Support Vector Machine for regression. More...

#include <svr.h>

Inheritance diagram for SVR:

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

protected

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

Parameters

alpha_2
instances

overridevirtual

train model given dataset and param.

Parameters

dataset	training dataset
param	param for training

Implements SvmModel.

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