ClassesClasses | | Operators

write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmm (Operator)

Name

write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmm — Write the training data of a Gaussian Mixture Model to a file.

Signature

write_samples_class_gmm( : : GMMHandle, FileName : )

Herror write_samples_class_gmm(const Hlong GMMHandle, const char* FileName)

Herror T_write_samples_class_gmm(const Htuple GMMHandle, const Htuple FileName)

void WriteSamplesClassGmm(const HTuple& GMMHandle, const HTuple& FileName)

void HClassGmm::WriteSamplesClassGmm(const HString& FileName) const

void HClassGmm::WriteSamplesClassGmm(const char* FileName) const

static void HOperatorSet.WriteSamplesClassGmm(HTuple GMMHandle, HTuple fileName)

void HClassGmm.WriteSamplesClassGmm(string fileName)

Description

write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmm writes the training samples stored in the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandleGMMHandle to the file given by FileNameFileNameFileNameFileNamefileName. write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmm can be used to build up a database of training samples, and hence to improve the performance of the GMM by training it with an extended data set (see train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmm).

The file FileNameFileNameFileNameFileNamefileName is overwritten by write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmm. Nevertheless, extending the database of training samples is easy because read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmm and add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmm add the training samples to the training samples that are already stored in memory with the GMM.

The created file can be read with read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp if the classifier of a multilayer perceptron (MLP) should be used. The class of a training sample in the GMM corresponds to a component of the target vector in the MLP being 1.0.

Execution Information

Parameters

GMMHandleGMMHandleGMMHandleGMMHandleGMMHandle (input_control)  class_gmm HClassGmm, HTupleHTupleHtuple (integer) (IntPtr) (Hlong) (Hlong)

GMM handle.

FileNameFileNameFileNameFileNamefileName (input_control)  filename.write HTupleHTupleHtuple (string) (string) (HString) (char*)

File name.

Result

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

Possible Predecessors

add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmm

Possible Successors

clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmm

See also

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmm, read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmm, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp, write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlp

Module

Foundation


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