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CInference Class Referenceabstract

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 CInference::get_marginal_likelihood_estimate.

Definition at line 81 of file Inference.h.

Inheritance diagram for CInference:
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Public Member Functions

 CInference ()
 
 CInference (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
 
virtual ~CInference ()
 
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_alpha ()=0
 
virtual SGMatrix< float64_tget_cholesky ()=0
 
virtual SGVector< float64_tget_diagonal_vector ()=0
 
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 void register_minimizer (Minimizer *minimizer)
 
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 ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
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
 
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 void compute_gradient ()
 
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)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

Static Protected Member Functions

static void * get_derivative_helper (void *p)
 

Protected Attributes

Minimizerm_minimizer
 
CKernelm_kernel
 
CMeanFunctionm_mean
 
CLikelihoodModelm_model
 
CFeaturesm_features
 
CLabelsm_labels
 
SGVector< float64_tm_alpha
 
SGMatrix< float64_tm_L
 
float64_t m_log_scale
 
SGMatrix< float64_tm_ktrtr
 
SGMatrix< float64_tm_E
 
bool m_gradient_update
 

Constructor & Destructor Documentation

default constructor

Definition at line 55 of file Inference.cpp.

CInference ( 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 79 of file Inference.cpp.

~CInference ( )
virtual

Definition at line 91 of file Inference.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 630 of file SGObject.cpp.

void check_members ( ) const
protectedvirtual

check if members of object are valid for inference

Reimplemented in CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, and CVarDTCInferenceMethod.

Definition at line 322 of file Inference.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 747 of file SGObject.cpp.

void compute_gradient ( )
protectedvirtual
CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 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 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

virtual SGVector<float64_t> get_alpha ( )
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 \(meanf\) is the mean prior fomr MeanFunction

Implemented in CKLDualInferenceMethod, CSparseInference, CExactInferenceMethod, CEPInferenceMethod, CKLCovarianceInferenceMethod, CKLDiagonalInferenceMethod, CKLCholeskyInferenceMethod, and CLaplaceInference.

virtual SGMatrix<float64_t> get_cholesky ( )
pure virtual

get Cholesky decomposition matrix

Returns
Cholesky decomposition of matrix

Implemented in CKLInference, CSparseInference, CEPInferenceMethod, CExactInferenceMethod, and CLaplaceInference.

void * get_derivative_helper ( void *  p)
staticprotected

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

Definition at line 268 of file Inference.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 CInference class

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

Implemented in CKLInference, CEPInferenceMethod, CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleLaplaceInferenceMethod, and CSingleSparseInference.

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 CKLInference, CEPInferenceMethod, CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleLaplaceInferenceMethod, and CSingleSparseInference.

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 CKLInference, CEPInferenceMethod, CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleLaplaceInferenceMethod.

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 CKLInference, CEPInferenceMethod, CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CVarDTCInferenceMethod, CSingleFITCInference, and CSingleLaplaceInferenceMethod.

virtual SGVector<float64_t> get_diagonal_vector ( )
pure virtual
virtual CFeatures* get_features ( )
virtual

get features

Returns
features

Definition at line 266 of file Inference.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 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 245 of file Inference.h.

virtual EInferenceType get_inference_type ( ) const
virtual
virtual CKernel* get_kernel ( )
virtual

get kernel

Returns
kernel

Definition at line 283 of file Inference.h.

virtual CLabels* get_labels ( )
virtual

get labels

Returns
labels

Definition at line 317 of file Inference.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 CInference 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 139 of file Inference.cpp.

virtual CMeanFunction* get_mean ( )
virtual

get mean

Returns
mean

Definition at line 300 of file Inference.h.

CLikelihoodModel* get_model ( )

get likelihood model

Returns
likelihood

Definition at line 334 of file Inference.h.

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

Definition at line 531 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 555 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 568 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 71 of file Inference.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, CSparseFeatures< ST >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CSpecificityMeasure, CPrecisionMeasure, CPlif, CRecallMeasure, CDynamicObjectArray, CSingleFITCLaplaceNewtonOptimizer, CCrossCorrelationMeasure, CLaRank, CCSVFile, CF1Measure, CBinaryFile, CProtobufFile, CWRACCMeasure, CRBM, CBALMeasure, CBitString, CStreamingVwFeatures, CLibSVMFile, CStreamingSparseFeatures< T >, CErrorRateMeasure, CNeuralLayer, CMultitaskKernelPlifNormalizer, CMachine, CAccuracyMeasure, CStreamingFile, CStatistics, CQuadraticTimeMMD, CRandom, CStreamingMMD, CMemoryMappedFile< T >, CMultitaskKernelMaskNormalizer, CMemoryMappedFile< ST >, CAlphabet, CStructuredModel, CStreamingDenseFeatures< T >, CLMNNStatistics, CStreamingDenseFeatures< float64_t >, CStreamingDenseFeatures< float32_t >, CCombinedDotFeatures, CSingleLaplaceNewtonOptimizer, CFeatureSelection< ST >, CFeatureSelection< float64_t >, CMKL, CCache< T >, CCache< uint32_t >, CCache< ST >, CCache< float64_t >, CCache< uint8_t >, CCache< KERNELCACHE_ELEM >, CCache< char >, CCache< uint16_t >, CCache< shogun::SGSparseVectorEntry< T > >, CCache< shogun::SGSparseVectorEntry< float64_t > >, CCache< shogun::SGSparseVectorEntry< ST > >, CMultitaskKernelMaskPairNormalizer, CSVM, CNeuralNetwork, CMultitaskKernelNormalizer, CGaussian, CGMM, CHashedWDFeaturesTransposed, CBinaryStream< T >, CLinearHMM, CSimpleFile< T >, CStreamingStringFeatures< T >, CParameterCombination, CDeepBeliefNetwork, CNeuralLinearLayer, CStateModel, CMulticlassSVM, CNeuralConvolutionalLayer, CRandomKitchenSinksDotFeatures, COnlineLinearMachine, CVwParser, CPluginEstimate, CVowpalWabbit, CBinnedDotFeatures, CSVRLight, CPlifMatrix, CHashedWDFeatures, CImplicitWeightedSpecFeatures, CCombinedFeatures, CLeastAngleRegression, CSparseMatrixOperator< T >, CSNPFeatures, CKNN, CWDFeatures, CCrossValidationMulticlassStorage, CHashedDenseFeatures< ST >, CIOBuffer, CUAIFile, CTwoStateModel, CLossFunction, CHMSVMModel, CDeepAutoencoder, CPCA, CHashedSparseFeatures< ST >, CRandomFourierGaussPreproc, CMKLMulticlass, CAutoencoder, CHypothesisTest, CExplicitSpecFeatures, CLibLinearMTL, CLinearMachine, CModelSelectionParameters, CPositionalPWM, CCrossValidation, CNOCCO, CHashedDocDotFeatures, COnlineSVMSGD, CLibLinear, CIntegration, CJacobiEllipticFunctions, CLDA, CZeroMeanCenterKernelNormalizer, CSparsePolyFeatures, CHashedMultilabelModel, CSqrtDiagKernelNormalizer, CHuberLoss, CQDA, CCplex, CScatterKernelNormalizer, CKMeansBase, CFisherLDA, CHSIC, CLatentModel, CRationalApproximation, CStochasticProximityEmbedding, CTableFactorType, CSVMSGD, CMulticlassMachine, CDixonQTestRejectionStrategy, CGMNPLib, CVwCacheReader, CLBPPyrDotFeatures, CRidgeKernelNormalizer, CDependenceMaximization, CMulticlassSOLabels, CGraphCut, CSerializableAsciiFile, CKLDualInferenceMethod, CNeuralLayers, CSGDQN, CSNPStringKernel, CTime, CMatrixFeatures< ST >, CWeightedCommWordStringKernel, CHingeLoss, CTwoSampleTest, CSquaredLoss, CAbsoluteDeviationLoss, CExponentialLoss, CCustomKernel, CMulticlassLabels, CHash, CFactor, CPlifArray, CLinearTimeMMD, CStreamingHashedDocDotFeatures, CKernelIndependenceTest, CCustomDistance, CStreamingVwFile, CWeightedDegreeStringKernel, CBaggingMachine, CNeuralLogisticLayer, CNeuralRectifiedLinearLayer, CTOPFeatures, CDiceKernelNormalizer, CHierarchicalMultilabelModel, CMultitaskKernelMklNormalizer, CTask, CVwEnvironment, CGaussianKernel, CBinaryLabels, CMultilabelModel, CMultilabelSOLabels, CDomainAdaptationSVMLinear, CGaussianProcessClassification, CDotKernel, CCHAIDTree, CKernelTwoSampleTest, CMAPInferImpl, CWeightedDegreePositionStringKernel, CTanimotoKernelNormalizer, CCircularBuffer, CGaussianDistribution, CStreamingHashedDenseFeatures< ST >, CStreamingHashedSparseFeatures< ST >, CBesselKernel, CAvgDiagKernelNormalizer, CVarianceKernelNormalizer, CMCLDA, CMulticlassModel, COnlineLibLinear, CIndexFeatures, CShiftInvariantKernel, CCARTree, CKernelRidgeRegression, CStreamingAsciiFile, CIndependenceTest, CHierarchical, CEuclideanDistance, CFKFeatures, CCombinedKernel, CSparseSpatialSampleStringKernel, CSpectrumMismatchRBFKernel, COperatorFunction< T >, CMultilabelCLRModel, COperatorFunction< float64_t >, CVwRegressor, CHashedDocConverter, CFactorGraphLabels, CCommWordStringKernel, CSubsequenceStringKernel, CSet< T >, CKLInference, CKRRNystrom, CDataGenerator, CSparseInference, CNeuralInputLayer, CSequenceLabels, CPolyFeatures, CNode, CContingencyTableEvaluation, CChi2Kernel, CPyramidChi2, CSignal, CLibSVR, CPeriodicKernel, CSalzbergWordStringKernel, CStructuredLabels, CSquaredHingeLoss, CDenseMatrixOperator< T >, CDenseMatrixOperator< float64_t >, CNewtonSVM, CLPBoost, CVwLearner, CIndexBlockTree, CKLDiagonalInferenceMethod, CCommUlongStringKernel, CCompressor, CExactInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CIterativeLinearSolver< T, ST >, CIterativeLinearSolver< float64_t, float64_t >, CIterativeLinearSolver< complex128_t, float64_t >, CIterativeLinearSolver< T, T >, CSVMLin, CHistogram, CGaussianShiftKernel, CGCArray< T >, CNeuralSoftmaxLayer, CHomogeneousKernelMap, CMahalanobisDistance, CAttributeFeatures, CRandomFourierDotFeatures, CFirstElementKernelNormalizer, CMap< K, T >, CLogLoss, CLogLossMargin, CSmoothHingeLoss, CMultiLaplaceInferenceMethod, CSingleFITCInference, CMap< shogun::TParameter *, shogun::SGVector< float64_t > >, CMap< shogun::TParameter *, shogun::CSGObject * >, CMap< std::string, T >, CVwNativeCacheReader, CLocallyLinearEmbedding, CDistanceKernel, CLatentLabels, CSoftMaxLikelihood, CScatterSVM, AdamUpdater, FirstOrderStochasticMinimizer, CSpectrumRBFKernel, CMultilabelLabels, CKLLowerTriangularInference, NesterovMomentumCorrection, CMMDKernelSelection, CSegmentLoss, CKernelDistance, CLogDetEstimator, CLinearRidgeRegression, CGNPPLib, CStreamingFileFromFeatures, CPolyMatchStringKernel, CNeuralLeakyRectifiedLinearLayer, CDomainAdaptationSVM, COligoStringKernel, CSimpleLocalityImprovedStringKernel, CKernelSelection, CStreamingVwCacheFile, CCircularKernel, CConstKernel, CDiagKernel, CExponentialARDKernel, CSphericalKernel, CEigenSolver, CC45ClassifierTree, CLPM, CEmbeddingConverter, CWeightedMajorityVote, CMulticlassOVREvaluation, CPolyKernel, CPolyMatchWordStringKernel, CLogitDVGLikelihood, CSingleFITCLaplaceInferenceMethod, CID3ClassifierTree, CANOVAKernel, CProductKernel, CSparseKernel< ST >, CGaussianMatchStringKernel, CRandomForest, CKernelPCA, CNearestCentroid, CMultidimensionalScaling, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CFixedDegreeStringKernel, CStringKernel< ST >, CTensorProductPairKernel, CLanczosEigenSolver, CGaussianNaiveBayes, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CKernelDensity, CParser, CTStudentKernel, CWaveletKernel, CTraceSampler, CMulticlassOneVsRestStrategy, CMinkowskiMetric, CExponentialKernel, CDiffusionMaps, CAttenuatedEuclideanDistance, CCauchyKernel, CLogKernel, CPowerKernel, CRationalQuadraticKernel, CWaveKernel, CEPInferenceMethod, CGaussianProcessRegression, CGEMPLP, CLaplacianEigenmaps, CDistantSegmentsKernel, CLocalityImprovedStringKernel, CMatchWordStringKernel, CRegulatoryModulesStringKernel, CLaplaceInference, CKernelMachine, CBAHSIC, MKLMulticlassGradient, CAUCKernel, CHistogramIntersectionKernel, CSigmoidKernel, CDistanceMachine, CStructuredOutputMachine, CKernelDependenceMaximization, CGaussianARDKernel, CInverseMultiQuadricKernel, CGaussianProcessMachine, CLabelsFactory, CFITCInferenceMethod, CVarDTCInferenceMethod, CFFDiag, CJADiag, CJADiagOrth, CTreeMachineNode< T >, CLibLinearRegression, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< id3TreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< CARTreeNodeData >, CTreeMachineNode< C45TreeNodeData >, CTreeMachineNode< CHAIDTreeNodeData >, CTreeMachineNode< NbodyTreeNodeData >, CMulticlassAccuracy, CGaussianShortRealKernel, CMultiquadricKernel, CLocalAlignmentStringKernel, CStudentsTLikelihood, CJediDiag, CQDiag, CUWedge, AdaDeltaUpdater, RmsPropUpdater, CSplineKernel, CDelimiterTokenizer, CSingleSparseInference, StandardMomentumCorrection, CDimensionReductionPreprocessor, CPerceptron, CICAConverter, CHistogramWordStringKernel, CDualVariationalGaussianLikelihood, CLogitVGPiecewiseBoundLikelihood, CStudentsTVGLikelihood, FirstOrderMinimizer, CTaskTree, CProbabilityDistribution, CConstMean, CStochasticGBMachine, CMatrixOperator< T >, CLogRationalApproximationIndividual, CTreeMachine< T >, CMultitaskROCEvaluation, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CCanberraMetric, CCosineDistance, CManhattanMetric, CJensenShannonKernel, CLinearKernel, CGaussianLikelihood, CIterativeShiftedLinearFamilySolver< T, ST >, CIterativeShiftedLinearFamilySolver< float64_t, complex128_t >, CGeodesicMetric, CJensenMetric, CTanimotoDistance, CLineReader, CIdentityKernelNormalizer, CLinearStringKernel, CNumericalVGLikelihood, CLinearStructuredOutputMachine, CCGMShiftedFamilySolver, CLogRationalApproximationCGM, L1Penalty, SMIDASMinimizer, SVRGMinimizer, CDecompressString< ST >, CKMeans, CNGramTokenizer, CMMDKernelSelectionMedian, CIsomap, CChiSquareDistance, CHammingWordDistance, CRandomSearchModelSelection, MKLMulticlassGLPK, CSparseDistance< ST >, CLatentFeatures, CLogitVGLikelihood, CProbitVGLikelihood, CBinaryTreeMachineNode< T >, CMMDKernelSelectionOpt, CSparseDistance< float64_t >, CAveragedPerceptron, CSOBI, CKernelLocallyLinearEmbedding, CBrayCurtisDistance, CChebyshewMetric, CFactorGraphFeatures, CRegressionLabels, CNbodyTree, SMDMinimizer, CSparsePreprocessor< ST >, CLeastSquaresRegression, MKLMulticlassOptimizationBase, CVwNativeCacheWriter, CFFSep, CSparseEuclideanDistance, CRealFileFeatures, CJobResultAggregator, CGaussianARDSparseKernel, CKLDualInferenceMethodMinimizer, CMulticlassOneVsOneStrategy, AdaptMomentumCorrection, CVwAdaptiveLearner, CJediSep, CUWedgeSep, CStringDistance< ST >, CSingleLaplaceInferenceMethod, CLinearLatentMachine, AdaGradUpdater, CPNorm, CRescaleFeatures, CSparseMultilabel, CStringDistance< uint16_t >, CVwNonAdaptiveLearner, CStructuredAccuracy, CWeightedDegreeRBFKernel, CDenseMatrixExactLog, CECOCRandomSparseEncoder, CMulticlassStrategy, ElasticNetPenalty, InverseScalingLearningRate, L1PenaltyForTG, CGradientCriterion, CIndependentJob, CProbitLikelihood, CGMNPSVM, SGDMinimizer, CLogPlusOne, CMAPInference, CMixtureModel, CFactorGraphObservation, CGradientModelSelection, CNormOne, CLibSVM, CDenseSubSamplesFeatures< ST >, CStringFileFeatures< ST >, CGaussianCompactKernel, CScalarResult< T >, CLogitLikelihood, CBallTree, CKDTree, CStringPreprocessor< ST >, CStringPreprocessor< uint16_t >, CStringPreprocessor< uint64_t >, CFactorAnalysis, CCanberraWordDistance, CManhattanWordDistance, CCrossValidationOutput, CLinearMulticlassMachine, CDirectLinearSolverComplex, CIndividualJobResultAggregator, CECOCDiscriminantEncoder, CRandomCARTree, GradientDescendUpdater, CSumOne, CResultSet, CTaskGroup, CFastICA, CRationalApproximationCGMJob, L2Penalty, PNormMappingFunction, CSortWordString, CCCSOSVM, CIntronList, CRealNumber, CStoreVectorAggregator< T >, CIndexBlock, CIndexBlockGroup, CZeroMean, CRationalApproximationIndividualJob, CLBFGSMinimizer, CPruneVarSubMean, CSequence, CStoreVectorAggregator< complex128_t >, CJade, CMeanSquaredError, CMeanSquaredLogError, CConjugateOrthogonalCGSolver, ConstLearningRate, CSortUlongString, CMeanAbsoluteError, CDummyFeatures, CListElement, CMulticlassLibLinear, CDenseDistance< ST >, CRealDistance, CCrossValidationResult, CStringMap< T >, CDenseExactLogJob, CMMDKernelSelectionMax, CDenseDistance< float64_t >, CSVMLightOneClass, CEMBase< T >, CEMMixtureModel, CIndependentComputationEngine, CVectorResult< T >, CKernelStructuredOutputMachine, CLMNN, CThresholdRejectionStrategy, CVwConditionalProbabilityTree, CEMBase< MixModelData >, CLinearLocalTangentSpaceAlignment, CNeighborhoodPreservingEmbedding, CCombinationRule, CClusteringAccuracy, CClusteringMutualInformation, CMultilabelAccuracy, CMeanShiftDataGenerator, CFactorGraphModel, CHessianLocallyLinearEmbedding, CCustomMahalanobisDistance, CSubsetStack, CStoreScalarAggregator< T >, CGridSearchModelSelection, CStochasticSOSVM, CKMeansMiniBatch, CLocalTangentSpaceAlignment, CMajorityVote, CLinearOperator< T >, CConjugateGradientSolver, CLinearOperator< float64_t >, CLinearOperator< complex128_t >, CMeanRule, CGradientEvaluation, CLinearSolver< T, ST >, CMulticlassLibSVM, CMKLRegression, CFactorDataSource, CFactorGraph, CTaskRelation, CLinearSolver< float64_t, float64_t >, CLinearSolver< complex128_t, float64_t >, CLinearSolver< T, T >, CLocalityPreservingProjections, CSerialComputationEngine, CIndexBlockRelation, CDirectEigenSolver, CECOCEncoder, CGradientResult, CROCEvaluation, CGaussianBlobsDataGenerator, CBalancedConditionalProbabilityTree, CFactorType, CSOSVMHelper, CMKLOneClass, CLibSVMOneClass, CMPDSVM, CKernelMulticlassMachine, CNormalSampler, CECOCIHDDecoder, CConditionalProbabilityTree, CRelaxedTree, CFWSOSVM, CDomainAdaptationMulticlassLibLinear, CMKLClassification, CSubset, CECOCRandomDenseEncoder, CShareBoost, CGNPPSVM, CFactorGraphDataGenerator, CPRCEvaluation, CStratifiedCrossValidationSplitting, CDirectSparseLinearSolver, CDisjointSet, CCrossValidationSplitting, CDenseSubsetFeatures< ST >, CECOCForestEncoder, CCrossValidationPrintOutput, CTDistributedStochasticNeighborEmbedding, CCrossValidationMKLStorage, SerializableAsciiReader00, CJobResult, CFunction, CECOCAEDDecoder, CECOCDecoder, CECOCEDDecoder, CManifoldSculpting, CData, CNativeMulticlassMachine, CECOCStrategy, CConverter, CBaseMulticlassMachine, CECOCSimpleDecoder, CLOOCrossValidationSplitting, CECOCLLBDecoder, CStructuredData, CECOCHDDecoder, CRandomConditionalProbabilityTree, CECOCOVOEncoder, CECOCOVREncoder, 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 CSingleFITCLaplaceInferenceMethod, CMultiLaplaceInferenceMethod, CKLInference, CExactInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, CEPInferenceMethod, and CSingleLaplaceInferenceMethod.

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 198 of file Inference.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 CSparseInference, CEPInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleFITCLaplaceInferenceMethod, CKLInference, and CLaplaceInference.

virtual SGVector<float64_t> get_posterior_mean ( )
pure virtual

returns mean vector \(\mu\) 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 CSparseInference, CEPInferenceMethod, CExactInferenceMethod, CMultiLaplaceInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleLaplaceInferenceMethod, and CKLInference.

float64_t get_scale ( ) const
virtual

get kernel scale

Returns
kernel scale

Definition at line 60 of file Inference.cpp.

virtual SGVector<float64_t> get_value ( )
virtual

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file Inference.h.

bool has ( const std::string &  name) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string &  name) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.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 329 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
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
Returns
TRUE if done, otherwise FALSE

Definition at line 402 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 459 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 454 of file SGObject.cpp.

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

Definition at line 295 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_minimizer ( Minimizer minimizer)
virtual

Set a minimizer

Parameters
minimizerminimizer used in inference method

Reimplemented in CEPInferenceMethod, CKLInference, CExactInferenceMethod, CKLDualInferenceMethod, CSingleFITCLaplaceInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 128 of file Inference.cpp.

void register_param ( Tag< T > &  _tag,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
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
Returns
TRUE if done, otherwise FALSE

Definition at line 347 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 469 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 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag,
const T &  value 
)
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name,
const T &  value 
)
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

virtual void set_features ( CFeatures feat)
virtual

set features

Parameters
featfeatures to set

Definition at line 272 of file Inference.h.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

virtual void set_kernel ( CKernel kern)
virtual

set kernel

Parameters
kernkernel to set

Reimplemented in CSingleSparseInference.

Definition at line 289 of file Inference.h.

virtual void set_labels ( CLabels lab)
virtual

set labels

Parameters
lablabel to set

Definition at line 323 of file Inference.h.

virtual void set_mean ( CMeanFunction m)
virtual

set mean

Parameters
mmean function to set

Definition at line 306 of file Inference.h.

virtual void set_model ( CLikelihoodModel mod)
virtual

set likelihood model

Parameters
modmodel to set

Reimplemented in CKLDualInferenceMethod, and CKLInference.

Definition at line 340 of file Inference.h.

void set_scale ( float64_t  scale)
virtual

set kernel scale

Parameters
scalescale to be set

Definition at line 65 of file Inference.cpp.

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 225 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, CKLInference, CSingleFITCLaplaceInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 371 of file Inference.h.

virtual bool supports_multiclass ( ) const
virtual

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

Returns
false

Reimplemented in CMultiLaplaceInferenceMethod.

Definition at line 378 of file Inference.h.

virtual bool supports_regression ( ) const
virtual

whether combination of inference method and given likelihood function supports regression

Returns
false

Reimplemented in CExactInferenceMethod, CKLInference, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleFITCLaplaceInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 364 of file Inference.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update ( )
virtual
virtual void update_alpha ( )
protectedpure virtual
virtual void update_chol ( )
protectedpure virtual
virtual void update_deriv ( )
protectedpure virtual
void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

void update_train_kernel ( )
protectedvirtual

update train kernel matrix

Reimplemented in CSparseInference.

Definition at line 337 of file Inference.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

SGVector<float64_t> m_alpha
protected

alpha vector used in process mean calculation

Definition at line 484 of file Inference.h.

SGMatrix<float64_t> m_E
protected

the matrix used for multi classification

Definition at line 496 of file Inference.h.

CFeatures* m_features
protected

features to use

Definition at line 478 of file Inference.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

bool m_gradient_update
protected

Whether gradients are updated

Definition at line 499 of file Inference.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CKernel* m_kernel
protected

covariance function

Definition at line 469 of file Inference.h.

SGMatrix<float64_t> m_ktrtr
protected

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

Definition at line 493 of file Inference.h.

SGMatrix<float64_t> m_L
protected

upper triangular factor of Cholesky decomposition

Definition at line 487 of file Inference.h.

CLabels* m_labels
protected

labels of features

Definition at line 481 of file Inference.h.

float64_t m_log_scale
protected

kernel scale

Definition at line 490 of file Inference.h.

CMeanFunction* m_mean
protected

mean function

Definition at line 472 of file Inference.h.

Minimizer* m_minimizer
protected

minimizer

Definition at line 466 of file Inference.h.

CLikelihoodModel* m_model
protected

likelihood function to use

Definition at line 475 of file Inference.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation