Detailed Description

Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit.

VW is a fast online learning algorithm which operates on sparse features. It uses an online gradient descent technique.

For more details, refer to the tutorial at https://github.com/JohnLangford/vowpal_wabbit/wiki/v5.1_tutorial.pdf

Definition at line 38 of file VowpalWabbit.h.

Inheritance diagram for CVowpalWabbit:

Public Member Functions
	CVowpalWabbit ()

	CVowpalWabbit (CStreamingVwFeatures *feat)

	~CVowpalWabbit ()

void	reinitialize_weights ()

void	set_no_training (bool dont_train)

void	set_adaptive (bool adaptive_learning)

void	set_exact_adaptive_norm (bool exact_adaptive)

void	set_num_passes (int32_t passes)

void	load_regressor (char *file_name)

void	set_regressor_out (char *file_name, bool is_text=true)

void	set_prediction_out (char *file_name)

void	add_quadratic_pair (char *pair)

virtual bool	train_machine (CFeatures *feat=NULL)

virtual float32_t	predict_and_finalize (VwExample *ex)

float32_t	compute_exact_norm (VwExample *&ex, float32_t &sum_abs_x)

float32_t	compute_exact_norm_quad (float32_t *weights, VwFeature &page_feature, v_array< VwFeature > &offer_features, vw_size_t mask, float32_t g, float32_t &sum_abs_x)

virtual CVwEnvironment *	get_env ()

virtual const char *	get_name () const

Public Member Functions inherited from COnlineLinearMachine
	COnlineLinearMachine ()

virtual	~COnlineLinearMachine ()

virtual void	get_w (float32_t *&dst_w, int32_t &dst_dims)

virtual void	get_w (float64_t *&dst_w, int32_t &dst_dims)

virtual SGVector< float32_t >	get_w ()

virtual void	set_w (float32_t *src_w, int32_t src_w_dim)

virtual void	set_w (float64_t *src_w, int32_t src_w_dim)

virtual void	set_bias (float32_t b)

virtual float32_t	get_bias ()

virtual bool	load (FILE *srcfile)

virtual bool	save (FILE *dstfile)

virtual void	set_features (CStreamingDotFeatures *feat)

virtual CLabels *	apply ()

virtual CLabels *	apply (CFeatures *data)

virtual float64_t	apply (int32_t vec_idx)
	get output for example "vec_idx" More...

virtual float32_t	apply (float32_t *vec, int32_t len)

virtual float32_t	apply_to_current_example ()

virtual CStreamingDotFeatures *	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 ()

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)

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

Protected Attributes
CStreamingVwFeatures *	features
	Features. More...

CVwEnvironment *	env
	Environment for VW, i.e., globals. More...

CVwLearner *	learner
	Learner to use. More...

CVwRegressor *	reg
	Regressor. More...

Protected Attributes inherited from COnlineLinearMachine
int32_t	w_dim

float32_t *	w

float32_t	bias

CStreamingDotFeatures *	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

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)

Constructor & Destructor Documentation

CVowpalWabbit ( )

Default constructor

Definition at line 20 of file VowpalWabbit.cpp.

CVowpalWabbit ( CStreamingVwFeatures * feat )

Constructor, taking a features object as argument

Parameters

feat	StreamingVwFeatures object

Definition at line 28 of file VowpalWabbit.cpp.

~CVowpalWabbit ( )

Destructor

Definition at line 36 of file VowpalWabbit.cpp.

Member Function Documentation

void add_quadratic_pair ( char * pair )

Add a pair of namespaces whose features should be crossed for quadratic updates

Parameters

pair	a string with the two namespace names concatenated

Definition at line 101 of file VowpalWabbit.cpp.

float32_t compute_exact_norm	(	VwExample *&	ex,
		float32_t &	sum_abs_x
	)

Computes the exact norm during adaptive learning

Parameters

ex	example
sum_abs_x	set by reference, sum of abs of features

Returns: norm

Definition at line 382 of file VowpalWabbit.cpp.

float32_t compute_exact_norm_quad	(	float32_t *	weights,
		VwFeature &	page_feature,
		v_array< VwFeature > &	offer_features,
		vw_size_t	mask,
		float32_t	g,
		float32_t &	sum_abs_x
	)

Computes the exact norm for quadratic features during adaptive learning

Parameters

weights	weights
page_feature	current feature
offer_features	paired features
mask	mask
g	square of gradient
sum_abs_x	sum of absolute value of features

Returns: norm

Definition at line 419 of file VowpalWabbit.cpp.

virtual CVwEnvironment* get_env ( )

virtual

Get the environment

Returns: environment as CVwEnvironment*

Definition at line 176 of file VowpalWabbit.h.

virtual const char* get_name ( ) const

virtual

Return the name of the object

Returns: VowpalWabbit

Reimplemented from COnlineLinearMachine.

Definition at line 187 of file VowpalWabbit.h.

void load_regressor ( char * file_name )

Load regressor from a dump file

Parameters

file_name name of regressor file

Definition at line 80 of file VowpalWabbit.cpp.

float32_t predict_and_finalize ( VwExample * ex )

virtual

Predict for an example

Parameters

ex	VwExample to predict for

Returns: prediction

Definition at line 178 of file VowpalWabbit.cpp.

void reinitialize_weights ( )

Reinitialize the weight vectors. Call after updating env variables eg. stride.

Definition at line 43 of file VowpalWabbit.cpp.

void set_adaptive ( bool adaptive_learning )

Set whether learning is adaptive or not

Parameters

adaptive_learning true if adaptive

Definition at line 56 of file VowpalWabbit.cpp.

void set_exact_adaptive_norm ( bool exact_adaptive )

Set whether to use the more expensive exact norm for adaptive learning

Parameters

exact_adaptive true if exact norm is required

Definition at line 69 of file VowpalWabbit.cpp.

void set_no_training ( bool dont_train )

Set whether one desires to not train and only make passes over all examples instead.

This is useful if one wants to create a cache file from data.

Parameters

dont_train true if one doesn't want to train

Definition at line 73 of file VowpalWabbit.h.

void set_num_passes ( int32_t passes )

Set number of passes (only works for cached input)

Parameters

passes number of passes

Definition at line 95 of file VowpalWabbit.h.

void set_prediction_out ( char * file_name )

Set file name of prediction output

Parameters

file_name name of file to save predictions to

Definition at line 93 of file VowpalWabbit.cpp.

void set_regressor_out	(	char *	file_name,
		bool	is_text = `true`
	)

Set regressor output parameters

Parameters

file_name	name of file to save regressor to
is_text	human readable or not, bool

Definition at line 87 of file VowpalWabbit.cpp.

bool train_machine ( CFeatures * feat = NULL )

virtual

Train on a StreamingVwFeatures object

Parameters

feat	StreamingVwFeatures to train using

Reimplemented from CMachine.

Definition at line 106 of file VowpalWabbit.cpp.

Member Data Documentation

CVwEnvironment* env

protected

Environment for VW, i.e., globals.

Definition at line 266 of file VowpalWabbit.h.

CStreamingVwFeatures* features

protected

Features.

Definition at line 263 of file VowpalWabbit.h.

CVwLearner* learner

protected

Learner to use.

Definition at line 269 of file VowpalWabbit.h.

CVwRegressor* reg

protected

Regressor.

Definition at line 272 of file VowpalWabbit.h.

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

Detailed Description

Public Member Functions

Protected Attributes

Additional Inherited Members

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation