get_sample_class_svmT_get_sample_class_svmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm (算子名称)

名称

get_sample_class_svmT_get_sample_class_svmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm — Return a training sample from the training data of a support vector machine.

参数签名

描述

get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm reads out a training sample from the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandlesvmhandle that was added with add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm or read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvmread_samples_class_svm. The index of the sample is specified with IndexSampleIndexSampleIndexSampleIndexSampleindexSampleindex_sample. The index is counted from 0, i.e., IndexSampleIndexSampleIndexSampleIndexSampleindexSampleindex_sample must be a number between 0 and NumSamplesNumSamplesNumSamplesNumSamplesnumSamplesnum_samples - 1, where NumSamplesNumSamplesNumSamplesNumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm. The training sample is returned in 特征特征特征特征特征特征 and TargetTargetTargetTargettargettarget. 特征特征特征特征特征特征 is a feature vector of length NumFeaturesNumFeaturesNumFeaturesNumFeaturesnumFeaturesnum_features (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm), while TargetTargetTargetTargettargettarget is the index of the class, ranging between 0 and NumClassesNumClassesNumClassesNumClassesnumClassesnum_classes-1 (see add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm).

get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm can, for example, be used to reclassify the training data with classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm 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)

* Train an SVM
create_class_svm (NumFeatures, 'rbf', 0.01, 0.01, NumClasses,\
                  'one-versus-all', 'normalization', NumFeatures,\
                  SVMHandle)
read_samples_class_svm (SVMHandle, 'samples.mtf')
train_class_svm (SVMHandle, 0.001, 'default')
* Reclassify the training samples
get_sample_num_class_svm (SVMHandle, NumSamples)
for I := 0 to NumSamples-1 by 1
    get_sample_class_svm (SVMHandle, I, Data, Target)
    classify_class_svm (SVMHandle, Data, 1, Class)
    if (Class != Target)
        * Sample has been classified incorrectly
    endif
endfor

结果

If the parameters are valid 该算子 get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm 返回值 2 ( H_MSG_TRUE) . If necessary, an exception is raised.

可能的前置算子

add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm, read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvmread_samples_class_svm, get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm, get_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvmGetSupportVectorClassSvmget_support_vector_class_svm

可能的后置算子

classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmClassifyClassSvmclassify_class_svm

参考其它

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm

模块

Foundation

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