SHOGUN  6.1.3
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CGaussianNaiveBayes Class Reference

Detailed Description

Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier.

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

\[ P(c) \prod_{i} P(x_i|c) \]

Definition at line 37 of file GaussianNaiveBayes.h.

Inheritance diagram for CGaussianNaiveBayes:
[legend]

Public Types

typedef rxcpp::subjects::subject< ObservedValueSGSubject
 
typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > SGObservable
 
typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > SGSubscriber
 

Public Member Functions

 CGaussianNaiveBayes ()
 
 CGaussianNaiveBayes (CFeatures *train_examples, CLabels *train_labels)
 
virtual ~CGaussianNaiveBayes ()
 
virtual void set_features (CFeatures *features)
 
virtual CFeaturesget_features ()
 
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
 
virtual float64_t apply_one (int32_t idx)
 
virtual const char * get_name () const
 
virtual EMachineType get_classifier_type ()
 
virtual void set_labels (CLabels *lab)
 
bool set_machine (int32_t num, CMachine *machine)
 
CMachineget_machine (int32_t num) const
 
virtual CBinaryLabelsget_submachine_outputs (int32_t i)
 
virtual float64_t get_submachine_output (int32_t i, int32_t num)
 
virtual CMultilabelLabelsapply_multilabel_output (CFeatures *data=NULL, int32_t n_outputs=5)
 
CMulticlassStrategyget_multiclass_strategy () const
 
CRejectionStrategyget_rejection_strategy () const
 
void set_rejection_strategy (CRejectionStrategy *rejection_strategy)
 
EProbHeuristicType get_prob_heuris ()
 
void set_prob_heuris (EProbHeuristicType prob_heuris)
 
int32_t get_num_machines () const
 
virtual EProblemType get_machine_problem_type () const
 
virtual bool is_label_valid (CLabels *lab) const
 
virtual bool train (CFeatures *data=NULL)
 
virtual CLabelsapply (CFeatures *data=NULL)
 
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
 
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
 
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
 
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
 
virtual CLabelsget_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 CLabelsapply_locked (SGVector< index_t > indices)
 
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
 
virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)
 
virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)
 
virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)
 
virtual CLatentLabelsapply_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
 
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 CSGObjectshallow_copy () const
 
virtual CSGObjectdeep_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)
 
SGIOget_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallelget_global_parallel ()
 
void set_global_version (Version *version)
 
Versionget_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 >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
SGObservableget_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 CSGObjectclone ()
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
 
void init_strategy ()
 
void clear_machines ()
 
virtual bool init_machine_for_train (CFeatures *data)
 
virtual bool init_machines_for_apply (CFeatures *data)
 
virtual bool is_ready ()
 
virtual CMachineget_machine_from_trained (CMachine *machine)
 
virtual int32_t get_num_rhs_vectors ()
 
virtual void add_machine_subset (SGVector< index_t > subset)
 
virtual void remove_machine_subset ()
 
virtual bool is_acceptable_machine (CMachine *machine)
 
virtual void store_model_features ()
 
virtual bool train_require_labels () 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

CDotFeaturesm_features
 features for training or classifying More...
 
int32_t m_min_label
 minimal label More...
 
int32_t m_num_classes
 number of different classes (labels) More...
 
int32_t m_dim
 dimensionality of feature space More...
 
SGMatrix< float64_tm_means
 means for normal distributions of features More...
 
SGMatrix< float64_tm_variances
 variances for normal distributions of features More...
 
SGVector< float64_tm_label_prob
 a priori probabilities of labels More...
 
SGVector< float64_tm_rates
 label rates More...
 
CMulticlassStrategym_multiclass_strategy
 
CMachinem_machine
 
CDynamicObjectArraym_machines
 
float64_t m_max_train_time
 
CLabelsm_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

Definition at line 130 of file SGObject.h.

◆ SGSubject

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

◆ CGaussianNaiveBayes() [1/2]

default constructor

Definition at line 24 of file GaussianNaiveBayes.cpp.

◆ CGaussianNaiveBayes() [2/2]

CGaussianNaiveBayes ( CFeatures train_examples,
CLabels train_labels 
)

constructor

Parameters
train_examplestrain examples
train_labelslabels corresponding to train_examples

Definition at line 31 of file GaussianNaiveBayes.cpp.

◆ ~CGaussianNaiveBayes()

~CGaussianNaiveBayes ( )
virtual

destructor

Definition at line 46 of file GaussianNaiveBayes.cpp.

Member Function Documentation

◆ add_machine_subset()

virtual void add_machine_subset ( SGVector< index_t subset)
protectedvirtualinherited

set subset to the features of the machine, deletes old one

Parameters
subsetsubset indices to set

Implements CMulticlassMachine.

Definition at line 68 of file NativeMulticlassMachine.h.

◆ 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 machine to data in means of binary classification problem

Reimplemented in CKernelMachine, CNeuralNetwork, COnlineLinearMachine, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.

Definition at line 215 of file Machine.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
indicesindex 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)
virtual

classify specified examples

Parameters
dataexamples to be classified
Returns
labels corresponding to data

Reimplemented from CMulticlassMachine.

Definition at line 164 of file GaussianNaiveBayes.cpp.

◆ apply_multilabel_output()

CMultilabelLabels * apply_multilabel_output ( CFeatures data = NULL,
int32_t  n_outputs = 5 
)
virtualinherited

classify all examples with multiple output

Returns
resulting labels

Definition at line 195 of file MulticlassMachine.cpp.

◆ apply_one()

float64_t apply_one ( int32_t  idx)
virtual

classifiy specified example

Parameters
idxexample index
Returns
label

Reimplemented from CMulticlassMachine.

Definition at line 186 of file GaussianNaiveBayes.cpp.

◆ apply_regression()

CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CNeuralNetwork, CLinearMachine, COnlineLinearMachine, CCHAIDTree, CStochasticGBMachine, CCARTree, CGaussianProcessRegression, and CBaggingMachine.

Definition at line 221 of file Machine.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.

◆ 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
dictdictionary of parameters to be built.

Definition at line 635 of file SGObject.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.

◆ clear_machines()

void clear_machines ( )
protectedinherited

clear machines

Definition at line 47 of file NativeMulticlassMachine.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
labslabels used for locking
featuresfeatures 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
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 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
_tagname 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
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 404 of file SGObject.h.

◆ get_classifier_type()

virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type

Reimplemented from CMachine.

Definition at line 89 of file GaussianNaiveBayes.h.

◆ get_features()

CFeatures * get_features ( )
virtual

get features for classify

Returns
current features

Definition at line 51 of file GaussianNaiveBayes.cpp.

◆ 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_machine()

CMachine* get_machine ( int32_t  num) const
inherited

get machine

Parameters
numindex of machine to get
Returns
SVM at number num

Definition at line 74 of file MulticlassMachine.h.

◆ get_machine_from_trained()

virtual CMachine* get_machine_from_trained ( CMachine machine)
protectedvirtualinherited

obtain machine from trained one

Implements CMulticlassMachine.

Definition at line 59 of file NativeMulticlassMachine.h.

◆ get_machine_problem_type()

EProblemType get_machine_problem_type ( ) const
virtualinherited

get problem type

Reimplemented from CMachine.

Reimplemented in CCHAIDTree, and CCARTree.

Definition at line 32 of file BaseMulticlassMachine.cpp.

◆ 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_namename 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_namename 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_multiclass_strategy()

CMulticlassStrategy* get_multiclass_strategy ( ) const
inherited

get the type of multiclass'ness

Returns
multiclass type one vs one etc

Definition at line 114 of file MulticlassMachine.h.

◆ get_name()

virtual const char* get_name ( ) const
virtual

get name

Returns
classifier name

Reimplemented from CNativeMulticlassMachine.

Definition at line 84 of file GaussianNaiveBayes.h.

◆ get_num_machines()

int32_t get_num_machines ( ) const
inherited

get number of machines

Returns
number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

◆ get_num_rhs_vectors()

virtual int32_t get_num_rhs_vectors ( )
protectedvirtualinherited

get num rhs vectors

Implements CMulticlassMachine.

Definition at line 62 of file NativeMulticlassMachine.h.

◆ get_parameters_observable()

SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

◆ get_prob_heuris()

EProbHeuristicType get_prob_heuris ( )
inherited

get prob output heuristic of multiclass strategy

Definition at line 145 of file MulticlassMachine.h.

◆ get_rejection_strategy()

CRejectionStrategy* get_rejection_strategy ( ) const
inherited

returns rejection strategy

Returns
rejection strategy

Definition at line 124 of file MulticlassMachine.h.

◆ get_solver_type()

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 109 of file Machine.cpp.

◆ get_submachine_output()

float64_t get_submachine_output ( int32_t  i,
int32_t  num 
)
virtualinherited

get output of i-th submachine for num-th vector

Parameters
inumber of submachine
numnumber of feature vector
Returns
output

Definition at line 80 of file MulticlassMachine.cpp.

◆ get_submachine_outputs()

CBinaryLabels * get_submachine_outputs ( int32_t  i)
virtualinherited

get outputs of i-th submachine

Parameters
inumber of submachine
Returns
outputs

Reimplemented in CDomainAdaptationMulticlassLibLinear.

Definition at line 71 of file MulticlassMachine.cpp.

◆ has() [1/3]

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

Definition at line 326 of file SGObject.h.

◆ init_machine_for_train()

virtual bool init_machine_for_train ( CFeatures data)
protectedvirtualinherited

abstract init machine for training method

Implements CMulticlassMachine.

Definition at line 50 of file NativeMulticlassMachine.h.

◆ init_machines_for_apply()

virtual bool init_machines_for_apply ( CFeatures data)
protectedvirtualinherited

abstract init machines for applying method

Implements CMulticlassMachine.

Definition at line 53 of file NativeMulticlassMachine.h.

◆ init_strategy()

void init_strategy ( )
protectedinherited

init strategy

Definition at line 44 of file NativeMulticlassMachine.h.

◆ is_acceptable_machine()

virtual bool is_acceptable_machine ( CMachine machine)
protectedvirtualinherited

whether the machine is acceptable in set_machine

Reimplemented from CMulticlassMachine.

Definition at line 74 of file NativeMulticlassMachine.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
genericset 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()

bool is_label_valid ( CLabels lab) const
virtualinherited

check whether the labels is valid.

Parameters
labthe labels being checked, guaranteed to be non-NULL

Reimplemented from CMachine.

Reimplemented in CCARTree, and CCHAIDTree.

Definition at line 37 of file BaseMulticlassMachine.cpp.

◆ is_ready()

virtual bool is_ready ( )
protectedvirtualinherited

check whether machine is ready

Implements CMulticlassMachine.

Definition at line 56 of file NativeMulticlassMachine.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
filewhere to load from
prefixprefix 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
ShogunExceptionwill 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
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 455 of file SGObject.cpp.

◆ observe()

void observe ( const ObservedValue  value)
protectedinherited

Observe a parameter value and emit them to observer.

Parameters
valueObserved 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
prefixprefix 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
namethe param name
typethe param type
descriptiona 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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 485 of file SGObject.h.

◆ remove_machine_subset()

virtual void remove_machine_subset ( )
protectedvirtualinherited

deletes any subset set to the features of the machine

Implements CMulticlassMachine.

Definition at line 71 of file NativeMulticlassMachine.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
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 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
ShogunExceptionwill 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
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 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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 368 of file SGObject.h.

◆ set_features()

void set_features ( CFeatures features)
virtual

set features for classify

Parameters
featuresfeatures to be set

Definition at line 57 of file GaussianNaiveBayes.cpp.

◆ 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
ioio 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
parallelparallel 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
versionversion object to use

Definition at line 317 of file SGObject.cpp.

◆ set_labels()

void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabels

Reimplemented from CMachine.

Definition at line 52 of file MulticlassMachine.cpp.

◆ set_machine()

bool set_machine ( int32_t  num,
CMachine machine 
)
inherited

set machine

Parameters
numindex of machine
machinemachine to set
Returns
if setting was successful

Definition at line 59 of file MulticlassMachine.h.

◆ set_max_train_time()

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 89 of file Machine.cpp.

◆ set_prob_heuris()

void set_prob_heuris ( EProbHeuristicType  prob_heuris)
inherited

set prob output heuristic of multiclass strategy

Parameters
prob_heuristype of probability heuristic

Definition at line 153 of file MulticlassMachine.h.

◆ set_rejection_strategy()

void set_rejection_strategy ( CRejectionStrategy rejection_strategy)
inherited

sets rejection strategy

Parameters
rejection_strategyrejection strategy to be set

Definition at line 133 of file MulticlassMachine.h.

◆ set_solver_type()

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver 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_modelwhether model should be stored after training

Definition at line 114 of file Machine.cpp.

◆ 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.

◆ 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)
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 43 of file Machine.cpp.

◆ 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
indicesindex 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)
protectedvirtual

train classifier

Parameters
datatrain examples
Returns
true if successful

Reimplemented from CMulticlassMachine.

Definition at line 67 of file GaussianNaiveBayes.cpp.

◆ train_require_labels()

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CKMeansBase, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.

Definition at line 397 of file Machine.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

◆ 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_dim

int32_t m_dim
protected

dimensionality of feature space

Definition at line 114 of file GaussianNaiveBayes.h.

◆ m_features

CDotFeatures* m_features
protected

features for training or classifying

Definition at line 105 of file GaussianNaiveBayes.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_label_prob

SGVector<float64_t> m_label_prob
protected

a priori probabilities of labels

Definition at line 123 of file GaussianNaiveBayes.h.

◆ m_labels

CLabels* m_labels
protectedinherited

labels

Definition at line 436 of file Machine.h.

◆ m_machine

CMachine* m_machine
protectedinherited

machine

Definition at line 208 of file MulticlassMachine.h.

◆ m_machines

CDynamicObjectArray* m_machines
protectedinherited

machines

Definition at line 56 of file BaseMulticlassMachine.h.

◆ m_max_train_time

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 433 of file Machine.h.

◆ m_means

SGMatrix<float64_t> m_means
protected

means for normal distributions of features

Definition at line 117 of file GaussianNaiveBayes.h.

◆ m_min_label

int32_t m_min_label
protected

minimal label

Definition at line 108 of file GaussianNaiveBayes.h.

◆ m_model_selection_parameters

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

◆ m_multiclass_strategy

CMulticlassStrategy* m_multiclass_strategy
protectedinherited

type of multiclass strategy

Definition at line 205 of file MulticlassMachine.h.

◆ m_mutex

std::mutex m_mutex
protectedinherited

Mutex used to pause threads

Definition at line 457 of file Machine.h.

◆ m_num_classes

int32_t m_num_classes
protected

number of different classes (labels)

Definition at line 111 of file GaussianNaiveBayes.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_rates

SGVector<float64_t> m_rates
protected

label rates

Definition at line 126 of file GaussianNaiveBayes.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_variances

SGMatrix<float64_t> m_variances
protected

variances for normal distributions of features

Definition at line 120 of file GaussianNaiveBayes.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:

SHOGUN Machine Learning Toolbox - Documentation