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

Inheritance diagram for OneClassSVC:

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

vector< float_type > OneClassSVC::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.

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