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CInferenceMethod类 参考abstract

详细描述

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.

在文件 InferenceMethod.h51 行定义.

类 CInferenceMethod 继承关系图:
Inheritance graph
[图例]

Public 成员函数

 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 ()
 
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="", 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 属性

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
ParameterMapm_parameter_map
 
uint32_t m_hash
 

Protected 成员函数

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)
 

静态 Protected 成员函数

static void * get_derivative_helper (void *p)
 

Protected 属性

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
 

构造及析构函数说明

default constructor

在文件 InferenceMethod.cpp35 行定义.

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

constructor

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

在文件 InferenceMethod.cpp48 行定义.

~CInferenceMethod ( )
virtual

在文件 InferenceMethod.cpp60 行定义.

成员函数说明

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.

参数
dictdictionary of parameters to be built.

在文件 SGObject.cpp1243 行定义.

void check_members ( ) const
protectedvirtual

check if members of object are valid for inference

CFITCInferenceMethod , 以及 CExactInferenceMethod 重载.

在文件 InferenceMethod.cpp275 行定义.

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.

返回
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

在文件 SGObject.cpp1360 行定义.

CSGObject * deep_copy ( ) const
virtualinherited

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

在文件 SGObject.cpp200 行定义.

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.

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

在文件 SGObject.cpp1264 行定义.

void * get_derivative_helper ( void *  p)
staticprotected

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

在文件 InferenceMethod.cpp221 行定义.

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

参数
paramparameter of CInferenceMethod class
返回
derivative of negative log marginal likelihood

CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod , 以及 CSingleLaplacianInferenceMethod 内被实现.

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

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

参数
paramparameter of given kernel
返回
derivative of negative log marginal likelihood

CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod , 以及 CSingleLaplacianInferenceMethod 内被实现.

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

参数
paramparameter of given likelihood model
返回
derivative of negative log marginal likelihood

CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod , 以及 CSingleLaplacianInferenceMethod 内被实现.

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

参数
paramparameter of given mean function
返回
derivative of negative log marginal likelihood

CKLInferenceMethod, CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod , 以及 CSingleLaplacianInferenceMethod 内被实现.

virtual CFeatures* get_features ( )
virtual

get features

返回
features

在文件 InferenceMethod.h236 行定义.

SGIO * get_global_io ( )
inherited

get the io object

返回
io object

在文件 SGObject.cpp237 行定义.

Parallel * get_global_parallel ( )
inherited

get the parallel object

返回
parallel object

在文件 SGObject.cpp278 行定义.

Version * get_global_version ( )
inherited

get the version object

返回
version object

在文件 SGObject.cpp291 行定义.

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

get the gradient

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

实现了 CDifferentiableFunction.

在文件 InferenceMethod.h215 行定义.

virtual EInferenceType get_inference_type ( ) const
virtual

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

返回
inference type

CKLInferenceMethod, CLaplacianInferenceBase, CExactInferenceMethod, CFITCInferenceMethod , 以及 CEPInferenceMethod 重载.

在文件 InferenceMethod.h74 行定义.

virtual CKernel* get_kernel ( )
virtual

get kernel

返回
kernel

在文件 InferenceMethod.h253 行定义.

virtual CLabels* get_labels ( )
virtual

get labels

返回
labels

在文件 InferenceMethod.h287 行定义.

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.

参数
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.
返回
unbiased estimate of the marginal likelihood function \( p(y|\theta),\) in log-domain.

在文件 InferenceMethod.cpp91 行定义.

virtual CMeanFunction* get_mean ( )
virtual

get mean

返回
mean

在文件 InferenceMethod.h270 行定义.

CLikelihoodModel* get_model ( )

get likelihood model

返回
likelihood

在文件 InferenceMethod.h304 行定义.

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

在文件 SGObject.cpp1135 行定义.

char * get_modsel_param_descr ( const char *  param_name)
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

参数
param_namename of the parameter
返回
description of the parameter

在文件 SGObject.cpp1159 行定义.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

在文件 SGObject.cpp1172 行定义.

SGMatrix< float64_t > get_multiclass_E ( )
virtual

get the E matrix used for multi classification

返回
the matrix for multi classification

在文件 InferenceMethod.cpp40 行定义.

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'.

返回
name of the SGSerializable

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

virtual float64_t get_negative_log_marginal_likelihood ( )
pure virtual

get negative log marginal likelihood

返回
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.

CKLInferenceMethod, CFITCInferenceMethod, CMultiLaplacianInferenceMethod, CExactInferenceMethod, CSingleLaplacianInferenceMethod , 以及 CEPInferenceMethod 内被实现.

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

get log marginal likelihood gradient

返回
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.

在文件 InferenceMethod.cpp150 行定义.

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.

返回
covariance matrix

CFITCInferenceMethod, CEPInferenceMethod, CKLInferenceMethod, CExactInferenceMethod , 以及 CLaplacianInferenceBase 内被实现.

virtual SGVector<float64_t> get_posterior_mean ( )
pure virtual

get alpha vector

返回
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.

返回
mean vector

CFITCInferenceMethod, CEPInferenceMethod, CExactInferenceMethod, CLaplacianInferenceBase , 以及 CKLInferenceMethod 内被实现.

virtual float64_t get_scale ( ) const
virtual

get kernel scale

返回
kernel scale

在文件 InferenceMethod.h321 行定义.

virtual SGVector<float64_t> get_value ( )
virtual

get the function value

返回
vector that represents the function value

实现了 CDifferentiableFunction.

在文件 InferenceMethod.h225 行定义.

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.

参数
genericset to the type of the generic if returning TRUE
返回
TRUE if a class template.

在文件 SGObject.cpp297 行定义.

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)

参数
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
返回
(sorted) array of created TParameter instances with file data

在文件 SGObject.cpp704 行定义.

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

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

在文件 SGObject.cpp545 行定义.

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!

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

在文件 SGObject.cpp374 行定义.

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.

异常
ShogunExceptionwill be thrown if an error occurs.

CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.

在文件 SGObject.cpp1062 行定义.

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.

异常
ShogunExceptionwill be thrown if an error occurs.

CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.

在文件 SGObject.cpp1057 行定义.

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

参数
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

在文件 SGObject.cpp742 行定义.

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

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

在文件 SGObject.cpp949 行定义.

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)

参数
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

在文件 SGObject.cpp889 行定义.

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

在文件 SGObject.cpp263 行定义.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

在文件 SGObject.cpp1111 行定义.

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

prints registered parameters out

参数
prefixprefix for members

在文件 SGObject.cpp309 行定义.

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

Save this object to file.

参数
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)
返回
TRUE if done, otherwise FALSE

在文件 SGObject.cpp315 行定义.

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.

异常
ShogunExceptionwill be thrown if an error occurs.

CKernel 重载.

在文件 SGObject.cpp1072 行定义.

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.

异常
ShogunExceptionwill be thrown if an error occurs.

CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.

在文件 SGObject.cpp1067 行定义.

virtual void set_features ( CFeatures feat)
virtual

set features

参数
featfeatures to set

在文件 InferenceMethod.h242 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp42 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp47 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp52 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp57 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp62 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp67 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp72 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp77 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp82 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp87 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp92 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp97 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp102 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp107 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp112 行定义.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

参数
ioio object to use

在文件 SGObject.cpp230 行定义.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

参数
parallelparallel object to use

在文件 SGObject.cpp243 行定义.

void set_global_version ( Version version)
inherited

set the version object

参数
versionversion object to use

在文件 SGObject.cpp284 行定义.

virtual void set_kernel ( CKernel kern)
virtual

set kernel

参数
kernkernel to set

在文件 InferenceMethod.h259 行定义.

virtual void set_labels ( CLabels lab)
virtual

set labels

参数
lablabel to set

在文件 InferenceMethod.h293 行定义.

virtual void set_mean ( CMeanFunction m)
virtual

set mean

参数
mmean function to set

在文件 InferenceMethod.h276 行定义.

virtual void set_model ( CLikelihoodModel mod)
virtual

set likelihood model

参数
modmodel to set

CKLInferenceMethod , 以及 CKLDualInferenceMethod 重载.

在文件 InferenceMethod.h310 行定义.

virtual void set_scale ( float64_t  scale)
virtual

set kernel scale

参数
scalescale to be set

在文件 InferenceMethod.h327 行定义.

CSGObject * shallow_copy ( ) const
virtualinherited

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

CGaussianKernel 重载.

在文件 SGObject.cpp194 行定义.

virtual bool supports_binary ( ) const
virtual

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

返回
false

CEPInferenceMethod, CKLInferenceMethod , 以及 CSingleLaplacianInferenceMethod 重载.

在文件 InferenceMethod.h341 行定义.

virtual bool supports_multiclass ( ) const
virtual

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

返回
false

在文件 InferenceMethod.h348 行定义.

virtual bool supports_regression ( ) const
virtual

whether combination of inference method and given likelihood function supports regression

返回
false

CFITCInferenceMethod, CKLInferenceMethod, CExactInferenceMethod , 以及 CSingleLaplacianInferenceMethod 重载.

在文件 InferenceMethod.h334 行定义.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

在文件 SGObject.cpp304 行定义.

void update ( )
virtual
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

CEPInferenceMethod, CFITCInferenceMethod, CExactInferenceMethod, CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CSingleLaplacianInferenceMethod , 以及 CKLLowerTriangularInferenceMethod 内被实现.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

在文件 SGObject.cpp250 行定义.

void update_train_kernel ( )
protectedvirtual

update train kernel matrix

CFITCInferenceMethod 重载.

在文件 InferenceMethod.cpp291 行定义.

类成员变量说明

SGIO* io
inherited

io

在文件 SGObject.h496 行定义.

SGVector<float64_t> m_alpha
protected

alpha vector used in process mean calculation

在文件 InferenceMethod.h443 行定义.

SGMatrix<float64_t> m_E
protected

the matrix used for multi classification

在文件 InferenceMethod.h455 行定义.

CFeatures* m_features
protected

features to use

在文件 InferenceMethod.h437 行定义.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

在文件 SGObject.h511 行定义.

uint32_t m_hash
inherited

Hash of parameter values

在文件 SGObject.h517 行定义.

CKernel* m_kernel
protected

covariance function

在文件 InferenceMethod.h428 行定义.

SGMatrix<float64_t> m_ktrtr
protected

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

在文件 InferenceMethod.h452 行定义.

SGMatrix<float64_t> m_L
protected

upper triangular factor of Cholesky decomposition

在文件 InferenceMethod.h446 行定义.

CLabels* m_labels
protected

labels of features

在文件 InferenceMethod.h440 行定义.

CMeanFunction* m_mean
protected

mean function

在文件 InferenceMethod.h431 行定义.

CLikelihoodModel* m_model
protected

likelihood function to use

在文件 InferenceMethod.h434 行定义.

Parameter* m_model_selection_parameters
inherited

model selection parameters

在文件 SGObject.h508 行定义.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

在文件 SGObject.h514 行定义.

Parameter* m_parameters
inherited

parameters

在文件 SGObject.h505 行定义.

float64_t m_scale
protected

kernel scale

在文件 InferenceMethod.h449 行定义.

Parallel* parallel
inherited

parallel

在文件 SGObject.h499 行定义.

Version* version
inherited

version

在文件 SGObject.h502 行定义.


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