add_sample_class_svmT_add_sample_class_svmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm (算子名称)

名称

add_sample_class_svmT_add_sample_class_svmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm — Add a training sample to the training data of a support vector machine.

参数签名

描述

add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm adds a training sample to the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandlesvmhandle. The training sample is given by 特征特征特征特征特征特征 and ClassClassClassClassclassValclass. 特征特征特征特征特征特征 is the feature vector of the sample, and consequently must be a real vector of length NumFeaturesNumFeaturesNumFeaturesNumFeaturesnumFeaturesnum_features, as specified in create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm. ClassClassClassClassclassValclass is the target of the sample, which must be in the range of 0 to NumClassesNumClassesNumClassesNumClassesnumClassesnum_classes-1 (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm). In the special case of 'novelty-detection'"novelty-detection""novelty-detection""novelty-detection""novelty-detection""novelty-detection" the class is to be set to 0 as only one class is assumed. Before the SVM can be trained with train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm, training samples must be added to the SVM with add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm. The usage of support vectors of an already trained SVM as training samples is described in train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm.

The number of currently stored training samples can be queried with get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm. Stored training samples can be read out again with get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm.

Normally, it is useful to save the training samples in a file with write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvmWriteSamplesClassSvmwrite_samples_class_svm to facilitate reusing the samples and to facilitate that, if necessary, new training samples can be added to the data set, and hence to facilitate that a newly created SVM can be trained with the extended data set.

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。

This operator modifies the state of the following input parameter:

• SVMHandleSVMHandleSVMHandleSVMHandleSVMHandlesvmhandle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

参数表

结果

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

可能的前置算子

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm

可能的后置算子

train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvmtrain_class_svm, write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvmWriteSamplesClassSvmwrite_samples_class_svm, get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm, get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm

可替代算子

read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvmread_samples_class_svm

参考其它

clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvmClearSamplesClassSvmclear_samples_class_svm, get_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvmGetSupportVectorClassSvmget_support_vector_class_svm

模块

Foundation

---