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SHOGUN
4.2.0
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SHOGUN
4.2.0
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Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
A linear classifier computes
\[ f({\bf x})= {\bf w} \cdot {\bf x} + b \]
where \({\bf w}\) are the weights assigned to each feature in training and \(b\) the bias.
To implement a linear classifier all that is required is to define the train() function that delivers \({\bf w}\) above.
Note that this framework works with linear classifiers of arbitraty feature type, e.g. dense and sparse and even string based features. This is implemented by using CDotFeatures that may provide a mapping function \(\Phi({\bf x})\mapsto {\cal R^D}\) encapsulating all the required operations (like the dot product). The decision function is thus
\[ f({\bf x})= {\bf w} \cdot \Phi({\bf x}) + b. \]
The following linear classifiers are implemented
Definition at line 63 of file LinearMachine.h.
Public Member Functions | |
| CLinearMachine () | |
| CLinearMachine (bool compute_bias) | |
| virtual | ~CLinearMachine () |
| CLinearMachine (CLinearMachine *machine) | |
| virtual bool | train (CFeatures *data=NULL) |
| virtual SGVector< float64_t > | get_w () const |
| virtual void | set_w (const SGVector< float64_t > src_w) |
| virtual void | set_bias (float64_t b) |
| virtual float64_t | get_bias () |
| virtual void | set_compute_bias (bool compute_bias) |
| virtual bool | get_compute_bias () |
| virtual void | set_features (CDotFeatures *feat) |
| virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
| virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
| virtual float64_t | apply_one (int32_t vec_idx) |
| virtual CDotFeatures * | get_features () |
| virtual const char * | get_name () const |
| virtual CLabels * | apply (CFeatures *data=NULL) |
| virtual CMulticlassLabels * | apply_multiclass (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 () |
| virtual EMachineType | get_classifier_type () |
| 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 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 SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
| virtual void | store_model_features () |
| void | compute_bias (CFeatures *data) |
| virtual bool | train_machine (CFeatures *data=NULL) |
| 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 > | w |
| float64_t | bias |
| CDotFeatures * | features |
| bool | m_compute_bias |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
| CLinearMachine | ( | ) |
default constructor
Definition at line 20 of file LinearMachine.cpp.
| CLinearMachine | ( | bool | compute_bias | ) |
Constructor
| compute_bias | new m_compute_bias Determines if bias_compution is considered or not |
Definition at line 25 of file LinearMachine.cpp.
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virtual |
destructor
Definition at line 54 of file LinearMachine.cpp.
| CLinearMachine | ( | CLinearMachine * | machine | ) |
copy constructor
Definition at line 32 of file LinearMachine.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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virtual |
apply linear machine to data for binary classification problem
| data | (test)data to be classified |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 70 of file LinearMachine.cpp.
apply get outputs
| data | features to compute outputs |
Definition at line 76 of file LinearMachine.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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virtual |
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virtual |
apply linear machine to data for regression problem
| data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 64 of file LinearMachine.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.
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protected |
Computes the added bias. The bias is computed as the mean error between the predictions and the true labels.
Definition at line 145 of file LinearMachine.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 classifier type
Reimplemented in CLaRank, CSVMLight, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CQDA, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKernelRidgeRegression, CLibSVR, CKNN, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CScatterSVM, CGaussianProcessRegression, CSGDQN, CSVMSGD, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CKMeansBase, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CDomainAdaptationSVM, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLightOneClass, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGNPPSVM, and CCPLEXSVM.
Definition at line 92 of file Machine.cpp.
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inherited |
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inherited |
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virtualinherited |
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returns type of problem machine solves
Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.
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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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Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
Reimplemented from CMachine.
Reimplemented in CLeastAngleRegression, CLibLinearMTL, CLibLinear, CLDA, CSVMSGD, CSGDQN, CDomainAdaptationSVMLinear, CNewtonSVM, CLPBoost, CSVMLin, CLinearRidgeRegression, CLPM, CLibLinearRegression, CPerceptron, CAveragedPerceptron, CLeastSquaresRegression, and CLinearLatentMachine.
Definition at line 161 of file LinearMachine.h.
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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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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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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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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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Set m_compute_bias
Determines if bias compution is considered or not
| compute_bias | new m_compute_bias |
Definition at line 118 of file LinearMachine.cpp.
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set features
| feat | features to set |
Reimplemented in CLDA, CLPBoost, and CLPM.
Definition at line 128 of file LinearMachine.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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inherited |
Definition at line 134 of file SGObject.cpp.
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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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set maximum training time
| t | maximimum training time |
Definition at line 82 of file Machine.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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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. Does nothing because Linear machines store the normal vector of the separating hyperplane and therefore the model anyway
Reimplemented from CMachine.
Definition at line 141 of file LinearMachine.cpp.
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virtualinherited |
Reimplemented in CKernelMachine.
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Train machine
Reimplemented from CMachine.
Reimplemented in CSGDQN.
Definition at line 169 of file LinearMachine.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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protectedvirtualinherited |
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 in CSVMLight, CNeuralNetwork, CLaRank, CLibLinearMTL, CMKL, COnlineLinearMachine, CKNN, CCARTree, CCHAIDTree, CSVRLight, CPluginEstimate, CRelaxedTree, CLeastAngleRegression, CLibLinear, CQDA, CCCSOSVM, CMulticlassMachine, CLDA, CMKLMulticlass, CC45ClassifierTree, CLibLinearRegression, CSVMSGD, CStochasticGBMachine, CMulticlassLibLinear, CVowpalWabbit, CRandomForest, CMCLDA, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CBaggingMachine, CID3ClassifierTree, CKernelRidgeRegression, CHierarchical, CLinearLatentMachine, CLibSVR, CNewtonSVM, CLPBoost, CDomainAdaptationSVM, CSVMLin, CScatterSVM, CStochasticSOSVM, CLinearRidgeRegression, CLPM, CGaussianNaiveBayes, CFWSOSVM, CNearestCentroid, CKMeansMiniBatch, CVwConditionalProbabilityTree, CConditionalProbabilityTree, CGaussianProcessRegression, CPerceptron, CAveragedPerceptron, CGMNPSVM, CLibSVM, CSVMLightOneClass, CShareBoost, CLibSVMOneClass, CMulticlassLibSVM, CMPDSVM, CGNPPSVM, and CCPLEXSVM.
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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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protected |
bias
Definition at line 192 of file LinearMachine.h.
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features
Definition at line 194 of file LinearMachine.h.
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io
Definition at line 537 of file SGObject.h.
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If true, bias is computed in train method
Definition at line 196 of file LinearMachine.h.
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protectedinherited |
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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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protectedinherited |
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model selection parameters
Definition at line 549 of file SGObject.h.
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parameters
Definition at line 546 of file SGObject.h.
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protectedinherited |
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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.
w
Definition at line 190 of file LinearMachine.h.