read_samples_class_svmT_read_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm (Operator)

Name

read_samples_class_svmT_read_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm — Read the training data of a support vector machine from a file.

Signature

read_samples_class_svm( : : SVMHandle, FileName : )

Description

read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvm reads training samples from the file given by FileNameFileNameFileNameFileNamefileName and adds them to the training samples that have already been added to the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle. The SVM must be created with create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm before calling read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvm. As described with train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvm and write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvmWriteSamplesClassSvm, the operators read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmReadSamplesClassSvm, add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvm, and write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvmWriteSamplesClassSvm can be used to build up a extensive set of training samples, and hence to improve the performance of the SVM by retraining the SVM with extended data sets.

It should be noted that the training samples must have the correct dimensionality. The feature vectors and target vectors stored in FileNameFileNameFileNameFileNamefileName must have the lengths NumFeatures and NumClasses that were specified with create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm. The target is stored in vector form for compatibility reason with the MLP (see read_samples_class_mlpread_samples_class_mlpReadSamplesClassMlpReadSamplesClassMlpReadSamplesClassMlp). If the dimensions are incorrect an error message is returned.

Execution Information

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.

This operator modifies the state of the following input parameter:

SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle

The value of this parameter may not be shared across multiple threads without external synchronization.

Parameters

SVMHandleSVMHandleSVMHandleSVMHandleSVMHandle (input_control, state is modified) class_svm → (handle)

SVM handle.

FileNameFileNameFileNameFileNamefileName (input_control) filename.read → (string)

File name.

Result

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

Possible Predecessors

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm

Possible Successors

train_class_svmtrain_class_svmTrainClassSvmTrainClassSvmTrainClassSvm

Alternatives

add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmAddSampleClassSvm

See also

write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvmWriteSamplesClassSvm, clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvmClearSamplesClassSvm

Module

Foundation