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CGMNPSVM Class Reference

Detailed Description

Class GMNPSVM implements a one vs. rest MultiClass SVM.

It uses CGMNPLib for training (in true multiclass-SVM fashion).

Definition at line 24 of file GMNPSVM.h.

Inheritance diagram for CGMNPSVM:
Inheritance graph
[legend]

List of all members.

Public Member Functions

 CGMNPSVM ()
 CGMNPSVM (float64_t C, CKernel *k, CLabels *lab)
virtual ~CGMNPSVM ()
virtual EMachineType get_classifier_type ()
float64_tget_basealphas_ptr (index_t *y, index_t *x)
virtual const char * get_name () const
 MACHINE_PROBLEM_TYPE (PT_MULTICLASS)
bool create_multiclass_svm (int32_t num_classes)
bool set_svm (int32_t num, CSVM *svm)
CSVMget_svm (int32_t num)
bool load (FILE *svm_file)
bool save (FILE *svm_file)
SGVector< float64_tget_linear_term ()
float64_t get_tube_epsilon ()
float64_t get_epsilon ()
float64_t get_nu ()
float64_t get_C ()
int32_t get_qpsize ()
bool get_shrinking_enabled ()
float64_t get_objective ()
bool get_bias_enabled ()
bool get_linadd_enabled ()
bool get_batch_computation_enabled ()
void set_defaults (int32_t num_sv=0)
void set_linear_term (SGVector< float64_t > linear_term)
void set_C (float64_t C)
void set_epsilon (float64_t eps)
void set_nu (float64_t nue)
void set_tube_epsilon (float64_t eps)
void set_qpsize (int32_t qps)
void set_shrinking_enabled (bool enable)
void set_objective (float64_t v)
void set_bias_enabled (bool enable_bias)
void set_linadd_enabled (bool enable)
void set_batch_computation_enabled (bool enable)
void set_kernel (CKernel *k)
CKernelget_kernel ()
virtual void store_model_features ()
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 CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual
CMulticlassMultipleOutputLabels
apply_multiclass_multiple_output (CFeatures *data=NULL, int32_t n_outputs=5)
virtual float64_t apply_one (int32_t vec_idx)
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
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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)
virtual bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
CSVMsvm_proto ()
SGVector< int32_t > svm_svs ()
virtual bool init_machines_for_apply (CFeatures *data)
virtual bool is_acceptable_machine (CMachine *machine)
virtual bool init_machine_for_train (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 ()
void init_strategy ()
void clear_machines ()
virtual bool train_require_labels () const
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
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)

Protected Attributes

float64_tm_basealphas
index_t m_basealphas_y
index_t m_basealphas_x
float64_t m_C
CKernelm_kernel
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

Constructor & Destructor Documentation

CGMNPSVM ( )

default constructor

Definition at line 25 of file GMNPSVM.cpp.

CGMNPSVM ( float64_t  C,
CKernel k,
CLabels lab 
)

constructor

Parameters:
Cconstant C
kkernel
lablabels

Definition at line 31 of file GMNPSVM.cpp.

~CGMNPSVM ( ) [virtual]

default destructor

Definition at line 37 of file GMNPSVM.cpp.


Member Function Documentation

void add_machine_subset ( SGVector< index_t subset) [protected, virtual, inherited]

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

Parameters:
subsetsubset indices to set

Implements CMulticlassMachine.

Definition at line 175 of file KernelMulticlassMachine.cpp.

CLabels * apply ( CFeatures data = NULL) [virtual, inherited]

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 162 of file Machine.cpp.

CBinaryLabels * apply_binary ( CFeatures data = NULL) [virtual, inherited]

apply machine to data in means of binary classification problem

Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CLinearMachine, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, CGaussianProcessBinaryClassification, and CBaggingMachine.

Definition at line 218 of file Machine.cpp.

CLatentLabels * apply_latent ( CFeatures data = NULL) [virtual, inherited]

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 242 of file Machine.cpp.

CLabels * apply_locked ( SGVector< index_t indices) [virtual, inherited]

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 197 of file Machine.cpp.

CBinaryLabels * apply_locked_binary ( SGVector< index_t indices) [virtual, inherited]

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 248 of file Machine.cpp.

CLatentLabels * apply_locked_latent ( SGVector< index_t indices) [virtual, inherited]

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

Definition at line 276 of file Machine.cpp.

CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t indices) [virtual, inherited]

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

Definition at line 262 of file Machine.cpp.

CRegressionLabels * apply_locked_regression ( SGVector< index_t indices) [virtual, inherited]

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

Reimplemented in CKernelMachine.

Definition at line 255 of file Machine.cpp.

CStructuredLabels * apply_locked_structured ( SGVector< index_t indices) [virtual, inherited]

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

Definition at line 269 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures data = NULL) [virtual, inherited]

classify all examples

Returns:
resulting labels

Reimplemented from CMachine.

Reimplemented in CGaussianNaiveBayes, CMCLDA, and CQDA.

Definition at line 94 of file MulticlassMachine.cpp.

CMulticlassMultipleOutputLabels * apply_multiclass_multiple_output ( CFeatures data = NULL,
int32_t  n_outputs = 5 
) [virtual, inherited]

classify all examples with multiple output

Returns:
resulting labels

Definition at line 196 of file MulticlassMachine.cpp.

float64_t apply_one ( int32_t  vec_idx) [virtual, inherited]

classify one example

Parameters:
vec_idx
Returns:
label

Reimplemented from CMachine.

Reimplemented in CScatterSVM, and CGaussianNaiveBayes.

Definition at line 284 of file MulticlassMachine.cpp.

CRegressionLabels * apply_regression ( CFeatures data = NULL) [virtual, inherited]

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.

Definition at line 224 of file Machine.cpp.

CStructuredLabels * apply_structured ( CFeatures data = NULL) [virtual, inherited]

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 236 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:
dictdictionary of parameters to be built.

Definition at line 1156 of file SGObject.cpp.

void clear_machines ( ) [protected, inherited]

clear machines

Reimplemented in CNativeMulticlassMachine.

CSGObject * clone ( ) [virtual, inherited]

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 1273 of file SGObject.cpp.

bool create_multiclass_svm ( int32_t  num_classes) [inherited]

create multiclass SVM. Appends the appropriate number of svm pointer (depending on multiclass strategy) to m_machines. All pointers are initialized with NULL.

Parameters:
num_classesnumber of classes in SVM
Returns:
if creation was successful

Definition at line 48 of file MulticlassSVM.cpp.

void data_lock ( CLabels labs,
CFeatures features 
) [virtual, inherited]

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 122 of file Machine.cpp.

void data_unlock ( ) [virtual, inherited]

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 153 of file Machine.cpp.

virtual CSGObject* deep_copy ( ) const [virtual, inherited]

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

Definition at line 126 of file SGObject.h.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0 
) [virtual, inherited]

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

Definition at line 1177 of file SGObject.cpp.

required for CMKLMulticlass constraint computation

Parameters:
yheight of basealphas
xwidth of basealphas
Returns:
basealphas basealphas[k][j] is the alpha for class k and sample j which is untransformed compared to the alphas stored in CSVM* members

Definition at line 186 of file GMNPSVM.cpp.

bool get_batch_computation_enabled ( ) [inherited]

get batch computation option of base SVM

Returns:
whether batch computation of base SVM is enabled

Definition at line 144 of file MulticlassSVM.h.

bool get_bias_enabled ( ) [inherited]

get bias enabled options of base SVM

Returns:
whether bias of base SVM is enabled

Definition at line 134 of file MulticlassSVM.h.

float64_t get_C ( ) [inherited]

get C of base SVM

Returns:
C of base SVM

Definition at line 113 of file MulticlassSVM.h.

virtual EMachineType get_classifier_type ( ) [virtual]

get classifier type

Returns:
classifier type GMNPSVM

Reimplemented from CMachine.

Definition at line 47 of file GMNPSVM.h.

float64_t get_epsilon ( ) [inherited]

get epsilon of base SVM

Returns:
epsilon of base SVM

Definition at line 103 of file MulticlassSVM.h.

SGIO * get_global_io ( ) [inherited]

get the io object

Returns:
io object

Definition at line 174 of file SGObject.cpp.

Parallel * get_global_parallel ( ) [inherited]

get the parallel object

Returns:
parallel object

Definition at line 209 of file SGObject.cpp.

Version * get_global_version ( ) [inherited]

get the version object

Returns:
version object

Definition at line 222 of file SGObject.cpp.

CKernel * get_kernel ( ) [inherited]

get kernel

Returns:
kernel

Definition at line 119 of file KernelMulticlassMachine.cpp.

CLabels * get_labels ( ) [virtual, inherited]

get labels

Returns:
labels

Definition at line 86 of file Machine.cpp.

bool get_linadd_enabled ( ) [inherited]

get linadd option of base SVM

Returns:
whether linadd of base SVM is enabled

Definition at line 139 of file MulticlassSVM.h.

get linear term of base SVM

Returns:
linear term of base SVM

Definition at line 93 of file MulticlassSVM.h.

CMachine* get_machine ( int32_t  num) const [inherited]

get machine

Parameters:
numindex of machine to get
Returns:
SVM at number num

Definition at line 72 of file MulticlassMachine.h.

CMachine * get_machine_from_trained ( CMachine machine) [protected, virtual, inherited]

construct kernel machine from given kernel machine

Implements CMulticlassMachine.

Definition at line 165 of file KernelMulticlassMachine.cpp.

EProblemType get_machine_problem_type ( ) const [virtual, inherited]

get problem type

Reimplemented from CMachine.

Definition at line 32 of file BaseMulticlassMachine.cpp.

float64_t get_max_train_time ( ) [inherited]

get maximum training time

Returns:
maximum training time

Definition at line 97 of file Machine.cpp.

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

Definition at line 1060 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 1084 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 1097 of file SGObject.cpp.

get the type of multiclass'ness

Returns:
multiclass type one vs one etc

Definition at line 112 of file MulticlassMachine.h.

virtual const char* get_name ( ) const [virtual]
Returns:
object name

Reimplemented from CMulticlassSVM.

Definition at line 61 of file GMNPSVM.h.

float64_t get_nu ( ) [inherited]

get nu of base SVM

Returns:
nu of base SVM

Definition at line 108 of file MulticlassSVM.h.

int32_t get_num_machines ( ) const [inherited]

get number of machines

Returns:
number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

int32_t get_num_rhs_vectors ( ) [protected, virtual, inherited]

return number of rhs feature vectors

Implements CMulticlassMachine.

Definition at line 170 of file KernelMulticlassMachine.cpp.

float64_t get_objective ( ) [inherited]

get objective of base SVM

Returns:
objective of base SVM

Definition at line 128 of file MulticlassSVM.h.

get prob output heuristic of multiclass strategy

Definition at line 143 of file MulticlassMachine.h.

int32_t get_qpsize ( ) [inherited]

get qpsize of base SVM

Returns:
qpsize of base SVM

Definition at line 118 of file MulticlassSVM.h.

returns rejection strategy

Returns:
rejection strategy

Definition at line 122 of file MulticlassMachine.h.

bool get_shrinking_enabled ( ) [inherited]

get shrinking option of base SVM

Returns:
whether shrinking of base SVM is enabled

Definition at line 123 of file MulticlassSVM.h.

ESolverType get_solver_type ( ) [inherited]

get solver type

Returns:
solver

Definition at line 112 of file Machine.cpp.

float64_t get_submachine_output ( int32_t  i,
int32_t  num 
) [virtual, inherited]

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

Parameters:
inumber of submachine
numnumber of feature vector
Returns:
output

Definition at line 81 of file MulticlassMachine.cpp.

CBinaryLabels * get_submachine_outputs ( int32_t  i) [virtual, inherited]

get outputs of i-th submachine

Parameters:
inumber of submachine
Returns:
outputs

Reimplemented in CDomainAdaptationMulticlassLibLinear.

Definition at line 72 of file MulticlassMachine.cpp.

CSVM* get_svm ( int32_t  num) [inherited]

get SVM

Parameters:
numwhich SVM to get
Returns:
SVM at number num

Definition at line 74 of file MulticlassSVM.h.

float64_t get_tube_epsilon ( ) [inherited]

get tube epsilon of base SVM

Returns:
tube epsilon of base SVM

Definition at line 98 of file MulticlassSVM.h.

bool init_machine_for_train ( CFeatures data) [protected, virtual, inherited]

init machine for training with kernel init

Implements CMulticlassMachine.

Definition at line 125 of file KernelMulticlassMachine.cpp.

bool init_machines_for_apply ( CFeatures data) [protected, virtual, inherited]

initializes machines (OvO, OvR) for apply

Reimplemented from CKernelMulticlassMachine.

Definition at line 73 of file MulticlassSVM.cpp.

void init_strategy ( ) [protected, inherited]

init strategy

Reimplemented in CNativeMulticlassMachine.

Definition at line 66 of file MulticlassMachine.cpp.

virtual bool is_acceptable_machine ( CMachine machine) [protected, virtual, inherited]

is machine an SVM instance

Reimplemented from CMulticlassMachine.

Definition at line 230 of file MulticlassSVM.h.

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

Definition at line 289 of file Machine.h.

bool is_generic ( EPrimitiveType *  generic) const [virtual, inherited]

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 228 of file SGObject.cpp.

bool is_label_valid ( CLabels lab) const [virtual, inherited]

check whether the labels is valid.

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

Reimplemented from CMachine.

Definition at line 37 of file BaseMulticlassMachine.cpp.

bool is_ready ( ) [protected, virtual, inherited]

check kernel availability

Implements CMulticlassMachine.

Definition at line 157 of file KernelMulticlassMachine.cpp.

bool load ( FILE *  svm_file) [inherited]

load a Multiclass SVM from file

Parameters:
svm_filethe file handle

Definition at line 111 of file MulticlassSVM.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters:
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns:
(sorted) array of created TParameter instances with file data

Definition at line 633 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters:
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns:
new array with TParameter instances with the attached data

Definition at line 474 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns:
TRUE if done, otherwise FALSE

Definition at line 305 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 989 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 984 of file SGObject.cpp.

problem type

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
) [inherited]

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters:
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 671 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
) [protected, virtual, inherited]

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters:
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns:
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 878 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
) [protected, virtual, inherited]

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters:
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 818 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
) [virtual, inherited]

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 280 of file Machine.h.

void print_modsel_params ( ) [inherited]

prints all parameter registered for model selection and their type

Definition at line 1036 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "") [virtual, inherited]

prints registered parameters out

Parameters:
prefixprefix for members

Definition at line 240 of file SGObject.cpp.

void remove_machine_subset ( ) [protected, virtual, inherited]

deletes any subset set to the features of the machine

Implements CMulticlassMachine.

Definition at line 180 of file KernelMulticlassMachine.cpp.

bool save ( FILE *  svm_file) [inherited]

write a Multiclass SVM to a file

Parameters:
svm_filethe file handle

Definition at line 259 of file MulticlassSVM.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns:
TRUE if done, otherwise FALSE

Definition at line 246 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

Reimplemented in CKernel.

Definition at line 999 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 994 of file SGObject.cpp.

void set_batch_computation_enabled ( bool  enable) [inherited]

set batch computation option

Parameters:
enablewhether batch computation should be enabled

Definition at line 205 of file MulticlassSVM.h.

void set_bias_enabled ( bool  enable_bias) [inherited]

set bias option

Parameters:
enable_biaswhether bias should be enabled

Definition at line 195 of file MulticlassSVM.h.

void set_C ( float64_t  C) [inherited]

set C parameters

Parameters:
Cset regularization parameter

Definition at line 160 of file MulticlassSVM.h.

void set_defaults ( int32_t  num_sv = 0) [inherited]

set default number of support vectors

Parameters:
num_svnumber of support vectors

Definition at line 150 of file MulticlassSVM.h.

void set_epsilon ( float64_t  eps) [inherited]

set epsilon value

Parameters:
epsepsilon value

Definition at line 165 of file MulticlassSVM.h.

void set_generic< complex128_t > ( ) [inherited]

set generic type to T

Definition at line 41 of file SGObject.cpp.

void set_global_io ( SGIO io) [inherited]

set the io object

Parameters:
ioio object to use

Definition at line 167 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel) [inherited]

set the parallel object

Parameters:
parallelparallel object to use

Definition at line 180 of file SGObject.cpp.

void set_global_version ( Version version) [inherited]

set the version object

Parameters:
versionversion object to use

Definition at line 215 of file SGObject.cpp.

void set_kernel ( CKernel k) [inherited]

set kernel

Parameters:
kkernel

Definition at line 111 of file KernelMulticlassMachine.cpp.

void set_labels ( CLabels lab) [virtual, inherited]

set labels

Parameters:
lablabels

Reimplemented from CMachine.

Definition at line 53 of file MulticlassMachine.cpp.

void set_linadd_enabled ( bool  enable) [inherited]

set linadd option

Parameters:
enablewhether linadd should be enabled

Definition at line 200 of file MulticlassSVM.h.

void set_linear_term ( SGVector< float64_t linear_term) [inherited]

set linear term

Parameters:
linear_termlinear term vector

Definition at line 155 of file MulticlassSVM.h.

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 57 of file MulticlassMachine.h.

void set_max_train_time ( float64_t  t) [inherited]

set maximum training time

Parameters:
tmaximimum training time

Definition at line 92 of file Machine.cpp.

void set_nu ( float64_t  nue) [inherited]

set nu value

Parameters:
nuenu value

Definition at line 170 of file MulticlassSVM.h.

void set_objective ( float64_t  v) [inherited]

set objective value

Parameters:
vobjective value

Definition at line 190 of file MulticlassSVM.h.

void set_prob_heuris ( EProbHeuristicType  prob_heuris) [inherited]

set prob output heuristic of multiclass strategy

Parameters:
prob_heuristype of probability heuristic

Definition at line 151 of file MulticlassMachine.h.

void set_qpsize ( int32_t  qps) [inherited]

set set QP size

Parameters:
qpsqp size

Definition at line 180 of file MulticlassSVM.h.

void set_rejection_strategy ( CRejectionStrategy rejection_strategy) [inherited]

sets rejection strategy

Parameters:
rejection_strategyrejection strategy to be set

Definition at line 131 of file MulticlassMachine.h.

void set_shrinking_enabled ( bool  enable) [inherited]

set shrinking option

Parameters:
enablewhether shrinking should be enabled

Definition at line 185 of file MulticlassSVM.h.

void set_solver_type ( ESolverType  st) [inherited]

set solver type

Parameters:
stsolver type

Definition at line 107 of file Machine.cpp.

void set_store_model_features ( bool  store_model) [virtual, inherited]

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

Parameters:
store_modelwhether model should be stored after training

Definition at line 117 of file Machine.cpp.

bool set_svm ( int32_t  num,
CSVM svm 
) [inherited]

set SVM

Parameters:
numnumber to set
svmSVM to set
Returns:
if setting was successful

Definition at line 63 of file MulticlassSVM.cpp.

void set_tube_epsilon ( float64_t  eps) [inherited]

set tube epsilon value

Parameters:
epstube epsilon value

Definition at line 175 of file MulticlassSVM.h.

virtual CSGObject* shallow_copy ( ) const [virtual, inherited]

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

Reimplemented in CGaussianKernel.

Definition at line 117 of file SGObject.h.

void store_model_features ( ) [virtual, inherited]

Stores feature data of underlying model.

Need to store the SVs for all sub-machines. We make a union of the SVs for all sub-machines, store the union and adjust the sub-machines to index into the union.

Reimplemented from CMachine.

Definition at line 17 of file KernelMulticlassMachine.cpp.

virtual bool supports_locking ( ) const [virtual, inherited]
Returns:
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 286 of file Machine.h.

CSVM* svm_proto ( ) [protected, inherited]

casts m_machine to SVM

Definition at line 216 of file MulticlassSVM.h.

SGVector<int32_t> svm_svs ( ) [protected, inherited]

returns support vectors

Definition at line 221 of file MulticlassSVM.h.

bool train ( CFeatures data = NULL) [virtual, inherited]

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, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

virtual bool train_locked ( SGVector< index_t indices) [virtual, inherited]

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, and CMultitaskLinearMachine.

Definition at line 232 of file Machine.h.

bool train_machine ( CFeatures data = NULL) [protected, virtual]

train SVM

Parameters:
datatraining 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 CMulticlassMachine.

Definition at line 53 of file GMNPSVM.cpp.

virtual bool train_require_labels ( ) const [protected, virtual, inherited]

returns whether machine require labels for training

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

Definition at line 347 of file Machine.h.

void unset_generic ( ) [inherited]

unset generic type

this has to be called in classes specializing a template class

Definition at line 235 of file SGObject.cpp.

bool update_parameter_hash ( ) [virtual, inherited]

Updates the hash of current parameter combination.

Returns:
bool if parameter combination has changed since last update.

Definition at line 187 of file SGObject.cpp.


Member Data Documentation

SGIO* io [inherited]

io

Definition at line 473 of file SGObject.h.

float64_t* m_basealphas [protected]

required for CMKLMulticlass stores the untransformed alphas of this algorithm whereas CSVM* members stores a transformed version of it m_basealphas[k][j] is the alpha for class k and sample j

Definition at line 81 of file GMNPSVM.h.

index_t m_basealphas_x [protected]

base alphas x

Definition at line 85 of file GMNPSVM.h.

index_t m_basealphas_y [protected]

base alphas y

Definition at line 83 of file GMNPSVM.h.

float64_t m_C [protected, inherited]

C regularization constant

Definition at line 245 of file MulticlassSVM.h.

bool m_data_locked [protected, inherited]

whether data is locked

Definition at line 363 of file Machine.h.

parameters wrt which we can compute gradients

Definition at line 488 of file SGObject.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 494 of file SGObject.h.

CKernel* m_kernel [protected, inherited]

kernel

Definition at line 100 of file KernelMulticlassMachine.h.

CLabels* m_labels [protected, inherited]

labels

Definition at line 354 of file Machine.h.

CMachine* m_machine [protected, inherited]

machine

Definition at line 206 of file MulticlassMachine.h.

CDynamicObjectArray* m_machines [protected, inherited]

machines

Definition at line 53 of file BaseMulticlassMachine.h.

float64_t m_max_train_time [protected, inherited]

maximum training time

Definition at line 351 of file Machine.h.

model selection parameters

Definition at line 485 of file SGObject.h.

CMulticlassStrategy* m_multiclass_strategy [protected, inherited]

type of multiclass strategy

Definition at line 203 of file MulticlassMachine.h.

map for different parameter versions

Definition at line 491 of file SGObject.h.

Parameter* m_parameters [inherited]

parameters

Definition at line 482 of file SGObject.h.

ESolverType m_solver_type [protected, inherited]

solver type

Definition at line 357 of file Machine.h.

bool m_store_model_features [protected, inherited]

whether model features should be stored after training

Definition at line 360 of file Machine.h.

Parallel* parallel [inherited]

parallel

Definition at line 476 of file SGObject.h.

Version* version [inherited]

version

Definition at line 479 of file SGObject.h.


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