Detailed Description

class OnlineSVMSGD

Definition at line 35 of file OnlineSVMSGD.h.

Inheritance diagram for COnlineSVMSGD:

Public Types
typedef rxcpp::subjects::subject< ObservedValue >	SGSubject

typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > >	SGObservable

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > >	SGSubscriber

Public Member Functions
	MACHINE_PROBLEM_TYPE (PT_BINARY)

	COnlineSVMSGD ()

	COnlineSVMSGD (float64_t C)

	COnlineSVMSGD (float64_t C, CStreamingDotFeatures *traindat)

virtual	~COnlineSVMSGD ()

virtual EMachineType	get_classifier_type ()

virtual bool	train (CFeatures *data=NULL)

void	set_C (float64_t c_neg, float64_t c_pos)

float64_t	get_C1 ()

float64_t	get_C2 ()

void	set_epochs (int32_t e)

int32_t	get_epochs ()

void	set_lambda (float64_t l)

float64_t	get_lambda ()

void	set_bias_enabled (bool enable_bias)

bool	get_bias_enabled ()

void	set_regularized_bias_enabled (bool enable_bias)

bool	get_regularized_bias_enabled ()

void	set_loss_function (CLossFunction *loss_func)

CLossFunction *	get_loss_function ()

const char *	get_name () const

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

virtual SGVector< float32_t >	get_w () const

virtual void	set_w (const SGVector< float32_t > w)

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 void	set_features (CStreamingDotFeatures *feat)

virtual CRegressionLabels *	apply_regression (CFeatures *data=NULL)

virtual CBinaryLabels *	apply_binary (CFeatures *data=NULL)

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

virtual float32_t	apply_one (float32_t *vec, int32_t len)

virtual float32_t	apply_to_current_example ()

virtual CStreamingDotFeatures *	get_features ()

virtual void	start_train ()

virtual void	stop_train ()

virtual void	train_example (CStreamingDotFeatures *feature, float64_t label)

virtual CLabels *	apply (CFeatures *data=NULL)

virtual CMulticlassLabels *	apply_multiclass (CFeatures *data=NULL)

virtual CStructuredLabels *	apply_structured (CFeatures *data=NULL)

virtual CLatentLabels *	apply_latent (CFeatures *data=NULL)

virtual void	set_labels (CLabels *lab)

virtual CLabels *	get_labels ()

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)

virtual bool	train_locked (SGVector< index_t > indices)

virtual CLabels *	apply_locked (SGVector< index_t > indices)

virtual CBinaryLabels *	apply_locked_binary (SGVector< index_t > indices)

virtual CRegressionLabels *	apply_locked_regression (SGVector< index_t > indices)

virtual CMulticlassLabels *	apply_locked_multiclass (SGVector< index_t > indices)

virtual CStructuredLabels *	apply_locked_structured (SGVector< index_t > indices)

virtual CLatentLabels *	apply_locked_latent (SGVector< index_t > indices)

virtual void	data_lock (CLabels labs, CFeatures features)

virtual void	post_lock (CLabels labs, CFeatures features)

virtual void	data_unlock ()

virtual bool	supports_locking () const

bool	is_data_locked () const

virtual EProblemType	get_machine_problem_type () const

SG_FORCED_INLINE bool	cancel_computation () const

SG_FORCED_INLINE void	pause_computation ()

SG_FORCED_INLINE void	resume_computation ()

int32_t	ref ()

int32_t	ref_count ()

int32_t	unref ()

virtual CSGObject *	shallow_copy () const

virtual CSGObject *	deep_copy () const

virtual bool	is_generic (EPrimitiveType *generic) const

template<class T >
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 ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
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 ()

SGStringList< char >	get_modelsel_names ()

void	print_modsel_params ()

char *	get_modsel_param_descr (const char *param_name)

index_t	get_modsel_param_index (const char *param_name)

void	build_gradient_parameter_dictionary (CMap< TParameter , CSGObject > *dict)

bool	has (const std::string &name) const

template<typename T >
bool	has (const Tag< T > &tag) const

template<typename T , typename U = void>
bool	has (const std::string &name) const

template<typename T >
void	set (const Tag< T > &_tag, const T &value)

template<typename T , typename U = void>
void	set (const std::string &name, const T &value)

template<typename T >
T	get (const Tag< T > &_tag) const

template<typename T , typename U = void>
T	get (const std::string &name) const

SGObservable *	get_parameters_observable ()

void	subscribe_to_parameters (ParameterObserverInterface *obs)

void	list_observable_parameters ()

virtual void	update_parameter_hash ()

virtual bool	parameter_hash_changed ()

virtual bool	equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObject *	clone ()

Public Attributes
SGIO *	io

Parallel *	parallel

Version *	version

Parameter *	m_parameters

Parameter *	m_model_selection_parameters

Parameter *	m_gradient_parameters

uint32_t	m_hash

Protected Member Functions
void	calibrate (int32_t max_vec_num=1000)

virtual bool	train_machine (CFeatures *data=NULL)

SGVector< float64_t >	apply_get_outputs (CFeatures *data)

virtual bool	train_require_labels () const

virtual void	store_model_features ()

virtual bool	is_label_valid (CLabels *lab) const

rxcpp::subscription	connect_to_signal_handler ()

void	reset_computation_variables ()

virtual void	on_next ()

virtual void	on_pause ()

virtual void	on_complete ()

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)

template<typename T >
void	register_param (Tag< T > &_tag, const T &value)

template<typename T >
void	register_param (const std::string &name, const T &value)

bool	clone_parameters (CSGObject *other)

void	observe (const ObservedValue value)

void	register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description)

Protected Attributes
SGVector< float32_t >	m_w

float32_t	bias

CStreamingDotFeatures *	features

float64_t	m_max_train_time

CLabels *	m_labels

ESolverType	m_solver_type

bool	m_store_model_features

bool	m_data_locked

std::atomic< bool >	m_cancel_computation

std::atomic< bool >	m_pause_computation_flag

std::condition_variable	m_pause_computation

std::mutex	m_mutex

Member Typedef Documentation

◆ SGObservable

typedef rxcpp::observable<ObservedValue, rxcpp::dynamic_observable<ObservedValue> > SGObservable

inherited

Definition at line 130 of file SGObject.h.

◆ SGSubject

typedef rxcpp::subjects::subject<ObservedValue> SGSubject

inherited

Definition at line 127 of file SGObject.h.

◆ SGSubscriber

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer<ObservedValue, void, void, void, void> > SGSubscriber

inherited

Definition at line 133 of file SGObject.h.

Constructor & Destructor Documentation

◆ COnlineSVMSGD() [1/3]

COnlineSVMSGD ( )

default constructor

Definition at line 32 of file OnlineSVMSGD.cpp.

◆ COnlineSVMSGD() [2/3]

COnlineSVMSGD ( float64_t C )

constructor

Parameters

C	constant C

Definition at line 38 of file OnlineSVMSGD.cpp.

◆ COnlineSVMSGD() [3/3]

COnlineSVMSGD	(	float64_t	C,
		CStreamingDotFeatures *	traindat
	)

constructor

Parameters

C	constant C
traindat	training features

Definition at line 47 of file OnlineSVMSGD.cpp.

◆ ~COnlineSVMSGD()

~COnlineSVMSGD ( )

virtual

Definition at line 57 of file OnlineSVMSGD.cpp.

Member Function Documentation

◆ apply()

CLabels * apply ( CFeatures * data = NULL )

virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters

data	(test)data to be classified

Returns: classified labels

Definition at line 159 of file Machine.cpp.

◆ apply_binary()

CBinaryLabels * apply_binary ( CFeatures * data = NULL )

virtualinherited

apply linear machine to data for binary classification problems

Parameters

data	(test)data to be classified

Returns: classified labels

Reimplemented from CMachine.

Definition at line 35 of file OnlineLinearMachine.cpp.

◆ apply_get_outputs()

SGVector< float64_t > apply_get_outputs ( CFeatures * data )

protectedinherited

get real outputs

Parameters

data	features to compute outputs

Returns: outputs

Definition at line 47 of file OnlineLinearMachine.cpp.

◆ apply_latent()

CLatentLabels * apply_latent ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 239 of file Machine.cpp.

◆ apply_locked()

CLabels * apply_locked ( SGVector< index_t > indices )

virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters

indices index vector (of locked features) that is predicted

Definition at line 194 of file Machine.cpp.

◆ apply_locked_binary()

CBinaryLabels * apply_locked_binary ( SGVector< index_t > indices )

virtualinherited

applies a locked machine on a set of indices for binary problems

Reimplemented in CKernelMachine.

Definition at line 245 of file Machine.cpp.

◆ apply_locked_latent()

CLatentLabels * apply_locked_latent ( SGVector< index_t > indices )

virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 273 of file Machine.cpp.

◆ apply_locked_multiclass()

CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t > indices )

virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 259 of file Machine.cpp.

◆ apply_locked_regression()

CRegressionLabels * apply_locked_regression ( SGVector< index_t > indices )

virtualinherited

applies a locked machine on a set of indices for regression problems

Reimplemented in CKernelMachine.

Definition at line 252 of file Machine.cpp.

◆ apply_locked_structured()

CStructuredLabels * apply_locked_structured ( SGVector< index_t > indices )

virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 266 of file Machine.cpp.

◆ apply_multiclass()

CMulticlassLabels * apply_multiclass ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of multiclass classification problem

Reimplemented in CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CKNN, CMulticlassMachine, CC45ClassifierTree, CID3ClassifierTree, CQDA, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CRelaxedTree, and CBaggingMachine.

Definition at line 227 of file Machine.cpp.

◆ apply_one() [1/2]

virtual float64_t apply_one ( int32_t vec_idx )

virtualinherited

get output for example "vec_idx"

Reimplemented from CMachine.

Definition at line 153 of file OnlineLinearMachine.h.

◆ apply_one() [2/2]

float32_t apply_one	(	float32_t *	vec,
		int32_t	len
	)

virtualinherited

apply linear machine to one vector

Parameters

vec	feature vector
len	length of vector

Returns: classified label

Definition at line 77 of file OnlineLinearMachine.cpp.

◆ apply_regression()

CRegressionLabels * apply_regression ( CFeatures * data = NULL )

virtualinherited

apply linear machine to data for regression problems

Parameters

data	(test)data to be classified

Returns: classified labels

Reimplemented from CMachine.

Definition at line 41 of file OnlineLinearMachine.cpp.

◆ apply_structured()

CStructuredLabels * apply_structured ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 233 of file Machine.cpp.

◆ apply_to_current_example()

float32_t apply_to_current_example ( )

virtualinherited

apply linear machine to vector currently being processed

Returns: classified label

Definition at line 83 of file OnlineLinearMachine.cpp.

◆ build_gradient_parameter_dictionary()

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject *> * dict )

inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters

dict	dictionary of parameters to be built.

Definition at line 635 of file SGObject.cpp.

◆ calibrate()

void calibrate ( int32_t max_vec_num = 1000 )

protected

calibrate

Parameters

max_vec_num Maximum number of vectors to calibrate using (optional) if set to -1, tries to calibrate using all vectors

Definition at line 163 of file OnlineSVMSGD.cpp.

◆ cancel_computation()

SG_FORCED_INLINE bool cancel_computation ( ) const

inherited

Returns: whether the algorithm needs to be stopped

Definition at line 319 of file Machine.h.

◆ clone()

CSGObject * clone ( )

virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns: an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicObjectArray, CAlphabet, and CMKL.

Definition at line 734 of file SGObject.cpp.

◆ clone_parameters()

bool clone_parameters ( CSGObject * other )

protectedinherited

Definition at line 759 of file SGObject.cpp.

◆ connect_to_signal_handler()

rxcpp::subscription connect_to_signal_handler ( )

protectedinherited

connect the machine instance to the signal handler

Definition at line 280 of file Machine.cpp.

◆ data_lock()

void data_lock	(	CLabels *	labs,
		CFeatures *	features
	)

virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters

labs	labels used for locking
features	features used for locking

Reimplemented in CKernelMachine.

Definition at line 119 of file Machine.cpp.

◆ data_unlock()

void data_unlock ( )

virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 150 of file Machine.cpp.

◆ deep_copy()

CSGObject * deep_copy ( ) const

virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 232 of file SGObject.cpp.

◆ equals()

bool equals	(	CSGObject *	other,
		float64_t	accuracy = `0.0`,
		bool	tolerant = `false`
	)

virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters

other	object to compare with
accuracy	accuracy to use for comparison (optional)
tolerant	allows linient check on float equality (within accuracy)

Returns: true if all parameters were equal, false if not

Definition at line 656 of file SGObject.cpp.

◆ get() [1/2]

T get ( const Tag< T > & _tag ) const

inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter

Returns: value of the parameter identified by the input tag

Definition at line 381 of file SGObject.h.

◆ get() [2/2]

T get ( const std::string & name ) const

inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter

Returns: value of the parameter corresponding to the input name and type

Definition at line 404 of file SGObject.h.

◆ get_bias()

virtual float32_t get_bias ( )

virtualinherited

get bias

Returns: bias

Definition at line 120 of file OnlineLinearMachine.h.

◆ get_bias_enabled()

bool get_bias_enabled ( )

check if bias is enabled

Returns: if bias is enabled

Definition at line 129 of file OnlineSVMSGD.h.

◆ get_C1()

float64_t get_C1 ( )

get C1

Returns: C1

Definition at line 87 of file OnlineSVMSGD.h.

◆ get_C2()

float64_t get_C2 ( )

get C2

Returns: C2

Definition at line 93 of file OnlineSVMSGD.h.

◆ get_classifier_type()

virtual EMachineType get_classifier_type ( )

virtual

get classifier type

Returns: classifier type OnlineSVMSGD

Reimplemented from CMachine.

Definition at line 63 of file OnlineSVMSGD.h.

◆ get_epochs()

int32_t get_epochs ( )

get epochs

Returns: the number of training epochs

Definition at line 105 of file OnlineSVMSGD.h.

◆ get_features()

virtual CStreamingDotFeatures* get_features ( )

virtualinherited

get features

Returns: features

Definition at line 180 of file OnlineLinearMachine.h.

◆ get_global_io()

SGIO * get_global_io ( )

inherited

get the io object

Returns: io object

Definition at line 269 of file SGObject.cpp.

◆ get_global_parallel()

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns: parallel object

Definition at line 311 of file SGObject.cpp.

◆ get_global_version()

Version * get_global_version ( )

inherited

get the version object

Returns: version object

Definition at line 324 of file SGObject.cpp.

◆ get_labels()

CLabels * get_labels ( )

virtualinherited

get labels

Returns: labels

Definition at line 83 of file Machine.cpp.

◆ get_lambda()

float64_t get_lambda ( )

get lambda

Returns: the regularization parameter lambda

Definition at line 117 of file OnlineSVMSGD.h.

◆ get_loss_function()

CLossFunction* get_loss_function ( )

Return the loss function

Returns: loss function as CLossFunction*

Definition at line 153 of file OnlineSVMSGD.h.

◆ get_machine_problem_type()

virtual EProblemType get_machine_problem_type ( ) const

virtualinherited

returns type of problem machine solves

Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.

Definition at line 311 of file Machine.h.

◆ get_max_train_time()

float64_t get_max_train_time ( )

inherited

get maximum training time

Returns: maximum training time

Definition at line 94 of file Machine.cpp.

◆ get_modelsel_names()

SGStringList< char > get_modelsel_names ( )

inherited

Returns: vector of names of all parameters which are registered for model selection

Definition at line 536 of file SGObject.cpp.

◆ get_modsel_param_descr()

char * get_modsel_param_descr ( const char * param_name )

inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters

param_name name of the parameter

Returns: description of the parameter

Definition at line 560 of file SGObject.cpp.

◆ get_modsel_param_index()

index_t get_modsel_param_index ( const char * param_name )

inherited

Returns index of model selection parameter with provided index

Parameters

param_name name of model selection parameter

Returns: index of model selection parameter with provided name, -1 if there is no such

Definition at line 573 of file SGObject.cpp.

◆ get_name()

const char* get_name ( ) const

virtual

Returns: object name

Reimplemented from COnlineLinearMachine.

Definition at line 156 of file OnlineSVMSGD.h.

◆ get_parameters_observable()

SGObservable* get_parameters_observable ( )

inherited

Get parameters observable

Returns: RxCpp observable

Definition at line 415 of file SGObject.h.

◆ get_regularized_bias_enabled()

bool get_regularized_bias_enabled ( )

check if regularized bias is enabled

Returns: if regularized bias is enabled

Definition at line 141 of file OnlineSVMSGD.h.

◆ get_solver_type()

ESolverType get_solver_type ( )

inherited

get solver type

Returns: solver

Definition at line 109 of file Machine.cpp.

◆ get_w() [1/2]

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

virtualinherited

Get w as a _new_ float64_t array

Parameters

dst_w	store w in this argument
dst_dims	dimension of w

Definition at line 66 of file OnlineLinearMachine.h.

◆ get_w() [2/2]

virtual SGVector<float32_t> get_w ( ) const

virtualinherited

get w

Returns: weight vector

Definition at line 79 of file OnlineLinearMachine.h.

◆ has() [1/3]

bool has ( const std::string & name ) const

inherited

Checks if object has a class parameter identified by a name.

Parameters

name	name of the parameter

Returns: true if the parameter exists with the input name

Definition at line 304 of file SGObject.h.

◆ has() [2/3]

bool has ( const Tag< T > & tag ) const

inherited

Checks if object has a class parameter identified by a Tag.

Parameters

tag	tag of the parameter containing name and type information

Returns: true if the parameter exists with the input tag

Definition at line 315 of file SGObject.h.

◆ has() [3/3]

bool has ( const std::string & name ) const

inherited

Checks if a type exists for a class parameter identified by a name.

Parameters

name	name of the parameter

Returns: true if the parameter exists with the input name and type

Definition at line 326 of file SGObject.h.

◆ is_data_locked()

bool is_data_locked ( ) const

inherited

Returns: whether this machine is locked

Definition at line 308 of file Machine.h.

◆ is_generic()

bool is_generic ( EPrimitiveType * generic ) const

virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters

generic set to the type of the generic if returning TRUE

Returns: TRUE if a class template.

Definition at line 330 of file SGObject.cpp.

◆ is_label_valid()

virtual bool is_label_valid ( CLabels * lab ) const

protectedvirtualinherited

check whether the labels is valid.

Subclasses can override this to implement their check of label types.

Parameters

lab	the labels being checked, guaranteed to be non-NULL

Reimplemented in CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 391 of file Machine.h.

◆ list_observable_parameters()

void list_observable_parameters ( )

inherited

Print to stdout a list of observable parameters

Definition at line 878 of file SGObject.cpp.

◆ load_serializable()

bool load_serializable	(	CSerializableFile *	file,
		const char *	prefix = `""`
	)

virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters

file	where to load from
prefix	prefix for members

Returns: TRUE if done, otherwise FALSE

Definition at line 403 of file SGObject.cpp.

◆ load_serializable_post()

void load_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 460 of file SGObject.cpp.

◆ load_serializable_pre()

void load_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 455 of file SGObject.cpp.

◆ MACHINE_PROBLEM_TYPE()

MACHINE_PROBLEM_TYPE ( PT_BINARY )

returns type of problem machine solves

◆ observe()

void observe ( const ObservedValue value )

protectedinherited

Observe a parameter value and emit them to observer.

Parameters

value Observed parameter's value

Definition at line 828 of file SGObject.cpp.

◆ on_complete()

virtual void on_complete ( )

protectedvirtualinherited

The action which will be done when the user decides to return to prompt and terminate the program execution

Definition at line 427 of file Machine.h.

◆ on_next()

virtual void on_next ( )

protectedvirtualinherited

The action which will be done when the user decides to premature stop the CMachine execution

Definition at line 411 of file Machine.h.

◆ on_pause()

virtual void on_pause ( )

protectedvirtualinherited

The action which will be done when the user decides to pause the CMachine execution

Definition at line 418 of file Machine.h.

◆ parameter_hash_changed()

bool parameter_hash_changed ( )

virtualinherited

Returns: whether parameter combination has changed since last update

Definition at line 296 of file SGObject.cpp.

◆ pause_computation()

SG_FORCED_INLINE void pause_computation ( )

inherited

Pause the algorithm if the flag is set

Definition at line 327 of file Machine.h.

◆ post_lock()

virtual void post_lock	(	CLabels *	labs,
		CFeatures *	features
	)

virtualinherited

post lock

Definition at line 299 of file Machine.h.

◆ print_modsel_params()

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

◆ print_serializable()

void print_serializable ( const char * prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix prefix for members

Definition at line 342 of file SGObject.cpp.

◆ ref()

int32_t ref ( )

inherited

increase reference counter

Returns: reference count

Definition at line 186 of file SGObject.cpp.

◆ ref_count()

int32_t ref_count ( )

inherited

display reference counter

Returns: reference count

Definition at line 193 of file SGObject.cpp.

◆ register_observable_param()

void register_observable_param	(	const std::string &	name,
		const SG_OBS_VALUE_TYPE	type,
		const std::string &	description
	)

protectedinherited

Register which params this object can emit.

Parameters

name	the param name
type	the param type
description	a user oriented description

Definition at line 871 of file SGObject.cpp.

◆ register_param() [1/2]

void register_param	(	Tag< T > &	_tag,
		const T &	value
	)

protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 472 of file SGObject.h.

◆ register_param() [2/2]

void register_param	(	const std::string &	name,
		const T &	value
	)

protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 485 of file SGObject.h.

◆ reset_computation_variables()

void reset_computation_variables ( )

protectedinherited

reset the computation variables

Definition at line 403 of file Machine.h.

◆ resume_computation()

SG_FORCED_INLINE void resume_computation ( )

inherited

Resume current computation (sets the flag)

Definition at line 340 of file Machine.h.

◆ save_serializable()

bool save_serializable	(	CSerializableFile *	file,
		const char *	prefix = `""`
	)

virtualinherited

Save this object to file.

Parameters

file	where to save the object; will be closed during returning if PREFIX is an empty string.
prefix	prefix for members

Returns: TRUE if done, otherwise FALSE

Definition at line 348 of file SGObject.cpp.

◆ save_serializable_post()

void save_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 470 of file SGObject.cpp.

◆ save_serializable_pre()

void save_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 465 of file SGObject.cpp.

◆ set() [1/2]

void set	(	const Tag< T > &	_tag,
		const T &	value
	)

inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 342 of file SGObject.h.

◆ set() [2/2]

void set	(	const std::string &	name,
		const T &	value
	)

inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 368 of file SGObject.h.

◆ set_bias()

virtual void set_bias ( float32_t b )

virtualinherited

set bias

Parameters

b new bias

Definition at line 111 of file OnlineLinearMachine.h.

◆ set_bias_enabled()

void set_bias_enabled ( bool enable_bias )

set if bias shall be enabled

Parameters

enable_bias if bias shall be enabled

Definition at line 123 of file OnlineSVMSGD.h.

◆ set_C()

void set_C	(	float64_t	c_neg,
		float64_t	c_pos
	)

set C

Parameters

c_neg	new C constant for negatively labeled examples
c_pos	new C constant for positively labeled examples

Definition at line 81 of file OnlineSVMSGD.h.

◆ set_epochs()

void set_epochs ( int32_t e )

set epochs

Parameters

e	new number of training epochs

Definition at line 99 of file OnlineSVMSGD.h.

◆ set_features()

virtual void set_features ( CStreamingDotFeatures * feat )

virtualinherited

set features

Parameters

feat	features to set

Definition at line 129 of file OnlineLinearMachine.h.

◆ set_generic() [1/16]

void set_generic ( )

inherited

Definition at line 73 of file SGObject.cpp.

◆ set_generic() [2/16]

void set_generic ( )

inherited

Definition at line 78 of file SGObject.cpp.

◆ set_generic() [3/16]

void set_generic ( )

inherited

Definition at line 83 of file SGObject.cpp.

◆ set_generic() [4/16]

void set_generic ( )

inherited

Definition at line 88 of file SGObject.cpp.

◆ set_generic() [5/16]

void set_generic ( )

inherited

Definition at line 93 of file SGObject.cpp.

◆ set_generic() [6/16]

void set_generic ( )

inherited

Definition at line 98 of file SGObject.cpp.

◆ set_generic() [7/16]

void set_generic ( )

inherited

Definition at line 103 of file SGObject.cpp.

◆ set_generic() [8/16]

void set_generic ( )

inherited

Definition at line 108 of file SGObject.cpp.

◆ set_generic() [9/16]

void set_generic ( )

inherited

Definition at line 113 of file SGObject.cpp.

◆ set_generic() [10/16]

void set_generic ( )

inherited

Definition at line 118 of file SGObject.cpp.

◆ set_generic() [11/16]

void set_generic ( )

inherited

Definition at line 123 of file SGObject.cpp.

◆ set_generic() [12/16]

void set_generic ( )

inherited

Definition at line 128 of file SGObject.cpp.

◆ set_generic() [13/16]

void set_generic ( )

inherited

Definition at line 133 of file SGObject.cpp.

◆ set_generic() [14/16]

void set_generic ( )

inherited

Definition at line 138 of file SGObject.cpp.

◆ set_generic() [15/16]

void set_generic ( )

inherited

Definition at line 143 of file SGObject.cpp.

◆ set_generic() [16/16]

void set_generic ( )

inherited

set generic type to T

◆ set_global_io()

void set_global_io ( SGIO * io )

inherited

set the io object

Parameters

io	io object to use

Definition at line 262 of file SGObject.cpp.

◆ set_global_parallel()

void set_global_parallel ( Parallel * parallel )

inherited

set the parallel object

Parameters

parallel parallel object to use

Definition at line 275 of file SGObject.cpp.

◆ set_global_version()

void set_global_version ( Version * version )

inherited

set the version object

Parameters

version version object to use

Definition at line 317 of file SGObject.cpp.

◆ set_labels()

void set_labels ( CLabels * lab )

virtualinherited

set labels

Parameters

lab labels

Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 72 of file Machine.cpp.

◆ set_lambda()

void set_lambda ( float64_t l )

set lambda

Parameters

l	value of regularization parameter lambda

Definition at line 111 of file OnlineSVMSGD.h.

◆ set_loss_function()

void set_loss_function ( CLossFunction * loss_func )

Set the loss function to use

Parameters

loss_func object derived from CLossFunction

Definition at line 62 of file OnlineSVMSGD.cpp.

◆ set_max_train_time()

void set_max_train_time ( float64_t t )

inherited

set maximum training time

Parameters

t	maximimum training time

Definition at line 89 of file Machine.cpp.

◆ set_regularized_bias_enabled()

void set_regularized_bias_enabled ( bool enable_bias )

set if regularized bias shall be enabled

Parameters

enable_bias if regularized bias shall be enabled

Definition at line 135 of file OnlineSVMSGD.h.

◆ set_solver_type()

void set_solver_type ( ESolverType st )

inherited

set solver type

Parameters

st	solver type

Definition at line 104 of file Machine.cpp.

◆ set_store_model_features()

void set_store_model_features ( bool store_model )

virtualinherited

Setter for store-model-features-after-training flag

Parameters

store_model whether model should be stored after training

Definition at line 114 of file Machine.cpp.

◆ set_w() [1/2]

virtual void set_w ( const SGVector< float32_t > w )

virtualinherited

set w

Parameters

src_w	new w
src_w_dim	dimension of new w

Definition at line 89 of file OnlineLinearMachine.h.

◆ set_w() [2/2]

virtual void set_w	(	float64_t *	src_w,
		int32_t	src_w_dim
	)

virtualinherited

Set weight vector from a float64_t vector

Parameters

src_w	new w
src_w_dim	dimension of new w

Definition at line 100 of file OnlineLinearMachine.h.

◆ shallow_copy()

CSGObject * shallow_copy ( ) const

virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 226 of file SGObject.cpp.

◆ start_train()

virtual void start_train ( )

virtualinherited

Start training of the online machine, sub-class should override this if some preparations are to be done

Reimplemented in COnlineLibLinear.

Definition at line 192 of file OnlineLinearMachine.h.

◆ stop_train()

virtual void stop_train ( )

virtualinherited

Stop training of the online machine, sub-class should override this if some clean up is needed

Reimplemented in COnlineLibLinear.

Definition at line 197 of file OnlineLinearMachine.h.

◆ store_model_features()

virtual void store_model_features ( )

protectedvirtualinherited

Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.

Base method, has to be implemented in order to allow cross-validation and model selection.

NOT IMPLEMENTED! Has to be done in subclasses

Reimplemented in CKernelMachine, CKNN, CLinearMachine, CLinearMulticlassMachine, CKMeansBase, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CGaussianProcessMachine, CHierarchical, CDistanceMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.

Definition at line 378 of file Machine.h.

◆ subscribe_to_parameters()

void subscribe_to_parameters ( ParameterObserverInterface * obs )

inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

◆ supports_locking()

virtual bool supports_locking ( ) const

virtualinherited

Returns: whether this machine supports locking

Reimplemented in CKernelMachine.

Definition at line 305 of file Machine.h.

◆ train()

bool train ( CFeatures * data = NULL )

virtual

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 69 of file OnlineSVMSGD.cpp.

◆ train_example()

virtual void train_example	(	CStreamingDotFeatures *	feature,
		float64_t	label
	)

virtualinherited

train on one example

Parameters

feature the feature object containing the current example. Note that get_next_example is already called so relevalent methods like dot() and dense_dot() can be directly called. WARN: this function should only process ONE example, and get_next_example() should NEVER be called here. Use the label passed in the 2nd parameter, instead of get_label() from feature, because sometimes the features might not have associated labels or the caller might want to provide some other labels.

label label of this example

Reimplemented in COnlineLibLinear.

Definition at line 208 of file OnlineLinearMachine.h.

◆ train_locked()

virtual bool train_locked ( SGVector< index_t > indices )

virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

NOT IMPLEMENTED

Parameters

indices index vector (of locked features) that is used for training

Returns: whether training was successful

Reimplemented in CKernelMachine.

Definition at line 248 of file Machine.h.

◆ train_machine()

bool train_machine ( CFeatures * data = NULL )

protectedvirtualinherited

Train classifier

Parameters

data	Training data, can be avoided if already initialized with it

Returns: Whether training was successful

Reimplemented from CMachine.

Reimplemented in CVowpalWabbit.

Definition at line 88 of file OnlineLinearMachine.cpp.

◆ train_require_labels()

virtual bool train_require_labels ( ) const

protectedvirtualinherited

whether train require labels

Reimplemented from CMachine.

Definition at line 229 of file OnlineLinearMachine.h.

◆ unref()

int32_t unref ( )

inherited

decrement reference counter and deallocate object if refcount is zero before or after decrementing it

Returns: reference count

Definition at line 200 of file SGObject.cpp.

◆ unset_generic()

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 337 of file SGObject.cpp.

◆ update_parameter_hash()

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

Member Data Documentation

◆ bias

float32_t bias

protectedinherited

bias

Definition at line 235 of file OnlineLinearMachine.h.

◆ features

CStreamingDotFeatures* features

protectedinherited

features

Definition at line 237 of file OnlineLinearMachine.h.

◆ io

SGIO* io

inherited

io

Definition at line 600 of file SGObject.h.

◆ m_cancel_computation

std::atomic<bool> m_cancel_computation

protectedinherited

Cancel computation

Definition at line 448 of file Machine.h.

◆ m_data_locked

bool m_data_locked

protectedinherited

whether data is locked

Definition at line 445 of file Machine.h.

◆ m_gradient_parameters

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 615 of file SGObject.h.

◆ m_hash

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

◆ m_labels

CLabels* m_labels

protectedinherited

labels

Definition at line 436 of file Machine.h.

◆ m_max_train_time

float64_t m_max_train_time

protectedinherited

maximum training time

Definition at line 433 of file Machine.h.

◆ m_model_selection_parameters

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 612 of file SGObject.h.

◆ m_mutex

std::mutex m_mutex

protectedinherited

Mutex used to pause threads

Definition at line 457 of file Machine.h.

◆ m_parameters

Parameter* m_parameters

inherited

parameters

Definition at line 609 of file SGObject.h.

◆ m_pause_computation

std::condition_variable m_pause_computation

protectedinherited

Conditional variable to make threads wait

Definition at line 454 of file Machine.h.

◆ m_pause_computation_flag

std::atomic<bool> m_pause_computation_flag

protectedinherited

Pause computation flag

Definition at line 451 of file Machine.h.

◆ m_solver_type

ESolverType m_solver_type

protectedinherited

solver type

Definition at line 439 of file Machine.h.

◆ m_store_model_features

bool m_store_model_features

protectedinherited

whether model features should be stored after training

Definition at line 442 of file Machine.h.

◆ m_w

SGVector<float32_t> m_w

protectedinherited

w

Definition at line 233 of file OnlineLinearMachine.h.

◆ parallel

Parallel* parallel

inherited

parallel

Definition at line 603 of file SGObject.h.

◆ version

Version* version

inherited

version

Definition at line 606 of file SGObject.h.

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

Detailed Description

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Member Typedef Documentation

◆ SGObservable

◆ SGSubject

◆ SGSubscriber

Constructor & Destructor Documentation

◆ COnlineSVMSGD() [1/3]

◆ COnlineSVMSGD() [2/3]

◆ COnlineSVMSGD() [3/3]

◆ ~COnlineSVMSGD()

Member Function Documentation

◆ apply()

◆ apply_binary()

◆ apply_get_outputs()

◆ apply_latent()

◆ apply_locked()

◆ apply_locked_binary()

◆ apply_locked_latent()

◆ apply_locked_multiclass()

◆ apply_locked_regression()

◆ apply_locked_structured()

◆ apply_multiclass()

◆ apply_one() [1/2]

◆ apply_one() [2/2]

◆ apply_regression()

◆ apply_structured()

◆ apply_to_current_example()

◆ build_gradient_parameter_dictionary()

◆ calibrate()

◆ cancel_computation()

◆ clone()

◆ clone_parameters()

◆ connect_to_signal_handler()

◆ data_lock()

◆ data_unlock()

◆ deep_copy()

◆ equals()

◆ get() [1/2]

◆ get() [2/2]

◆ get_bias()

◆ get_bias_enabled()

◆ get_C1()

◆ get_C2()

◆ get_classifier_type()

◆ get_epochs()

◆ get_features()

◆ get_global_io()

◆ get_global_parallel()

◆ get_global_version()

◆ get_labels()

◆ get_lambda()

◆ get_loss_function()

◆ get_machine_problem_type()

◆ get_max_train_time()

◆ get_modelsel_names()

◆ get_modsel_param_descr()

◆ get_modsel_param_index()

◆ get_name()

◆ get_parameters_observable()

◆ get_regularized_bias_enabled()

◆ get_solver_type()

◆ get_w() [1/2]

◆ get_w() [2/2]

◆ has() [1/3]

◆ has() [2/3]

◆ has() [3/3]

◆ is_data_locked()

◆ is_generic()

◆ is_label_valid()

◆ list_observable_parameters()

◆ load_serializable()

◆ load_serializable_post()

◆ load_serializable_pre()

◆ MACHINE_PROBLEM_TYPE()

◆ observe()