CPluginEstimate Class Reference

Detailed Description

class PluginEstimate

The class PluginEstimate takes as input two probabilistic models (of type CLinearHMM, even though general models are possible ) and classifies examples according to the rule

See Also

CLinearHMM

CDistribution

Definition at line 34 of file PluginEstimate.h.

Inheritance diagram for CPluginEstimate:

Public Member Functions
	CPluginEstimate (float64_t pos_pseudo=1e-10, float64_t neg_pseudo=1e-10)
virtual	~CPluginEstimate ()
CLabels *	apply ()
virtual CLabels *	apply (CFeatures *data)
virtual void	set_features (CStringFeatures< uint16_t > *feat)
virtual CStringFeatures < uint16_t > *	get_features ()
float64_t	apply (int32_t vec_idx)
	classify the test feature vector indexed by vec_idx
float64_t	posterior_log_odds_obsolete (uint16_t *vector, int32_t len)
float64_t	get_parameterwise_log_odds (uint16_t obs, int32_t position)
float64_t	log_derivative_pos_obsolete (uint16_t obs, int32_t pos)
float64_t	log_derivative_neg_obsolete (uint16_t obs, int32_t pos)
bool	get_model_params (float64_t *&pos_params, float64_t *&neg_params, int32_t &seq_length, int32_t &num_symbols)
void	set_model_params (float64_t *pos_params, float64_t *neg_params, int32_t seq_length, int32_t num_symbols)
int32_t	get_num_params ()
bool	check_models ()
virtual const char *	get_name () const
Public Member Functions inherited from CMachine
	CMachine ()
virtual	~CMachine ()
virtual bool	train (CFeatures *data=NULL)
virtual bool	load (FILE *srcfile)
virtual bool	save (FILE *dstfile)
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 ()
virtual EClassifierType	get_classifier_type ()
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 CMachine
virtual void	store_model_features ()
Protected Member Functions inherited from CSGObject
virtual void	load_serializable_pre () throw (ShogunException)
virtual void	load_serializable_post () throw (ShogunException)
virtual void	save_serializable_pre () throw (ShogunException)
virtual void	save_serializable_post () throw (ShogunException)

Protected Attributes
float64_t	m_pos_pseudo
float64_t	m_neg_pseudo
CLinearHMM *	pos_model
CLinearHMM *	neg_model
CStringFeatures< uint16_t > *	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

CPluginEstimate	(	float64_t	pos_pseudo = 1e-10,
		float64_t	neg_pseudo = 1e-10
	)

default constructor

Parameters

pos_pseudo	pseudo for positive model
neg_pseudo	pseudo for negative model

Definition at line 20 of file PluginEstimate.cpp.

~CPluginEstimate ( )

virtual

Definition at line 38 of file PluginEstimate.cpp.

Member Function Documentation

CLabels * apply ( )

virtual

classify objects using the currently set features

Returns

classified labels

Implements CMachine.

Definition at line 96 of file PluginEstimate.cpp.

CLabels * apply ( CFeatures *  data )

virtual

classify objects

Parameters

data	(test)data to be classified

Returns

classified labels

Implements CMachine.

Definition at line 108 of file PluginEstimate.cpp.

float64_t apply ( int32_t  vec_idx )

virtual

classify the test feature vector indexed by vec_idx

Reimplemented from CMachine.

Definition at line 123 of file PluginEstimate.cpp.

bool check_models

(

)

check models

Returns

if one of the two models is invalid

Definition at line 195 of file PluginEstimate.h.

virtual CStringFeatures<uint16_t>* get_features ( )

virtual

get features

Returns

features

Definition at line 72 of file PluginEstimate.h.

bool get_model_params	(	float64_t *&	pos_params,
		float64_t *&	neg_params,
		int32_t &	seq_length,
		int32_t &	num_symbols
	)

get model parameters

Parameters

pos_params	parameters of positive model
neg_params	parameters of negative model
seq_length	sequence length
num_symbols	numbe of symbols

Returns

if operation was successful

Definition at line 131 of file PluginEstimate.h.

virtual const char* get_name ( ) const

virtual

Returns

object name

Implements CSGObject.

Definition at line 201 of file PluginEstimate.h.

int32_t get_num_params

(

)

get number of parameters

Returns

number of parameters

Definition at line 186 of file PluginEstimate.h.

float64_t get_parameterwise_log_odds	(	uint16_t	obs,
		int32_t	position
	)

get log odds parameter-wise

Parameters

obs	observation
position	position

Returns

log odd at position

Definition at line 95 of file PluginEstimate.h.

float64_t log_derivative_neg_obsolete	(	uint16_t	obs,
		int32_t	pos
	)

get obsolete negative log derivative

Parameters

obs	observation
pos	position

Returns

negative log derivative

Definition at line 118 of file PluginEstimate.h.

float64_t log_derivative_pos_obsolete	(	uint16_t	obs,
		int32_t	pos
	)

get obsolete positive log derivative

Parameters

obs	observation
pos	position

Returns

positive log derivative

Definition at line 107 of file PluginEstimate.h.

float64_t posterior_log_odds_obsolete	(	uint16_t *	vector,
		int32_t	len
	)

obsolete posterior log odds

Parameters

vector	vector
len	len

Returns

something floaty

Definition at line 83 of file PluginEstimate.h.

virtual void set_features ( CStringFeatures< uint16_t > *  feat )

virtual

set features

Parameters

feat	features to set

Definition at line 61 of file PluginEstimate.h.

void set_model_params	(	float64_t *	pos_params,
		float64_t *	neg_params,
		int32_t	seq_length,
		int32_t	num_symbols
	)

set model parameters

Parameters

pos_params	parameters of positive model
neg_params	parameters of negative model
seq_length	sequence length
num_symbols	numbe of symbols

Definition at line 159 of file PluginEstimate.h.

bool train_machine ( CFeatures *  data = NULL )

protectedvirtual

train plugin estimate 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 46 of file PluginEstimate.cpp.

Member Data Documentation

CStringFeatures<uint16_t>* features

protected

features

Definition at line 226 of file PluginEstimate.h.

float64_t m_neg_pseudo

protected

pseudo count for negative class

Definition at line 218 of file PluginEstimate.h.

float64_t m_pos_pseudo

protected

pseudo count for positive class

Definition at line 216 of file PluginEstimate.h.

CLinearHMM* neg_model

protected

negative model

Definition at line 223 of file PluginEstimate.h.

CLinearHMM* pos_model

protected

positive model

Definition at line 221 of file PluginEstimate.h.

---

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

• PluginEstimate.h
• PluginEstimate.cpp