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SHOGUN
4.2.0
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SHOGUN
4.2.0
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Class CFWSOSVM solves SOSVM using Frank-Wolfe algorithm [1].
[1] S. Lacoste-Julien, M. Jaggi, M. Schmidt and P. Pletscher. Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. ICML 2013.
Public Member Functions | |
| CFWSOSVM () | |
| CFWSOSVM (CStructuredModel *model, CStructuredLabels *labs, bool do_line_search=true, bool verbose=false) | |
| ~CFWSOSVM () | |
| virtual const char * | get_name () const |
| virtual EMachineType | get_classifier_type () |
| float64_t | get_lambda () const |
| void | set_lambda (float64_t lbda) |
| int32_t | get_num_iter () const |
| void | set_num_iter (int32_t num_iter) |
| float64_t | get_gap_threshold () const |
| void | set_gap_threshold (float64_t gap_threshold) |
| float64_t | get_ell () const |
| void | set_ell (float64_t ell) |
| void | set_w (SGVector< float64_t > w) |
| SGVector< float64_t > | get_w () const |
| virtual CStructuredLabels * | apply_structured (CFeatures *data=NULL) |
| virtual void | store_model_features () |
| MACHINE_PROBLEM_TYPE (PT_STRUCTURED) | |
| void | set_model (CStructuredModel *model) |
| CStructuredModel * | get_model () const |
| virtual void | set_labels (CLabels *lab) |
| void | set_features (CFeatures *f) |
| CFeatures * | get_features () const |
| void | set_surrogate_loss (CLossFunction *loss) |
| CLossFunction * | get_surrogate_loss () const |
| virtual float64_t | risk (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0, EStructRiskType rtype=N_SLACK_MARGIN_RESCALING) |
| CSOSVMHelper * | get_helper () const |
| void | set_verbose (bool verbose) |
| bool | get_verbose () const |
| virtual bool | train (CFeatures *data=NULL) |
| virtual CLabels * | apply (CFeatures *data=NULL) |
| virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
| virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
| virtual CMulticlassLabels * | apply_multiclass (CFeatures *data=NULL) |
| virtual CLatentLabels * | apply_latent (CFeatures *data=NULL) |
| virtual CLabels * | get_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 float64_t | apply_one (int32_t i) |
| virtual CLabels * | apply_locked (SGVector< index_t > indices) |
| virtual CBinaryLabels * | apply_locked_binary (SGVector< index_t > indices) |
| virtual CRegressionLabels * | apply_locked_regression (SGVector< index_t > indices) |
| virtual CMulticlassLabels * | apply_locked_multiclass (SGVector< index_t > indices) |
| virtual CStructuredLabels * | apply_locked_structured (SGVector< index_t > indices) |
| virtual CLatentLabels * | apply_locked_latent (SGVector< index_t > indices) |
| virtual void | data_lock (CLabels *labs, CFeatures *features) |
| virtual void | post_lock (CLabels *labs, CFeatures *features) |
| virtual void | data_unlock () |
| virtual bool | supports_locking () const |
| bool | is_data_locked () const |
| virtual EProblemType | get_machine_problem_type () const |
| virtual CSGObject * | shallow_copy () const |
| virtual CSGObject * | deep_copy () const |
| virtual bool | is_generic (EPrimitiveType *generic) const |
| template<class T > | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| template<> | |
| void | set_generic () |
| void | unset_generic () |
| virtual void | print_serializable (const char *prefix="") |
| virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
| virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
| void | set_global_io (SGIO *io) |
| SGIO * | get_global_io () |
| void | set_global_parallel (Parallel *parallel) |
| Parallel * | get_global_parallel () |
| void | set_global_version (Version *version) |
| Version * | get_global_version () |
| SGStringList< char > | get_modelsel_names () |
| void | print_modsel_params () |
| char * | get_modsel_param_descr (const char *param_name) |
| index_t | get_modsel_param_index (const char *param_name) |
| void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
| bool | has (const std::string &name) const |
| template<typename T > | |
| bool | has (const Tag< T > &tag) const |
| template<typename T , typename U = void> | |
| bool | has (const std::string &name) const |
| template<typename T > | |
| void | set (const Tag< T > &_tag, const T &value) |
| template<typename T , typename U = void> | |
| void | set (const std::string &name, const T &value) |
| template<typename T > | |
| T | get (const Tag< T > &_tag) const |
| template<typename T , typename U = void> | |
| T | get (const std::string &name) const |
| virtual void | update_parameter_hash () |
| virtual bool | parameter_hash_changed () |
| virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
| virtual CSGObject * | clone () |
Public Attributes | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| uint32_t | m_hash |
Protected Member Functions | |
| virtual bool | train_machine (CFeatures *data=NULL) |
| virtual float64_t | risk_nslack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_nslack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_1slack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_1slack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_customized_formulation (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual bool | is_label_valid (CLabels *lab) const |
| virtual bool | train_require_labels () const |
| virtual void | load_serializable_pre () throw (ShogunException) |
| virtual void | load_serializable_post () throw (ShogunException) |
| virtual void | save_serializable_pre () throw (ShogunException) |
| virtual void | save_serializable_post () throw (ShogunException) |
| template<typename T > | |
| void | register_param (Tag< T > &_tag, const T &value) |
| template<typename T > | |
| void | register_param (const std::string &name, const T &value) |
Protected Attributes | |
| SGVector< float64_t > | m_w |
| CStructuredModel * | m_model |
| CLossFunction * | m_surrogate_loss |
| CSOSVMHelper * | m_helper |
| bool | m_verbose |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
| CFWSOSVM | ( | ) |
default constructor
Definition at line 18 of file FWSOSVM.cpp.
| CFWSOSVM | ( | CStructuredModel * | model, |
| CStructuredLabels * | labs, | ||
| bool | do_line_search = true, |
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| bool | verbose = false |
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| ) |
standard constructor
| model | structured model with application specific functions |
| labs | structured labels |
| do_line_search | whether do analytical line search |
| verbose | whether compute debug information, such as primal value, duality gap etc. |
Definition at line 24 of file FWSOSVM.cpp.
| ~CFWSOSVM | ( | ) |
destructor
Definition at line 58 of file FWSOSVM.cpp.
apply machine to data if data is not specified apply to the current features
| data | (test)data to be classified |
Definition at line 152 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.
Definition at line 208 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 232 of file Machine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
Definition at line 187 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine.
Definition at line 238 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
Definition at line 266 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
Definition at line 252 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 245 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
Definition at line 259 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of multiclass classification problem
Reimplemented in CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CKNN, CMulticlassMachine, CC45ClassifierTree, CID3ClassifierTree, CQDA, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CRelaxedTree, and CBaggingMachine.
Definition at line 220 of file Machine.cpp.
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virtualinherited |
applies to one vector
Reimplemented in CKernelMachine, CRelaxedTree, COnlineLinearMachine, CLinearMachine, CKNN, CMulticlassMachine, CDistanceMachine, CScatterSVM, CGaussianNaiveBayes, and CPluginEstimate.
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virtualinherited |
apply machine to data in means of regression problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CNeuralNetwork, CLinearMachine, CCHAIDTree, CStochasticGBMachine, CCARTree, CGaussianProcessRegression, and CBaggingMachine.
Definition at line 214 of file Machine.cpp.
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virtualinherited |
apply structured machine to data for Structured Output (SO) problem
| data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 45 of file LinearStructuredOutputMachine.cpp.
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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.
| dict | dictionary of parameters to be built. |
Definition at line 630 of file SGObject.cpp.
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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.
Definition at line 747 of file SGObject.cpp.
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
| labs | labels used for locking |
| features | features used for locking |
Reimplemented in CKernelMachine.
Definition at line 112 of file Machine.cpp.
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virtualinherited |
Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 143 of file Machine.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
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.
| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
| tolerant | allows linient check on float equality (within accuracy) |
Definition at line 651 of file SGObject.cpp.
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inherited |
Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
| _tag | name and type information of parameter |
Definition at line 367 of file SGObject.h.
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inherited |
Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
| name | name of the parameter |
Definition at line 388 of file SGObject.h.
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virtual |
get classifier type
Reimplemented from CMachine.
Definition at line 62 of file FWSOSVM.cpp.
| float64_t get_ell | ( | ) | const |
Definition at line 240 of file FWSOSVM.cpp.
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inherited |
| float64_t get_gap_threshold | ( | ) | const |
Definition at line 230 of file FWSOSVM.cpp.
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inherited |
Definition at line 186 of file StructuredOutputMachine.cpp.
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virtualinherited |
| float64_t get_lambda | ( | ) | const |
Definition at line 210 of file FWSOSVM.cpp.
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returns type of problem machine solves
Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.
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get structured model
Definition at line 50 of file StructuredOutputMachine.cpp.
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Definition at line 531 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
Definition at line 555 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
Definition at line 568 of file SGObject.cpp.
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virtual |
Reimplemented from CLinearStructuredOutputMachine.
| int32_t get_num_iter | ( | ) | const |
Definition at line 220 of file FWSOSVM.cpp.
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get surrogate loss function
Definition at line 91 of file StructuredOutputMachine.cpp.
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get verbose
Definition at line 203 of file StructuredOutputMachine.cpp.
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Checks if object has a class parameter identified by a name.
| name | name of the parameter |
Definition at line 289 of file SGObject.h.
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inherited |
Checks if object has a class parameter identified by a Tag.
| tag | tag of the parameter containing name and type information |
Definition at line 301 of file SGObject.h.
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inherited |
Checks if a type exists for a class parameter identified by a name.
| name | name of the parameter |
Definition at line 312 of file SGObject.h.
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inherited |
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
Definition at line 329 of file SGObject.cpp.
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protectedvirtualinherited |
check whether the labels is valid.
Subclasses can override this to implement their check of label types.
| lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented in CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from |
| prefix | prefix for members |
Definition at line 402 of file SGObject.cpp.
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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.
| ShogunException | will 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.
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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.
| ShogunException | will 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.
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inherited |
problem type
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virtualinherited |
Definition at line 295 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
| prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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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.
| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 439 of file SGObject.h.
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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.
| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
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computes the value of the risk function and sub-gradient at given point
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
| rtype | The type of structured risk |
Definition at line 157 of file StructuredOutputMachine.cpp.
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protectedvirtualinherited |
1-slack formulation and margin rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
Definition at line 139 of file StructuredOutputMachine.cpp.
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protectedvirtualinherited |
1-slack formulation and slack rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
Definition at line 145 of file StructuredOutputMachine.cpp.
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protectedvirtualinherited |
customized risk type
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
Definition at line 151 of file StructuredOutputMachine.cpp.
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protectedvirtualinherited |
n-slack formulation and margin rescaling
The value of the risk is evaluated as
\[ R({\bf w}) = \sum_{i=1}^{m} \max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) - \Psi(x_i, y_i) \rangle \right] \]
The subgradient is by Danskin's theorem given as
\[ R'({\bf w}) = \sum_{i=1}^{m} \Psi(x_i, \hat{y}_i) - \Psi(x_i, y_i), \]
where \( \hat{y}_i \) is the most violated label, i.e.
\[ \hat{y}_i = \arg\max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) \rangle \right] \]
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
Definition at line 97 of file StructuredOutputMachine.cpp.
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protectedvirtualinherited |
n-slack formulation and slack rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
Definition at line 133 of file StructuredOutputMachine.cpp.
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Save this object to file.
| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
Definition at line 347 of file SGObject.cpp.
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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.
| ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 469 of file SGObject.cpp.
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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.
| ShogunException | will 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.
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inherited |
Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
| _tag | name and type information of parameter |
| value | value of the parameter |
Definition at line 328 of file SGObject.h.
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inherited |
Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
| name | name of the parameter |
| value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
| void set_ell | ( | float64_t | ell | ) |
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inherited |
| void set_gap_threshold | ( | float64_t | gap_threshold | ) |
set threshold of the duality gap
| gap_threshold | threshold of the duality gap |
Definition at line 235 of file FWSOSVM.cpp.
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Definition at line 74 of file SGObject.cpp.
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Definition at line 79 of file SGObject.cpp.
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Definition at line 84 of file SGObject.cpp.
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Definition at line 89 of file SGObject.cpp.
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Definition at line 94 of file SGObject.cpp.
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Definition at line 99 of file SGObject.cpp.
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Definition at line 104 of file SGObject.cpp.
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Definition at line 109 of file SGObject.cpp.
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Definition at line 114 of file SGObject.cpp.
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Definition at line 119 of file SGObject.cpp.
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Definition at line 124 of file SGObject.cpp.
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Definition at line 129 of file SGObject.cpp.
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Definition at line 134 of file SGObject.cpp.
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Definition at line 139 of file SGObject.cpp.
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Definition at line 144 of file SGObject.cpp.
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set generic type to T
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set the parallel object
| parallel | parallel object to use |
Definition at line 274 of file SGObject.cpp.
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set the version object
| version | version object to use |
Definition at line 316 of file SGObject.cpp.
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virtualinherited |
set labels
| lab | labels |
Reimplemented from CMachine.
Definition at line 67 of file StructuredOutputMachine.cpp.
| void set_lambda | ( | float64_t | lbda | ) |
set regularization const
| lbda | regularization const lambda |
Definition at line 215 of file FWSOSVM.cpp.
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inherited |
set maximum training time
| t | maximimum training time |
Definition at line 82 of file Machine.cpp.
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set structured model
| model | structured model to set |
Definition at line 43 of file StructuredOutputMachine.cpp.
| void set_num_iter | ( | int32_t | num_iter | ) |
set max number of iterations
| num_iter | number of iterations |
Definition at line 225 of file FWSOSVM.cpp.
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inherited |
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virtualinherited |
Setter for store-model-features-after-training flag
| store_model | whether model should be stored after training |
Definition at line 107 of file Machine.cpp.
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inherited |
set surrogate loss function
| loss | loss function to set |
Definition at line 84 of file StructuredOutputMachine.cpp.
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inherited |
set verbose NOTE that track verbose information including primal objectives, training errors and duality gaps will make the training 2x or 3x slower.
| verbose | flag enabling/disabling verbose information |
Definition at line 198 of file StructuredOutputMachine.cpp.
set w (useful for modular interfaces)
| w | weight vector to set |
Definition at line 35 of file LinearStructuredOutputMachine.cpp.
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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.
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virtualinherited |
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 78 of file LinearStructuredOutputMachine.cpp.
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virtualinherited |
Reimplemented in CKernelMachine.
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virtualinherited |
train machine
| data | training 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. |
Reimplemented in CRelaxedTree, CAutoencoder, CLinearMachine, CSGDQN, and COnlineSVMSGD.
Definition at line 39 of file Machine.cpp.
Trains a locked machine on a set of indices. Error if machine is not locked
NOT IMPLEMENTED
| indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine.
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protectedvirtual |
train primal SO-SVM
| data | training data |
Reimplemented from CMachine.
Definition at line 67 of file FWSOSVM.cpp.
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protectedvirtualinherited |
returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CKMeansBase, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
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inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 336 of file SGObject.cpp.
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virtualinherited |
Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
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inherited |
io
Definition at line 537 of file SGObject.h.
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protectedinherited |
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inherited |
parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
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inherited |
Hash of parameter values
Definition at line 555 of file SGObject.h.
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protectedinherited |
the helper that records primal objectives, duality gaps etc
Definition at line 223 of file StructuredOutputMachine.h.
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protectedinherited |
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protectedinherited |
the model that contains the application dependent modules
Definition at line 214 of file StructuredOutputMachine.h.
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inherited |
model selection parameters
Definition at line 549 of file SGObject.h.
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inherited |
parameters
Definition at line 546 of file SGObject.h.
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protectedinherited |
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protectedinherited |
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protectedinherited |
the surrogate loss, for SOSVM, fixed to Hinge loss, other non-convex losses such as Ramp loss are also applicable, will be extended in the future
Definition at line 220 of file StructuredOutputMachine.h.
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protectedinherited |
verbose outputs and statistics
Definition at line 226 of file StructuredOutputMachine.h.
weight vector
Definition at line 82 of file LinearStructuredOutputMachine.h.
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inherited |
parallel
Definition at line 540 of file SGObject.h.
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inherited |
version
Definition at line 543 of file SGObject.h.