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CKLDualInferenceMethodMinimizer Class Reference

Detailed Description

Build-in minimizer for KLDualInference.

Definition at line 48 of file KLDualInferenceMethod.h.

Inheritance diagram for CKLDualInferenceMethodMinimizer:
[legend]

Public Member Functions

 CKLDualInferenceMethodMinimizer ()
 
 CKLDualInferenceMethodMinimizer (FirstOrderCostFunction *fun)
 
virtual ~CKLDualInferenceMethodMinimizer ()
 
virtual float64_t minimize ()
 
virtual const char * get_name () const
 
virtual bool supports_batch_update () const
 
virtual void set_lbfgs_parameters (int32_t m=100, int32_t max_linesearch=1000, ELBFGSLineSearch linesearch=BACKTRACKING_STRONG_WOLFE, int32_t max_iterations=1000, float64_t delta=0.0, int32_t past=0, float64_t epsilon=1e-5, float64_t min_step=1e-20, float64_t max_step=1e+20, float64_t ftol=1e-4, float64_t wolfe=0.9, float64_t gtol=0.9, float64_t xtol=1e-16, float64_t orthantwise_c=0.0, int32_t orthantwise_start=0, int32_t orthantwise_end=1)
 
virtual void set_cost_function (FirstOrderCostFunction *fun)
 
virtual void unset_cost_function (bool is_unref=true)
 
virtual void set_penalty_weight (float64_t penalty_weight)
 
virtual void set_penalty_type (Penalty *penalty_type)
 
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
 
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 void init_minimization ()
 
virtual float64_t get_penalty (SGVector< float64_t > var)
 
virtual void update_gradient (SGVector< float64_t > gradient, SGVector< float64_t > var)
 
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

int32_t m_m
 
int32_t m_max_linesearch
 
int8_t m_linesearch_id
 
int32_t m_max_iterations
 
float64_t m_delta
 
int32_t m_past
 
float64_t m_epsilon
 
float64_t m_min_step
 
float64_t m_max_step
 
float64_t m_ftol
 
float64_t m_wolfe
 
float64_t m_gtol
 
float64_t m_xtol
 
float64_t m_orthantwise_c
 
int32_t m_orthantwise_start
 
int32_t m_orthantwise_end
 
SGVector< float64_tm_target_variable
 
FirstOrderCostFunctionm_fun
 
Penaltym_penalty_type
 
float64_t m_penalty_weight
 

Constructor & Destructor Documentation

Definition at line 51 of file KLDualInferenceMethod.h.

Constructor

Parameters
funa cost function

Definition at line 56 of file KLDualInferenceMethod.h.

virtual ~CKLDualInferenceMethodMinimizer ( )
virtual

Definition at line 58 of file KLDualInferenceMethod.h.

Member Function Documentation

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.

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.

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.

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

returns the name of the class

Returns
name CLBFGSMinimizer

Reimplemented from CLBFGSMinimizer.

Definition at line 66 of file KLDualInferenceMethod.h.

float64_t get_penalty ( SGVector< float64_t var)
protectedvirtualinherited

Get the penalty given target variables For L2 penalty, the target variable is \(w\) and the value of penalty is \(\lambda \frac{w^t w}{2}\), where \(\lambda\) is the weight of penalty

Parameters
varthe variable used in regularization

Definition at line 69 of file FirstOrderMinimizer.cpp.

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.

void init_minimization ( )
protectedvirtual

Init before minimization

Reimplemented from CLBFGSMinimizer.

Definition at line 122 of file KLDualInferenceMethod.cpp.

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.

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.

float64_t minimize ( )
virtual

Do minimization and get the optimal value

Returns
optimal value

Reimplemented from CLBFGSMinimizer.

Definition at line 132 of file KLDualInferenceMethod.cpp.

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

Definition at line 295 of file SGObject.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.

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_cost_function ( FirstOrderCostFunction fun)
virtualinherited

Set cost function used in the minimizer

Parameters
funthe cost function

Definition at line 42 of file FirstOrderMinimizer.cpp.

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_lbfgs_parameters ( int32_t  m = 100,
int32_t  max_linesearch = 1000,
ELBFGSLineSearch  linesearch = BACKTRACKING_STRONG_WOLFE,
int32_t  max_iterations = 1000,
float64_t  delta = 0.0,
int32_t  past = 0,
float64_t  epsilon = 1e-5,
float64_t  min_step = 1e-20,
float64_t  max_step = 1e+20,
float64_t  ftol = 1e-4,
float64_t  wolfe = 0.9,
float64_t  gtol = 0.9,
float64_t  xtol = 1e-16,
float64_t  orthantwise_c = 0.0,
int32_t  orthantwise_start = 0,
int32_t  orthantwise_end = 1 
)
virtualinherited

set L-BFGS parameters For details please see shogun/optimization/lbfgs/lbfgs.h

Parameters
mThe number of corrections to approximate the inverse hessian matrix. Default value is 100.
max_linesearchThe maximum number of trials to do line search for each L-BFGS update. Default value is 1000.
linesearchThe line search algorithm. Default value is using the backtracking with the strong Wolfe condition line search
max_iterationsThe maximum number of iterations for L-BFGS update. Default value is 1000.
deltaDelta for convergence test based on the change of function value. Default value is 0.
pastDistance for delta-based convergence test. Default value is 0.
epsilonEpsilon for convergence test based on the change of gradient. Default value is 1e-5
min_stepThe minimum step of the line search. The default value is 1e-20
max_stepThe maximum step of the line search. The default value is 1e+20
ftolA parameter used in Armijo condition. Default value is 1e-4
wolfeA parameter used in curvature condition. Default value is 0.9
gtolA parameter used in Morethuente linesearch to control the accuracy. Default value is 0.9
xtolThe machine precision for floating-point values. Default value is 1e-16.
orthantwise_cCoeefficient for the L1 norm of variables. This parameter should be set to zero for standard minimization problems. Setting this parameter to a positive value activates Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. Default value is 0.
orthantwise_startStart index for computing L1 norm of the variables. This parameter is valid only for OWL-QN method. Default value is 0.
orthantwise_endEnd index for computing L1 norm of the variables. Default value is 1.

Definition at line 100 of file LBFGSMinimizer.cpp.

void set_penalty_type ( Penalty penalty_type)
virtualinherited

Set the type of penalty For example, L2 penalty

Parameters
penalty_typethe type of penalty. If NULL is given, regularization is not enabled.

Definition at line 53 of file FirstOrderMinimizer.cpp.

void set_penalty_weight ( float64_t  penalty_weight)
virtualinherited

Set the weight of penalty

Parameters
penalty_weightthe weight of penalty, which is positive

Definition at line 63 of file FirstOrderMinimizer.cpp.

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 bool supports_batch_update ( ) const
virtualinherited

Does minimizer support batch update?

Returns
whether minimizer supports batch update

Implements FirstOrderMinimizer.

Definition at line 71 of file LBFGSMinimizer.h.

virtual void unset_cost_function ( bool  is_unref = true)
virtualinherited

Unset cost function used in the minimizer

Definition at line 94 of file FirstOrderMinimizer.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_gradient ( SGVector< float64_t gradient,
SGVector< float64_t var 
)
protectedvirtualinherited

Add gradient of the penalty wrt target variables to unpenalized gradient For least sqaure with L2 penalty,

\[ L2f(w)=f(w) + L2(w) \]

where \( f(w)=\sum_i{(y_i-w^T x_i)^2}\) is the least sqaure cost function and \(L2(w)=\lambda \frac{w^t w}{2}\) is the L2 penalty

Target variables is \(w\) Unpenalized gradient is \(\frac{\partial f(w) }{\partial w}\) Gradient of the penalty wrt target variables is \(\frac{\partial L2(w) }{\partial w}\)

Parameters
gradientunpenalized gradient wrt its target variable
varthe target variable

Definition at line 81 of file FirstOrderMinimizer.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

float64_t m_delta
protectedinherited

Delta for convergence test based on the change of function value.

Definition at line 157 of file LBFGSMinimizer.h.

float64_t m_epsilon
protectedinherited

Epsilon for convergence test based on the change of gradient.

Definition at line 163 of file LBFGSMinimizer.h.

float64_t m_ftol
protectedinherited

A parameter used in Armijo condition.

Definition at line 172 of file LBFGSMinimizer.h.

FirstOrderCostFunction* m_fun
protectedinherited

Cost function

Definition at line 146 of file FirstOrderMinimizer.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

float64_t m_gtol
protectedinherited

A parameter used in Morethuente linesearch to control the accuracy.

Definition at line 178 of file LBFGSMinimizer.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

int8_t m_linesearch_id
protectedinherited

Id for the line search algorithm.

Definition at line 151 of file LBFGSMinimizer.h.

int32_t m_m
protectedinherited

The number of corrections to approximate the inverse hessian matrix.

Definition at line 145 of file LBFGSMinimizer.h.

int32_t m_max_iterations
protectedinherited

The maximum number of iterations for L-BFGS update.

Definition at line 154 of file LBFGSMinimizer.h.

int32_t m_max_linesearch
protectedinherited

The maximum number of trials to do line search for each L-BFGS update.

Definition at line 148 of file LBFGSMinimizer.h.

float64_t m_max_step
protectedinherited

The maximum step of the line search.

Definition at line 169 of file LBFGSMinimizer.h.

float64_t m_min_step
protectedinherited

The minimum step of the line search.

Definition at line 166 of file LBFGSMinimizer.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

float64_t m_orthantwise_c
protectedinherited

Coeefficient for the L1 norm of variables.

Definition at line 184 of file LBFGSMinimizer.h.

int32_t m_orthantwise_end
protectedinherited

End index for computing L1 norm of the variables.

Definition at line 190 of file LBFGSMinimizer.h.

int32_t m_orthantwise_start
protectedinherited

Start index for computing L1 norm of the variables.

Definition at line 187 of file LBFGSMinimizer.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

int32_t m_past
protectedinherited

Distance for delta-based convergence test.

Definition at line 160 of file LBFGSMinimizer.h.

Penalty* m_penalty_type
protectedinherited

the type of penalty

Definition at line 149 of file FirstOrderMinimizer.h.

float64_t m_penalty_weight
protectedinherited

the weight of penalty

Definition at line 152 of file FirstOrderMinimizer.h.

SGVector<float64_t> m_target_variable
protectedinherited

Target variable

Definition at line 193 of file LBFGSMinimizer.h.

float64_t m_wolfe
protectedinherited

A parameter used in curvature condition.

Definition at line 175 of file LBFGSMinimizer.h.

float64_t m_xtol
protectedinherited

The machine precision for floating-point values.

Definition at line 181 of file LBFGSMinimizer.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation