CPerceptron Class Reference

Detailed Description

Class Perceptron implements the standard linear (online) perceptron.

Given a maximum number of iterations (the standard perceptron algorithm is not guaranteed to converge) and a fixed lerning rate, the result is a linear classifier.

See Also

CLinearMachine

http://en.wikipedia.org/wiki/Perceptron

Definition at line 30 of file Perceptron.h.

Inheritance diagram for CPerceptron:

Public Member Functions
	CPerceptron ()
	CPerceptron (CDotFeatures *traindat, CLabels *trainlab)
virtual	~CPerceptron ()
virtual EClassifierType	get_classifier_type ()
void	set_learn_rate (float64_t r)
	set learn rate of gradient descent training algorithm
void	set_max_iter (int32_t i)
	set maximum number of iterations
virtual const char *	get_name () const
Public Member Functions inherited from CLinearMachine
	CLinearMachine ()
virtual	~CLinearMachine ()
void	get_w (float64_t *&dst_w, int32_t &dst_dims)
SGVector< float64_t >	get_w ()
void	set_w (SGVector< float64_t > src_w)
void	set_bias (float64_t b)
float64_t	get_bias ()
virtual bool	load (FILE *srcfile)
virtual bool	save (FILE *dstfile)
virtual void	set_features (CDotFeatures *feat)
virtual CLabels *	apply ()
virtual CLabels *	apply (CFeatures *data)
virtual float64_t	apply (int32_t vec_idx)
	get output for example "vec_idx"
virtual CDotFeatures *	get_features ()
Public Member Functions inherited from CMachine
	CMachine ()
virtual	~CMachine ()
virtual bool	train (CFeatures *data=NULL)
virtual void	set_labels (CLabels *lab)
virtual CLabels *	get_labels ()
virtual float64_t	get_label (int32_t i)
void	set_max_train_time (float64_t t)
float64_t	get_max_train_time ()
void	set_solver_type (ESolverType st)
ESolverType	get_solver_type ()
virtual void	set_store_model_features (bool store_model)
Public Member Functions inherited from CSGObject
	CSGObject ()
	CSGObject (const CSGObject &orig)
virtual	~CSGObject ()
virtual bool	is_generic (EPrimitiveType *generic) const
template<class T >
void	set_generic ()
void	unset_generic ()
virtual void	print_serializable (const char *prefix="")
virtual bool	save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool	load_serializable (CSerializableFile *file, const char *prefix="")
void	set_global_io (SGIO *io)
SGIO *	get_global_io ()
void	set_global_parallel (Parallel *parallel)
Parallel *	get_global_parallel ()
void	set_global_version (Version *version)
Version *	get_global_version ()
SGVector< char * >	get_modelsel_names ()
char *	get_modsel_param_descr (const char *param_name)
index_t	get_modsel_param_index (const char *param_name)

Protected Member Functions
virtual bool	train_machine (CFeatures *data=NULL)
Protected Member Functions inherited from CLinearMachine
virtual void	store_model_features ()

Protected Attributes
float64_t	learn_rate
int32_t	max_iter
Protected Attributes inherited from CLinearMachine
int32_t	w_dim
float64_t *	w
float64_t	bias
CDotFeatures *	features
Protected Attributes inherited from CMachine
float64_t	max_train_time
CLabels *	labels
ESolverType	solver_type
bool	m_store_model_features

Additional Inherited Members
Public Attributes inherited from CSGObject
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters

Constructor & Destructor Documentation

CPerceptron

(

)

default constructor

Definition at line 17 of file Perceptron.cpp.

CPerceptron	(	CDotFeatures *	traindat,
		CLabels *	trainlab
	)

constructor

Parameters

traindat	training features
trainlab	labels for training features

Definition at line 22 of file Perceptron.cpp.

~CPerceptron ( )

virtual

Definition at line 29 of file Perceptron.cpp.

Member Function Documentation

virtual EClassifierType get_classifier_type ( )

virtual

get classifier type

Returns

classifier type PERCEPTRON

Reimplemented from CMachine.

Definition at line 48 of file Perceptron.h.

virtual const char* get_name ( ) const

virtual

Returns

object name

Reimplemented from CLinearMachine.

Definition at line 63 of file Perceptron.h.

void set_learn_rate

(

float64_t

r

)

set learn rate of gradient descent training algorithm

Definition at line 51 of file Perceptron.h.

void set_max_iter

(

int32_t

i

)

set maximum number of iterations

Definition at line 57 of file Perceptron.h.

bool train_machine ( CFeatures *  data = NULL )

protectedvirtual

train classifier

Parameters

data	training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)

Returns

whether training was successful

Reimplemented from CMachine.

Definition at line 33 of file Perceptron.cpp.

Member Data Documentation

float64_t learn_rate

protected

learning rate

Definition at line 78 of file Perceptron.h.

int32_t max_iter

protected

maximum number of iterations

Definition at line 80 of file Perceptron.h.

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

• Perceptron.h
• Perceptron.cpp