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SHOGUN
4.2.0
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All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Modules Pages
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SHOGUN
4.2.0
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This class implements the Random Forests algorithm. In Random Forests algorithm, we train a number of randomized CART trees (see class CRandomCARTree) using the supplied training data. The number of trees to be trained is a parameter (called number of bags) controlled by the user. Test feature vectors are classified/regressed by combining the outputs of all these trained candidate trees using a combination rule (see class CCombinationRule). The feature for calculating out-of-box error is also provided to help determine the appropriate number of bags. The evaluatin criteria for calculating this out-of-box error is specified by the user (see class CEvaluation).
Definition at line 46 of file RandomForest.h.
Public Member Functions | |
| CRandomForest () | |
| CRandomForest (int32_t num_rand_feats, int32_t num_bags=10) | |
| CRandomForest (CFeatures *features, CLabels *labels, int32_t num_bags=10, int32_t num_rand_feats=0) | |
| CRandomForest (CFeatures *features, CLabels *labels, SGVector< float64_t > weights, int32_t num_bags=10, int32_t num_rand_feats=0) | |
| virtual | ~CRandomForest () |
| virtual const char * | get_name () const |
| virtual void | set_machine (CMachine *machine) |
| void | set_weights (SGVector< float64_t > weights) |
| SGVector< float64_t > | get_weights () const |
| void | set_feature_types (SGVector< bool > ft) |
| SGVector< bool > | get_feature_types () const |
| virtual EProblemType | get_machine_problem_type () const |
| void | set_machine_problem_type (EProblemType mode) |
| void | set_num_random_features (int32_t rand_featsize) |
| int32_t | get_num_random_features () const |
| virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
| virtual CMulticlassLabels * | apply_multiclass (CFeatures *data=NULL) |
| virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
| void | set_num_bags (int32_t num_bags) |
| int32_t | get_num_bags () const |
| virtual void | set_bag_size (int32_t bag_size) |
| virtual int32_t | get_bag_size () const |
| CMachine * | get_machine () const |
| void | set_combination_rule (CCombinationRule *rule) |
| CCombinationRule * | get_combination_rule () const |
| virtual EMachineType | get_classifier_type () |
| float64_t | get_oob_error (CEvaluation *eval) const |
| virtual bool | train (CFeatures *data=NULL) |
| virtual CLabels * | apply (CFeatures *data=NULL) |
| virtual CStructuredLabels * | apply_structured (CFeatures *data=NULL) |
| virtual CLatentLabels * | apply_latent (CFeatures *data=NULL) |
| virtual void | set_labels (CLabels *lab) |
| 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 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 void | set_machine_parameters (CMachine *m, SGVector< index_t > idx) |
| SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
| void | register_parameters () |
| CDynamicArray< index_t > * | get_oob_indices (const SGVector< index_t > &in_bag) |
| virtual void | store_model_features () |
| 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 | |
| CDynamicObjectArray * | m_bags |
| CFeatures * | m_features |
| CMachine * | m_machine |
| int32_t | m_num_bags |
| int32_t | m_bag_size |
| CCombinationRule * | m_combination_rule |
| SGVector< bool > | m_all_oob_idx |
| CDynamicObjectArray * | m_oob_indices |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
| CRandomForest | ( | ) |
constructor
Definition at line 36 of file RandomForest.cpp.
| CRandomForest | ( | int32_t | num_rand_feats, |
| int32_t | num_bags = 10 |
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| ) |
constructor
| num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
| num_bags | number of trees in forest |
Definition at line 42 of file RandomForest.cpp.
| CRandomForest | ( | CFeatures * | features, |
| CLabels * | labels, | ||
| int32_t | num_bags = 10, |
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| int32_t | num_rand_feats = 0 |
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| ) |
constructor
| features | training features |
| labels | training labels |
| num_bags | number of trees in forest |
| num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
Definition at line 54 of file RandomForest.cpp.
| CRandomForest | ( | CFeatures * | features, |
| CLabels * | labels, | ||
| SGVector< float64_t > | weights, | ||
| int32_t | num_bags = 10, |
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| int32_t | num_rand_feats = 0 |
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| ) |
constructor
| features | training features |
| labels | training labels |
| weights | weights of training feature vectors |
| num_bags | number of trees in forest |
| num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
Definition at line 69 of file RandomForest.cpp.
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virtual |
destructor
Definition at line 85 of file RandomForest.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 from CMachine.
Definition at line 45 of file BaggingMachine.cpp.
helper function for the apply_{regression,..} functions that computes the output
| data | the data to compute the output for |
Definition at line 70 of file BaggingMachine.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 from CMachine.
Definition at line 53 of file BaggingMachine.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 from CMachine.
Definition at line 61 of file BaggingMachine.cpp.
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virtualinherited |
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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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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virtualinherited |
Get number of feature vectors that are use for training each bag/machine
Definition at line 236 of file BaggingMachine.cpp.
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virtualinherited |
get classifier type
Reimplemented from CMachine.
Definition at line 110 of file BaggingMachine.h.
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inherited |
Get the combination rule that is used for aggregating the results
Definition at line 274 of file BaggingMachine.cpp.
| SGVector< bool > get_feature_types | ( | ) | const |
get feature types of various features
Definition at line 110 of file RandomForest.cpp.
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inherited |
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inherited |
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inherited |
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virtualinherited |
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inherited |
Get machine for bagging
Definition at line 241 of file BaggingMachine.cpp.
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virtual |
get problem type - multiclass classification or regression
Reimplemented from CMachine.
Definition at line 116 of file RandomForest.cpp.
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inherited |
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inherited |
Definition at line 531 of file SGObject.cpp.
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inherited |
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 |
get name
Reimplemented from CBaggingMachine.
Definition at line 85 of file RandomForest.h.
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inherited |
Get number of bags/machines
Definition at line 226 of file BaggingMachine.cpp.
| int32_t get_num_random_features | ( | ) | const |
get number of random features to be chosen during node splits
Definition at line 136 of file RandomForest.cpp.
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inherited |
get out-of-bag error CombinationRule is used for combining the predictions.
| eval | Evaluation method to use for calculating the error |
Definition at line 280 of file BaggingMachine.cpp.
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protectedinherited |
get the vector of indices for feature vectors that are out of bag
| in_bag | vector of indices that are in bag. NOTE: in_bag is a randomly generated with replacement |
Definition at line 360 of file BaggingMachine.cpp.
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inherited |
get weights
Definition at line 99 of file RandomForest.cpp.
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inherited |
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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virtualinherited |
Definition at line 295 of file SGObject.cpp.
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inherited |
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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protectedinherited |
Register paramaters
Definition at line 206 of file BaggingMachine.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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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.
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virtualinherited |
Set number of feature vectors to use for each bag/machine
| bag_size | number of vectors to use for a bag |
Definition at line 231 of file BaggingMachine.cpp.
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inherited |
Set the combination rule to use for aggregating the classification results
| rule | combination rule |
Definition at line 267 of file BaggingMachine.cpp.
| void set_feature_types | ( | SGVector< bool > | ft | ) |
set feature types of various features
| ft | bool vector true for nominal feature false for continuous feature type |
Definition at line 104 of file RandomForest.cpp.
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inherited |
Definition at line 74 of file SGObject.cpp.
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inherited |
Definition at line 79 of file SGObject.cpp.
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inherited |
Definition at line 84 of file SGObject.cpp.
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inherited |
Definition at line 89 of file SGObject.cpp.
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inherited |
Definition at line 94 of file SGObject.cpp.
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inherited |
Definition at line 99 of file SGObject.cpp.
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inherited |
Definition at line 104 of file SGObject.cpp.
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inherited |
Definition at line 109 of file SGObject.cpp.
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inherited |
Definition at line 114 of file SGObject.cpp.
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inherited |
Definition at line 119 of file SGObject.cpp.
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inherited |
Definition at line 124 of file SGObject.cpp.
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inherited |
Definition at line 129 of file SGObject.cpp.
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inherited |
Definition at line 134 of file SGObject.cpp.
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inherited |
Definition at line 139 of file SGObject.cpp.
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inherited |
Definition at line 144 of file SGObject.cpp.
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inherited |
set generic type to T
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inherited |
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inherited |
set the parallel object
| parallel | parallel object to use |
Definition at line 274 of file SGObject.cpp.
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inherited |
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 in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 65 of file Machine.cpp.
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virtual |
machine is set to modified CART(RandomCART) and cannot be changed
| machine | the machine to use for bagging |
Reimplemented from CBaggingMachine.
Definition at line 89 of file RandomForest.cpp.
sets parameters of CARTree - sets machine labels and weights here
| m | machine |
| idx | indices of training vectors chosen in current bag |
Reimplemented from CBaggingMachine.
Definition at line 142 of file RandomForest.cpp.
| void set_machine_problem_type | ( | EProblemType | mode | ) |
set problem type - multiclass classification or regression
| mode | EProblemType PT_MULTICLASS or PT_REGRESSION |
Definition at line 122 of file RandomForest.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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inherited |
Set number of bags/machine to create
| num_bags | number of bags |
Definition at line 221 of file BaggingMachine.cpp.
| void set_num_random_features | ( | int32_t | rand_featsize | ) |
set number of random features to be chosen during node splits
| rand_featsize | number of randomly chosen features during each node split |
Definition at line 128 of file RandomForest.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.
set weights
| weights | of training feature vectors |
Definition at line 94 of file RandomForest.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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protectedvirtualinherited |
Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.
Base method, has to be implemented in order to allow cross-validation and model selection.
NOT IMPLEMENTED! Has to be done in subclasses
Reimplemented in CKernelMachine, CKNN, CLinearMachine, CLinearMulticlassMachine, CKMeansBase, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CGaussianProcessMachine, CHierarchical, CDistanceMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.
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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 machine
| data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data) |
NOT IMPLEMENTED!
Reimplemented from CBaggingMachine.
Definition at line 167 of file RandomForest.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.
|
inherited |
io
Definition at line 537 of file SGObject.h.
|
protectedinherited |
indices of all feature vectors that are out of bag
Definition at line 177 of file BaggingMachine.h.
|
protectedinherited |
number of vectors to use from the training features
Definition at line 171 of file BaggingMachine.h.
|
protectedinherited |
bags array
Definition at line 159 of file BaggingMachine.h.
|
protectedinherited |
combination rule to use
Definition at line 174 of file BaggingMachine.h.
|
protectedinherited |
|
protectedinherited |
features to train on
Definition at line 162 of file BaggingMachine.h.
|
inherited |
parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
|
inherited |
Hash of parameter values
Definition at line 555 of file SGObject.h.
|
protectedinherited |
machine to use for bagging
Definition at line 165 of file BaggingMachine.h.
|
protectedinherited |
|
inherited |
model selection parameters
Definition at line 549 of file SGObject.h.
|
protectedinherited |
number of bags to create
Definition at line 168 of file BaggingMachine.h.
|
protectedinherited |
array of oob indices
Definition at line 180 of file BaggingMachine.h.
|
inherited |
parameters
Definition at line 546 of file SGObject.h.
|
protectedinherited |
|
protectedinherited |
|
inherited |
parallel
Definition at line 540 of file SGObject.h.
|
inherited |
version
Definition at line 543 of file SGObject.h.