Support Vector Machine for classification. More...

#include <svc.h>

Inheritance diagram for SVC:

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)

Protected Member Functions
virtual void	train_binary (const DataSet &dataset, int i, int j, SyncArray< float_type > &alpha, float_type &rho)

void	model_setup (const DataSet &dataset, SvmParam &param) override

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 classification.

Member Function Documentation

overrideprotectedvirtual

called at the begining of train(), do initialization

Parameters

dataset
param

Reimplemented from SvmModel.

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

overridevirtual

predict label given instances.

Parameters

instances	instances used
batch_size	the 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.

overridevirtual

train model given dataset and param.

Parameters

dataset	training dataset
param	param for training

Implements SvmModel.

protectedvirtual

train a binary SVC model \(SVM_{i,j}\) for class i and class j.

Parameters

[in]	dataset	original dataset
[in]	i
[in]	j
[out]	alpha	optimization variables \(\boldsymbol{\alpha}\) in dual problem, should be initialized with the same size of the number of instances in this binary problem
[out]	rho	bias term \(b\) in dual problem

Reimplemented in NuSVC.

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