SHOGUN  6.1.3
CLDA Class Reference

## Detailed Description

Class LDA implements regularized Linear Discriminant Analysis.

LDA learns a linear classifier and requires examples to be CDenseFeatures. The learned linear classification rule is optimal under the assumption that both classes a gaussian distributed with equal co-variance. To find a linear separation $${\bf w}$$ in training, the in-between class variance is maximized and the within class variance is minimized.

This class provides 3 method options to compute the LDA : FLD_LDA : Two class Fisher Discriminant Analysis.

$J({\bf w})=\frac{{\bf w^T} S_B {\bf w}}{{\bf w^T} S_W {\bf w}}$

is maximized, where

$S_b := ({\bf m_{+1}} - {\bf m_{-1}})({\bf m_{+1}} - {\bf m_{-1}})^T$

is the between class scatter matrix and

$S_w := \sum_{c\in\{-1,+1\}}\sum_{{\bf x}\in X_{c}}({\bf x} - {\bf m_c})({\bf x} - {\bf m_c})^T$

is the within class scatter matrix with mean $${\bf m_c} := \frac{1}{N}\sum_{j=1}^N {\bf x_j^c}$$ and $$X_c:=\{x_1^c, \dots, x_N^c\}$$ the set of examples of class c.

LDA is very fast for low-dimensional samples. The regularization parameter $$\gamma$$ (especially useful in the low sample case) should be tuned in cross-validation.

SVD_LDA : Singular Valued decomposition method. The above derivation of Fisher's LDA requires the invertibility of the within class matrix. However, this condition gets void when there are fewer data-points than dimensions. A solution is to require that $${\bf W}$$ lies only in the subspace spanned by the data. A basis of the data $${\bf X}$$ is found using the thin-SVD technique which returns an orthonormal non-square basis matrix $${\bf Q}$$. We then require the solution $${\bf w}$$ to be expressed in this basis.

${\bf W} := {\bf Q} {\bf{W^\prime}}$

The between class Matrix is replaced with:

${\bf S_b^\prime} \equiv {\bf Q^T}{\bf S_b}{\bf Q}$

The within class Matrix is replaced with:

${\bf S_w^\prime} \equiv {\bf Q^T}{\bf S_w}{\bf Q}$

In this case { S_w^} is guranteed invertible since { S_w} has been projected down to the basis that spans the data. see: Bayesian Reasoning and Machine Learning, section 16.3.1.

AUTO_LDA : This mode automagically chooses one of the above modes for the users based on whether N > D (chooses FLD_LDA) or N < D(chooses SVD_LDA) Note that even if N > D FLD_LDA may fail being the covariance matrix not invertible, in such case one should use SVD_LDA.

CLinearMachine
http://en.wikipedia.org/wiki/Linear_discriminant_analysis

Definition at line 99 of file LDA.h.

Inheritance diagram for CLDA:
[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

MACHINE_PROBLEM_TYPE (PT_BINARY)

CLDA (float64_t gamma=0, ELDAMethod method=AUTO_LDA, bool bdc_svd=true)

CLDA (float64_t gamma, CDenseFeatures< float64_t > *traindat, CLabels *trainlab, ELDAMethod method=AUTO_LDA, bool bdc_svd=true)

virtual ~CLDA ()

void set_gamma (float64_t gamma)

float64_t get_gamma ()

virtual EMachineType get_classifier_type ()

virtual void set_features (CDotFeatures *feat)

virtual const char * get_name () const

virtual bool train (CFeatures *data=NULL)

virtual SGVector< float64_tget_w () const

virtual void set_w (const SGVector< float64_t > src_w)

virtual void set_bias (float64_t b)

virtual float64_t get_bias ()

virtual void set_compute_bias (bool compute_bias)

virtual bool get_compute_bias ()

virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)

virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)

virtual float64_t apply_one (int32_t vec_idx)

virtual CDotFeaturesget_features ()

virtual CLabelsapply (CFeatures *data=NULL)

virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)

virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)

virtual CLatentLabelsapply_latent (CFeatures *data=NULL)

virtual void set_labels (CLabels *lab)

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

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 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

uint32_t m_hash

## Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)

template<typename ST >
bool train_machine_templated ()

template<typename ST >
bool solver_svd ()

template<typename ST >
bool solver_classic ()

void init ()

virtual SGVector< float64_tapply_get_outputs (CFeatures *data)

virtual void store_model_features ()

void compute_bias (CFeatures *data)

virtual bool is_label_valid (CLabels *lab) const

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

float64_t m_gamma

ELDAMethod m_method

bool m_bdc_svd

float64_t bias

CDotFeaturesfeatures

bool m_compute_bias

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

## ◆ SGObservable

 inherited

Definition at line 130 of file SGObject.h.

## ◆ SGSubject

 inherited

Definition at line 127 of file SGObject.h.

## ◆ SGSubscriber

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

Definition at line 133 of file SGObject.h.

## ◆ CLDA() [1/2]

 CLDA ( float64_t gamma = 0, ELDAMethod method = AUTO_LDA, bool bdc_svd = true )

constructor

Parameters
 gamma gamma method LDA using Fisher's algorithm or Singular Value Decomposition : FLD_LDA/SVD_LDA/AUTO_LDA[default] bdc_svd when using SVD solver switch between Bidiagonal Divide and Conquer algorithm (BDC) and Jacobi's algorithm, for the differences
linalg::SVDAlgorithm. [default = BDC-SVD]

Definition at line 24 of file LDA.cpp.

## ◆ CLDA() [2/2]

 CLDA ( float64_t gamma, CDenseFeatures< float64_t > * traindat, CLabels * trainlab, ELDAMethod method = AUTO_LDA, bool bdc_svd = true )

constructor

Parameters
 gamma gamma traindat training features trainlab labels for training features method LDA using Fisher's algorithm or Singular Value Decomposition : FLD_LDA/SVD_LDA/AUTO_LDA[default] bdc_svd when using SVD solver switch between Bidiagonal Divide and Conquer algorithm (BDC-SVD) and Jacobi's algorithm, for the differences
linalg::SVDAlgorithm. [default = BDC-SVD]

Definition at line 33 of file LDA.cpp.

## ◆ ~CLDA()

 ~CLDA ( )
virtual

Definition at line 60 of file LDA.cpp.

## ◆ 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 problem

Parameters
 data (test)data to be classified
Returns
classified labels

Reimplemented from CMachine.

Definition at line 70 of file LinearMachine.cpp.

## ◆ apply_get_outputs()

 SGVector< float64_t > apply_get_outputs ( CFeatures * data )
protectedvirtualinherited

apply get outputs

Parameters
 data features to compute outputs
Returns
outputs

Definition at line 76 of file LinearMachine.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

Definition at line 227 of file Machine.cpp.

## ◆ apply_one()

 float64_t apply_one ( int32_t vec_idx )
virtualinherited

applies to one vector

Reimplemented from CMachine.

Definition at line 59 of file LinearMachine.cpp.

## ◆ apply_regression()

 CRegressionLabels * apply_regression ( CFeatures * data = NULL )
virtualinherited

apply linear machine to data for regression problem

Parameters
 data (test)data to be classified
Returns
classified labels

Reimplemented from CMachine.

Definition at line 64 of file LinearMachine.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.

 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.

## ◆ 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

Definition at line 734 of file SGObject.cpp.

## ◆ clone_parameters()

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 759 of file SGObject.cpp.

## ◆ compute_bias()

 void compute_bias ( CFeatures * data )
protectedinherited

Computes the added bias. The bias is computed as the mean error between the predictions and the true labels.

Definition at line 145 of file LinearMachine.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()

 float64_t get_bias ( )
virtualinherited

get bias

Returns
bias

Definition at line 113 of file LinearMachine.cpp.

## ◆ get_classifier_type()

 virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type LDA

Reimplemented from CMachine.

Definition at line 158 of file LDA.h.

## ◆ get_compute_bias()

 bool get_compute_bias ( )
virtualinherited

Get compute bias

Returns
compute_bias

Definition at line 123 of file LinearMachine.cpp.

## ◆ get_features()

 CDotFeatures * get_features ( )
virtualinherited

get features

Returns
features

Definition at line 135 of file LinearMachine.cpp.

## ◆ get_gamma()

 float64_t get_gamma ( )

get gamma

Returns
gamma

Definition at line 149 of file LDA.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_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()

 virtual const char* get_name ( ) const
virtual
Returns
object name

Reimplemented from CLinearMachine.

Definition at line 177 of file LDA.h.

## ◆ get_parameters_observable()

 SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

## ◆ get_solver_type()

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 109 of file Machine.cpp.

## ◆ get_w()

 SGVector< float64_t > get_w ( ) const
virtualinherited

get w

Returns
weight vector

Definition at line 98 of file LinearMachine.cpp.

## ◆ 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.

## ◆ init()

 void init ( )
protected

Definition at line 46 of file LDA.cpp.

## ◆ 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.

 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.

 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.

Definition at line 460 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
 ShogunException will be thrown if an error occurs.

Definition at line 455 of file SGObject.cpp.

## ◆ MACHINE_PROBLEM_TYPE()

 MACHINE_PROBLEM_TYPE ( PT_BINARY )

## ◆ 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.

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()

 void set_bias ( float64_t b )
virtualinherited

set bias

Parameters
 b new bias

Definition at line 108 of file LinearMachine.cpp.

## ◆ set_compute_bias()

 void set_compute_bias ( bool compute_bias )
virtualinherited

Set m_compute_bias

Determines if bias compution is considered or not

Parameters
 compute_bias new m_compute_bias

Definition at line 118 of file LinearMachine.cpp.

## ◆ set_features()

 virtual void set_features ( CDotFeatures * feat )
virtual

set features

Parameters
 feat features to set

Reimplemented from CLinearMachine.

Definition at line 167 of file LDA.h.

## ◆ set_gamma()

 void set_gamma ( float64_t gamma )

set gamma

Parameters
 gamma the new gamma

Definition at line 140 of file LDA.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_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_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()

 void set_w ( const SGVector< float64_t > src_w )
virtualinherited

set w

Parameters
 src_w new w

Definition at line 103 of file LinearMachine.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.

## ◆ solver_classic()

 bool solver_classic ( )
protected

Train the machine with the classic method based on the cholesky decomposition of the covariance matrix.

Parameters
 features training data labels labels for training data

Definition at line 153 of file LDA.cpp.

## ◆ solver_svd()

 bool solver_svd ( )
protected

Train the machine with the svd-based solver (

CFisherLDA).
Parameters
 features training data labels labels for training data

Definition at line 120 of file LDA.cpp.

## ◆ store_model_features()

 void store_model_features ( )
protectedvirtualinherited

Stores feature data of underlying model. Does nothing because Linear machines store the normal vector of the separating hyperplane and therefore the model anyway

Reimplemented from CMachine.

Definition at line 141 of file LinearMachine.cpp.

## ◆ 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

Returns
whether training was successful

Reimplemented from CMachine.

Reimplemented in CSGDQN.

Definition at line 169 of file LinearMachine.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
 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 )
protectedvirtual

train LDA 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 64 of file LDA.cpp.

## ◆ train_machine_templated()

 bool train_machine_templated ( )
protected

A templated specialization of the train_machine method

Parameters
 features training data labels labels for training data
train_machine

Definition at line 105 of file LDA.cpp.

## ◆ train_require_labels()

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

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.

## ◆ bias

 float64_t bias
protectedinherited

bias

Definition at line 195 of file LinearMachine.h.

## ◆ features

 CDotFeatures* features
protectedinherited

features

Definition at line 197 of file LinearMachine.h.

## ◆ io

 SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

## ◆ m_bdc_svd

 bool m_bdc_svd
protected

use bdc-svd algorithm

Definition at line 225 of file LDA.h.

## ◆ m_cancel_computation

 std::atomic m_cancel_computation
protectedinherited

Cancel computation

Definition at line 448 of file Machine.h.

## ◆ m_compute_bias

 bool m_compute_bias
protectedinherited

If true, bias is computed in train method

Definition at line 199 of file LinearMachine.h.

## ◆ m_data_locked

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 445 of file Machine.h.

## ◆ m_gamma

 float64_t m_gamma
protected

gamma

Definition at line 221 of file LDA.h.

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_method

 ELDAMethod m_method
protected

LDA mode

Definition at line 223 of file LDA.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

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 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.

## ◆ 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