[Sequence[float], int]

Herror T_get_sample_class_gmm( const Htuple GMMHandle, const Htuple NumSample, Htuple* Features, Htuple* ClassID )

HTuple HClassGmm::GetSampleClassGmm( Hlong NumSample, Hlong* ClassID ) const

HTuple HClassGmm.GetSampleClassGmm( int numSample, out int classID )

Description

get_sample_class_gmmGetSampleClassGmm reads out a training sample from the Gaussian Mixture Model (GMM) given by GMMHandleGMMHandlegmmhandle that was stored with add_sample_class_gmmAddSampleClassGmm or add_samples_image_class_gmmAddSamplesImageClassGmm. The index of the sample is specified with NumSamplenumSamplenum_sample. The index is counted from 0, i.e., NumSamplenumSamplenum_sample must be a number between 0 and NumSamplesnumSamplesnum_samples \(-\) 1, where NumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_gmmGetSampleNumClassGmm. The training sample is returned in Featuresfeaturesfeatures and ClassIDclassIDclass_id. Featuresfeaturesfeatures is a feature vector of length NumDimnumDimnum_dim, while ClassIDclassIDclass_id is its class (see add_sample_class_gmmAddSampleClassGmm and create_class_gmmCreateClassGmm).

get_sample_class_gmmGetSampleClassGmm can, for example, be used to reclassify the training data with classify_class_gmmClassifyClassGmm in order to determine which training samples, if any, are classified incorrectly.

Execution information

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

GMMHandleGMMHandlegmmhandle (input_control) class_gmm → (handle)HTuple (HHandle)HClassGmm, HTuple (IntPtr)HHandleHtuple (handle)

GMM handle.

NumSamplenumSamplenum_sample (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Index of the stored training sample.

Featuresfeaturesfeatures (output_control) real-array → (real)HTuple (double)HTuple (double)Sequence[float]Htuple (double)

Feature vector of the training sample.

ClassIDclassIDclass_id (output_control) number → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Class of the training sample.

Example

(HDevelop)

create_class_gmm (2, 2, [1,10], 'spherical', 'none', 2, 42, GMMHandle)
read_samples_class_gmm (GMMHandle, 'samples.gsf')
train_class_gmm (GMMHandle, 100, 1e-4, 'training', 1e-4, Centers, Iter)
* Reclassify the training samples
get_sample_num_class_gmm (GMMHandle, NumSamples)
for I := 0 to NumSamples-1 by 1
  get_sample_class_gmm (GMMHandle, I, Features, Class)
  classify_class_gmm (GMMHandle, Features, 2, ClassID, ClassProb,\
                      Density, KSigmaProb)
  if (not (Class == ClassProb[0]))
    * classified incorrectly
  endif
endfor

Result

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

Combinations with other operators

Combinations

Possible predecessors

add_sample_class_gmmAddSampleClassGmm, add_samples_image_class_gmmAddSamplesImageClassGmm, read_samples_class_gmmReadSamplesClassGmm, get_sample_num_class_gmmGetSampleNumClassGmm

Possible successors

classify_class_gmmClassifyClassGmm, evaluate_class_gmmEvaluateClassGmm

See also

create_class_gmmCreateClassGmm

Module

Foundation