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CInferenceMethod Class Reference

Detailed Description

The Inference Method base class.

The Inference Method computes (a Gaussian approximation to) the posterior distribution for a given Gaussian Process.

It is possible to sample the (true) log-marginal likelihood on the base of any implemented approximation. See CInferenceMethod::get_marginal_likelihood_estimate.

Definition at line 51 of file InferenceMethod.h.

Inheritance diagram for CInferenceMethod:
Inheritance graph
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Public Member Functions

 CInferenceMethod ()
 CInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual ~CInferenceMethod ()
virtual EInferenceType get_inference_type () const
virtual float64_t get_negative_log_marginal_likelihood ()=0
float64_t get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
virtual CMap< TParameter
*, SGVector< float64_t > > * 
get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters)
virtual SGVector< float64_tget_posterior_mean ()=0
virtual SGMatrix< float64_tget_posterior_covariance ()=0
virtual CMap< TParameter
*, SGVector< float64_t > > * 
get_gradient (CMap< TParameter *, CSGObject * > *parameters)
virtual SGVector< float64_tget_value ()
virtual CFeaturesget_features ()
virtual void set_features (CFeatures *feat)
virtual CKernelget_kernel ()
virtual void set_kernel (CKernel *kern)
virtual CMeanFunctionget_mean ()
virtual void set_mean (CMeanFunction *m)
virtual CLabelsget_labels ()
virtual void set_labels (CLabels *lab)
CLikelihoodModelget_model ()
virtual void set_model (CLikelihoodModel *mod)
virtual float64_t get_scale () const
virtual void set_scale (float64_t scale)
virtual bool supports_regression () const
virtual bool supports_binary () const
virtual bool supports_multiclass () const
virtual void update ()
virtual SGMatrix< float64_tget_multiclass_E ()
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual const char * get_name () const =0
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual void update_parameter_hash ()
virtual bool parameter_hash_changed ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual void check_members () const
virtual void update_alpha ()=0
virtual void update_chol ()=0
virtual void update_deriv ()=0
virtual void update_train_kernel ()
virtual SGVector< float64_tget_derivative_wrt_inference_method (const TParameter *param)=0
virtual SGVector< float64_tget_derivative_wrt_likelihood_model (const TParameter *param)=0
virtual SGVector< float64_tget_derivative_wrt_kernel (const TParameter *param)=0
virtual SGVector< float64_tget_derivative_wrt_mean (const TParameter *param)=0
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
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)

Static Protected Member Functions

static void * get_derivative_helper (void *p)

Protected Attributes

CKernelm_kernel
CMeanFunctionm_mean
CLikelihoodModelm_model
CFeaturesm_features
CLabelsm_labels
SGVector< float64_tm_alpha
SGMatrix< float64_tm_L
float64_t m_scale
SGMatrix< float64_tm_ktrtr
SGMatrix< float64_tm_E

Constructor & Destructor Documentation

default constructor

Definition at line 35 of file InferenceMethod.cpp.

CInferenceMethod ( CKernel kernel,
CFeatures features,
CMeanFunction mean,
CLabels labels,
CLikelihoodModel model 
)

constructor

Parameters
kernelcovariance function
featuresfeatures to use in inference
meanmean function
labelslabels of the features
modellikelihood model to use

Definition at line 48 of file InferenceMethod.cpp.

~CInferenceMethod ( )
virtual

Definition at line 60 of file InferenceMethod.cpp.

Member Function Documentation

void build_gradient_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.

Parameters
dictdictionary of parameters to be built.

Definition at line 1243 of file SGObject.cpp.

void check_members ( ) const
protectedvirtual

check if members of object are valid for inference

Reimplemented in CFITCInferenceMethod, and CExactInferenceMethod.

Definition at line 275 of file InferenceMethod.cpp.

CSGObject * clone ( )
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.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 1360 of file SGObject.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 200 of file SGObject.cpp.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0,
bool  tolerant = false 
)
virtualinherited

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.

Parameters
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 1264 of file SGObject.cpp.

void * get_derivative_helper ( void *  p)
staticprotected

pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter

Definition at line 221 of file InferenceMethod.cpp.

virtual SGVector<float64_t> get_derivative_wrt_inference_method ( const TParameter param)
protectedpure virtual

returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class

Parameters
paramparameter of CInferenceMethod class
Returns
derivative of negative log marginal likelihood

Implemented in CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, and CSingleLaplacianInferenceMethod.

virtual SGVector<float64_t> get_derivative_wrt_kernel ( const TParameter param)
protectedpure virtual

returns derivative of negative log marginal likelihood wrt kernel's parameter

Parameters
paramparameter of given kernel
Returns
derivative of negative log marginal likelihood

Implemented in CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, and CSingleLaplacianInferenceMethod.

virtual SGVector<float64_t> get_derivative_wrt_likelihood_model ( const TParameter param)
protectedpure virtual

returns derivative of negative log marginal likelihood wrt parameter of likelihood model

Parameters
paramparameter of given likelihood model
Returns
derivative of negative log marginal likelihood

Implemented in CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, and CSingleLaplacianInferenceMethod.

virtual SGVector<float64_t> get_derivative_wrt_mean ( const TParameter param)
protectedpure virtual

returns derivative of negative log marginal likelihood wrt mean function's parameter

Parameters
paramparameter of given mean function
Returns
derivative of negative log marginal likelihood

Implemented in CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, and CSingleLaplacianInferenceMethod.

virtual CFeatures* get_features ( )
virtual

get features

Returns
features

Definition at line 236 of file InferenceMethod.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 237 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 278 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 291 of file SGObject.cpp.

virtual CMap<TParameter*, SGVector<float64_t> >* get_gradient ( CMap< TParameter *, CSGObject * > *  parameters)
virtual

get the gradient

Parameters
parametersparameter's dictionary
Returns
map of gradient. Keys are names of parameters, values are values of derivative with respect to that parameter.

Implements CDifferentiableFunction.

Definition at line 215 of file InferenceMethod.h.

virtual EInferenceType get_inference_type ( ) const
virtual

return what type of inference we are, e.g. exact, FITC, Laplacian, etc.

Returns
inference type

Reimplemented in CKLInferenceMethod, CLaplacianInferenceBase, CExactInferenceMethod, CFITCInferenceMethod, and CEPInferenceMethod.

Definition at line 74 of file InferenceMethod.h.

virtual CKernel* get_kernel ( )
virtual

get kernel

Returns
kernel

Definition at line 253 of file InferenceMethod.h.

virtual CLabels* get_labels ( )
virtual

get labels

Returns
labels

Definition at line 287 of file InferenceMethod.h.

float64_t get_marginal_likelihood_estimate ( int32_t  num_importance_samples = 1,
float64_t  ridge_size = 1e-15 
)

Computes an unbiased estimate of the marginal-likelihood (in log-domain),

\[ p(y|X,\theta), \]

where \(y\) are the labels, \(X\) are the features (omitted from in the following expressions), and \(\theta\) represent hyperparameters.

This is done via a Gaussian approximation to the posterior \(q(f|y, \theta)\approx p(f|y, \theta)\), which is computed by the underlying CInferenceMethod instance (if implemented, otherwise error), and then using an importance sample estimator

\[ p(y|\theta)=\int p(y|f)p(f|\theta)df =\int p(y|f)\frac{p(f|\theta)}{q(f|y, \theta)}q(f|y, \theta)df \approx\frac{1}{n}\sum_{i=1}^n p(y|f^{(i)})\frac{p(f^{(i)}|\theta)} {q(f^{(i)}|y, \theta)}, \]

where \( f^{(i)} \) are samples from the posterior approximation \( q(f|y, \theta) \). The resulting estimator has a low variance if \( q(f|y, \theta) \) is a good approximation. It has large variance otherwise (while still being consistent). Storing all number of log-domain ensures numerical stability.

Parameters
num_importance_samplesthe number of importance samples \(n\) from \( q(f|y, \theta) \).
ridge_sizescalar that is added to the diagonal of the involved Gaussian distribution's covariance of GP prior and posterior approximation to stabilise things. Increase if covariance matrix is not numerically positive semi-definite.
Returns
unbiased estimate of the marginal likelihood function \( p(y|\theta),\) in log-domain.

Definition at line 91 of file InferenceMethod.cpp.

virtual CMeanFunction* get_mean ( )
virtual

get mean

Returns
mean

Definition at line 270 of file InferenceMethod.h.

CLikelihoodModel* get_model ( )

get likelihood model

Returns
likelihood

Definition at line 304 of file InferenceMethod.h.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1135 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

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 1159 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

Parameters
param_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1172 of file SGObject.cpp.

SGMatrix< float64_t > get_multiclass_E ( )
virtual

get the E matrix used for multi classification

Returns
the matrix for multi classification

Definition at line 40 of file InferenceMethod.cpp.

virtual const char* get_name ( ) const
pure virtualinherited

Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.

Returns
name of the SGSerializable

Implemented in CMath, CHMM, CStringFeatures< ST >, CStringFeatures< T >, CStringFeatures< uint8_t >, CStringFeatures< char >, CStringFeatures< uint16_t >, CSVMLight, CTrie< Trie >, CTrie< DNATrie >, CTrie< POIMTrie >, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CMultitaskKernelTreeNormalizer, CList, CDynProg, CDenseFeatures< ST >, CDenseFeatures< uint32_t >, CDenseFeatures< float64_t >, CDenseFeatures< T >, CDenseFeatures< uint16_t >, CFile, CStatistics, CSparseFeatures< ST >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CSpecificityMeasure, CPrecisionMeasure, CPlif, CRecallMeasure, CDynamicObjectArray, CCrossCorrelationMeasure, CF1Measure, CCSVFile, CProtobufFile, CLaRank, CBinaryFile, CWRACCMeasure, CRBM, CTaxonomy, CBALMeasure, CBitString, CStreamingVwFeatures, CLibSVMFile, CStreamingSparseFeatures< T >, CMultitaskKernelPlifNormalizer, CErrorRateMeasure, CWDSVMOcas, CMachine, CNeuralLayer, CAccuracyMeasure, CStreamingFile, CQuadraticTimeMMD, CRandom, CStreamingMMD, CMultitaskKernelMaskNormalizer, CMemoryMappedFile< T >, CMemoryMappedFile< ST >, CAlphabet, CMKL, CStreamingDenseFeatures< T >, CLMNNStatistics, CStructuredModel, CStreamingDenseFeatures< float64_t >, CStreamingDenseFeatures< float32_t >, CCombinedDotFeatures, CFeatureSelection< ST >, CFeatureSelection< float64_t >, CGUIStructure, CCache< 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 > >, CMultitaskKernelMaskPairNormalizer, CSVM, CMultitaskKernelNormalizer, CNeuralNetwork, CGUIClassifier, CGaussian, CGUIFeatures, CGMM, CBinaryStream< T >, CHashedWDFeaturesTransposed, CLinearHMM, CSimpleFile< T >, CParameterCombination, CDeepBeliefNetwork, CStreamingStringFeatures< T >, CNeuralLinearLayer, CMulticlassSVM, CStateModel, CRandomKitchenSinksDotFeatures, COnlineLinearMachine, CVwParser, CPluginEstimate, CVowpalWabbit, CBinnedDotFeatures, CSVMOcas, CNeuralConvolutionalLayer, CSVRLight, CHashedWDFeatures, CPlifMatrix, CCrossValidation, CImplicitWeightedSpecFeatures, CCombinedFeatures, CSparseMatrixOperator< T >, CSNPFeatures, CWDFeatures, CKMeans, CCrossValidationMulticlassStorage, CHashedDenseFeatures< ST >, CIOBuffer, CUAIFile, CLossFunction, CTwoStateModel, CPCA, CHMSVMModel, CDeepAutoencoder, CLeastAngleRegression, CGUIKernel, CKNN, CRandomFourierGaussPreproc, CMKLMulticlass, CHashedSparseFeatures< ST >, CAutoencoder, CHypothesisTest, CExplicitSpecFeatures, CModelSelectionParameters, CLibLinearMTL, CNOCCO, CPositionalPWM, CHashedDocDotFeatures, CGUIHMM, COnlineSVMSGD, CIntegration, CJacobiEllipticFunctions, CLibLinear, CLDA, CZeroMeanCenterKernelNormalizer, CSparsePolyFeatures, CHashedMultilabelModel, CSqrtDiagKernelNormalizer, CHuberLoss, CScatterKernelNormalizer, CCplex, CFisherLDA, CHSIC, CRationalApproximation, CStochasticProximityEmbedding, CLatentModel, CGMNPLib, CMulticlassMachine, CDixonQTestRejectionStrategy, CTableFactorType, CSVMSGD, CVwCacheReader, CLBPPyrDotFeatures, CRidgeKernelNormalizer, CDependenceMaximization, CLinearMachine, CGraphCut, CMulticlassSOLabels, CSerializableAsciiFile, CSGDQN, CSNPStringKernel, CTime, CMatrixFeatures< ST >, CWeightedCommWordStringKernel, CHingeLoss, CTwoSampleTest, CSquaredLoss, CAbsoluteDeviationLoss, CExponentialLoss, CQPBSVMLib, CCustomKernel, CMulticlassLabels, CHash, CLinearTimeMMD, CFactor, CPlifArray, CStreamingVwFile, CStreamingHashedDocDotFeatures, CKernelIndependenceTest, CCustomDistance, CWeightedDegreeStringKernel, CKernelRidgeRegression, CBaggingMachine, CQDA, CNeuralLayers, CNeuralLogisticLayer, CNeuralRectifiedLinearLayer, CHierarchicalMultilabelModel, CTOPFeatures, CDiceKernelNormalizer, CMultitaskKernelMklNormalizer, CTask, CGaussianProcessClassification, CVwEnvironment, CBinaryLabels, CMultilabelModel, CMultilabelSOLabels, CDomainAdaptationSVMLinear, CCHAIDTree, CKernelTwoSampleTest, CWeightedDegreePositionStringKernel, CMAPInferImpl, CBesselKernel, CTanimotoKernelNormalizer, CCircularBuffer, CMCLDA, CGaussianDistribution, CStreamingHashedDenseFeatures< ST >, CStreamingHashedSparseFeatures< ST >, CAvgDiagKernelNormalizer, CVarianceKernelNormalizer, CMulticlassModel, COnlineLibLinear, CIndexFeatures, CCARTree, CStreamingAsciiFile, CHierarchical, CIndependenceTest, CFKFeatures, CSpectrumMismatchRBFKernel, COperatorFunction< T >, CMultilabelCLRModel, COperatorFunction< float64_t >, CCombinedKernel, CSparseSpatialSampleStringKernel, CVwRegressor, CHashedDocConverter, CFactorGraphLabels, CKLInferenceMethod, CSubsequenceStringKernel, CDotKernel, CGaussianKernel, CCommWordStringKernel, CSet< T >, CDataGenerator, CNeuralInputLayer, CSequenceLabels, CNode, CContingencyTableEvaluation, CPolyFeatures, CDenseMatrixOperator< T >, CLibSVR, CDenseMatrixOperator< float64_t >, CChi2Kernel, CPyramidChi2, CSignal, CSalzbergWordStringKernel, CStructuredLabels, CSquaredHingeLoss, CLPBoost, CNewtonSVM, CKLApproxDiagonalInferenceMethod, CVwLearner, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CIterativeLinearSolver< T, ST >, CIterativeLinearSolver< float64_t, float64_t >, CIterativeLinearSolver< complex128_t, float64_t >, CIterativeLinearSolver< T, T >, CCommUlongStringKernel, CCompressor, CHomogeneousKernelMap, CSVMLin, CHistogram, CGaussianShiftKernel, CGCArray< T >, CIndexBlockTree, CMultiLaplacianInferenceMethod, CNeuralSoftmaxLayer, CLocallyLinearEmbedding, CMahalanobisDistance, CAttributeFeatures, CRandomFourierDotFeatures, CFirstElementKernelNormalizer, CMap< K, T >, CLogLoss, CLogLossMargin, CSmoothHingeLoss, CScatterSVM, CMap< TParameter *, CSGObject * >, CMap< TParameter *, SGVector< float64_t > >, CGNPPLib, CVwNativeCacheReader, CDistanceKernel, CLatentLabels, CMultilabelLabels, CKLLowerTriangularInferenceMethod, CSingleLaplacianInferenceMethodWithLBFGS, CSoftMaxLikelihood, CMMDKernelSelection, CSpectrumRBFKernel, CLogDetEstimator, CSegmentLoss, CKernelDistance, CStreamingFileFromFeatures, CLinearRidgeRegression, CDomainAdaptationSVM, CPolyMatchStringKernel, CSimpleLocalityImprovedStringKernel, CKernelSelection, CStreamingVwCacheFile, COligoStringKernel, CKLDualInferenceMethod, CEigenSolver, CLPM, CCircularKernel, CConstKernel, CDiagKernel, CSphericalKernel, CLogitDVGLikelihood, CC45ClassifierTree, CMultitaskClusteredLogisticRegression, CEmbeddingConverter, CEuclideanDistance, CWeightedMajorityVote, CMulticlassOVREvaluation, CPolyKernel, CPolyMatchWordStringKernel, CLanczosEigenSolver, CID3ClassifierTree, CNearestCentroid, CMultidimensionalScaling, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CANOVAKernel, CProductKernel, CSparseKernel< ST >, CGaussianMatchStringKernel, CRandomForest, CKernelPCA, CFixedDegreeStringKernel, CStringKernel< ST >, CTensorProductPairKernel, CTraceSampler, CGaussianNaiveBayes, CMulticlassOneVsRestStrategy, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CKernelDensity, CParser, CTStudentKernel, CWaveletKernel, CGaussianProcessRegression, MKLMulticlassGradient, CDiffusionMaps, CMinkowskiMetric, CExponentialKernel, CLaplacianEigenmaps, CAttenuatedEuclideanDistance, CCauchyKernel, CLogKernel, CPowerKernel, CRationalQuadraticKernel, CDistantSegmentsKernel, CWaveKernel, CLaplacianInferenceBase, CKernelMachine, CBAHSIC, CLocalityImprovedStringKernel, CMatchWordStringKernel, CRegulatoryModulesStringKernel, CDistanceMachine, CStructuredOutputMachine, CKernelDependenceMaximization, CAUCKernel, CHistogramIntersectionKernel, CSigmoidKernel, CGaussianProcessMachine, CInverseMultiQuadricKernel, CFFDiag, CJADiag, CJADiagOrth, CLibLinearRegression, CMMDKernelSelectionCombOpt, CLocalAlignmentStringKernel, CLabelsFactory, CJediDiag, CQDiag, CUWedge, CTreeMachineNode< T >, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< id3TreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< CARTreeNodeData >, CTreeMachineNode< C45TreeNodeData >, CTreeMachineNode< CHAIDTreeNodeData >, CTreeMachineNode< NbodyTreeNodeData >, CMulticlassAccuracy, CGaussianARDKernel, CGaussianShortRealKernel, CMultiquadricKernel, CExactInferenceMethod, CPerceptron, CICAConverter, CSplineKernel, CDelimiterTokenizer, CDualVariationalGaussianLikelihood, CLogitVGPiecewiseBoundLikelihood, CLogRationalApproximationIndividual, CDimensionReductionPreprocessor, CHistogramWordStringKernel, CMatrixOperator< T >, CTaskTree, CMatrixOperator< float64_t >, CProbabilityDistribution, CConstMean, CStochasticGBMachine, CLinearOperator< RetType, OperandType >, CCGMShiftedFamilySolver, CIterativeShiftedLinearFamilySolver< T, ST >, CLogRationalApproximationCGM, CTreeMachine< T >, CMMDKernelSelectionCombMaxL2, CMultitaskL12LogisticRegression, CMultitaskROCEvaluation, CLinearOperator< SGVector< complex128_t >, SGVector< complex128_t > >, CLinearOperator< SGVector< T >, SGVector< T > >, CLinearOperator< SGVector< float64_t >, SGVector< float64_t > >, CIterativeShiftedLinearFamilySolver< float64_t, complex128_t >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CCanberraMetric, CCosineDistance, CManhattanMetric, CLineReader, CJensenShannonKernel, CLinearKernel, CNumericalVGLikelihood, CLinearStructuredOutputMachine, CDualLibQPBMSOSVM, CGeodesicMetric, CJensenMetric, CTanimotoDistance, CIdentityKernelNormalizer, CLinearStringKernel, CFITCInferenceMethod, CDecompressString< ST >, CGUIConverter, CNGramTokenizer, CStudentsTVGLikelihood, CMMDKernelSelectionMedian, MKLMulticlassGLPK, CChiSquareDistance, CHammingWordDistance, CLogitVGLikelihood, CProbitVGLikelihood, CRandomSearchModelSelection, CGUILabels, CAveragedPerceptron, CSOBI, CKernelLocallyLinearEmbedding, CSparseDistance< ST >, CCrossValidationResult, CLatentFeatures, CBinaryTreeMachineNode< T >, CMMDKernelSelectionOpt, CSparseDistance< float64_t >, CFFSep, CBrayCurtisDistance, CChebyshewMetric, CFactorGraphFeatures, CRegressionLabels, CJobResultAggregator, CMulticlassOneVsOneStrategy, CNbodyTree, CSparsePreprocessor< ST >, CLeastSquaresRegression, MKLMulticlassOptimizationBase, CVwNativeCacheWriter, CJediSep, CUWedgeSep, CSparseEuclideanDistance, CRealFileFeatures, CLinearARDKernel, CSingleLaplacianInferenceMethod, CDenseMatrixExactLog, CPNorm, CSparseMultilabel, CGUIPluginEstimate, CVwAdaptiveLearner, CStringDistance< ST >, CStructuredAccuracy, CLinearLatentMachine, CMulticlassStrategy, CRescaleFeatures, CStringDistance< uint16_t >, CVwNonAdaptiveLearner, CWeightedDegreeRBFKernel, CIndependentJob, CECOCRandomSparseEncoder, CLogPlusOne, CGradientCriterion, CLatentSVM, CEPInferenceMethod, CGMNPSVM, CNormOne, CMixtureModel, CFactorGraphObservation, CScalarResult< T >, CDirectLinearSolverComplex, CIndividualJobResultAggregator, CMAPInference, CMultitaskTraceLogisticRegression, CLibSVM, CStringFileFeatures< ST >, CLinearMulticlassMachine, CRationalApproximationCGMJob, CBallTree, CKDTree, CStringPreprocessor< ST >, CSumOne, CMultitaskLogisticRegression, CStringPreprocessor< uint16_t >, CStringPreprocessor< uint64_t >, CFastICA, CCanberraWordDistance, CManhattanWordDistance, CCrossValidationOutput, CRationalApproximationIndividualJob, CECOCDiscriminantEncoder, CRandomCARTree, CSortWordString, CResultSet, CTaskGroup, CGUIDistance, CStoreVectorAggregator< T >, CConjugateOrthogonalCGSolver, CPruneVarSubMean, CCCSOSVM, CIntronList, CRealNumber, CStoreVectorAggregator< complex128_t >, CJade, CIndexBlock, CIndexBlockGroup, CGradientModelSelection, CSortUlongString, CSequence, CGUIPreprocessor, CFeatureBlockLogisticRegression, CMeanSquaredError, CMeanSquaredLogError, CLatentSOSVM, CStudentsTLikelihood, CDenseExactLogJob, CMulticlassLibLinear, CMeanAbsoluteError, CDummyFeatures, CListElement, CIsomap, CDenseDistance< ST >, CRealDistance, CIndependentComputationEngine, CVectorResult< T >, CKernelStructuredOutputMachine, CLMNN, CThresholdRejectionStrategy, CMMDKernelSelectionMax, CDenseDistance< float64_t >, CSVMLightOneClass, CLinearLocalTangentSpaceAlignment, CNeighborhoodPreservingEmbedding, CEMBase< T >, CEMMixtureModel, CClusteringAccuracy, CClusteringMutualInformation, CMultilabelAccuracy, CMeanShiftDataGenerator, CVwConditionalProbabilityTree, CEMBase< MixModelData >, CHessianLocallyLinearEmbedding, CCustomMahalanobisDistance, CCombinationRule, CStoreScalarAggregator< T >, CConjugateGradientSolver, CMMDKernelSelectionComb, CFactorGraphModel, CMultitaskLeastSquaresRegression, CLocalTangentSpaceAlignment, CSubsetStack, CGaussianLikelihood, CGridSearchModelSelection, CStochasticSOSVM, CMultitaskLinearMachine, CMajorityVote, CMeanRule, CDirectEigenSolver, CLinearSolver< T, ST >, CLinearSolver< float64_t, float64_t >, CLinearSolver< complex128_t, float64_t >, CLinearSolver< T, T >, CLocalityPreservingProjections, CGradientEvaluation, CSerialComputationEngine, CECOCEncoder, CMulticlassLibSVM, CMKLRegression, CFactorDataSource, CFactorGraph, CTaskRelation, CGaussianBlobsDataGenerator, CIndexBlockRelation, CKernelMeanMatching, CLibSVMOneClass, CROCEvaluation, CKernelMulticlassMachine, CNormalSampler, CBalancedConditionalProbabilityTree, CFactorType, CSOSVMHelper, CDomainAdaptationMulticlassLibLinear, CMKLOneClass, CGPBTSVM, CMPDSVM, CGradientResult, CECOCIHDDecoder, CMulticlassTreeGuidedLogisticRegression, CConditionalProbabilityTree, CRelaxedTree, CFWSOSVM, CMKLClassification, CSubset, CDirectSparseLinearSolver, CECOCRandomDenseEncoder, CMulticlassLogisticRegression, CMulticlassOCAS, CShareBoost, CGNPPSVM, CStratifiedCrossValidationSplitting, CPRCEvaluation, CProbitLikelihood, CSparseInverseCovariance, CCrossValidationSplitting, CDisjointSet, CDenseSubsetFeatures< ST >, CECOCForestEncoder, CGUIMath, CGUITime, CLogitLikelihood, CTDistributedStochasticNeighborEmbedding, CCrossValidationPrintOutput, CJobResult, CECOCDecoder, CFactorAnalysis, CManifoldSculpting, CCrossValidationMKLStorage, SerializableAsciiReader00, CNativeMulticlassMachine, CFunction, CECOCAEDDecoder, CECOCEDDecoder, CECOCStrategy, CData, CZeroMean, CConverter, CLOOCrossValidationSplitting, CBaseMulticlassMachine, CECOCSimpleDecoder, CECOCLLBDecoder, CStructuredData, CECOCHDDecoder, CECOCOVOEncoder, CECOCOVREncoder, CRandomConditionalProbabilityTree, and CRejectionStrategy.

virtual float64_t get_negative_log_marginal_likelihood ( )
pure virtual

get negative log marginal likelihood

Returns
the negative log of the marginal likelihood function:

\[ -log(p(y|X, \theta)) \]

where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.

Implemented in CKLInferenceMethod, CFITCInferenceMethod, CMultiLaplacianInferenceMethod, CExactInferenceMethod, CSingleLaplacianInferenceMethod, and CEPInferenceMethod.

CMap< TParameter *, SGVector< float64_t > > * get_negative_log_marginal_likelihood_derivatives ( CMap< TParameter *, CSGObject * > *  parameters)
virtual

get log marginal likelihood gradient

Returns
vector of the marginal likelihood function gradient with respect to hyperparameters (under the current approximation to the posterior \(q(f|y)\approx p(f|y)\):

\[ -\frac{\partial log(p(y|X, \theta))}{\partial \theta} \]

where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.

Definition at line 150 of file InferenceMethod.cpp.

virtual SGMatrix<float64_t> get_posterior_covariance ( )
pure virtual

returns covariance matrix \(\Sigma\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:

\[ p(f|y) \approx q(f|y) = \mathcal{N}(\mu,\Sigma) \]

in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.

Returns
covariance matrix

Implemented in CFITCInferenceMethod, CEPInferenceMethod, CKLInferenceMethod, CExactInferenceMethod, and CLaplacianInferenceBase.

virtual SGVector<float64_t> get_posterior_mean ( )
pure virtual

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha+meanf \]

where \(\mu\) is the mean, \(K\) is the prior covariance matrix, and \(meanf$\f is the mean prior fomr MeanFunction */ virtual SGVector<float64_t> get_alpha()=0; /** get Cholesky decomposition matrix @return Cholesky decomposition of matrix */ virtual SGMatrix<float64_t> get_cholesky()=0; /** get diagonal vector @return diagonal of matrix used to calculate posterior covariance matrix */ virtual SGVector<float64_t> get_diagonal_vector()=0; /** returns mean vector \)$ of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:

\[ p(f|y) \approx q(f|y) = \mathcal{N}(\mu,\Sigma) \]

in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.

Returns
mean vector

Implemented in CFITCInferenceMethod, CEPInferenceMethod, CExactInferenceMethod, CLaplacianInferenceBase, and CKLInferenceMethod.

virtual float64_t get_scale ( ) const
virtual

get kernel scale

Returns
kernel scale

Definition at line 321 of file InferenceMethod.h.

virtual SGVector<float64_t> get_value ( )
virtual

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 225 of file InferenceMethod.h.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 297 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)

Parameters
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 704 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

Parameters
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 545 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
filewhere to load from
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 374 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException)
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.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 1062 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException)
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.

Exceptions
ShogunExceptionwill 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 1057 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

Parameters
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 742 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
)
protectedvirtualinherited

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

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 949 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 
)
protectedvirtualinherited

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)

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 889 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 263 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1111 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 309 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
virtualinherited

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 315 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException)
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.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 1072 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException)
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.

Exceptions
ShogunExceptionwill 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 1067 of file SGObject.cpp.

virtual void set_features ( CFeatures feat)
virtual

set features

Parameters
featfeatures to set

Definition at line 242 of file InferenceMethod.h.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 42 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 230 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 243 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 284 of file SGObject.cpp.

virtual void set_kernel ( CKernel kern)
virtual

set kernel

Parameters
kernkernel to set

Definition at line 259 of file InferenceMethod.h.

virtual void set_labels ( CLabels lab)
virtual

set labels

Parameters
lablabel to set

Definition at line 293 of file InferenceMethod.h.

virtual void set_mean ( CMeanFunction m)
virtual

set mean

Parameters
mmean function to set

Definition at line 276 of file InferenceMethod.h.

virtual void set_model ( CLikelihoodModel mod)
virtual

set likelihood model

Parameters
modmodel to set

Reimplemented in CKLInferenceMethod, and CKLDualInferenceMethod.

Definition at line 310 of file InferenceMethod.h.

virtual void set_scale ( float64_t  scale)
virtual

set kernel scale

Parameters
scalescale to be set

Definition at line 327 of file InferenceMethod.h.

CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 194 of file SGObject.cpp.

virtual bool supports_binary ( ) const
virtual

whether combination of inference method and given likelihood function supports binary classification

Returns
false

Reimplemented in CEPInferenceMethod, CKLInferenceMethod, and CSingleLaplacianInferenceMethod.

Definition at line 341 of file InferenceMethod.h.

virtual bool supports_multiclass ( ) const
virtual

whether combination of inference method and given likelihood function supports multiclass classification

Returns
false

Definition at line 348 of file InferenceMethod.h.

virtual bool supports_regression ( ) const
virtual

whether combination of inference method and given likelihood function supports regression

Returns
false

Reimplemented in CFITCInferenceMethod, CKLInferenceMethod, CExactInferenceMethod, and CSingleLaplacianInferenceMethod.

Definition at line 334 of file InferenceMethod.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 304 of file SGObject.cpp.

void update ( )
virtual

update all matrices

Reimplemented in CEPInferenceMethod, CFITCInferenceMethod, CKLInferenceMethod, CExactInferenceMethod, and CLaplacianInferenceBase.

Definition at line 269 of file InferenceMethod.cpp.

virtual void update_alpha ( )
protectedpure virtual
virtual void update_chol ( )
protectedpure virtual
virtual void update_deriv ( )
protectedpure virtual

update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter

Implemented in CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CSingleLaplacianInferenceMethod, and CKLLowerTriangularInferenceMethod.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 250 of file SGObject.cpp.

void update_train_kernel ( )
protectedvirtual

update train kernel matrix

Reimplemented in CFITCInferenceMethod.

Definition at line 291 of file InferenceMethod.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 496 of file SGObject.h.

SGVector<float64_t> m_alpha
protected

alpha vector used in process mean calculation

Definition at line 443 of file InferenceMethod.h.

SGMatrix<float64_t> m_E
protected

the matrix used for multi classification

Definition at line 455 of file InferenceMethod.h.

CFeatures* m_features
protected

features to use

Definition at line 437 of file InferenceMethod.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 511 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 517 of file SGObject.h.

CKernel* m_kernel
protected

covariance function

Definition at line 428 of file InferenceMethod.h.

SGMatrix<float64_t> m_ktrtr
protected

kernel matrix from features (non-scalled by inference scalling)

Definition at line 452 of file InferenceMethod.h.

SGMatrix<float64_t> m_L
protected

upper triangular factor of Cholesky decomposition

Definition at line 446 of file InferenceMethod.h.

CLabels* m_labels
protected

labels of features

Definition at line 440 of file InferenceMethod.h.

CMeanFunction* m_mean
protected

mean function

Definition at line 431 of file InferenceMethod.h.

CLikelihoodModel* m_model
protected

likelihood function to use

Definition at line 434 of file InferenceMethod.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 508 of file SGObject.h.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 514 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 505 of file SGObject.h.

float64_t m_scale
protected

kernel scale

Definition at line 449 of file InferenceMethod.h.

Parallel* parallel
inherited

parallel

Definition at line 499 of file SGObject.h.

Version* version
inherited

version

Definition at line 502 of file SGObject.h.


The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation