write_samples_class_gmmT_write_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm (算子)

名称

write_samples_class_gmmT_write_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm — 将高斯混合模型的训练数据写入文件。

签名

write_samples_class_gmm( : : GMMHandle, 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

void HClassGmm::WriteSamplesClassGmm(const wchar_t* FileName) const   ( Windows only)

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

void HClassGmm.WriteSamplesClassGmm(string fileName)

def write_samples_class_gmm(gmmhandle: HHandle, file_name: str) -> None

描述

write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm writes the training samples stored in the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle to the file given by FileNameFileNameFileNameFileNamefileNamefile_name. write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm 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_gmmTrainClassGmmTrainClassGmmTrainClassGmmtrain_class_gmm).

The file FileNameFileNameFileNameFileNamefileNamefile_name is overwritten by write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm. Nevertheless, extending the database of training samples is easy because read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm and add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm 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_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp 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.

执行信息

参数

GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (输入控制)  class_gmm HClassGmm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

GMM 句柄。

FileNameFileNameFileNameFileNamefileNamefile_name (输入控制)  filename.write HTuplestrHTupleHtuple (string) (string) (HString) (char*)

File name.

结果

如果参数有效,算子 write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmmwrite_samples_class_gmm 返回值 2 ( H_MSG_TRUE )。如有必要,则抛出异常。

可能的前趋

add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm

可能的后继

clear_samples_class_gmmclear_samples_class_gmmClearSamplesClassGmmClearSamplesClassGmmClearSamplesClassGmmclear_samples_class_gmm

另见

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm, read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm, read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlpread_samples_class_mlp, write_samples_class_mlpwrite_samples_class_mlpWriteSamplesClassMlpWriteSamplesClassMlpWriteSamplesClassMlpwrite_samples_class_mlp

模块

基础