SHOGUN
4.1.0
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The Variational Likelihood base class.
The class computes approximately the following variational expection \(E_q{log{P(y|f)}}=\sum_{i}E_{q}{log{P(y_i|f_i)}}\) and the distribution \(p(y|f)\), where \(y\) are the labels, \(f\) is the prediction function, q is a variational distribution and p is a modeling distribution.
在文件 VariationalLikelihood.h 第 54 行定义.
Public 属性 | |
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 成员函数 | |
virtual void | init_likelihood ()=0 |
virtual void | set_likelihood (CLikelihoodModel *lik) |
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) |
Protected 属性 | |
SGVector< float64_t > | m_lab |
CLikelihoodModel * | m_likelihood |
default constructor
在文件 VariationalLikelihood.cpp 第 38 行定义.
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virtual |
在文件 VariationalLikelihood.cpp 第 44 行定义.
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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. |
在文件 SGObject.cpp 第 1244 行定义.
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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.
在文件 SGObject.cpp 第 1361 行定义.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 200 行定义.
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) |
在文件 SGObject.cpp 第 1265 行定义.
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virtual |
get derivative of log likelihood \(log(p(y|f))\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 88 行定义.
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pure virtual |
get derivative of log likelihood \(log(p(y|f))\) with respect to given hyperparameter Note that variational parameters are NOT considered as hyperparameters
param | parameter |
在 CLogitVGPiecewiseBoundLikelihood, CNumericalVGLikelihood , 以及 CDualVariationalGaussianLikelihood 内被实现.
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virtual |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
i | index i |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 140 行定义.
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virtualinherited |
returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_first_moment multiple times.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
在文件 LikelihoodModel.cpp 第 72 行定义.
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inherited |
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inherited |
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inherited |
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virtual |
get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)
lab | labels used |
func | function location |
i | index, choices are 1, 2, and 3 for first, second, and third derivatives respectively |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 125 行定义.
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virtual |
Returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\).
One can evaluate log-likelihood like: \( log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)
lab | labels \(y_i\) |
func | values of the function \(f_i\) |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 118 行定义.
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virtualinherited |
Returns the log-likelihood \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\) for each of the provided functions \( f \) in the given matrix.
Wrapper method which calls get_log_probability_f multiple times.
lab | labels \(y_i\) |
F | values of the function \(f_i\) where each column of the matrix is one function \( f \). |
在文件 LikelihoodModel.cpp 第 51 行定义.
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virtual |
returns the zeroth moment of a given (unnormalized) probability distribution:
\[ log(Z_i) = log\left(\int p(y_i|f_i) \mathcal{N}(f_i|\mu,\sigma^2) df_i\right) \]
for each \(f_i\).
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 132 行定义.
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virtual |
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inherited |
在文件 SGObject.cpp 第 1136 行定义.
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
在文件 SGObject.cpp 第 1160 行定义.
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inherited |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
在文件 SGObject.cpp 第 1173 行定义.
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pure virtualinherited |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
在 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 , 以及 CRejectionStrategy 内被实现.
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virtualinherited |
returns the logarithm of the predictive density of \(y_*\):
\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]
which approximately equals to
\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]
where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
被 CSoftMaxLikelihood 重载.
在文件 LikelihoodModel.cpp 第 45 行定义.
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virtual |
returns mean of the predictive marginal \(p(y_*|X,y,x_*)\)
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 72 行定义.
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virtual |
returns variance of the predictive marginal \(p(y_*|X,y,x_*)\)
NOTE: if lab equals to NULL, then each \(y_*\) equals to one.
mu | posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
s2 | posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\) |
lab | labels \(y_*\) |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 80 行定义.
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virtual |
get derivative of the first derivative of log likelihood with respect to function location, i.e. \(\frac{\partial log(p(y|f))}{\partial f}\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 96 行定义.
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virtual |
returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
This method is useful for EP local likelihood approximation.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
i | index i |
实现了 CLikelihoodModel.
在文件 VariationalLikelihood.cpp 第 148 行定义.
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virtualinherited |
returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).
Wrapper method which calls get_second_moment multiple times.
mu | mean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
s2 | variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\) |
lab | labels \(y_i\) |
在文件 LikelihoodModel.cpp 第 89 行定义.
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virtual |
get derivative of the second derivative of log likelihood with respect to function location, i.e. \(\frac{\partial^{2} log(p(y|f))}{\partial f^{2}}\) with respect to given parameter
lab | labels used |
func | function location |
param | parameter |
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 104 行定义.
returns the expection of the logarithm of a given probability distribution wrt the variational distribution.
在 CLogitVGPiecewiseBoundLikelihood, CNumericalVGLikelihood , 以及 CDualVariationalGaussianLikelihood 内被实现.
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pure virtual |
get derivative of the variational expection of log likelihood with respect to given parameter
param | parameter |
在 CLogitVGPiecewiseBoundLikelihood, CNumericalVGLikelihood , 以及 CDualVariationalGaussianLikelihood 内被实现.
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protectedpure virtual |
this method is called to initialize m_likelihood in init()
在 CLogitDVGLikelihood, CLogitVGPiecewiseBoundLikelihood, CNumericalVGLikelihood, CVariationalGaussianLikelihood, CLogitVGLikelihood, CProbitVGLikelihood , 以及 CStudentsTVGLikelihood 内被实现.
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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 |
在文件 SGObject.cpp 第 298 行定义.
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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 |
在文件 SGObject.cpp 第 705 行定义.
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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 |
在文件 SGObject.cpp 第 546 行定义.
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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) |
在文件 SGObject.cpp 第 375 行定义.
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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. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 1063 行定义.
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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. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 1058 行定义.
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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 |
在文件 SGObject.cpp 第 743 行定义.
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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 |
在文件 SGObject.cpp 第 950 行定义.
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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 |
在文件 SGObject.cpp 第 890 行定义.
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virtualinherited |
在文件 SGObject.cpp 第 264 行定义.
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inherited |
prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1112 行定义.
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virtualinherited |
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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) |
在文件 SGObject.cpp 第 316 行定义.
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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. |
被 CKernel 重载.
在文件 SGObject.cpp 第 1073 行定义.
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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. |
被 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.cpp 第 1068 行定义.
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inherited |
在文件 SGObject.cpp 第 42 行定义.
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inherited |
在文件 SGObject.cpp 第 47 行定义.
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inherited |
在文件 SGObject.cpp 第 52 行定义.
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inherited |
在文件 SGObject.cpp 第 57 行定义.
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inherited |
在文件 SGObject.cpp 第 62 行定义.
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inherited |
在文件 SGObject.cpp 第 67 行定义.
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inherited |
在文件 SGObject.cpp 第 72 行定义.
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inherited |
在文件 SGObject.cpp 第 77 行定义.
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inherited |
在文件 SGObject.cpp 第 82 行定义.
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inherited |
在文件 SGObject.cpp 第 87 行定义.
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inherited |
在文件 SGObject.cpp 第 92 行定义.
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inherited |
在文件 SGObject.cpp 第 97 行定义.
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inherited |
在文件 SGObject.cpp 第 102 行定义.
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inherited |
在文件 SGObject.cpp 第 107 行定义.
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inherited |
在文件 SGObject.cpp 第 112 行定义.
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inherited |
set generic type to T
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inherited |
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inherited |
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inherited |
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protectedvirtual |
this method used to set m_likelihood
在文件 VariationalLikelihood.cpp 第 49 行定义.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 194 行定义.
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virtual |
return whether likelihood function supports binary classification
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 162 行定义.
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pure virtual |
return whether likelihood function supports computing the derivative wrt hyperparameter Note that variational parameters are NOT considered as hyperparameters
在 CLogitVGPiecewiseBoundLikelihood, CDualVariationalGaussianLikelihood, CProbitVGLikelihood, CStudentsTVGLikelihood , 以及 CLogitVGLikelihood 内被实现.
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virtual |
return whether likelihood function supports multiclass classification
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 168 行定义.
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virtual |
return whether likelihood function supports regression
重载 CLikelihoodModel .
在文件 VariationalLikelihood.cpp 第 156 行定义.
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inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 305 行定义.
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virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 250 行定义.
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inherited |
io
在文件 SGObject.h 第 496 行定义.
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inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 511 行定义.
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inherited |
Hash of parameter values
在文件 SGObject.h 第 517 行定义.
the label of data
在文件 VariationalLikelihood.h 第 277 行定义.
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protected |
the distribution used to model data
在文件 VariationalLikelihood.h 第 280 行定义.
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inherited |
model selection parameters
在文件 SGObject.h 第 508 行定义.
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inherited |
map for different parameter versions
在文件 SGObject.h 第 514 行定义.
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inherited |
parameters
在文件 SGObject.h 第 505 行定义.
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inherited |
parallel
在文件 SGObject.h 第 499 行定义.
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inherited |
version
在文件 SGObject.h 第 502 行定义.