get_sample_class_svmT_get_sample_class_svmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm (Operator)

Name

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

Signature

Description

get_sample_class_svmget_sample_class_svmGetSampleClassSvmGetSampleClassSvmget_sample_class_svm reads out a training sample from the support vector machine (SVM) given by SVMHandleSVMHandleSVMHandleSVMHandlesvmhandle that was added with add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm or read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmread_samples_class_svm. The index of the sample is specified with IndexSampleIndexSampleIndexSampleindexSampleindex_sample. The index is counted from 0, i.e., IndexSampleIndexSampleIndexSampleindexSampleindex_sample must be a number between 0 and NumSamplesNumSamplesNumSamplesnumSamplesnum_samples - 1, where NumSamplesNumSamplesNumSamplesnumSamplesnum_samples can be determined with get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm. The training sample is returned in FeaturesFeaturesFeaturesfeaturesfeatures and TargetTargetTargettargettarget. FeaturesFeaturesFeaturesfeaturesfeatures is a feature vector of length NumFeaturesNumFeaturesNumFeaturesnumFeaturesnum_features (see create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmcreate_class_svm), while TargetTargetTargettargettarget is the index of the class, ranging between 0 and NumClassesNumClassesNumClassesnumClassesnum_classes-1 (see add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm).

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

Execution Information

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

Parameters

Example (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

Result

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

Possible Predecessors

add_sample_class_svmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvmadd_sample_class_svm, read_samples_class_svmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvmread_samples_class_svm, get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvmget_sample_num_class_svm, get_support_vector_class_svmget_support_vector_class_svmGetSupportVectorClassSvmGetSupportVectorClassSvmget_support_vector_class_svm

Possible Successors

classify_class_svmclassify_class_svmClassifyClassSvmClassifyClassSvmclassify_class_svm

See also

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmcreate_class_svm

Module

Foundation