ThunderSVM
ThunderSVM: An Open-Source SVM Library on GPUs and CPUs
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OneClassSVC Class Reference

Support Vector Machine for outlier detection (density estimation) More...

#include <oneclass_svc.h>

Inheritance diagram for OneClassSVC:
SvmModel

Public Member Functions

void train (const DataSet &dataset, SvmParam param) override
 
vector< float_type > predict (const DataSet::node2d &instances, int batch_size) override
 
- Public Member Functions inherited from SvmModel
void predict_dec_values (const DataSet::node2d &instances, SyncArray< float_type > &dec_values, int batch_size) const
 
virtual vector< float_type > cross_validation (DataSet dataset, SvmParam param, int n_fold)
 
virtual void save_to_file (string path)
 
virtual void load_from_file (string path)
 

Additional Inherited Members

- Protected Member Functions inherited from SvmModel
virtual void model_setup (const DataSet &dataset, SvmParam &param)
 
- Protected Attributes inherited from SvmModel
SvmParam param
 
SyncArray< float_type > coef
 
DataSet::node2d sv
 
SyncArray< int > n_sv
 the number of support vectors for each class
 
int n_total_sv
 the number of support vectors for all classes
 
SyncArray< float_type > rho
 the bias term for each binary model
 
int n_classes = 2
 the number of classes
 
size_t n_binary_models
 the number of binary models, equal to \(k(k-1)/2\), where \(k\) is the number of classes
 
vector< float_type > probA
 be used to predict probability for each binary model
 
vector< float_type > probB
 be used to predict probability for each binary model
 
vector< int > label
 only for SVC, maps logical label (0,1,2,...) to real label in dataset (maybe 2,4,5,...)
 

Detailed Description

Support Vector Machine for outlier detection (density estimation)

Member Function Documentation

◆ predict()

vector< float_type > OneClassSVC::predict ( const DataSet::node2d &  instances,
int  batch_size 
)
overridevirtual

predict label given instances.

Parameters
instancesinstances used
batch_sizethe number of instances to predict parallel, higher value needs more memory
Returns
label (SVC, NuSVC), real number (SVR, NuSVR), {-1,+1} (OneClassSVC)

Reimplemented from SvmModel.

◆ train()

void OneClassSVC::train ( const DataSet dataset,
SvmParam  param 
)
overridevirtual

train model given dataset and param.

Parameters
datasettraining dataset
paramparam for training

Implements SvmModel.


The documentation for this class was generated from the following files: