get_sample_class_gmmT_get_sample_class_gmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmm (算子名称)

名称

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

参数签名

描述

get_sample_class_gmmget_sample_class_gmmGetSampleClassGmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmm reads out a training sample from the Gaussian Mixture Model (GMM) given by GMMHandleGMMHandleGMMHandleGMMHandleGMMHandlegmmhandle that was stored with add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm or add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm. The index of the sample is specified with NumSampleNumSampleNumSampleNumSamplenumSamplenum_sample. The index is counted from 0, i.e., NumSampleNumSampleNumSampleNumSamplenumSamplenum_sample must be a number between 0 and NumSamplesNumSamplesNumSamplesNumSamplesnumSamplesnum_samples - 1, where NumSamplesNumSamplesNumSamplesNumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_gmmget_sample_num_class_gmmGetSampleNumClassGmmGetSampleNumClassGmmGetSampleNumClassGmmget_sample_num_class_gmm. The training sample is returned in 特征特征特征特征特征特征 and ClassIDClassIDClassIDClassIDclassIDclass_id. 特征特征特征特征特征特征 is a feature vector of length NumDimNumDimNumDimNumDimnumDimnum_dim, while ClassIDClassIDClassIDClassIDclassIDclass_id is its class (see add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm and create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm).

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

运行信息

• 多线程类型:可重入(与非独占操作符并行运行)。
• 多线程作用域:全局(可以从任何线程调用)。
• 未经并行化处理。

参数表

例程 (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

结果

如果参数均有效，算子 get_sample_class_gmmget_sample_class_gmmGetSampleClassGmmGetSampleClassGmmGetSampleClassGmmget_sample_class_gmm 返回值 2 ( H_MSG_TRUE) . If necessary an exception is raised.

可能的前置算子

add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmmadd_sample_class_gmm, add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmAddSamplesImageClassGmmadd_samples_image_class_gmm, read_samples_class_gmmread_samples_class_gmmReadSamplesClassGmmReadSamplesClassGmmReadSamplesClassGmmread_samples_class_gmm, get_sample_num_class_gmmget_sample_num_class_gmmGetSampleNumClassGmmGetSampleNumClassGmmGetSampleNumClassGmmget_sample_num_class_gmm

可能的后置算子

classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmmclassify_class_gmm, evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm

参考其它

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmmcreate_class_gmm

模块

Foundation

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