Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms.
A linear classifier computes
![\[ f({\bf x})= {\bf w} \cdot {\bf x} + b \]](form_205.png)
where 
are the weights assigned to each feature in training and 
the bias.
To implement a linear classifier all that is required is to define the train() function that delivers above.
Note that this framework works with linear classifiers of arbitrary feature type, e.g. dense and sparse and even string based features. This is implemented by using CStreamingDotFeatures that may provide a mapping function 
encapsulating all the required operations (like the dot product). The decision function is thus
![\[ f({\bf x})= {\bf w} \cdot \Phi({\bf x}) + b. \]](form_207.png)
Definition at line 50 of file OnlineLinearMachine.h.
default constructor
Definition at line 16 of file OnlineLinearMachine.cpp.
| ~COnlineLinearMachine | ( | ) | [virtual] |
Definition at line 25 of file OnlineLinearMachine.cpp.
apply machine to data if data is not specified apply to the current features
| data | (test)data to be classified |
Definition at line 162 of file Machine.cpp.
| CBinaryLabels * apply_binary | ( | CFeatures * | data = NULL |
) | [virtual] |
apply linear machine to data for binary classification problems
| data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 34 of file OnlineLinearMachine.cpp.
get real outputs
| data | features to compute outputs |
Definition at line 46 of file OnlineLinearMachine.cpp.
| CLatentLabels * apply_latent | ( | CFeatures * | data = NULL |
) | [virtual, inherited] |
apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 242 of file Machine.cpp.
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 197 of file Machine.cpp.
| CBinaryLabels * apply_locked_binary | ( | SGVector< index_t > | indices | ) | [virtual, inherited] |
applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, CMultitaskCompositeMachine, and CMultitaskLinearMachine.
Definition at line 248 of file Machine.cpp.
| CLatentLabels * apply_locked_latent | ( | SGVector< index_t > | indices | ) | [virtual, inherited] |
applies a locked machine on a set of indices for latent problems
Definition at line 276 of file Machine.cpp.
| CMulticlassLabels * apply_locked_multiclass | ( | SGVector< index_t > | indices | ) | [virtual, inherited] |
applies a locked machine on a set of indices for multiclass problems
Definition at line 262 of file Machine.cpp.
| CRegressionLabels * apply_locked_regression | ( | SGVector< index_t > | indices | ) | [virtual, inherited] |
applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 255 of file Machine.cpp.
| CStructuredLabels * apply_locked_structured | ( | SGVector< index_t > | indices | ) | [virtual, inherited] |
applies a locked machine on a set of indices for structured problems
Definition at line 269 of file Machine.cpp.
| CMulticlassLabels * apply_multiclass | ( | CFeatures * | data = NULL |
) | [virtual, inherited] |
apply machine to data in means of multiclass classification problem
Reimplemented in CDistanceMachine, CMulticlassMachine, CConjugateIndex, CGaussianNaiveBayes, CKNN, CQDA, CConditionalProbabilityTree, CRelaxedTree, and CVwConditionalProbabilityTree.
Definition at line 230 of file Machine.cpp.
| virtual float64_t apply_one | ( | int32_t | vec_idx | ) | [virtual] |
get output for example "vec_idx"
Reimplemented from CMachine.
Definition at line 168 of file OnlineLinearMachine.h.
apply linear machine to one vector
| vec | feature vector | |
| len | length of vector |
Definition at line 81 of file OnlineLinearMachine.cpp.
| CRegressionLabels * apply_regression | ( | CFeatures * | data = NULL |
) | [virtual] |
apply linear machine to data for regression problems
| data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 40 of file OnlineLinearMachine.cpp.
| CStructuredLabels * apply_structured | ( | CFeatures * | data = NULL |
) | [virtual, inherited] |
apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 236 of file Machine.cpp.
| float32_t apply_to_current_example | ( | ) | [virtual] |
apply linear machine to vector currently being processed
Definition at line 86 of file OnlineLinearMachine.cpp.
| void build_parameter_dictionary | ( | CMap< TParameter *, CSGObject * > & | dict | ) | [inherited] |
Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
| dict | dictionary of parameters to be built. |
Definition at line 1201 of file SGObject.cpp.
| virtual CMachine* clone | ( | ) | [virtual, inherited] |
clone
Reimplemented in CKernelMachine, and CLinearMachine.
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 122 of file Machine.cpp.
| void data_unlock | ( | ) | [virtual, inherited] |
Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 153 of file Machine.cpp.
| virtual CSGObject* deep_copy | ( | ) | const [virtual, inherited] |
A deep copy. All the instance variables will also be copied.
Definition at line 131 of file SGObject.h.
| virtual float32_t get_bias | ( | ) | [virtual] |
| EMachineType get_classifier_type | ( | ) | [virtual, inherited] |
get classifier type
Reimplemented in CAveragedPerceptron, CLDA, CLPBoost, CLPM, CMKLClassification, CMKLMulticlass, CMKLOneClass, CPerceptron, CSubGradientLPM, CCPLEXSVM, CGNPPSVM, CGPBTSVM, CLibLinear, CLibSVM, CLibSVMOneClass, CMPDSVM, CNewtonSVM, COnlineSVMSGD, CSGDQN, CSubGradientSVM, CSVMLight, CSVMLightOneClass, CSVMLin, CSVMOcas, CSVMSGD, CWDSVMOcas, CHierarchical, CKMeans, CConjugateIndex, CGaussianNaiveBayes, CGMNPSVM, CKNN, CLaRank, CMulticlassLibSVM, CQDA, CScatterSVM, CGaussianProcessRegression, CKernelRidgeRegression, CLeastAngleRegression, CLeastSquaresRegression, CLinearRidgeRegression, CLibSVR, CMKLRegression, CSVRLight, CDomainAdaptationSVM, CDomainAdaptationSVMLinear, and CLibLinearMTL.
Definition at line 102 of file Machine.cpp.
| virtual CStreamingDotFeatures* get_features | ( | ) | [virtual] |
| SGIO * get_global_io | ( | ) | [inherited] |
| Parallel * get_global_parallel | ( | ) | [inherited] |
| Version * get_global_version | ( | ) | [inherited] |
| CLabels * get_labels | ( | ) | [virtual, inherited] |
| virtual EProblemType get_machine_problem_type | ( | ) | const [virtual, inherited] |
returns type of problem machine solves
Reimplemented in CBaseMulticlassMachine.
| float64_t get_max_train_time | ( | ) | [inherited] |
| SGStringList< char > get_modelsel_names | ( | ) | [inherited] |
Definition at line 1108 of file SGObject.cpp.
| char * get_modsel_param_descr | ( | const char * | param_name | ) | [inherited] |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
Definition at line 1132 of file SGObject.cpp.
| index_t get_modsel_param_index | ( | const char * | param_name | ) | [inherited] |
Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
Definition at line 1145 of file SGObject.cpp.
| virtual const char* get_name | ( | ) | const [virtual] |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
Reimplemented from CMachine.
Reimplemented in COnlineLibLinear, COnlineSVMSGD, and CVowpalWabbit.
Definition at line 202 of file OnlineLinearMachine.h.
| ESolverType get_solver_type | ( | ) | [inherited] |
| virtual void get_w | ( | float64_t *& | dst_w, | |
| int32_t & | dst_dims | |||
| ) | [virtual] |
Get w as a _new_ float64_t array
| dst_w | store w in this argument | |
| dst_dims | dimension of w |
Definition at line 75 of file OnlineLinearMachine.h.
| virtual void get_w | ( | float32_t *& | dst_w, | |
| int32_t & | dst_dims | |||
| ) | [virtual] |
get w
| dst_w | store w in this argument | |
| dst_dims | dimension of w |
Definition at line 62 of file OnlineLinearMachine.h.
| bool is_data_locked | ( | ) | const [inherited] |
| bool is_generic | ( | EPrimitiveType * | generic | ) | const [virtual, inherited] |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
Definition at line 278 of file SGObject.cpp.
| virtual bool is_label_valid | ( | CLabels * | lab | ) | const [protected, virtual, inherited] |
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 CBaseMulticlassMachine.
| DynArray< TParameter * > * load_all_file_parameters | ( | int32_t | file_version, | |
| int32_t | current_version, | |||
| CSerializableFile * | file, | |||
| const char * | prefix = "" | |||
| ) | [inherited] |
maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
| file_version | parameter version of the file | |
| current_version | version from which mapping begins (you want to use VERSION_PARAMETER for this in most cases) | |
| file | file to load from | |
| prefix | prefix for members |
Definition at line 679 of file SGObject.cpp.
| DynArray< TParameter * > * load_file_parameters | ( | const SGParamInfo * | param_info, | |
| int32_t | file_version, | |||
| CSerializableFile * | file, | |||
| const char * | prefix = "" | |||
| ) | [inherited] |
loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
| param_info | information of parameter | |
| file_version | parameter version of the file, must be <= provided parameter version | |
| file | file to load from | |
| prefix | prefix for members |
Definition at line 523 of file SGObject.cpp.
| bool load_serializable | ( | CSerializableFile * | file, | |
| const char * | prefix = "", |
|||
| int32_t | param_version = VERSION_PARAMETER | |||
| ) | [virtual, inherited] |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from | |
| prefix | prefix for members | |
| param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Reimplemented in CModelSelectionParameters.
Definition at line 354 of file SGObject.cpp.
| void load_serializable_post | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CLinearHMM, CAlphabet, CANOVAKernel, CCircularKernel, CExponentialKernel, CGaussianKernel, CInverseMultiQuadricKernel, CKernel, CWeightedDegreePositionStringKernel, and CList.
Definition at line 1033 of file SGObject.cpp.
| void load_serializable_pre | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
| ShogunException | Will be thrown if an error occurres. |
Definition at line 1028 of file SGObject.cpp.
| void map_parameters | ( | DynArray< TParameter * > * | param_base, | |
| int32_t & | base_version, | |||
| DynArray< const SGParamInfo * > * | target_param_infos | |||
| ) | [inherited] |
Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
| param_base | set of TParameter instances that are mapped to the provided target parameter infos | |
| base_version | version of the parameter base | |
| target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 717 of file SGObject.cpp.
| TParameter * migrate | ( | DynArray< TParameter * > * | param_base, | |
| const SGParamInfo * | target | |||
| ) | [protected, virtual, inherited] |
creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
| param_base | set of TParameter instances to use for migration | |
| target | parameter info for the resulting TParameter |
Definition at line 923 of file SGObject.cpp.
| void one_to_one_migration_prepare | ( | DynArray< TParameter * > * | param_base, | |
| const SGParamInfo * | target, | |||
| TParameter *& | replacement, | |||
| TParameter *& | to_migrate, | |||
| char * | old_name = NULL | |||
| ) | [protected, virtual, inherited] |
This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
| param_base | set of TParameter instances to use for migration | |
| target | parameter info for the resulting TParameter | |
| replacement | (used as output) here the TParameter instance which is returned by migration is created into | |
| to_migrate | the only source that is used for migration | |
| old_name | with this parameter, a name change may be specified |
Definition at line 864 of file SGObject.cpp.
post lock
Reimplemented in CMultitaskCompositeMachine, and CMultitaskLinearMachine.
| void print_modsel_params | ( | ) | [inherited] |
prints all parameter registered for model selection and their type
Definition at line 1084 of file SGObject.cpp.
| void print_serializable | ( | const char * | prefix = "" |
) | [virtual, inherited] |
prints registered parameters out
| prefix | prefix for members |
Definition at line 290 of file SGObject.cpp.
| bool save_serializable | ( | CSerializableFile * | file, | |
| const char * | prefix = "", |
|||
| int32_t | param_version = VERSION_PARAMETER | |||
| ) | [virtual, inherited] |
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 | |
| param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Reimplemented in CModelSelectionParameters.
Definition at line 296 of file SGObject.cpp.
| void save_serializable_post | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CKernel.
Definition at line 1043 of file SGObject.cpp.
| void save_serializable_pre | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
| ShogunException | Will be thrown if an error occurres. |
Reimplemented in CKernel.
Definition at line 1038 of file SGObject.cpp.
| virtual void set_bias | ( | float32_t | b | ) | [virtual] |
| virtual void set_features | ( | CStreamingDotFeatures * | feat | ) | [virtual] |
| void set_generic< floatmax_t > | ( | ) | [inherited] |
set generic type to T
| void set_global_io | ( | SGIO * | io | ) | [inherited] |
| void set_global_parallel | ( | Parallel * | parallel | ) | [inherited] |
set the parallel object
| parallel | parallel object to use |
Definition at line 230 of file SGObject.cpp.
| void set_global_version | ( | Version * | version | ) | [inherited] |
set the version object
| version | version object to use |
Definition at line 265 of file SGObject.cpp.
| void set_labels | ( | CLabels * | lab | ) | [virtual, inherited] |
set labels
| lab | labels |
Reimplemented in CMulticlassMachine, and CRelaxedTree.
Definition at line 75 of file Machine.cpp.
| void set_max_train_time | ( | float64_t | t | ) | [inherited] |
set maximum training time
| t | maximimum training time |
Definition at line 92 of file Machine.cpp.
| void set_solver_type | ( | ESolverType | st | ) | [inherited] |
| void set_store_model_features | ( | bool | store_model | ) | [virtual, inherited] |
Setter for store-model-features-after-training flag
| store_model | whether model should be stored after training |
Definition at line 117 of file Machine.cpp.
| virtual void set_w | ( | float32_t * | src_w, | |
| int32_t | src_w_dim | |||
| ) | [virtual] |
set w
| src_w | new w | |
| src_w_dim | dimension of new w |
Definition at line 98 of file OnlineLinearMachine.h.
| virtual void set_w | ( | float64_t * | src_w, | |
| int32_t | src_w_dim | |||
| ) | [virtual] |
Set weight vector from a float64_t vector
| src_w | new w | |
| src_w_dim | dimension of new w |
Definition at line 112 of file OnlineLinearMachine.h.
| virtual CSGObject* shallow_copy | ( | ) | const [virtual, inherited] |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 122 of file SGObject.h.
| virtual void start_train | ( | ) | [virtual] |
Start training of the online machine, sub-class should override this if some preparations are to be done
Reimplemented in COnlineLibLinear.
Definition at line 207 of file OnlineLinearMachine.h.
| virtual void stop_train | ( | ) | [virtual] |
Stop training of the online machine, sub-class should override this if some clean up is needed
Reimplemented in COnlineLibLinear.
Definition at line 212 of file OnlineLinearMachine.h.
| virtual void store_model_features | ( | ) | [protected, virtual, inherited] |
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 CHierarchical, CKMeans, CDistanceMachine, CKernelMachine, CKernelMulticlassMachine, CLinearMachine, CLinearMulticlassMachine, and CKNN.
| virtual bool supports_locking | ( | ) | const [virtual, inherited] |
Reimplemented in CKernelMachine, CMultitaskCompositeMachine, and CMultitaskLinearMachine.
| bool train | ( | CFeatures * | data = NULL |
) | [virtual, inherited] |
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 COnlineSVMSGD, CSGDQN, and CRelaxedTree.
Definition at line 49 of file Machine.cpp.
| virtual void train_example | ( | CStreamingDotFeatures * | feature, | |
| float64_t | label | |||
| ) | [virtual] |
train on one example
| feature | the feature object containing the current example. Note that get_next_example is already called so relevalent methods like dot() and dense_dot() can be directly called. WARN: this function should only process ONE example, and get_next_example() should NEVER be called here. Use the label passed in the 2nd parameter, instead of get_label() from feature, because sometimes the features might not have associated labels or the caller might want to provide some other labels. | |
| label | label of this example |
Reimplemented in COnlineLibLinear.
Definition at line 223 of file OnlineLinearMachine.h.
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, CMultitaskCompositeMachine, and CMultitaskLinearMachine.
| bool train_machine | ( | CFeatures * | data = NULL |
) | [protected, virtual] |
Train classifier
| data | Training data, can be avoided if already initialized with it |
Reimplemented from CMachine.
Reimplemented in CVowpalWabbit.
Definition at line 91 of file OnlineLinearMachine.cpp.
| virtual bool train_require_labels | ( | ) | const [protected, virtual] |
whether train require labels
Reimplemented from CMachine.
Definition at line 244 of file OnlineLinearMachine.h.
| void unset_generic | ( | ) | [inherited] |
unset generic type
this has to be called in classes specializing a template class
Definition at line 285 of file SGObject.cpp.
| bool update_parameter_hash | ( | ) | [protected, virtual, inherited] |
Updates the hash of current parameter combination.
Definition at line 237 of file SGObject.cpp.
bias
Definition at line 252 of file OnlineLinearMachine.h.
CStreamingDotFeatures* features [protected] |
io
Definition at line 462 of file SGObject.h.
bool m_data_locked [protected, inherited] |
uint32_t m_hash [inherited] |
Hash of parameter values
Definition at line 480 of file SGObject.h.
float64_t m_max_train_time [protected, inherited] |
Parameter* m_model_selection_parameters [inherited] |
model selection parameters
Definition at line 474 of file SGObject.h.
ParameterMap* m_parameter_map [inherited] |
map for different parameter versions
Definition at line 477 of file SGObject.h.
Parameter* m_parameters [inherited] |
parameters
Definition at line 471 of file SGObject.h.
ESolverType m_solver_type [protected, inherited] |
bool m_store_model_features [protected, inherited] |
parallel
Definition at line 465 of file SGObject.h.
version
Definition at line 468 of file SGObject.h.
w
Definition at line 250 of file OnlineLinearMachine.h.
int32_t w_dim [protected] |
dimension of w
Definition at line 248 of file OnlineLinearMachine.h.
