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SHOGUN
4.1.0
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TODO doc
在文件 LinearStructuredOutputMachine.h 第 26 行定义.
Public 属性 | |
| SGIO * | io |
| Parallel * | parallel |
| Version * | version |
| Parameter * | m_parameters |
| Parameter * | m_model_selection_parameters |
| Parameter * | m_gradient_parameters |
| uint32_t | m_hash |
Protected 成员函数 | |
| virtual float64_t | risk_nslack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_nslack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_1slack_margin_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_1slack_slack_rescale (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| virtual float64_t | risk_customized_formulation (float64_t *subgrad, float64_t *W, TMultipleCPinfo *info=0) |
| 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) |
Protected 属性 | |
| SGVector< float64_t > | m_w |
| CStructuredModel * | m_model |
| CLossFunction * | m_surrogate_loss |
| CSOSVMHelper * | m_helper |
| bool | m_verbose |
| float64_t | m_max_train_time |
| CLabels * | m_labels |
| ESolverType | m_solver_type |
| bool | m_store_model_features |
| bool | m_data_locked |
default constructor
在文件 LinearStructuredOutputMachine.cpp 第 17 行定义.
| CLinearStructuredOutputMachine | ( | CStructuredModel * | model, |
| CStructuredLabels * | labs | ||
| ) |
standard constructor
| model | structured model with application specific functions |
| labs | structured labels |
在文件 LinearStructuredOutputMachine.cpp 第 23 行定义.
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virtual |
destructor
在文件 LinearStructuredOutputMachine.cpp 第 31 行定义.
apply machine to data if data is not specified apply to the current features
| data | (test)data to be classified |
在文件 Machine.cpp 第 152 行定义.
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virtualinherited |
apply machine to data in means of binary classification problem
被 CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate , 以及 CBaggingMachine 重载.
在文件 Machine.cpp 第 208 行定义.
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apply machine to data in means of latent problem
被 CLinearLatentMachine 重载.
在文件 Machine.cpp 第 232 行定义.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
在文件 Machine.cpp 第 187 行定义.
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applies a locked machine on a set of indices for binary problems
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
在文件 Machine.cpp 第 238 行定义.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
在文件 Machine.cpp 第 266 行定义.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
在文件 Machine.cpp 第 252 行定义.
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applies a locked machine on a set of indices for regression problems
被 CKernelMachine 重载.
在文件 Machine.cpp 第 245 行定义.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
在文件 Machine.cpp 第 259 行定义.
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virtualinherited |
apply machine to data in means of multiclass classification problem
被 CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CMulticlassMachine, CKNN, CC45ClassifierTree, CID3ClassifierTree, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree , 以及 CBaggingMachine 重载.
在文件 Machine.cpp 第 220 行定义.
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applies to one vector
被 CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate , 以及 CFeatureBlockLogisticRegression 重载.
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apply machine to data in means of regression problem
被 CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, CGaussianProcessRegression , 以及 CBaggingMachine 重载.
在文件 Machine.cpp 第 214 行定义.
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virtual |
apply structured machine to data for Structured Output (SO) problem
| data | (test)data to be classified |
重载 CMachine .
在文件 LinearStructuredOutputMachine.cpp 第 45 行定义.
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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. |
在文件 SGObject.cpp 第 597 行定义.
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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.
在文件 SGObject.cpp 第 714 行定义.
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 |
被 CKernelMachine 重载.
在文件 Machine.cpp 第 112 行定义.
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virtualinherited |
Unlocks a locked machine and restores previous state
被 CKernelMachine 重载.
在文件 Machine.cpp 第 143 行定义.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 198 行定义.
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) |
在文件 SGObject.cpp 第 618 行定义.
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get classifier type
被 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 , 以及 CCPLEXSVM 重载.
在文件 Machine.cpp 第 92 行定义.
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在文件 StructuredOutputMachine.cpp 第 186 行定义.
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virtualinherited |
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virtualinherited |
returns type of problem machine solves
被 CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree , 以及 CBaseMulticlassMachine 重载.
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在文件 SGObject.cpp 第 498 行定义.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
在文件 SGObject.cpp 第 522 行定义.
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Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
在文件 SGObject.cpp 第 535 行定义.
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被 CDualLibQPBMSOSVM, CCCSOSVM, CStochasticSOSVM , 以及 CFWSOSVM 重载.
在文件 LinearStructuredOutputMachine.h 第 71 行定义.
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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 |
在文件 SGObject.cpp 第 296 行定义.
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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 |
被 CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression , 以及 CBaseMulticlassMachine 重载.
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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 |
在文件 SGObject.cpp 第 369 行定义.
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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. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 426 行定义.
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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. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 421 行定义.
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problem type
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virtualinherited |
在文件 SGObject.cpp 第 262 行定义.
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 474 行定义.
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computes the value of the risk function and sub-gradient at given point
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
| rtype | The type of structured risk |
在文件 StructuredOutputMachine.cpp 第 157 行定义.
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protectedvirtualinherited |
1-slack formulation and margin rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
在文件 StructuredOutputMachine.cpp 第 139 行定义.
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protectedvirtualinherited |
1-slack formulation and slack rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
在文件 StructuredOutputMachine.cpp 第 145 行定义.
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protectedvirtualinherited |
customized risk type
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
在文件 StructuredOutputMachine.cpp 第 151 行定义.
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protectedvirtualinherited |
n-slack formulation and margin rescaling
The value of the risk is evaluated as
\[ R({\bf w}) = \sum_{i=1}^{m} \max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) - \Psi(x_i, y_i) \rangle \right] \]
The subgradient is by Danskin's theorem given as
\[ R'({\bf w}) = \sum_{i=1}^{m} \Psi(x_i, \hat{y}_i) - \Psi(x_i, y_i), \]
where \( \hat{y}_i \) is the most violated label, i.e.
\[ \hat{y}_i = \arg\max_{y \in \mathcal{Y}} \left[ \ell(y_i, y) + \langle {\bf w}, \Psi(x_i, y) \rangle \right] \]
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
在文件 StructuredOutputMachine.cpp 第 97 行定义.
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protectedvirtualinherited |
n-slack formulation and slack rescaling
| subgrad | Subgradient computed at given point W |
| W | Given weight vector |
| info | Helper info for multiple cutting plane models algorithm |
在文件 StructuredOutputMachine.cpp 第 133 行定义.
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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 |
在文件 SGObject.cpp 第 314 行定义.
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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. |
被 CKernel 重载.
在文件 SGObject.cpp 第 436 行定义.
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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. |
被 CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 431 行定义.
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在文件 SGObject.cpp 第 41 行定义.
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在文件 SGObject.cpp 第 46 行定义.
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在文件 SGObject.cpp 第 51 行定义.
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在文件 SGObject.cpp 第 56 行定义.
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在文件 SGObject.cpp 第 61 行定义.
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在文件 SGObject.cpp 第 66 行定义.
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在文件 SGObject.cpp 第 71 行定义.
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在文件 SGObject.cpp 第 76 行定义.
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在文件 SGObject.cpp 第 81 行定义.
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在文件 SGObject.cpp 第 86 行定义.
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在文件 SGObject.cpp 第 91 行定义.
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在文件 SGObject.cpp 第 96 行定义.
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在文件 SGObject.cpp 第 101 行定义.
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在文件 SGObject.cpp 第 106 行定义.
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在文件 SGObject.cpp 第 111 行定义.
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set generic type to T
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Setter for store-model-features-after-training flag
| store_model | whether model should be stored after training |
在文件 Machine.cpp 第 107 行定义.
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set verbose NOTE that track verbose information including primal objectives, training errors and duality gaps will make the training 2x or 3x slower.
| verbose | flag enabling/disabling verbose information |
在文件 StructuredOutputMachine.cpp 第 198 行定义.
set w (useful for modular interfaces)
| w | weight vector to set |
在文件 LinearStructuredOutputMachine.cpp 第 35 行定义.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 192 行定义.
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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
重载 CMachine .
在文件 LinearStructuredOutputMachine.cpp 第 78 行定义.
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被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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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. |
被 CRelaxedTree, CAutoencoder, CSGDQN , 以及 COnlineSVMSGD 重载.
在文件 Machine.cpp 第 39 行定义.
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 |
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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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!
被 CSVMLight, CLaRank, CWDSVMOcas, CLibLinearMTL, CMKL, COnlineLinearMachine, CNeuralNetwork, CDualLibQPBMSOSVM, CCARTree, CCHAIDTree, CSVRLight, CPluginEstimate, CRelaxedTree, CKNN, CSVMOcas, CLibLinear, CCCSOSVM, CLeastAngleRegression, CMulticlassMachine, CLDA, CMKLMulticlass, CC45ClassifierTree, CLibLinearRegression, CSVMSGD, CQDA, CStochasticGBMachine, CMulticlassLibLinear, CVowpalWabbit, CMCLDA, CGaussianProcessClassification, CKernelRidgeRegression, CDomainAdaptationSVMLinear, CBaggingMachine, CMultitaskLinearMachine, CID3ClassifierTree, CHierarchical, CMulticlassOCAS, CLinearLatentMachine, CMulticlassTreeGuidedLogisticRegression, CLibSVR, CNewtonSVM, CLPBoost, CDomainAdaptationSVM, CSVMLin, CFeatureBlockLogisticRegression, CScatterSVM, CStochasticSOSVM, CLinearRidgeRegression, CLPM, CGaussianNaiveBayes, CMultitaskClusteredLogisticRegression, CFWSOSVM, CNearestCentroid, CMultitaskLogisticRegression, CVwConditionalProbabilityTree, CGaussianProcessRegression, CMulticlassLogisticRegression, CConditionalProbabilityTree, CMultitaskL12LogisticRegression, CMultitaskLeastSquaresRegression, CPerceptron, CAveragedPerceptron, CGMNPSVM, CMultitaskTraceLogisticRegression, CLibSVM, CSVMLightOneClass, CShareBoost, CLibSVMOneClass, CMulticlassLibSVM, CMPDSVM, CGPBTSVM, CGNPPSVM , 以及 CCPLEXSVM 重载.
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returns whether machine require labels for training
被 COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree , 以及 CLibSVMOneClass 重载.
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unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 303 行定义.
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Updates the hash of current parameter combination
在文件 SGObject.cpp 第 248 行定义.
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io
在文件 SGObject.h 第 369 行定义.
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parameters wrt which we can compute gradients
在文件 SGObject.h 第 384 行定义.
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Hash of parameter values
在文件 SGObject.h 第 387 行定义.
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the helper that records primal objectives, duality gaps etc
在文件 StructuredOutputMachine.h 第 223 行定义.
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the model that contains the application dependent modules
在文件 StructuredOutputMachine.h 第 214 行定义.
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model selection parameters
在文件 SGObject.h 第 381 行定义.
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parameters
在文件 SGObject.h 第 378 行定义.
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the surrogate loss, for SOSVM, fixed to Hinge loss, other non-convex losses such as Ramp loss are also applicable, will be extended in the future
在文件 StructuredOutputMachine.h 第 220 行定义.
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verbose outputs and statistics
在文件 StructuredOutputMachine.h 第 226 行定义.
weight vector
在文件 LinearStructuredOutputMachine.h 第 82 行定义.
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parallel
在文件 SGObject.h 第 372 行定义.
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version
在文件 SGObject.h 第 375 行定义.
