SHOGUN
4.1.0
|
The Inference Method base class.
The Inference Method computes (a Gaussian approximation to) the posterior distribution for a given Gaussian Process.
It is possible to sample the (true) log-marginal likelihood on the base of any implemented approximation. See CInferenceMethod::get_marginal_likelihood_estimate.
Definition at line 81 of file InferenceMethod.h.
Public Member Functions | |
CInferenceMethod () | |
CInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model) | |
virtual | ~CInferenceMethod () |
virtual EInferenceType | get_inference_type () const |
virtual float64_t | get_negative_log_marginal_likelihood ()=0 |
float64_t | get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15) |
virtual CMap< TParameter *, SGVector< float64_t > > * | get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters) |
virtual SGVector< float64_t > | get_posterior_mean ()=0 |
virtual SGMatrix< float64_t > | get_posterior_covariance ()=0 |
virtual CMap< TParameter *, SGVector< float64_t > > * | get_gradient (CMap< TParameter *, CSGObject * > *parameters) |
virtual SGVector< float64_t > | get_value () |
virtual CFeatures * | get_features () |
virtual void | set_features (CFeatures *feat) |
virtual CKernel * | get_kernel () |
virtual void | set_kernel (CKernel *kern) |
virtual CMeanFunction * | get_mean () |
virtual void | set_mean (CMeanFunction *m) |
virtual CLabels * | get_labels () |
virtual void | set_labels (CLabels *lab) |
CLikelihoodModel * | get_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_t > | get_multiclass_E () |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_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) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_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 CSGObject * | clone () |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Member Functions | |
virtual void | check_members () const |
virtual void | update_alpha ()=0 |
virtual void | update_chol ()=0 |
virtual void | update_deriv ()=0 |
virtual void | update_train_kernel () |
virtual SGVector< float64_t > | get_derivative_wrt_inference_method (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_likelihood_model (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_kernel (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_mean (const TParameter *param)=0 |
virtual void | compute_gradient () |
virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
Static Protected Member Functions | |
static void * | get_derivative_helper (void *p) |
Protected Attributes | |
CKernel * | m_kernel |
CMeanFunction * | m_mean |
CLikelihoodModel * | m_model |
CFeatures * | m_features |
CLabels * | m_labels |
SGVector< float64_t > | m_alpha |
SGMatrix< float64_t > | m_L |
float64_t | m_log_scale |
SGMatrix< float64_t > | m_ktrtr |
SGMatrix< float64_t > | m_E |
bool | m_gradient_update |
CInferenceMethod | ( | ) |
default constructor
Definition at line 56 of file InferenceMethod.cpp.
CInferenceMethod | ( | CKernel * | kernel, |
CFeatures * | features, | ||
CMeanFunction * | mean, | ||
CLabels * | labels, | ||
CLikelihoodModel * | model | ||
) |
constructor
kernel | covariance function |
features | features to use in inference |
mean | mean function |
labels | labels of the features |
model | likelihood model to use |
Definition at line 80 of file InferenceMethod.cpp.
|
virtual |
Definition at line 92 of file InferenceMethod.cpp.
|
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 1244 of file SGObject.cpp.
|
protectedvirtual |
check if members of object are valid for inference
Reimplemented in CSparseInferenceBase, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, and CMultiLaplacianInferenceMethod.
Definition at line 309 of file InferenceMethod.cpp.
|
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.
Definition at line 1361 of file SGObject.cpp.
|
protectedvirtual |
update gradients
Reimplemented in CKLInferenceMethod, CExactInferenceMethod, CEPInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod, CFITCInferenceMethod, and CLaplacianInferenceBase.
Definition at line 330 of file InferenceMethod.cpp.
|
virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 200 of file SGObject.cpp.
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.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 1265 of file SGObject.cpp.
|
staticprotected |
pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
Definition at line 255 of file InferenceMethod.cpp.
|
protectedpure virtual |
returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
param | parameter of CInferenceMethod class |
Implemented in CKLInferenceMethod, CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CSparseInferenceBase, CExactInferenceMethod, CMultiLaplacianInferenceMethod, CSingleLaplacianInferenceMethod, and CSingleSparseInferenceBase.
|
protectedpure virtual |
returns derivative of negative log marginal likelihood wrt kernel's parameter
param | parameter of given kernel |
Implemented in CKLInferenceMethod, CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CSparseInferenceBase, CExactInferenceMethod, CMultiLaplacianInferenceMethod, CSingleLaplacianInferenceMethod, and CSingleSparseInferenceBase.
|
protectedpure virtual |
returns derivative of negative log marginal likelihood wrt parameter of likelihood model
param | parameter of given likelihood model |
Implemented in CKLInferenceMethod, CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CSparseInferenceBase, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CMultiLaplacianInferenceMethod, CSparseVGInferenceMethod, and CSingleLaplacianInferenceMethod.
|
protectedpure virtual |
returns derivative of negative log marginal likelihood wrt mean function's parameter
param | parameter of given mean function |
Implemented in CKLInferenceMethod, CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CSparseInferenceBase, CExactInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianBase, CMultiLaplacianInferenceMethod, and CSingleLaplacianInferenceMethod.
|
virtual |
|
inherited |
|
inherited |
|
inherited |
|
virtual |
get the gradient
parameters | parameter's dictionary |
Implements CDifferentiableFunction.
Definition at line 245 of file InferenceMethod.h.
|
virtual |
return what type of inference we are, e.g. exact, FITC, Laplacian, etc.
Reimplemented in CKLApproxDiagonalInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CMultiLaplacianInferenceMethod, CKLInferenceMethod, CSparseInferenceBase, CKLDualInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CEPInferenceMethod, CLaplacianInferenceBase, and CSingleLaplacianInferenceMethod.
Definition at line 104 of file InferenceMethod.h.
|
virtual |
|
virtual |
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.
num_importance_samples | the number of importance samples \(n\) from \( q(f|y, \theta) \). |
ridge_size | scalar 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. |
Definition at line 126 of file InferenceMethod.cpp.
|
virtual |
CLikelihoodModel* get_model | ( | ) |
|
inherited |
Definition at line 1136 of file SGObject.cpp.
|
inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1160 of file SGObject.cpp.
|
inherited |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 1173 of file SGObject.cpp.
get the E matrix used for multi classification
Definition at line 72 of file InferenceMethod.cpp.
|
pure virtualinherited |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
Implemented in 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, CCSVFile, CF1Measure, CBinaryFile, CProtobufFile, CLaRank, CWRACCMeasure, CRBM, CTaxonomy, CBALMeasure, CBitString, CStreamingVwFeatures, CLibSVMFile, CStreamingSparseFeatures< T >, CErrorRateMeasure, CNeuralLayer, CMultitaskKernelPlifNormalizer, CWDSVMOcas, CMachine, 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, CNeuralNetwork, CMultitaskKernelNormalizer, CGUIClassifier, CGaussian, CGUIFeatures, CGMM, CHashedWDFeaturesTransposed, CBinaryStream< T >, CLinearHMM, CSimpleFile< T >, CDeepBeliefNetwork, CStreamingStringFeatures< T >, CParameterCombination, CNeuralLinearLayer, CStateModel, CMulticlassSVM, CNeuralConvolutionalLayer, CRandomKitchenSinksDotFeatures, COnlineLinearMachine, CVwParser, CPluginEstimate, CVowpalWabbit, CBinnedDotFeatures, CSVMOcas, 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, CNeuralLayers, CTwoSampleTest, CSquaredLoss, CAbsoluteDeviationLoss, CExponentialLoss, CCustomKernel, CMulticlassLabels, CHash, CFactor, CPlifArray, CLinearTimeMMD, CQPBSVMLib, CStreamingHashedDocDotFeatures, CStreamingVwFile, CKernelIndependenceTest, CCustomDistance, CWeightedDegreeStringKernel, CKernelRidgeRegression, CBaggingMachine, CQDA, CNeuralLogisticLayer, CNeuralRectifiedLinearLayer, CTOPFeatures, CDiceKernelNormalizer, CHierarchicalMultilabelModel, CMultitaskKernelMklNormalizer, CTask, CGaussianProcessClassification, CVwEnvironment, CBinaryLabels, CMultilabelModel, CMultilabelSOLabels, CDomainAdaptationSVMLinear, CDotKernel, CCHAIDTree, CKernelTwoSampleTest, CMAPInferImpl, CWeightedDegreePositionStringKernel, CGaussianDistribution, CTanimotoKernelNormalizer, CCircularBuffer, CMCLDA, CStreamingHashedDenseFeatures< ST >, CStreamingHashedSparseFeatures< ST >, CBesselKernel, CAvgDiagKernelNormalizer, CVarianceKernelNormalizer, CMulticlassModel, COnlineLibLinear, CIndexFeatures, CCARTree, CStreamingAsciiFile, CIndependenceTest, CHierarchical, CFKFeatures, CCombinedKernel, CSparseSpatialSampleStringKernel, CSpectrumMismatchRBFKernel, COperatorFunction< T >, CMultilabelCLRModel, COperatorFunction< float64_t >, CVwRegressor, CHashedDocConverter, CFactorGraphLabels, CKLInferenceMethod, CGaussianKernel, CCommWordStringKernel, CSubsequenceStringKernel, CSet< T >, CSparseInferenceBase, CDataGenerator, CNeuralInputLayer, CSequenceLabels, CPolyFeatures, CNode, CContingencyTableEvaluation, CChi2Kernel, CPyramidChi2, CDenseMatrixOperator< T >, CDenseMatrixOperator< float64_t >, CSignal, CLibSVR, CPeriodicKernel, CSalzbergWordStringKernel, CStructuredLabels, CSquaredHingeLoss, CNewtonSVM, CKLApproxDiagonalInferenceMethod, CLPBoost, CVwLearner, CExactInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CCommUlongStringKernel, CCompressor, CSingleFITCLaplacianBase, 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, CSoftMaxLikelihood, CMap< shogun::TParameter *, shogun::SGVector< float64_t > >, CMap< shogun::TParameter *, shogun::CSGObject * >, CVwNativeCacheReader, CDistanceKernel, CLatentLabels, CKLLowerTriangularInferenceMethod, CSpectrumRBFKernel, CMultilabelLabels, CSingleLaplacianInferenceMethodWithLBFGS, CMMDKernelSelection, CSegmentLoss, CKernelDistance, CLogDetEstimator, CNeuralLeakyRectifiedLinearLayer, CLinearRidgeRegression, CGNPPLib, CStreamingFileFromFeatures, CPolyMatchStringKernel, CScatterSVM, COligoStringKernel, CSimpleLocalityImprovedStringKernel, CKLDualInferenceMethod, CKernelSelection, CStreamingVwCacheFile, CCircularKernel, CConstKernel, CDiagKernel, CSphericalKernel, CLogitDVGLikelihood, CSingleFITCLaplacianInferenceMethod, CSingleFITCLaplacianInferenceMethodWithLBFGS, CEigenSolver, CC45ClassifierTree, CLPM, CEmbeddingConverter, CEuclideanDistance, CWeightedMajorityVote, CMulticlassOVREvaluation, CExponentialARDKernel, 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, CEPInferenceMethod, CLaplacianEigenmaps, CAttenuatedEuclideanDistance, CCauchyKernel, CLogKernel, CPowerKernel, CRationalQuadraticKernel, CWaveKernel, CLaplacianInferenceBase, CGEMPLP, CDistantSegmentsKernel, CLocalityImprovedStringKernel, CMatchWordStringKernel, CRegulatoryModulesStringKernel, CKernelMachine, CBAHSIC, MKLMulticlassGradient, CAUCKernel, CHistogramIntersectionKernel, CSigmoidKernel, CDistanceMachine, CGaussianProcessMachine, CFITCInferenceMethod, CSparseVGInferenceMethod, CStructuredOutputMachine, CKernelDependenceMaximization, CGaussianARDKernel, CInverseMultiQuadricKernel, CFFDiag, CJADiag, CJADiagOrth, CLabelsFactory, CStudentsTLikelihood, CJediDiag, CQDiag, CUWedge, CTreeMachineNode< T >, CLibLinearRegression, CMMDKernelSelectionCombOpt, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< id3TreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< CARTreeNodeData >, CTreeMachineNode< C45TreeNodeData >, CTreeMachineNode< CHAIDTreeNodeData >, CTreeMachineNode< NbodyTreeNodeData >, CMulticlassAccuracy, CGaussianShortRealKernel, CMultiquadricKernel, CLocalAlignmentStringKernel, CICAConverter, CSplineKernel, CDelimiterTokenizer, CDualVariationalGaussianLikelihood, CLogitVGPiecewiseBoundLikelihood, CDimensionReductionPreprocessor, CPerceptron, CHistogramWordStringKernel, CLogRationalApproximationIndividual, CMultitaskL12LogisticRegression, CTaskTree, CProbabilityDistribution, CConstMean, CGaussianLikelihood, CSingleSparseInferenceBase, 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, 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, CLinearStructuredOutputMachine, CDecompressString< ST >, CGUIConverter, CIsomap, 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, CJobResultAggregator, CGaussianARDSparseKernel, CSingleLaplacianInferenceMethod, CMulticlassOneVsOneStrategy, CGUIPluginEstimate, CVwAdaptiveLearner, CStringDistance< ST >, CLinearLatentMachine, CDenseMatrixExactLog, CPNorm, CRescaleFeatures, CSparseMultilabel, CStringDistance< uint16_t >, CVwNonAdaptiveLearner, CStructuredAccuracy, CWeightedDegreeRBFKernel, CProbitLikelihood, CECOCRandomSparseEncoder, CMulticlassStrategy, CGradientCriterion, CLatentSVM, CIndependentJob, CGMNPSVM, CLogPlusOne, CMAPInference, CMixtureModel, CFactorGraphObservation, CLogitLikelihood, CNormOne, CLibSVM, CFactorAnalysis, CDenseSubSamplesFeatures< ST >, 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, CZeroMean, CConjugateOrthogonalCGSolver, CGradientModelSelection, CPruneVarSubMean, CSequence, CMultitaskLogisticRegression, CGUIPreprocessor, CStoreVectorAggregator< complex128_t >, CMeanSquaredError, CMeanSquaredLogError, CLatentSOSVM, CSortUlongString, CFeatureBlockLogisticRegression, CMeanAbsoluteError, CDummyFeatures, CListElement, CDenseExactLogJob, CMulticlassLibLinear, 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 >, 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, CFactorGraphDataGenerator, CPRCEvaluation, CStratifiedCrossValidationSplitting, CSparseInverseCovariance, CDisjointSet, CCrossValidationSplitting, CDenseSubsetFeatures< ST >, CECOCForestEncoder, CGUIMath, CGUITime, CTDistributedStochasticNeighborEmbedding, CCrossValidationPrintOutput, CManifoldSculpting, CCrossValidationMKLStorage, SerializableAsciiReader00, CJobResult, CFunction, CECOCAEDDecoder, CECOCDecoder, CECOCEDDecoder, CData, CNativeMulticlassMachine, CECOCStrategy, CConverter, CBaseMulticlassMachine, CECOCSimpleDecoder, CLOOCrossValidationSplitting, CECOCLLBDecoder, CStructuredData, CECOCHDDecoder, CRandomConditionalProbabilityTree, CECOCOVOEncoder, CECOCOVREncoder, and CRejectionStrategy.
|
pure virtual |
get negative log marginal likelihood
\[ -log(p(y|X, \theta)) \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
Implemented in CSingleFITCLaplacianInferenceMethod, CMultiLaplacianInferenceMethod, CKLInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CEPInferenceMethod, and CSingleLaplacianInferenceMethod.
|
virtual |
get log marginal likelihood gradient
\[ -\frac{\partial log(p(y|X, \theta))}{\partial \theta} \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
Definition at line 185 of file InferenceMethod.cpp.
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.
Implemented in CSparseInferenceBase, CEPInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod, CKLInferenceMethod, and CLaplacianInferenceBase.
get alpha vector
\[ \mu = K\alpha+meanf \]
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.
Implemented in CSparseInferenceBase, CEPInferenceMethod, CExactInferenceMethod, CMultiLaplacianInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod, CSingleLaplacianInferenceMethod, and CKLInferenceMethod.
|
virtual |
get the function value
Implements CDifferentiableFunction.
Definition at line 255 of file InferenceMethod.h.
|
virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 298 of file SGObject.cpp.
|
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::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 705 of file SGObject.cpp.
|
inherited |
loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 546 of file SGObject.cpp.
|
virtualinherited |
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) |
Definition at line 375 of file SGObject.cpp.
|
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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1063 of file SGObject.cpp.
|
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.
ShogunException | will 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 1058 of file SGObject.cpp.
|
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 743 of file SGObject.cpp.
|
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
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 950 of file SGObject.cpp.
|
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)
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 890 of file SGObject.cpp.
|
virtualinherited |
Definition at line 264 of file SGObject.cpp.
|
inherited |
prints all parameter registered for model selection and their type
Definition at line 1112 of file SGObject.cpp.
|
virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 310 of file SGObject.cpp.
|
virtualinherited |
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) |
Definition at line 316 of file SGObject.cpp.
|
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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 1073 of file SGObject.cpp.
|
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.
ShogunException | will 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 1068 of file SGObject.cpp.
|
virtual |
|
inherited |
Definition at line 42 of file SGObject.cpp.
|
inherited |
Definition at line 47 of file SGObject.cpp.
|
inherited |
Definition at line 52 of file SGObject.cpp.
|
inherited |
Definition at line 57 of file SGObject.cpp.
|
inherited |
Definition at line 62 of file SGObject.cpp.
|
inherited |
Definition at line 67 of file SGObject.cpp.
|
inherited |
Definition at line 72 of file SGObject.cpp.
|
inherited |
Definition at line 77 of file SGObject.cpp.
|
inherited |
Definition at line 82 of file SGObject.cpp.
|
inherited |
Definition at line 87 of file SGObject.cpp.
|
inherited |
Definition at line 92 of file SGObject.cpp.
|
inherited |
Definition at line 97 of file SGObject.cpp.
|
inherited |
Definition at line 102 of file SGObject.cpp.
|
inherited |
Definition at line 107 of file SGObject.cpp.
|
inherited |
Definition at line 112 of file SGObject.cpp.
|
inherited |
set generic type to T
|
inherited |
|
inherited |
set the parallel object
parallel | parallel object to use |
Definition at line 243 of file SGObject.cpp.
|
inherited |
set the version object
version | version object to use |
Definition at line 285 of file SGObject.cpp.
|
virtual |
set kernel
kern | kernel to set |
Reimplemented in CSingleSparseInferenceBase.
Definition at line 289 of file InferenceMethod.h.
|
virtual |
|
virtual |
|
virtual |
set likelihood model
mod | model to set |
Reimplemented in CKLInferenceMethod, and CKLDualInferenceMethod.
Definition at line 340 of file InferenceMethod.h.
|
virtual |
|
virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 194 of file SGObject.cpp.
|
virtual |
whether combination of inference method and given likelihood function supports binary classification
Reimplemented in CEPInferenceMethod, CKLInferenceMethod, CSingleFITCLaplacianInferenceMethod, and CSingleLaplacianInferenceMethod.
Definition at line 371 of file InferenceMethod.h.
|
virtual |
whether combination of inference method and given likelihood function supports multiclass classification
Definition at line 378 of file InferenceMethod.h.
|
virtual |
whether combination of inference method and given likelihood function supports regression
Reimplemented in CExactInferenceMethod, CKLInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod, and CSingleLaplacianInferenceMethod.
Definition at line 364 of file InferenceMethod.h.
|
inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 305 of file SGObject.cpp.
|
virtual |
update matrices except gradients
Reimplemented in CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CKLInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CSparseInferenceBase, CLaplacianInferenceBase, and CSingleLaplacianInferenceMethod.
Definition at line 303 of file InferenceMethod.cpp.
|
protectedpure virtual |
update alpha vector
Implemented in CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethodWithLBFGS, CSingleFITCLaplacianInferenceMethodWithLBFGS, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.
|
protectedpure virtual |
update cholesky matrix
Implemented in CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInferenceMethod.
|
protectedpure virtual |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
Implemented in CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInferenceMethod.
|
virtualinherited |
Updates the hash of current parameter combination
Definition at line 250 of file SGObject.cpp.
|
protectedvirtual |
update train kernel matrix
Reimplemented in CSparseInferenceBase.
Definition at line 324 of file InferenceMethod.cpp.
|
inherited |
io
Definition at line 496 of file SGObject.h.
alpha vector used in process mean calculation
Definition at line 475 of file InferenceMethod.h.
the matrix used for multi classification
Definition at line 487 of file InferenceMethod.h.
|
protected |
features to use
Definition at line 469 of file InferenceMethod.h.
|
inherited |
parameters wrt which we can compute gradients
Definition at line 511 of file SGObject.h.
|
protected |
Whether gradients are updated
Definition at line 490 of file InferenceMethod.h.
|
inherited |
Hash of parameter values
Definition at line 517 of file SGObject.h.
|
protected |
covariance function
Definition at line 460 of file InferenceMethod.h.
kernel matrix from features (non-scalled by inference scalling)
Definition at line 484 of file InferenceMethod.h.
upper triangular factor of Cholesky decomposition
Definition at line 478 of file InferenceMethod.h.
|
protected |
labels of features
Definition at line 472 of file InferenceMethod.h.
|
protected |
kernel scale
Definition at line 481 of file InferenceMethod.h.
|
protected |
mean function
Definition at line 463 of file InferenceMethod.h.
|
protected |
likelihood function to use
Definition at line 466 of file InferenceMethod.h.
|
inherited |
model selection parameters
Definition at line 508 of file SGObject.h.
|
inherited |
map for different parameter versions
Definition at line 514 of file SGObject.h.
|
inherited |
parameters
Definition at line 505 of file SGObject.h.
|
inherited |
parallel
Definition at line 499 of file SGObject.h.
|
inherited |
version
Definition at line 502 of file SGObject.h.