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CVowpalWabbit Class Reference

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 40 of file VowpalWabbit.h.

Inheritance diagram for CVowpalWabbit:
[legend]

Public Member Functions

 MACHINE_PROBLEM_TYPE (PT_BINARY)
 
 CVowpalWabbit ()
 
 CVowpalWabbit (CStreamingVwFeatures *feat)
 
 CVowpalWabbit (CVowpalWabbit *vw)
 
 ~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
 
virtual void set_learner ()
 
CVwLearner * get_learner ()
 
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 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 bool train (CFeatures *data=NULL)
 
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 ()
 
virtual EMachineType get_classifier_type ()
 
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
 
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
 
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

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
 
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)
 

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...
 
int32_t w_dim
 
float32_t * w
 
float32_t bias
 
float64_t m_max_train_time
 
CLabels * m_labels
 
ESolverType m_solver_type
 
bool m_store_model_features
 
bool m_data_locked
 

Constructor & Destructor Documentation

CVowpalWabbit ( )

Default constructor

Definition at line 23 of file VowpalWabbit.cpp.

CVowpalWabbit ( CStreamingVwFeatures *  feat)

Constructor, taking a features object as argument

Parameters
featStreamingVwFeatures object

Definition at line 31 of file VowpalWabbit.cpp.

CVowpalWabbit ( CVowpalWabbit *  vw)

copy constructor

Parameters
vwanother VowpalWabbit object

Definition at line 39 of file VowpalWabbit.cpp.

~CVowpalWabbit ( )

Destructor

Definition at line 64 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
paira string with the two namespace names concatenated

Definition at line 131 of file VowpalWabbit.cpp.

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 152 of file Machine.cpp.

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 36 of file OnlineLinearMachine.cpp.

SGVector< float64_t > apply_get_outputs ( CFeatures *  data)
protectedinherited

get real outputs

Parameters
datafeatures to compute outputs
Returns
outputs

Definition at line 48 of file OnlineLinearMachine.cpp.

CLatentLabels * apply_latent ( CFeatures *  data = NULL)
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 232 of file Machine.cpp.

CLabels * apply_locked ( SGVector< index_t >  indices)
virtualinherited

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

Parameters
indicesindex vector (of locked features) that is predicted

Definition at line 187 of file Machine.cpp.

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 238 of file Machine.cpp.

CLatentLabels * apply_locked_latent ( SGVector< index_t >  indices)
virtualinherited

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

Definition at line 266 of file Machine.cpp.

CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t >  indices)
virtualinherited

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

Definition at line 252 of file Machine.cpp.

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 245 of file Machine.cpp.

CStructuredLabels * apply_locked_structured ( SGVector< index_t >  indices)
virtualinherited

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

Definition at line 259 of file Machine.cpp.

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 220 of file Machine.cpp.

virtual float64_t apply_one ( int32_t  vec_idx)
virtualinherited

get output for example "vec_idx"

Reimplemented from CMachine.

Definition at line 173 of file OnlineLinearMachine.h.

float32_t apply_one ( float32_t *  vec,
int32_t  len 
)
virtualinherited

apply linear machine to one vector

Parameters
vecfeature vector
lenlength of vector
Returns
classified label

Definition at line 84 of file OnlineLinearMachine.cpp.

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 42 of file OnlineLinearMachine.cpp.

CStructuredLabels * apply_structured ( CFeatures *  data = NULL)
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 226 of file Machine.cpp.

float32_t apply_to_current_example ( )
virtualinherited

apply linear machine to vector currently being processed

Returns
classified label

Definition at line 89 of file OnlineLinearMachine.cpp.

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
dictdictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

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

Definition at line 747 of file SGObject.cpp.

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

Computes the exact norm during adaptive learning

Parameters
exexample
sum_abs_xset by reference, sum of abs of features
Returns
norm

Definition at line 420 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
weightsweights
page_featurecurrent feature
offer_featurespaired features
maskmask
gsquare of gradient
sum_abs_xsum of absolute value of features
Returns
norm

Definition at line 457 of file VowpalWabbit.cpp.

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
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 112 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 143 of file Machine.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

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
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

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

Definition at line 388 of file SGObject.h.

virtual float32_t get_bias ( )
virtualinherited

get bias

Returns
bias

Definition at line 140 of file OnlineLinearMachine.h.

EMachineType get_classifier_type ( )
virtualinherited

get classifier type

Returns
classifier type NONE

Reimplemented in CLaRank, CSVMLight, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CQDA, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKernelRidgeRegression, CLibSVR, CKNN, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CScatterSVM, CGaussianProcessRegression, CSGDQN, CSVMSGD, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CKMeansBase, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CDomainAdaptationSVM, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLightOneClass, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGNPPSVM, and CCPLEXSVM.

Definition at line 92 of file Machine.cpp.

virtual CVwEnvironment* get_env ( )
virtual

Get the environment

Returns
environment as CVwEnvironment*

Definition at line 187 of file VowpalWabbit.h.

virtual CStreamingDotFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 200 of file OnlineLinearMachine.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 of file Machine.cpp.

CVwLearner* get_learner ( )

Get learner

Definition at line 209 of file VowpalWabbit.h.

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 299 of file Machine.h.

float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 87 of file Machine.cpp.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 531 of file SGObject.cpp.

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_namename of the parameter
Returns
description of the parameter

Definition at line 555 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

Definition at line 568 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual

Return the name of the object

Returns
VowpalWabbit

Reimplemented from COnlineLinearMachine.

Definition at line 198 of file VowpalWabbit.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 of file Machine.cpp.

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

get w

Parameters
dst_wstore w in this argument
dst_dimsdimension of w

Definition at line 65 of file OnlineLinearMachine.h.

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

Get w as a new float64_t array

Parameters
dst_wstore w in this argument
dst_dimsdimension of w

Definition at line 78 of file OnlineLinearMachine.h.

virtual SGVector<float32_t> get_w ( )
virtualinherited

get w

Returns
weight vector

Definition at line 91 of file OnlineLinearMachine.h.

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

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

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

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

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

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

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

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

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 296 of file Machine.h.

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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 329 of file SGObject.cpp.

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
labthe labels being checked, guaranteed to be non-NULL

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

Definition at line 348 of file Machine.h.

void load_regressor ( char *  file_name)

Load regressor from a dump file

Parameters
file_namename of regressor file

Definition at line 109 of file VowpalWabbit.cpp.

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
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 402 of file SGObject.cpp.

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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 459 of file SGObject.cpp.

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
ShogunExceptionwill 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 454 of file SGObject.cpp.

MACHINE_PROBLEM_TYPE ( PT_BINARY  )

problem type

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

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

post lock

Definition at line 287 of file Machine.h.

float32_t predict_and_finalize ( VwExample *  ex)
virtual

Predict for an example

Parameters
exVwExample to predict for
Returns
prediction

Definition at line 208 of file VowpalWabbit.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

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
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

void reinitialize_weights ( )

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

Definition at line 71 of file VowpalWabbit.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 347 of file SGObject.cpp.

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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

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
ShogunExceptionwill 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 464 of file SGObject.cpp.

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
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_adaptive ( bool  adaptive_learning)

Set whether learning is adaptive or not

Parameters
adaptive_learningtrue if adaptive

Definition at line 85 of file VowpalWabbit.cpp.

virtual void set_bias ( float32_t  b)
virtualinherited

set bias

Parameters
bnew bias

Definition at line 131 of file OnlineLinearMachine.h.

void set_exact_adaptive_norm ( bool  exact_adaptive)

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

Parameters
exact_adaptivetrue if exact norm is required

Definition at line 98 of file VowpalWabbit.cpp.

virtual void set_features ( CStreamingDotFeatures *  feat)
virtualinherited

set features

Parameters
featfeatures to set

Definition at line 149 of file OnlineLinearMachine.h.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO *  io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabels

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

Definition at line 65 of file Machine.cpp.

void set_learner ( )
virtual

Sets the train/update methods depending on parameters set, eg. adaptive or not

Definition at line 274 of file VowpalWabbit.cpp.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 82 of file Machine.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_traintrue if one doesn't want to train

Definition at line 84 of file VowpalWabbit.h.

void set_num_passes ( int32_t  passes)

Set number of passes (only works for cached input)

Parameters
passesnumber of passes

Definition at line 106 of file VowpalWabbit.h.

void set_prediction_out ( char *  file_name)

Set file name of prediction output

Parameters
file_namename of file to save predictions to

Definition at line 123 of file VowpalWabbit.cpp.

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

Set regressor output parameters

Parameters
file_namename of file to save regressor to
is_texthuman readable or not, bool

Definition at line 117 of file VowpalWabbit.cpp.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver type

Definition at line 97 of file Machine.cpp.

void set_store_model_features ( bool  store_model)
virtualinherited

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

Parameters
store_modelwhether model should be stored after training

Definition at line 107 of file Machine.cpp.

virtual void set_w ( float32_t *  src_w,
int32_t  src_w_dim 
)
virtualinherited

set w

Parameters
src_wnew w
src_w_dimdimension of new w

Definition at line 104 of file OnlineLinearMachine.h.

virtual void set_w ( float64_t *  src_w,
int32_t  src_w_dim 
)
virtualinherited

Set weight vector from a float64_t vector

Parameters
src_wnew w
src_w_dimdimension of new w

Definition at line 118 of file OnlineLinearMachine.h.

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 225 of file SGObject.cpp.

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 212 of file OnlineLinearMachine.h.

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 217 of file OnlineLinearMachine.h.

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 335 of file Machine.h.

virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine.

Definition at line 293 of file Machine.h.

bool train ( CFeatures *  data = NULL)
virtualinherited

train machine

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CAutoencoder, CLinearMachine, CSGDQN, and COnlineSVMSGD.

Definition at line 39 of file Machine.cpp.

virtual void train_example ( CStreamingDotFeatures *  feature,
float64_t  label 
)
virtualinherited

train on one example

Parameters
featurethe 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.
labellabel of this example

Reimplemented in COnlineLibLinear.

Definition at line 228 of file OnlineLinearMachine.h.

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
indicesindex vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented in CKernelMachine.

Definition at line 239 of file Machine.h.

bool train_machine ( CFeatures *  feat = NULL)
virtual

Train on a StreamingVwFeatures object

Parameters
featStreamingVwFeatures to train using

Reimplemented from COnlineLinearMachine.

Definition at line 136 of file VowpalWabbit.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

whether train require labels

Reimplemented from CMachine.

Definition at line 249 of file OnlineLinearMachine.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

float32_t bias
protectedinherited

bias

Definition at line 257 of file OnlineLinearMachine.h.

CVwEnvironment* env
protected

Environment for VW, i.e., globals.

Definition at line 282 of file VowpalWabbit.h.

CStreamingVwFeatures* features
protected

Features.

Definition at line 279 of file VowpalWabbit.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

CVwLearner* learner
protected

Learner to use.

Definition at line 285 of file VowpalWabbit.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 370 of file Machine.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 358 of file Machine.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

ESolverType m_solver_type
protectedinherited

solver type

Definition at line 364 of file Machine.h.

bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 367 of file Machine.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

CVwRegressor* reg
protected

Regressor.

Definition at line 288 of file VowpalWabbit.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.

float32_t* w
protectedinherited

w

Definition at line 255 of file OnlineLinearMachine.h.

int32_t w_dim
protectedinherited

dimension of w

Definition at line 253 of file OnlineLinearMachine.h.

The documentation for this class was generated from the following files:
SHOGUN Machine Learning Toolbox - Documentation