Public Member Functions | Public Attributes | Protected Member Functions | Protected Attributes

CInferenceMethod Class Reference

Detailed Description

The Inference Method base class.

The Inference Method computes approximately the posterior distribution for a given Gaussian Process.

Definition at line 32 of file InferenceMethod.h.

Inheritance diagram for CInferenceMethod:
Inheritance graph

List of all members.

Public Member Functions

 CInferenceMethod ()
 CInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual ~CInferenceMethod ()
virtual float64_t get_negative_marginal_likelihood ()=0
virtual CMap< TParameter
*, SGVector< float64_t > > 
get_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > &para_dict)=0
virtual SGVector< float64_tget_alpha ()=0
virtual SGMatrix< float64_tget_cholesky ()=0
virtual SGVector< float64_tget_diagonal_vector ()=0
virtual void set_features (CFeatures *feat)
virtual CFeaturesget_features ()
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 void set_scale (float64_t s)
virtual float64_t get_scale ()
virtual void set_latent_features (CFeatures *feat)
virtual CFeaturesget_latent_features ()
virtual CMap< TParameter
*, SGVector< float64_t > > 
get_gradient (CMap< TParameter *, CSGObject * > &para_dict)=0
virtual SGVector< float64_tget_quantity ()=0
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual const char * get_name () const =0
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=VERSION_PARAMETER)
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=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_parameter_dictionary (CMap< TParameter *, CSGObject * > &dict)

Public Attributes

uint32_t m_hash

Protected Member Functions

virtual void update_alpha ()
virtual void update_chol ()
virtual void update_train_kernel ()
virtual void update_data_means ()
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)
virtual bool update_parameter_hash ()

Protected Attributes

SGMatrix< float64_tm_feature_matrix
SGVector< float64_tm_data_means
SGVector< float64_tm_label_vector
SGVector< float64_tm_alpha
SGMatrix< float64_tm_L
float64_t m_scale
SGMatrix< float64_tm_ktrtr
SGMatrix< float64_tm_latent_matrix

Constructor & Destructor Documentation

Default Constructor

Definition at line 20 of file InferenceMethod.cpp.

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


kernel covariance function
features features to use in inference
mean Mean function
labels labels of the features
model Likelihood model to use

Definition at line 32 of file InferenceMethod.cpp.

~CInferenceMethod (  )  [virtual]


Definition at line 44 of file InferenceMethod.cpp.

Member Function Documentation

void build_parameter_dictionary ( CMap< TParameter *, CSGObject * > &  dict  )  [inherited]

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

dict dictionary of parameters to be built.

Definition at line 1201 of file SGObject.cpp.

virtual CSGObject* deep_copy (  )  const [virtual, inherited]

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

Definition at line 131 of file SGObject.h.

virtual SGVector<float64_t> get_alpha (  )  [pure virtual]

get Alpha Matrix

Matrix to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

where $\mu$ is the mean and $K$ is the prior covariance matrix

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual SGMatrix<float64_t> get_cholesky (  )  [pure virtual]

get Cholesky Decomposition Matrix

Cholesky Decomposition of Matrix:

\[ L = Cholesky(sW*K*sW+I) \]

Where $K$ is the prior covariance matrix, sW is the matrix returned by get_cholesky(), and $I$ is the identity matrix.

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual SGVector<float64_t> get_diagonal_vector (  )  [pure virtual]

get Diagonal Vector

Diagonal of matrix used to calculate posterior covariance matrix

\[ Cov = (K^{-1}+D^{2})^{-1}} \]

Where Cov is the posterior covariance matrix, K is the prior covariance matrix, and D is the diagonal matrix

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual CFeatures* get_features (  )  [virtual]

get features


Definition at line 123 of file InferenceMethod.h.

SGIO * get_global_io (  )  [inherited]

get the io object

io object

Definition at line 224 of file SGObject.cpp.

Parallel * get_global_parallel (  )  [inherited]

get the parallel object

parallel object

Definition at line 259 of file SGObject.cpp.

Version * get_global_version (  )  [inherited]

get the version object

version object

Definition at line 272 of file SGObject.cpp.

virtual CMap<TParameter*, SGVector<float64_t> > get_gradient ( CMap< TParameter *, CSGObject * > &  para_dict  )  [pure virtual, inherited]

Get the gradient

para_dict dictionary to be built
Map of gradient. Keys are names of parameters, values are values of derivative with respect to that parameter.

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual CKernel* get_kernel (  )  [virtual]

get kernel


Definition at line 133 of file InferenceMethod.h.

virtual CLabels* get_labels (  )  [virtual]

get labels


Definition at line 157 of file InferenceMethod.h.

virtual CFeatures* get_latent_features (  )  [virtual]

get latent features


Definition at line 199 of file InferenceMethod.h.

virtual CMap<TParameter*, SGVector<float64_t> > get_marginal_likelihood_derivatives ( CMap< TParameter *, CSGObject * > &  para_dict  )  [pure virtual]

get Log Marginal Likelihood Gradient

Vector of the Marginal Likelihood Function Gradient with respect to hyperparameters

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

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual CMeanFunction* get_mean (  )  [virtual]

get mean


Definition at line 145 of file InferenceMethod.h.

CLikelihoodModel* get_model (  ) 

get likelihood model


Definition at line 169 of file InferenceMethod.h.

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

Definition at line 1108 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name  )  [inherited]

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

param_name name of the parameter
description of the parameter

Definition at line 1132 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name  )  [inherited]

Returns index of model selection parameter with provided index

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

Definition at line 1145 of file SGObject.cpp.

virtual const char* get_name (  )  const [pure virtual, inherited]

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

name of the SGSerializable

Implemented in CAveragedPerceptron, CFeatureBlockLogisticRegression, CLDA, CLPBoost, CLPM, CMKL, CMKLMulticlass, MKLMulticlassGLPK, MKLMulticlassGradient, MKLMulticlassOptimizationBase, CPerceptron, CPluginEstimate, CSubGradientLPM, CGNPPLib, CGNPPSVM, CGPBTSVM, CLibLinear, CLibSVM, CLibSVMOneClass, CMPDSVM, CNewtonSVM, COnlineLibLinear, COnlineSVMSGD, CQPBSVMLib, CSGDQN, CSubGradientSVM, CSVM, CSVMLight, CSVMLin, CSVMOcas, CSVMSGD, CWDSVMOcas, CVwCacheReader, CVwNativeCacheReader, CVwNativeCacheWriter, CVwAdaptiveLearner, CVwNonAdaptiveLearner, CVowpalWabbit, CVwEnvironment, CVwLearner, CVwParser, CVwRegressor, CGMM, CHierarchical, CKMeans, CConverter, CDiffusionMaps, CEmbeddingConverter, CHessianLocallyLinearEmbedding, CIsomap, CKernelLocallyLinearEmbedding, CKernelLocalTangentSpaceAlignment, CLaplacianEigenmaps, CLinearLocalTangentSpaceAlignment, CLocalityPreservingProjections, CLocallyLinearEmbedding, CLocalTangentSpaceAlignment, CMultidimensionalScaling, CNeighborhoodPreservingEmbedding, CStochasticProximityEmbedding, CAttenuatedEuclideanDistance, CBrayCurtisDistance, CCanberraMetric, CCanberraWordDistance, CChebyshewMetric, CChiSquareDistance, CCosineDistance, CCustomDistance, CDenseDistance< ST >, CEuclideanDistance, CGeodesicMetric, CHammingWordDistance, CJensenMetric, CKernelDistance, CMahalanobisDistance, CManhattanMetric, CManhattanWordDistance, CMinkowskiMetric, CRealDistance, CSparseDistance< ST >, CSparseEuclideanDistance, CStringDistance< ST >, CTanimotoDistance, CGaussian, CGHMM, CHistogram, CHMM, CLinearHMM, CPositionalPWM, CClusteringAccuracy, CClusteringMutualInformation, CContingencyTableEvaluation, CAccuracyMeasure, CErrorRateMeasure, CBALMeasure, CWRACCMeasure, CF1Measure, CCrossCorrelationMeasure, CRecallMeasure, CPrecisionMeasure, CSpecificityMeasure, CCrossValidationResult, CCrossValidation, CCrossValidationMKLStorage, CCrossValidationMulticlassStorage, CCrossValidationOutput, CCrossValidationPrintOutput, CCrossValidationSplitting, CGradientCriterion, CGradientEvaluation, CGradientResult, CMeanAbsoluteError, CMeanSquaredError, CMeanSquaredLogError, CMulticlassAccuracy, CMulticlassOVREvaluation, CPRCEvaluation, CROCEvaluation, CStratifiedCrossValidationSplitting, CStructuredAccuracy, CAlphabet, CAttributeFeatures, CBinnedDotFeatures, CCombinedDotFeatures, CCombinedFeatures, CDataGenerator, CDenseFeatures< ST >, CDenseSubsetFeatures< ST >, CDummyFeatures, CExplicitSpecFeatures, CFKFeatures, CHashedWDFeatures, CHashedWDFeaturesTransposed, CImplicitWeightedSpecFeatures, CLatentFeatures, CLBPPyrDotFeatures, CMatrixFeatures< ST >, CPolyFeatures, CRealFileFeatures, CSNPFeatures, CSparseFeatures< ST >, CSparsePolyFeatures, CMeanShiftDataGenerator< T >, CStreamingDenseFeatures< T >, CStreamingSparseFeatures< T >, CStreamingStringFeatures< T >, CStreamingVwFeatures, CStringFeatures< ST >, CSubset, CSubsetStack, CTOPFeatures, CWDFeatures, CAsciiFile, CBinaryFile, CBinaryStream< T >, CFile, CIOBuffer, CMemoryMappedFile< T >, CSerializableAsciiFile, SerializableAsciiReader00, CSimpleFile< T >, CParseBuffer< T >, CStreamingAsciiFile, CStreamingFile, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromFeatures, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CStreamingVwCacheFile, CStreamingVwFile, CANOVAKernel, CAUCKernel, CBesselKernel, CCauchyKernel, CChi2Kernel, CCircularKernel, CCombinedKernel, CConstKernel, CCustomKernel, CDiagKernel, CDistanceKernel, CDotKernel, CExponentialKernel, CGaussianARDKernel, CGaussianKernel, CGaussianShiftKernel, CGaussianShortRealKernel, CHistogramIntersectionKernel, CInverseMultiQuadricKernel, CJensenShannonKernel, CLinearARDKernel, CLinearKernel, CLogKernel, CMultiquadricKernel, CAvgDiagKernelNormalizer, CDiceKernelNormalizer, CFirstElementKernelNormalizer, CIdentityKernelNormalizer, CRidgeKernelNormalizer, CScatterKernelNormalizer, CSqrtDiagKernelNormalizer, CTanimotoKernelNormalizer, CVarianceKernelNormalizer, CZeroMeanCenterKernelNormalizer, CPolyKernel, CPowerKernel, CProductKernel, CPyramidChi2, CRationalQuadraticKernel, CSigmoidKernel, CSparseKernel< ST >, CSphericalKernel, CSplineKernel, CCommUlongStringKernel, CCommWordStringKernel, CDistantSegmentsKernel, CFixedDegreeStringKernel, CGaussianMatchStringKernel, CHistogramWordStringKernel, CLinearStringKernel, CLocalAlignmentStringKernel, CLocalityImprovedStringKernel, CMatchWordStringKernel, COligoStringKernel, CPolyMatchStringKernel, CPolyMatchWordStringKernel, CRegulatoryModulesStringKernel, CSalzbergWordStringKernel, CSimpleLocalityImprovedStringKernel, CSNPStringKernel, CSparseSpatialSampleStringKernel, CSpectrumMismatchRBFKernel, CSpectrumRBFKernel, CStringKernel< ST >, CWeightedCommWordStringKernel, CWeightedDegreePositionStringKernel, CWeightedDegreeStringKernel, CTensorProductPairKernel, CTStudentKernel, CWaveKernel, CWaveletKernel, CWeightedDegreeRBFKernel, CBinaryLabels, CLatentLabels, CMulticlassLabels, CMulticlassMultipleOutputLabels, CRegressionLabels, CStructuredLabels, CLatentModel, CLatentSOSVM, CLatentSVM, CBitString, CCache< T >, CCompressor, CData, CDynamicArray< T >, CDynamicObjectArray, CGCArray< T >, CHash, CIndexBlock, CIndexBlockGroup, CIndexBlockRelation, CIndexBlockTree, CListElement, CList, CMap< K, T >, CSet< T >, CSignal, CStructuredData, CTime, CTrie< Trie >, CHingeLoss, CLogLoss, CLogLossMargin, CLossFunction, CSmoothHingeLoss, CSquaredHingeLoss, CSquaredLoss, CBaseMulticlassMachine, CDistanceMachine, CKernelMachine, CKernelMulticlassMachine, CKernelStructuredOutputMachine, CLinearLatentMachine, CLinearMachine, CLinearMulticlassMachine, CLinearStructuredOutputMachine, CMachine, CMulticlassMachine, CNativeMulticlassMachine, COnlineLinearMachine, CStructuredOutputMachine, CCplex, CMath, CSparseInverseCovariance, CStatistics, CGradientModelSelection, CGridSearchModelSelection, CModelSelectionParameters, CParameterCombination, CRandomSearchModelSelection, CConjugateIndex, CECOCAEDDecoder, CECOCDecoder, CECOCDiscriminantEncoder, CECOCEDDecoder, CECOCEncoder, CECOCForestEncoder, CECOCHDDecoder, CECOCIHDDecoder, CECOCLLBDecoder, CECOCOVOEncoder, CECOCOVREncoder, CECOCRandomDenseEncoder, CECOCRandomSparseEncoder, CECOCSimpleDecoder, CECOCStrategy, CGaussianNaiveBayes, CGMNPLib, CGMNPSVM, CKNN, CLaRank, CMulticlassLibLinear, CMulticlassLibSVM, CMulticlassLogisticRegression, CMulticlassOCAS, CMulticlassOneVsOneStrategy, CMulticlassOneVsRestStrategy, CMulticlassStrategy, CMulticlassTreeGuidedLogisticRegression, CQDA, CRejectionStrategy, CThresholdRejectionStrategy, CDixonQTestRejectionStrategy, CScatterSVM, CShareBoost, CBalancedConditionalProbabilityTree, CConditionalProbabilityTree, CRandomConditionalProbabilityTree, CRelaxedTree, CTreeMachine< T >, CTreeMachineNode< T >, CVwConditionalProbabilityTree, CDecompressString< ST >, CDimensionReductionPreprocessor, CHomogeneousKernelMap, CKernelPCA, CLogPlusOne, CNormOne, CPCA, CPNorm, CPruneVarSubMean, CRandomFourierGaussPreproc, CSortUlongString, CSortWordString, CSparsePreprocessor< ST >, CStringPreprocessor< ST >, CSumOne, CGaussianProcessRegression, CExactInferenceMethod, CFITCInferenceMethod, CGaussianLikelihood, CLaplacianInferenceMethod, CStudentsTLikelihood, CZeroMean, CKernelRidgeRegression, CLeastAngleRegression, CLeastSquaresRegression, CLinearRidgeRegression, CLibLinearRegression, CLibSVR, CSVRLight, CHSIC, CKernelIndependenceTestStatistic, CKernelMeanMatching, CKernelTwoSampleTestStatistic, CLinearTimeMMD, CQuadraticTimeMMD, CTestStatistic, CTwoDistributionsTestStatistic, CDualLibQPBMSOSVM, CDynProg, CSequence, CHMSVMLabels, CIntronList, CMulticlassModel, CRealNumber, CMulticlassSOLabels, CPlif, CPlifArray, CPlifMatrix, CSegmentLoss, CStateModel, CResultSet, CStructuredModel, CTwoStateModel, CDomainAdaptationMulticlassLibLinear, CDomainAdaptationSVM, CDomainAdaptationSVMLinear, CLibLinearMTL, CMultitaskClusteredLogisticRegression, CMultitaskCompositeMachine, CMultitaskKernelMaskNormalizer, CMultitaskKernelMaskPairNormalizer, CMultitaskKernelMklNormalizer, CMultitaskKernelNormalizer, CMultitaskKernelPlifNormalizer, CNode, CTaxonomy, CMultitaskKernelTreeNormalizer, CMultitaskL12LogisticRegression, CMultitaskLeastSquaresRegression, CMultitaskLinearMachine, CMultitaskLogisticRegression, CMultitaskROCEvaluation, CMultitaskTraceLogisticRegression, CTask, CTaskGroup, CTaskRelation, CTaskTree, CGUIClassifier, CGUIConverter, CGUIDistance, CGUIFeatures, CGUIHMM, CGUIKernel, CGUILabels, CGUIMath, CGUIPluginEstimate, CGUIPreprocessor, CGUIStructure, CGUITime, CDenseDistance< float64_t >, CSparseDistance< float64_t >, CStringDistance< uint16_t >, CDenseFeatures< uint32_t >, CDenseFeatures< float64_t >, CDenseFeatures< T >, CDenseFeatures< uint16_t >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CStreamingDenseFeatures< float32_t >, CStringFeatures< T >, CStringFeatures< uint8_t >, CStringFeatures< char >, CStringFeatures< uint16_t >, CMemoryMappedFile< ST >, CParseBuffer< VwExample >, CParseBuffer< float32_t >, CParseBuffer< SGSparseVectorEntry< T > >, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CCache< SGSparseVectorEntry< ST > >, CCache< uint32_t >, CCache< ST >, CCache< SGSparseVectorEntry< float64_t > >, CCache< float64_t >, CCache< uint8_t >, CCache< KERNELCACHE_ELEM >, CCache< char >, CCache< uint16_t >, CCache< SGSparseVectorEntry< T > >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, CMap< TParameter *, CSGObject * >, CMap< TParameter *, SGVector< float64_t > >, CTrie< DNATrie >, CTrie< POIMTrie >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CStringPreprocessor< uint16_t >, and CStringPreprocessor< uint64_t >.

virtual float64_t get_negative_marginal_likelihood (  )  [pure virtual]

get Negative Log Marginal Likelihood

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 CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual SGVector<float64_t> get_quantity (  )  [pure virtual, inherited]

Get the function value

Vector that represents the function value

Implemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

virtual float64_t get_scale (  )  [virtual]

get kernel scale

kernel scale

Definition at line 187 of file InferenceMethod.h.

bool is_generic ( EPrimitiveType *  generic  )  const [virtual, inherited]

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

generic set to the type of the generic if returning TRUE
TRUE if a class template.

Definition at line 278 of file SGObject.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

file_version parameter version of the file
current_version version from which mapping begins (you want to use VERSION_PARAMETER for this in most cases)
file file to load from
prefix prefix for members
(sorted) array of created TParameter instances with file data

Definition at line 679 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

param_info information of parameter
file_version parameter version of the file, must be <= provided parameter version
file file to load from
prefix prefix for members
new array with TParameter instances with the attached data

Definition at line 523 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = VERSION_PARAMETER 
) [virtual, inherited]

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

file where to load from
prefix prefix for members
param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
TRUE if done, otherwise FALSE

Reimplemented in CModelSelectionParameters.

Definition at line 354 of file SGObject.cpp.

void load_serializable_post (  )  throw (ShogunException) [protected, virtual, inherited]

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

ShogunException Will be thrown if an error occurres.

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

Definition at line 1033 of file SGObject.cpp.

void load_serializable_pre (  )  throw (ShogunException) [protected, virtual, inherited]

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

ShogunException Will be thrown if an error occurres.

Definition at line 1028 of file SGObject.cpp.

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
) [inherited]

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

param_base set of TParameter instances that are mapped to the provided target parameter infos
base_version version of the parameter base
target_param_infos set of SGParamInfo instances that specify the target parameter base

Definition at line 717 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
) [protected, virtual, inherited]

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

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

Definition at line 923 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
) [protected, virtual, inherited]

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

param_base set of TParameter instances to use for migration
target parameter info for the resulting TParameter
replacement (used as output) here the TParameter instance which is returned by migration is created into
to_migrate the only source that is used for migration
old_name with this parameter, a name change may be specified

Definition at line 864 of file SGObject.cpp.

void print_modsel_params (  )  [inherited]

prints all parameter registered for model selection and their type

Definition at line 1084 of file SGObject.cpp.

void print_serializable ( const char *  prefix = ""  )  [virtual, inherited]

prints registered parameters out

prefix prefix for members

Definition at line 290 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = VERSION_PARAMETER 
) [virtual, inherited]

Save this object to file.

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

Reimplemented in CModelSelectionParameters.

Definition at line 296 of file SGObject.cpp.

void save_serializable_post (  )  throw (ShogunException) [protected, virtual, inherited]

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1043 of file SGObject.cpp.

void save_serializable_pre (  )  throw (ShogunException) [protected, virtual, inherited]

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1038 of file SGObject.cpp.

void set_features ( CFeatures feat  )  [virtual]

set features

feat features to set

Definition at line 74 of file InferenceMethod.cpp.

void set_generic< floatmax_t > (  )  [inherited]

set generic type to T

void set_global_io ( SGIO io  )  [inherited]

set the io object

io io object to use

Definition at line 217 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel  )  [inherited]

set the parallel object

parallel parallel object to use

Definition at line 230 of file SGObject.cpp.

void set_global_version ( Version version  )  [inherited]

set the version object

version version object to use

Definition at line 265 of file SGObject.cpp.

void set_kernel ( CKernel kern  )  [virtual]

set kernel

kern kernel to set

Definition at line 130 of file InferenceMethod.cpp.

void set_labels ( CLabels lab  )  [virtual]

set labels

lab label to set

Definition at line 151 of file InferenceMethod.cpp.

void set_latent_features ( CFeatures feat  )  [virtual]

set latent features

feat features to set

Definition at line 102 of file InferenceMethod.cpp.

void set_mean ( CMeanFunction m  )  [virtual]

set mean

m mean function to set

Definition at line 140 of file InferenceMethod.cpp.

void set_model ( CLikelihoodModel mod  )  [virtual]

set likelihood model

mod model to set

Definition at line 167 of file InferenceMethod.cpp.

void set_scale ( float64_t  s  )  [virtual]

set kernel scale

s scale to be set

Definition at line 177 of file InferenceMethod.cpp.

virtual CSGObject* shallow_copy (  )  const [virtual, inherited]

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

Reimplemented in CGaussianKernel.

Definition at line 122 of file SGObject.h.

void unset_generic (  )  [inherited]

unset generic type

this has to be called in classes specializing a template class

Definition at line 285 of file SGObject.cpp.

virtual void update_alpha (  )  [protected, virtual]

Update alpha matrix

Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

Definition at line 209 of file InferenceMethod.h.

virtual void update_chol (  )  [protected, virtual]

Update cholesky matrix

Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

Definition at line 212 of file InferenceMethod.h.

void update_data_means (  )  [protected, virtual]

Update data means

Definition at line 185 of file InferenceMethod.cpp.

bool update_parameter_hash (  )  [protected, virtual, inherited]

Updates the hash of current parameter combination.

bool if parameter combination has changed since last update.

Definition at line 237 of file SGObject.cpp.

virtual void update_train_kernel (  )  [protected, virtual]

Update train kernel matrix

Reimplemented in CExactInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceMethod.

Definition at line 215 of file InferenceMethod.h.

Member Data Documentation

SGIO* io [inherited]


Definition at line 462 of file SGObject.h.

SGVector< float64_t > m_alpha [protected]

alpha matrix used in process mean calculation

Definition at line 262 of file InferenceMethod.h.

Means of Features

Definition at line 235 of file InferenceMethod.h.

Feature Matrix

Definition at line 232 of file InferenceMethod.h.

CFeatures* m_features [protected]

Features to use

Definition at line 229 of file InferenceMethod.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 480 of file SGObject.h.

CKernel* m_kernel [protected]

Covariance Function

Definition at line 226 of file InferenceMethod.h.

SGMatrix<float64_t> m_ktrtr [protected]

Kernel matrix from features

Definition at line 273 of file InferenceMethod.h.

SGMatrix<float64_t> m_L [protected]

Lower triangle Cholesky decomposition of feature matrix

Definition at line 267 of file InferenceMethod.h.

Vector of labels

Definition at line 238 of file InferenceMethod.h.

CLabels* m_labels [protected]

Labels of those features

Definition at line 242 of file InferenceMethod.h.

Latent Features for Approximation

Definition at line 248 of file InferenceMethod.h.

Kernel matrix from latent features

Definition at line 276 of file InferenceMethod.h.

CMeanFunction* m_mean [protected]

Mean Function

Definition at line 245 of file InferenceMethod.h.

CLikelihoodModel* m_model [protected]

Likelihood function to use

\[ p(y|f) \]

Where y are the labels and f is the prediction function

Definition at line 259 of file InferenceMethod.h.

model selection parameters

Definition at line 474 of file SGObject.h.

map for different parameter versions

Definition at line 477 of file SGObject.h.

Parameter* m_parameters [inherited]


Definition at line 471 of file SGObject.h.

float64_t m_scale [protected]

Kernel Scale

Definition at line 270 of file InferenceMethod.h.

Parallel* parallel [inherited]


Definition at line 465 of file SGObject.h.

Version* version [inherited]


Definition at line 468 of file SGObject.h.

The documentation for this class was generated from the following files:
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines

SHOGUN Machine Learning Toolbox - Documentation