The Inference Method base class.
The Inference Method computes approximately the posterior distribution for a given Gaussian Process.
Definition at line 32 of file InferenceMethod.h.
| CInferenceMethod | ( | ) |
Default Constructor
Definition at line 20 of file InferenceMethod.cpp.
| CInferenceMethod | ( | CKernel * | kernel, | |
| CFeatures * | features, | |||
| CMeanFunction * | mean, | |||
| CLabels * | labels, | |||
| CLikelihoodModel * | model | |||
| ) |
Constructor
| kernel | covariance function | |
| features | features to use in inference | |
| mean | Mean function | |
| labels | labels of the features | |
| model | Likelihood model to use |
Definition at line 32 of file InferenceMethod.cpp.
| ~CInferenceMethod | ( | ) | [virtual] |
destructor
Definition at line 44 of file InferenceMethod.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 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.
get Alpha Matrix
![\[ \mu = K\alpha \]](form_281.png)
where 
is the mean and 
is the prior covariance matrix
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
get Cholesky Decomposition Matrix
![\[ L = Cholesky(sW*K*sW+I) \]](form_283.png)
Where is the prior covariance matrix, sW is the matrix returned by get_cholesky(), and 
is the identity matrix.
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
get Diagonal Vector
![\[ Cov = (K^{-1}+D^{2})^{-1}} \]](form_284.png)
Where Cov is the posterior covariance matrix, K is the prior covariance matrix, and D is the diagonal matrix
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
| virtual CFeatures* get_features | ( | ) | [virtual] |
| SGIO * get_global_io | ( | ) | [inherited] |
| Parallel * get_global_parallel | ( | ) | [inherited] |
| Version * get_global_version | ( | ) | [inherited] |
| virtual CMap<TParameter*, SGVector<float64_t> > get_gradient | ( | CMap< TParameter *, CSGObject * > & | para_dict | ) | [pure virtual, inherited] |
Get the gradient
| para_dict | dictionary to be built |
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
| virtual CKernel* get_kernel | ( | ) | [virtual] |
| virtual CLabels* get_labels | ( | ) | [virtual] |
| virtual CFeatures* get_latent_features | ( | ) | [virtual] |
| virtual CMap<TParameter*, SGVector<float64_t> > get_marginal_likelihood_derivatives | ( | CMap< TParameter *, CSGObject * > & | para_dict | ) | [pure virtual] |
get Log Marginal Likelihood Gradient
![\[ -\frac{\partial {log(p(y|X, \theta))}}{\partial \theta} \]](form_280.png)
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
| virtual CMeanFunction* get_mean | ( | ) | [virtual] |
| CLikelihoodModel* get_model | ( | ) |
| 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 [pure virtual, inherited] |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
Implemented in CAveragedPerceptron, CFeatureBlockLogisticRegression, CLDA, CLPBoost, CLPM, CMKL, CMKLMulticlass, MKLMulticlassGLPK, MKLMulticlassGradient, MKLMulticlassOptimizationBase, CPerceptron, CPluginEstimate, CSubGradientLPM, CGNPPLib, CGNPPSVM, CGPBTSVM, CLibLinear, CLibSVM, CLibSVMOneClass, CMPDSVM, CNewtonSVM, COnlineLibLinear, COnlineSVMSGD, CQPBSVMLib, CSGDQN, CSubGradientSVM, CSVM, CSVMLight, CSVMLin, CSVMOcas, CSVMSGD, CWDSVMOcas, CVwCacheReader, CVwNativeCacheReader, CVwNativeCacheWriter, CVwAdaptiveLearner, CVwNonAdaptiveLearner, CVowpalWabbit, CVwEnvironment, CVwLearner, CVwParser, CVwRegressor, CGMM, CHierarchical, CKMeans, CConverter, CDiffusionMaps, CEmbeddingConverter, CHessianLocallyLinearEmbedding, CIsomap, CKernelLocallyLinearEmbedding, CKernelLocalTangentSpaceAlignment, CLaplacianEigenmaps, CLinearLocalTangentSpaceAlignment, CLocalityPreservingProjections, CLocallyLinearEmbedding, CLocalTangentSpaceAlignment, CMultidimensionalScaling, CNeighborhoodPreservingEmbedding, CStochasticProximityEmbedding, CAttenuatedEuclideanDistance, CBrayCurtisDistance, CCanberraMetric, CCanberraWordDistance, CChebyshewMetric, CChiSquareDistance, CCosineDistance, CCustomDistance, CDenseDistance< ST >, CEuclideanDistance, CGeodesicMetric, CHammingWordDistance, CJensenMetric, CKernelDistance, CMahalanobisDistance, CManhattanMetric, CManhattanWordDistance, CMinkowskiMetric, CRealDistance, CSparseDistance< ST >, CSparseEuclideanDistance, CStringDistance< ST >, CTanimotoDistance, CGaussian, CGHMM, CHistogram, CHMM, CLinearHMM, CPositionalPWM, CClusteringAccuracy, CClusteringMutualInformation, CContingencyTableEvaluation, CAccuracyMeasure, CErrorRateMeasure, CBALMeasure, CWRACCMeasure, CF1Measure, CCrossCorrelationMeasure, CRecallMeasure, CPrecisionMeasure, CSpecificityMeasure, CCrossValidationResult, CCrossValidation, CCrossValidationMKLStorage, CCrossValidationMulticlassStorage, CCrossValidationOutput, CCrossValidationPrintOutput, CCrossValidationSplitting, CGradientCriterion, CGradientEvaluation, CGradientResult, CMeanAbsoluteError, CMeanSquaredError, CMeanSquaredLogError, CMulticlassAccuracy, CMulticlassOVREvaluation, CPRCEvaluation, CROCEvaluation, CStratifiedCrossValidationSplitting, CStructuredAccuracy, CAlphabet, CAttributeFeatures, CBinnedDotFeatures, CCombinedDotFeatures, CCombinedFeatures, CDataGenerator, CDenseFeatures< ST >, CDenseSubsetFeatures< ST >, CDummyFeatures, CExplicitSpecFeatures, CFKFeatures, CHashedWDFeatures, CHashedWDFeaturesTransposed, CImplicitWeightedSpecFeatures, CLatentFeatures, CLBPPyrDotFeatures, CMatrixFeatures< ST >, CPolyFeatures, CRealFileFeatures, CSNPFeatures, CSparseFeatures< ST >, CSparsePolyFeatures, CMeanShiftDataGenerator< T >, CStreamingDenseFeatures< T >, CStreamingSparseFeatures< T >, CStreamingStringFeatures< T >, CStreamingVwFeatures, CStringFeatures< ST >, CSubset, CSubsetStack, CTOPFeatures, CWDFeatures, CAsciiFile, CBinaryFile, CBinaryStream< T >, CFile, CIOBuffer, CMemoryMappedFile< T >, CSerializableAsciiFile, SerializableAsciiReader00, CSimpleFile< T >, CParseBuffer< T >, CStreamingAsciiFile, CStreamingFile, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromFeatures, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CStreamingVwCacheFile, CStreamingVwFile, CANOVAKernel, CAUCKernel, CBesselKernel, CCauchyKernel, CChi2Kernel, CCircularKernel, CCombinedKernel, CConstKernel, CCustomKernel, CDiagKernel, CDistanceKernel, CDotKernel, CExponentialKernel, CGaussianARDKernel, CGaussianKernel, CGaussianShiftKernel, CGaussianShortRealKernel, CHistogramIntersectionKernel, CInverseMultiQuadricKernel, CJensenShannonKernel, CLinearARDKernel, CLinearKernel, CLogKernel, CMultiquadricKernel, CAvgDiagKernelNormalizer, CDiceKernelNormalizer, CFirstElementKernelNormalizer, CIdentityKernelNormalizer, CRidgeKernelNormalizer, CScatterKernelNormalizer, CSqrtDiagKernelNormalizer, CTanimotoKernelNormalizer, CVarianceKernelNormalizer, CZeroMeanCenterKernelNormalizer, CPolyKernel, CPowerKernel, CProductKernel, CPyramidChi2, CRationalQuadraticKernel, CSigmoidKernel, CSparseKernel< ST >, CSphericalKernel, CSplineKernel, CCommUlongStringKernel, CCommWordStringKernel, CDistantSegmentsKernel, CFixedDegreeStringKernel, CGaussianMatchStringKernel, CHistogramWordStringKernel, CLinearStringKernel, CLocalAlignmentStringKernel, CLocalityImprovedStringKernel, CMatchWordStringKernel, COligoStringKernel, CPolyMatchStringKernel, CPolyMatchWordStringKernel, CRegulatoryModulesStringKernel, CSalzbergWordStringKernel, CSimpleLocalityImprovedStringKernel, CSNPStringKernel, CSparseSpatialSampleStringKernel, CSpectrumMismatchRBFKernel, CSpectrumRBFKernel, CStringKernel< ST >, CWeightedCommWordStringKernel, CWeightedDegreePositionStringKernel, CWeightedDegreeStringKernel, CTensorProductPairKernel, CTStudentKernel, CWaveKernel, CWaveletKernel, CWeightedDegreeRBFKernel, CBinaryLabels, CLatentLabels, CMulticlassLabels, CMulticlassMultipleOutputLabels, CRegressionLabels, CStructuredLabels, CLatentModel, CLatentSOSVM, CLatentSVM, CBitString, CCache< T >, CCompressor, CData, CDynamicArray< T >, CDynamicObjectArray, CGCArray< T >, CHash, CIndexBlock, CIndexBlockGroup, CIndexBlockRelation, CIndexBlockTree, CListElement, CList, CMap< K, T >, CSet< T >, CSignal, CStructuredData, CTime, CTrie< Trie >, CHingeLoss, CLogLoss, CLogLossMargin, CLossFunction, CSmoothHingeLoss, CSquaredHingeLoss, CSquaredLoss, CBaseMulticlassMachine, CDistanceMachine, CKernelMachine, CKernelMulticlassMachine, CKernelStructuredOutputMachine, CLinearLatentMachine, CLinearMachine, CLinearMulticlassMachine, CLinearStructuredOutputMachine, CMachine, CMulticlassMachine, CNativeMulticlassMachine, COnlineLinearMachine, CStructuredOutputMachine, CCplex, CMath, CSparseInverseCovariance, CStatistics, CGradientModelSelection, CGridSearchModelSelection, CModelSelectionParameters, CParameterCombination, CRandomSearchModelSelection, CConjugateIndex, CECOCAEDDecoder, CECOCDecoder, CECOCDiscriminantEncoder, CECOCEDDecoder, CECOCEncoder, CECOCForestEncoder, CECOCHDDecoder, CECOCIHDDecoder, CECOCLLBDecoder, CECOCOVOEncoder, CECOCOVREncoder, CECOCRandomDenseEncoder, CECOCRandomSparseEncoder, CECOCSimpleDecoder, CECOCStrategy, CGaussianNaiveBayes, CGMNPLib, CGMNPSVM, CKNN, CLaRank, CMulticlassLibLinear, CMulticlassLibSVM, CMulticlassLogisticRegression, CMulticlassOCAS, CMulticlassOneVsOneStrategy, CMulticlassOneVsRestStrategy, CMulticlassStrategy, CMulticlassTreeGuidedLogisticRegression, CQDA, CRejectionStrategy, CThresholdRejectionStrategy, CDixonQTestRejectionStrategy, CScatterSVM, CShareBoost, CBalancedConditionalProbabilityTree, CConditionalProbabilityTree, CRandomConditionalProbabilityTree, CRelaxedTree, CTreeMachine< T >, CTreeMachineNode< T >, CVwConditionalProbabilityTree, CDecompressString< ST >, CDimensionReductionPreprocessor, CHomogeneousKernelMap, CKernelPCA, CLogPlusOne, CNormOne, CPCA, CPNorm, CPruneVarSubMean, CRandomFourierGaussPreproc, CSortUlongString, CSortWordString, CSparsePreprocessor< ST >, CStringPreprocessor< ST >, CSumOne, CGaussianProcessRegression, CExactInferenceMethod, CFITCInferenceMethod, CGaussianLikelihood, CLaplacianInferenceMethod, CStudentsTLikelihood, CZeroMean, CKernelRidgeRegression, CLeastAngleRegression, CLeastSquaresRegression, CLinearRidgeRegression, CLibLinearRegression, CLibSVR, CSVRLight, CHSIC, CKernelIndependenceTestStatistic, CKernelMeanMatching, CKernelTwoSampleTestStatistic, CLinearTimeMMD, CQuadraticTimeMMD, CTestStatistic, CTwoDistributionsTestStatistic, CDualLibQPBMSOSVM, CDynProg, CSequence, CHMSVMLabels, CIntronList, CMulticlassModel, CRealNumber, CMulticlassSOLabels, CPlif, CPlifArray, CPlifMatrix, CSegmentLoss, CStateModel, CResultSet, CStructuredModel, CTwoStateModel, CDomainAdaptationMulticlassLibLinear, CDomainAdaptationSVM, CDomainAdaptationSVMLinear, CLibLinearMTL, CMultitaskClusteredLogisticRegression, CMultitaskCompositeMachine, CMultitaskKernelMaskNormalizer, CMultitaskKernelMaskPairNormalizer, CMultitaskKernelMklNormalizer, CMultitaskKernelNormalizer, CMultitaskKernelPlifNormalizer, CNode, CTaxonomy, CMultitaskKernelTreeNormalizer, CMultitaskL12LogisticRegression, CMultitaskLeastSquaresRegression, CMultitaskLinearMachine, CMultitaskLogisticRegression, CMultitaskROCEvaluation, CMultitaskTraceLogisticRegression, CTask, CTaskGroup, CTaskRelation, CTaskTree, CGUIClassifier, CGUIConverter, CGUIDistance, CGUIFeatures, CGUIHMM, CGUIKernel, CGUILabels, CGUIMath, CGUIPluginEstimate, CGUIPreprocessor, CGUIStructure, CGUITime, CDenseDistance< float64_t >, CSparseDistance< float64_t >, CStringDistance< uint16_t >, CDenseFeatures< uint32_t >, CDenseFeatures< float64_t >, CDenseFeatures< T >, CDenseFeatures< uint16_t >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CStreamingDenseFeatures< float32_t >, CStringFeatures< T >, CStringFeatures< uint8_t >, CStringFeatures< char >, CStringFeatures< uint16_t >, CMemoryMappedFile< ST >, CParseBuffer< VwExample >, CParseBuffer< float32_t >, CParseBuffer< SGSparseVectorEntry< T > >, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CCache< SGSparseVectorEntry< ST > >, CCache< uint32_t >, CCache< ST >, CCache< SGSparseVectorEntry< float64_t > >, CCache< float64_t >, CCache< uint8_t >, CCache< KERNELCACHE_ELEM >, CCache< char >, CCache< uint16_t >, CCache< SGSparseVectorEntry< T > >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, CMap< TParameter *, CSGObject * >, CMap< TParameter *, SGVector< float64_t > >, CTrie< DNATrie >, CTrie< POIMTrie >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CStringPreprocessor< uint16_t >, and CStringPreprocessor< uint64_t >.
| virtual float64_t get_negative_marginal_likelihood | ( | ) | [pure virtual] |
get Negative Log Marginal Likelihood
![\[ -log(p(y|X, \theta)) Where y are the labels, X are the features, and \theta represent hyperparameters \]](form_279.png)
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
Get the function value
Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
| virtual float64_t get_scale | ( | ) | [virtual] |
| 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.
| 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.
| 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.
| void set_features | ( | CFeatures * | 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_kernel | ( | CKernel * | kern | ) | [virtual] |
| void set_labels | ( | CLabels * | lab | ) | [virtual] |
| void set_latent_features | ( | CFeatures * | feat | ) | [virtual] |
set latent features
| feat | features to set |
Definition at line 102 of file InferenceMethod.cpp.
| void set_mean | ( | CMeanFunction * | m | ) | [virtual] |
| void set_model | ( | CLikelihoodModel * | mod | ) | [virtual] |
set likelihood model
| mod | model to set |
Definition at line 167 of file InferenceMethod.cpp.
| void set_scale | ( | float64_t | s | ) | [virtual] |
| 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.
| 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.
| virtual void update_alpha | ( | ) | [protected, virtual] |
Update alpha matrix
Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
Definition at line 209 of file InferenceMethod.h.
| virtual void update_chol | ( | ) | [protected, virtual] |
Update cholesky matrix
Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
Definition at line 212 of file InferenceMethod.h.
| void update_data_means | ( | ) | [protected, virtual] |
Update data means
Definition at line 185 of file InferenceMethod.cpp.
| bool update_parameter_hash | ( | ) | [protected, virtual, inherited] |
Updates the hash of current parameter combination.
Definition at line 237 of file SGObject.cpp.
| virtual void update_train_kernel | ( | ) | [protected, virtual] |
Update train kernel matrix
Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.
Definition at line 215 of file InferenceMethod.h.
io
Definition at line 462 of file SGObject.h.
alpha matrix used in process mean calculation
Definition at line 262 of file InferenceMethod.h.
SGVector<float64_t> m_data_means [protected] |
Means of Features
Definition at line 235 of file InferenceMethod.h.
SGMatrix<float64_t> m_feature_matrix [protected] |
Feature Matrix
Definition at line 232 of file InferenceMethod.h.
CFeatures* m_features [protected] |
Features to use
Definition at line 229 of file InferenceMethod.h.
uint32_t m_hash [inherited] |
Hash of parameter values
Definition at line 480 of file SGObject.h.
Covariance Function
Definition at line 226 of file InferenceMethod.h.
Kernel matrix from features
Definition at line 273 of file InferenceMethod.h.
Lower triangle Cholesky decomposition of feature matrix
Definition at line 267 of file InferenceMethod.h.
SGVector<float64_t> m_label_vector [protected] |
Vector of labels
Definition at line 238 of file InferenceMethod.h.
Labels of those features
Definition at line 242 of file InferenceMethod.h.
CFeatures* m_latent_features [protected] |
Latent Features for Approximation
Definition at line 248 of file InferenceMethod.h.
SGMatrix<float64_t> m_latent_matrix [protected] |
Kernel matrix from latent features
Definition at line 276 of file InferenceMethod.h.
CMeanFunction* m_mean [protected] |
Mean Function
Definition at line 245 of file InferenceMethod.h.
CLikelihoodModel* m_model [protected] |
Likelihood function to use
![\[ p(y|f) \]](form_287.png)
Where y are the labels and f is the prediction function
Definition at line 259 of file InferenceMethod.h.
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.
Kernel Scale
Definition at line 270 of file InferenceMethod.h.
parallel
Definition at line 465 of file SGObject.h.
version
Definition at line 468 of file SGObject.h.
