ThunderSVM
ThunderSVM: An Open-Source SVM Library on GPUs and CPUs
Public Member Functions | Protected Member Functions | List of all members
SVR Class Reference

Support Vector Machine for regression. More...

#include <svr.h>

Inheritance diagram for SVR:
SvmModel NuSVR

Public Member Functions

void train (const DataSet &dataset, SvmParam param) override
 
- Public Member Functions inherited from SvmModel
virtual vector< float_type > predict (const DataSet::node2d &instances, int batch_size)
 
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)
 

Protected Member Functions

void save_svr_coef (const SyncArray< float_type > &alpha_2, const DataSet::node2d &instances)
 
- Protected Member Functions inherited from SvmModel
virtual void model_setup (const DataSet &dataset, SvmParam &param)
 

Additional Inherited Members

- 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 regression.

Member Function Documentation

◆ save_svr_coef()

void SVR::save_svr_coef ( const SyncArray< float_type > &  alpha_2,
const DataSet::node2d &  instances 
)
protected

save \(\boldsymbel{\alpha}\) into coef.

Parameters
alpha_2
instances

◆ train()

void SVR::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: