CGaussianNaiveBayes Class Reference

Detailed Description

Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier.

This classifier assumes that a posteriori conditional probabilities are gaussian pdfs. For each vector gaussian naive bayes chooses the class C with maximal

Definition at line 35 of file GaussianNaiveBayes.h.

Inheritance diagram for CGaussianNaiveBayes:

Public Member Functions
	CGaussianNaiveBayes ()
	CGaussianNaiveBayes (CFeatures *train_examples, CLabels *train_labels)
virtual	~CGaussianNaiveBayes ()
virtual void	set_features (CDotFeatures *features)
virtual CDotFeatures *	get_features ()
virtual bool	train (CFeatures *data=NULL)
virtual CLabels *	apply ()
virtual CLabels *	apply (CFeatures *data)
virtual float64_t	apply (int32_t idx)
virtual const char *	get_name () const
virtual EClassifierType	get_classifier_type ()
Public Member Functions inherited from CMachine
	CMachine ()
virtual	~CMachine ()
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 ()
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
float64_t	normal_exp (float64_t x, int32_t l_idx, int32_t f_idx)
Protected Member Functions inherited from CMachine
virtual bool	train_machine (CFeatures *data=NULL)
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
CDotFeatures *	m_features
	features for training or classifying
int32_t	m_min_label
	minimal label
int32_t	m_num_classes
	number of different classes (labels)
int32_t	m_dim
	dimensionality of feature space
SGVector< float64_t >	m_means
	means for normal distributions of features
SGVector< float64_t >	m_variances
	variances for normal distributions of features
SGVector< float64_t >	m_label_prob
	a priori probabilities of labels
SGVector< float64_t >	m_rates
	label rates
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

CGaussianNaiveBayes

(

)

default constructor

Definition at line 20 of file GaussianNaiveBayes.cpp.

CGaussianNaiveBayes	(	CFeatures *	train_examples,
		CLabels *	train_labels
	)

constructor

Parameters

train_examples	train examples
train_labels	labels corresponding to train_examples

Definition at line 28 of file GaussianNaiveBayes.cpp.

~CGaussianNaiveBayes ( )

virtual

destructor

Definition at line 40 of file GaussianNaiveBayes.cpp.

Member Function Documentation

CLabels * apply ( )

virtual

classify all examples

Returns

labels

Implements CMachine.

Definition at line 161 of file GaussianNaiveBayes.cpp.

CLabels * apply ( CFeatures *  data )

virtual

classify specified examples

Parameters

data	examples to be classified

Returns

labels corresponding to data

Implements CMachine.

Definition at line 176 of file GaussianNaiveBayes.cpp.

float64_t apply ( int32_t  idx )

virtual

classifiy specified example

Parameters

idx	example index

Returns

label

Reimplemented from CMachine.

Definition at line 191 of file GaussianNaiveBayes.cpp.

virtual EClassifierType get_classifier_type ( )

virtual

get classifier type

Returns

classifier type

Reimplemented from CMachine.

Definition at line 105 of file GaussianNaiveBayes.h.

virtual CDotFeatures* get_features ( )

virtual

get features for classify

Returns

current features

Definition at line 68 of file GaussianNaiveBayes.h.

virtual const char* get_name ( ) const

virtual

get name

Returns

classifier name

Implements CSGObject.

Definition at line 100 of file GaussianNaiveBayes.h.

float64_t normal_exp ( float64_t  x, int32_t  l_idx, int32_t  f_idx  )

protected

computes gaussian exponent by x, indexes, m_means and m_variances

Parameters

x	feature value
l_idx	index of label
f_idx	index of feature

Returns

exponent value

Definition at line 136 of file GaussianNaiveBayes.h.

virtual void set_features ( CDotFeatures *  features )

virtual

set features for classify

Parameters

features

features to be set

Definition at line 58 of file GaussianNaiveBayes.h.

bool train ( CFeatures *  data = NULL )

virtual

train classifier

Parameters

data	train examples

Returns

true if successful

Reimplemented from CMachine.

Definition at line 50 of file GaussianNaiveBayes.cpp.

Member Data Documentation

int32_t m_dim

protected

dimensionality of feature space

Definition at line 119 of file GaussianNaiveBayes.h.

CDotFeatures* m_features

protected

features for training or classifying

Definition at line 105 of file GaussianNaiveBayes.h.

SGVector<float64_t> m_label_prob

protected

a priori probabilities of labels

Definition at line 128 of file GaussianNaiveBayes.h.

SGVector<float64_t> m_means

protected

means for normal distributions of features

Definition at line 122 of file GaussianNaiveBayes.h.

int32_t m_min_label

protected

minimal label

Definition at line 113 of file GaussianNaiveBayes.h.

int32_t m_num_classes

protected

number of different classes (labels)

Definition at line 116 of file GaussianNaiveBayes.h.

SGVector<float64_t> m_rates

protected

label rates

Definition at line 142 of file GaussianNaiveBayes.h.

SGVector<float64_t> m_variances

protected

variances for normal distributions of features

Definition at line 125 of file GaussianNaiveBayes.h.

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

• GaussianNaiveBayes.h
• GaussianNaiveBayes.cpp