SHOGUN
4.1.0
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Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit.
VW is a fast online learning algorithm which operates on sparse features. It uses an online gradient descent technique.
For more details, refer to the tutorial at https://github.com/JohnLangford/vowpal_wabbit/wiki/v5.1_tutorial.pdf
Definition at line 40 of file VowpalWabbit.h.
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 | |
SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
virtual bool | train_require_labels () const |
virtual void | store_model_features () |
virtual bool | is_label_valid (CLabels *lab) 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) |
Protected Attributes | |
CStreamingVwFeatures * | features |
Features. More... | |
CVwEnvironment * | env |
Environment for VW, i.e., globals. More... | |
CVwLearner * | learner |
Learner to use. More... | |
CVwRegressor * | reg |
Regressor. More... | |
int32_t | w_dim |
float32_t * | w |
float32_t | bias |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
CVowpalWabbit | ( | ) |
Default constructor
Definition at line 23 of file VowpalWabbit.cpp.
CVowpalWabbit | ( | CStreamingVwFeatures * | feat | ) |
Constructor, taking a features object as argument
feat | StreamingVwFeatures object |
Definition at line 31 of file VowpalWabbit.cpp.
CVowpalWabbit | ( | CVowpalWabbit * | vw | ) |
copy constructor
vw | another VowpalWabbit object |
Definition at line 39 of file VowpalWabbit.cpp.
~CVowpalWabbit | ( | ) |
Destructor
Definition at line 64 of file VowpalWabbit.cpp.
void add_quadratic_pair | ( | char * | pair | ) |
Add a pair of namespaces whose features should be crossed for quadratic updates
pair | a string with the two namespace names concatenated |
Definition at line 131 of file VowpalWabbit.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 linear machine to data for binary classification problems
data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 36 of file OnlineLinearMachine.cpp.
get real outputs
data | features to compute outputs |
Definition at line 48 of file OnlineLinearMachine.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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applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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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apply machine to data in means of multiclass classification problem
Reimplemented in CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CMulticlassMachine, CKNN, CC45ClassifierTree, CID3ClassifierTree, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree, and CBaggingMachine.
Definition at line 220 of file Machine.cpp.
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virtualinherited |
get output for example "vec_idx"
Reimplemented from CMachine.
Definition at line 173 of file OnlineLinearMachine.h.
apply linear machine to one vector
vec | feature vector |
len | length of vector |
Definition at line 84 of file OnlineLinearMachine.cpp.
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virtualinherited |
apply linear machine to data for regression problems
data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 42 of file OnlineLinearMachine.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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virtualinherited |
apply linear machine to vector currently being processed
Definition at line 89 of file OnlineLinearMachine.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 597 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 714 of file SGObject.cpp.
Computes the exact norm during adaptive learning
ex | example |
sum_abs_x | set by reference, sum of abs of features |
Definition at line 420 of file VowpalWabbit.cpp.
float32_t compute_exact_norm_quad | ( | float32_t * | weights, |
VwFeature & | page_feature, | ||
v_array< VwFeature > & | offer_features, | ||
vw_size_t | mask, | ||
float32_t | g, | ||
float32_t & | sum_abs_x | ||
) |
Computes the exact norm for quadratic features during adaptive learning
weights | weights |
page_feature | current feature |
offer_features | paired features |
mask | mask |
g | square of gradient |
sum_abs_x | sum of absolute value of features |
Definition at line 457 of file VowpalWabbit.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 198 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 618 of file SGObject.cpp.
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get classifier type
Reimplemented in CLaRank, CSVMLight, CDualLibQPBMSOSVM, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKMeans, CLibSVR, CQDA, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CKNN, CScatterSVM, CGaussianProcessRegression, CSGDQN, CSVMSGD, CSVMOcas, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CWDSVMOcas, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CDomainAdaptationSVM, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLightOneClass, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGPBTSVM, CGNPPSVM, and CCPLEXSVM.
Definition at line 92 of file Machine.cpp.
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virtual |
Get the environment
Definition at line 187 of file VowpalWabbit.h.
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CVwLearner* get_learner | ( | ) |
Get learner
Definition at line 209 of file VowpalWabbit.h.
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returns type of problem machine solves
Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.
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Definition at line 498 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 522 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 535 of file SGObject.cpp.
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Return the name of the object
Reimplemented from COnlineLinearMachine.
Definition at line 198 of file VowpalWabbit.h.
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inherited |
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get w
dst_w | store w in this argument |
dst_dims | dimension of w |
Definition at line 65 of file OnlineLinearMachine.h.
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Get w as a new float64_t array
dst_w | store w in this argument |
dst_dims | dimension of w |
Definition at line 78 of file OnlineLinearMachine.h.
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inherited |
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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 296 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.
void load_regressor | ( | char * | file_name | ) |
Load regressor from a dump file
file_name | name of regressor file |
Definition at line 109 of file VowpalWabbit.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 369 of file SGObject.cpp.
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protectedvirtualinherited |
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 426 of file SGObject.cpp.
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protectedvirtualinherited |
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 421 of file SGObject.cpp.
MACHINE_PROBLEM_TYPE | ( | PT_BINARY | ) |
problem type
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virtualinherited |
Definition at line 262 of file SGObject.cpp.
Predict for an example
ex | VwExample to predict for |
Definition at line 208 of file VowpalWabbit.cpp.
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prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 308 of file SGObject.cpp.
void reinitialize_weights | ( | ) |
Reinitialize the weight vectors. Call after updating env variables eg. stride.
Definition at line 71 of file VowpalWabbit.cpp.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 314 of file SGObject.cpp.
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protectedvirtualinherited |
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 436 of file SGObject.cpp.
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protectedvirtualinherited |
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 431 of file SGObject.cpp.
void set_adaptive | ( | bool | adaptive_learning | ) |
Set whether learning is adaptive or not
adaptive_learning | true if adaptive |
Definition at line 85 of file VowpalWabbit.cpp.
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void set_exact_adaptive_norm | ( | bool | exact_adaptive | ) |
Set whether to use the more expensive exact norm for adaptive learning
exact_adaptive | true if exact norm is required |
Definition at line 98 of file VowpalWabbit.cpp.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 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 241 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 283 of file SGObject.cpp.
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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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Sets the train/update methods depending on parameters set, eg. adaptive or not
Definition at line 274 of file VowpalWabbit.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 82 of file Machine.cpp.
void set_no_training | ( | bool | dont_train | ) |
Set whether one desires to not train and only make passes over all examples instead.
This is useful if one wants to create a cache file from data.
dont_train | true if one doesn't want to train |
Definition at line 84 of file VowpalWabbit.h.
void set_num_passes | ( | int32_t | passes | ) |
Set number of passes (only works for cached input)
passes | number of passes |
Definition at line 106 of file VowpalWabbit.h.
void set_prediction_out | ( | char * | file_name | ) |
Set file name of prediction output
file_name | name of file to save predictions to |
Definition at line 123 of file VowpalWabbit.cpp.
void set_regressor_out | ( | char * | file_name, |
bool | is_text = true |
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Set regressor output parameters
file_name | name of file to save regressor to |
is_text | human readable or not, bool |
Definition at line 117 of file VowpalWabbit.cpp.
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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 w
src_w | new w |
src_w_dim | dimension of new w |
Definition at line 104 of file OnlineLinearMachine.h.
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Set weight vector from a float64_t vector
src_w | new w |
src_w_dim | dimension of new w |
Definition at line 118 of file OnlineLinearMachine.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 192 of file SGObject.cpp.
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Start training of the online machine, sub-class should override this if some preparations are to be done
Reimplemented in COnlineLibLinear.
Definition at line 212 of file OnlineLinearMachine.h.
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Stop training of the online machine, sub-class should override this if some clean up is needed
Reimplemented in COnlineLibLinear.
Definition at line 217 of file OnlineLinearMachine.h.
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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, CLinearMulticlassMachine, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CLinearMachine, CGaussianProcessMachine, CHierarchical, CDistanceMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.
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Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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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, CSGDQN, and COnlineSVMSGD.
Definition at line 39 of file Machine.cpp.
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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 228 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, and CMultitaskLinearMachine.
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Train on a StreamingVwFeatures object
feat | StreamingVwFeatures to train using |
Reimplemented from COnlineLinearMachine.
Definition at line 136 of file VowpalWabbit.cpp.
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whether train require labels
Reimplemented from CMachine.
Definition at line 249 of file OnlineLinearMachine.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 303 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 248 of file SGObject.cpp.
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bias
Definition at line 257 of file OnlineLinearMachine.h.
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Environment for VW, i.e., globals.
Definition at line 282 of file VowpalWabbit.h.
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Features.
Definition at line 279 of file VowpalWabbit.h.
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io
Definition at line 369 of file SGObject.h.
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Learner to use.
Definition at line 285 of file VowpalWabbit.h.
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parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
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Hash of parameter values
Definition at line 387 of file SGObject.h.
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model selection parameters
Definition at line 381 of file SGObject.h.
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parameters
Definition at line 378 of file SGObject.h.
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parallel
Definition at line 372 of file SGObject.h.
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Regressor.
Definition at line 288 of file VowpalWabbit.h.
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version
Definition at line 375 of file SGObject.h.
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w
Definition at line 255 of file OnlineLinearMachine.h.
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dimension of w
Definition at line 253 of file OnlineLinearMachine.h.