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SHOGUN
4.2.0
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SHOGUN
4.2.0
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the LaRank multiclass SVM machine This implementation uses LaRank algorithm from Bordes, Antoine, et al., 2007. "Solving multiclass support vector machines with LaRank."
Excellent results are usually obtained by performing just one or two training iterations. Stopping criterion here is the dual gap as in the original paper, with an arbitrary max_iteration (default: 1000) to safeguard against morbid training sets. This upper limit of training iterations is sufficient for most cases but can be adjusted with set_max_iteration() method. The current value of the upper limit can be queried with get_max_iteration() method.
Public Member Functions | |
| CLaRank () | |
| CLaRank (float64_t C, CKernel *k, CLabels *lab) | |
| virtual | ~CLaRank () |
| virtual int32_t | add (int32_t x_id, int32_t yi) |
| virtual int32_t | predict (int32_t x_id) |
| virtual void | destroy () |
| virtual float64_t | computeGap () |
| virtual uint32_t | getNumOutputs () const |
| int32_t | getNSV () |
| float64_t | computeW2 () |
| float64_t | getDual () |
| virtual EMachineType | get_classifier_type () |
| virtual const char * | get_name () const |
| void | set_batch_mode (bool enable) |
| bool | get_batch_mode () |
| void | set_tau (float64_t t) |
| float64_t | get_tau () |
| void | set_max_iteration (int32_t max_iter) |
| int32_t | get_max_iteration () |
| MACHINE_PROBLEM_TYPE (PT_MULTICLASS) | |
| bool | create_multiclass_svm (int32_t num_classes) |
| bool | set_svm (int32_t num, CSVM *svm) |
| CSVM * | get_svm (int32_t num) |
| bool | load (FILE *svm_file) |
| bool | save (FILE *svm_file) |
| SGVector< float64_t > | get_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) |
| CKernel * | get_kernel () |
| virtual void | store_model_features () |
| virtual void | set_labels (CLabels *lab) |
| bool | set_machine (int32_t num, CMachine *machine) |
| CMachine * | get_machine (int32_t num) const |
| virtual CBinaryLabels * | get_submachine_outputs (int32_t i) |
| virtual float64_t | get_submachine_output (int32_t i, int32_t num) |
| virtual CMulticlassLabels * | apply_multiclass (CFeatures *data=NULL) |
| virtual CMultilabelLabels * | apply_multilabel_output (CFeatures *data=NULL, int32_t n_outputs=5) |
| virtual float64_t | apply_one (int32_t vec_idx) |
| CMulticlassStrategy * | get_multiclass_strategy () const |
| CRejectionStrategy * | get_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 CLabels * | apply (CFeatures *data=NULL) |
| virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
| virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
| virtual CStructuredLabels * | apply_structured (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 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 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 | |
| bool | train_machine (CFeatures *data) |
| uint32_t | class_count () const |
| class count More... | |
| CSVM * | svm_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 CMachine * | get_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 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 | |
| std_hash_set< int32_t > | classes |
| classes More... | |
| float64_t | tau |
| tau More... | |
| int32_t | nb_train |
| nb train More... | |
| int64_t | cache |
| cache More... | |
| bool | batch_mode |
| whether to use online learning or batch training More... | |
| int32_t | step |
| progess output More... | |
| int32_t | max_iteration |
| Max number of iterations before training is stopped. More... | |
| float64_t | m_C |
| CKernel * | m_kernel |
| CMulticlassStrategy * | m_multiclass_strategy |
| CMachine * | m_machine |
| CDynamicObjectArray * | m_machines |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
| CLaRank | ( | ) |
Default constructor
Definition at line 587 of file LaRank.cpp.
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virtual |
Definition at line 604 of file LaRank.cpp.
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virtual |
set subset to the features of the machine, deletes old one
| subset | subset indices to set |
Implements CMulticlassMachine.
Definition at line 178 of file KernelMulticlassMachine.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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applies a locked machine on a set of indices for latent problems
Definition at line 266 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 252 of file Machine.cpp.
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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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applies a locked machine on a set of indices for structured problems
Definition at line 259 of file Machine.cpp.
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classify all examples
Reimplemented from CMachine.
Reimplemented in CQDA, CGaussianNaiveBayes, and CMCLDA.
Definition at line 93 of file MulticlassMachine.cpp.
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classify all examples with multiple output
Definition at line 195 of file MulticlassMachine.cpp.
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virtualinherited |
classify one example
| vec_idx |
Reimplemented from CMachine.
Reimplemented in CScatterSVM, and CGaussianNaiveBayes.
Definition at line 283 of file MulticlassMachine.cpp.
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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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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 226 of file Machine.cpp.
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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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clear machines
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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.
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computeGap
Definition at line 812 of file LaRank.cpp.
| float64_t computeW2 | ( | ) |
compute W2
Definition at line 871 of file LaRank.cpp.
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inherited |
create multiclass SVM. Appends the appropriate number of svm pointer (depending on multiclass strategy) to m_machines. All pointers are initialized with NULL.
| num_classes | number of classes in SVM |
Definition at line 48 of file MulticlassSVM.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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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 143 of file Machine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
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destroy
Definition at line 803 of file LaRank.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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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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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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get batch computation option of base SVM
Definition at line 146 of file MulticlassSVM.h.
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get bias enabled options of base SVM
Definition at line 136 of file MulticlassSVM.h.
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get linadd option of base SVM
Definition at line 141 of file MulticlassSVM.h.
get linear term of base SVM
Definition at line 95 of file MulticlassSVM.h.
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get machine
| num | index of machine to get |
Definition at line 74 of file MulticlassMachine.h.
construct kernel machine from given kernel machine
Implements CMulticlassMachine.
Definition at line 168 of file KernelMulticlassMachine.cpp.
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get problem type
Reimplemented from CMachine.
Reimplemented in CCHAIDTree, and CCARTree.
Definition at line 32 of file BaseMulticlassMachine.cpp.
| int32_t get_max_iteration | ( | ) |
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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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get the type of multiclass'ness
Definition at line 114 of file MulticlassMachine.h.
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get number of machines
Definition at line 27 of file BaseMulticlassMachine.cpp.
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return number of rhs feature vectors
Implements CMulticlassMachine.
Definition at line 173 of file KernelMulticlassMachine.cpp.
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get objective of base SVM
Definition at line 130 of file MulticlassSVM.h.
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get prob output heuristic of multiclass strategy
Definition at line 145 of file MulticlassMachine.h.
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returns rejection strategy
Definition at line 124 of file MulticlassMachine.h.
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get shrinking option of base SVM
Definition at line 125 of file MulticlassSVM.h.
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get output of i-th submachine for num-th vector
| i | number of submachine |
| num | number of feature vector |
Definition at line 80 of file MulticlassMachine.cpp.
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virtualinherited |
get outputs of i-th submachine
| i | number of submachine |
Reimplemented in CDomainAdaptationMulticlassLibLinear.
Definition at line 71 of file MulticlassMachine.cpp.
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get SVM
| num | which SVM to get |
Definition at line 76 of file MulticlassSVM.h.
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get tube epsilon of base SVM
Definition at line 100 of file MulticlassSVM.h.
| float64_t getDual | ( | ) |
get Dual
Definition at line 887 of file LaRank.cpp.
| int32_t getNSV | ( | ) |
get NSV
Definition at line 858 of file LaRank.cpp.
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get num outputs
Definition at line 843 of file LaRank.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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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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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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init machine for training with kernel init
Implements CMulticlassMachine.
Definition at line 128 of file KernelMulticlassMachine.cpp.
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initializes machines (OvO, OvR) for apply
Reimplemented from CKernelMulticlassMachine.
Definition at line 73 of file MulticlassSVM.cpp.
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protectedinherited |
init strategy
Definition at line 65 of file MulticlassMachine.cpp.
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is machine an SVM instance
Reimplemented from CMulticlassMachine.
Definition at line 232 of file MulticlassSVM.h.
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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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check whether the labels is valid.
| lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented from CMachine.
Reimplemented in CCARTree, and CCHAIDTree.
Definition at line 37 of file BaseMulticlassMachine.cpp.
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check kernel availability
Implements CMulticlassMachine.
Definition at line 160 of file KernelMulticlassMachine.cpp.
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load a Multiclass SVM from file
| svm_file | the file handle |
Definition at line 111 of file MulticlassSVM.cpp.
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virtualinherited |
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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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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prints registered parameters out
| prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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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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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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deletes any subset set to the features of the machine
Implements CMulticlassMachine.
Definition at line 183 of file KernelMulticlassMachine.cpp.
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write a Multiclass SVM to a file
| svm_file | the file handle |
Definition at line 259 of file MulticlassSVM.cpp.
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virtualinherited |
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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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.
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inherited |
set batch computation option
| enable | whether batch computation should be enabled |
Definition at line 207 of file MulticlassSVM.h.
| void set_batch_mode | ( | bool | enable | ) |
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set bias option
| enable_bias | whether bias should be enabled |
Definition at line 197 of file MulticlassSVM.h.
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set C parameters
| C | set regularization parameter |
Definition at line 162 of file MulticlassSVM.h.
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set default number of support vectors
| num_sv | number of support vectors |
Definition at line 152 of file MulticlassSVM.h.
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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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set labels
| lab | labels |
Reimplemented from CMachine.
Definition at line 52 of file MulticlassMachine.cpp.
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set linadd option
| enable | whether linadd should be enabled |
Definition at line 202 of file MulticlassSVM.h.
set linear term
| linear_term | linear term vector |
Definition at line 157 of file MulticlassSVM.h.
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set machine
| num | index of machine |
| machine | machine to set |
Definition at line 59 of file MulticlassMachine.h.
| void set_max_iteration | ( | int32_t | max_iter | ) |
Set max number of iterations before training is stopped
| max_iter |
Definition at line 849 of file LaRank.cpp.
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set maximum training time
| t | maximimum training time |
Definition at line 82 of file Machine.cpp.
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set prob output heuristic of multiclass strategy
| prob_heuris | type of probability heuristic |
Definition at line 153 of file MulticlassMachine.h.
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sets rejection strategy
| rejection_strategy | rejection strategy to be set |
Definition at line 133 of file MulticlassMachine.h.
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set shrinking option
| enable | whether shrinking should be enabled |
Definition at line 187 of file MulticlassSVM.h.
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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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set SVM
| num | number to set |
| svm | SVM to set |
Definition at line 63 of file MulticlassSVM.cpp.
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set tube epsilon value
| eps | tube epsilon value |
Definition at line 177 of file MulticlassSVM.h.
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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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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 20 of file KernelMulticlassMachine.cpp.
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Reimplemented in CKernelMachine.
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casts m_machine to SVM
Definition at line 218 of file MulticlassSVM.h.
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returns support vectors
Definition at line 223 of file MulticlassSVM.h.
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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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returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CKMeansBase, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
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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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Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
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io
Definition at line 537 of file SGObject.h.
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C regularization constant
Definition at line 247 of file MulticlassSVM.h.
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parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
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Hash of parameter values
Definition at line 555 of file SGObject.h.
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kernel
Definition at line 100 of file KernelMulticlassMachine.h.
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machine
Definition at line 208 of file MulticlassMachine.h.
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machines
Definition at line 56 of file BaseMulticlassMachine.h.
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model selection parameters
Definition at line 549 of file SGObject.h.
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type of multiclass strategy
Definition at line 205 of file MulticlassMachine.h.
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parameters
Definition at line 546 of file SGObject.h.
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parallel
Definition at line 540 of file SGObject.h.
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version
Definition at line 543 of file SGObject.h.