SHOGUN  4.1.0
所有成员列表 | Public 成员函数 | Public 属性 | Protected 成员函数
CLikelihoodModel类 参考abstract

详细描述

The Likelihood model base class.

The Likelihood model computes approximately the distribution \(p(y|f)\), where \(y\) are the labels, and \(f\) is the prediction function.

在文件 LikelihoodModel.h 第 62 行定义.

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

Public 成员函数

 CLikelihoodModel ()
 
virtual ~CLikelihoodModel ()
 
virtual SGVector< float64_t > get_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL)
 
virtual SGVector< float64_t > get_predictive_means (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
 
virtual SGVector< float64_t > get_predictive_variances (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
 
virtual ELikelihoodModelType get_model_type () const
 
virtual SGVector< float64_t > get_log_probability_f (const CLabels *lab, SGVector< float64_t > func) const =0
 
virtual SGVector< float64_t > get_log_probability_fmatrix (const CLabels *lab, SGMatrix< float64_t > F) const
 
virtual SGVector< float64_t > get_log_probability_derivative_f (const CLabels *lab, SGVector< float64_t > func, index_t i) const =0
 
virtual SGVector< float64_t > get_first_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_t > get_second_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_t > get_third_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_t > get_log_zeroth_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const =0
 
virtual float64_t get_first_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const =0
 
virtual SGVector< float64_t > get_first_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
 
virtual float64_t get_second_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const =0
 
virtual SGVector< float64_t > get_second_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
 
virtual bool supports_regression () const
 
virtual bool supports_binary () const
 
virtual bool supports_multiclass () const
 
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="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
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 属性

SGIO * io
 
Parallel * parallel
 
Version * version
 
Parameter * m_parameters
 
Parameter * m_model_selection_parameters
 
Parameter * m_gradient_parameters
 
uint32_t m_hash
 

Protected 成员函数

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)
 

构造及析构函数说明

default constructor

在文件 LikelihoodModel.cpp 第 37 行定义.

~CLikelihoodModel ( )
virtual

在文件 LikelihoodModel.cpp 第 41 行定义.

成员函数说明

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > *  dict)
inherited

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

参数
dictdictionary of parameters to be built.

在文件 SGObject.cpp 第 597 行定义.

CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

返回
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

在文件 SGObject.cpp 第 714 行定义.

CSGObject * deep_copy ( ) const
virtualinherited

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

在文件 SGObject.cpp 第 198 行定义.

bool equals ( CSGObject *  other,
float64_t  accuracy = 0.0,
bool  tolerant = false 
)
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

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

在文件 SGObject.cpp 第 618 行定义.

virtual SGVector<float64_t> get_first_derivative ( const CLabels *  lab,
SGVector< float64_t >  func,
const TParameter *  param 
) const
virtual

get derivative of log likelihood \(log(p(y|f))\) with respect to given parameter

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

被 CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h 第 192 行定义.

virtual float64_t get_first_moment ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab,
index_t  i 
) const
pure 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.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
iindex i
返回
first moment of \(q(f_i)\)

在 CStudentsTLikelihood, CGaussianLikelihood, CSoftMaxLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

SGVector< float64_t > get_first_moments ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab 
) const
virtual

returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) 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.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
the first moment of \(q(f_i)\) for each \(f_i\)

在文件 LikelihoodModel.cpp 第 72 行定义.

SGIO * get_global_io ( )
inherited

get the io object

返回
io object

在文件 SGObject.cpp 第 235 行定义.

Parallel * get_global_parallel ( )
inherited

get the parallel object

返回
parallel object

在文件 SGObject.cpp 第 277 行定义.

Version * get_global_version ( )
inherited

get the version object

返回
version object

在文件 SGObject.cpp 第 290 行定义.

virtual SGVector<float64_t> get_log_probability_derivative_f ( const CLabels *  lab,
SGVector< float64_t >  func,
index_t  i 
) const
pure virtual

get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)

参数
lablabels used
funcfunction location
iindex, choices are 1, 2, and 3 for first, second, and third derivatives respectively
返回
derivative

在 CSoftMaxLikelihood, CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

virtual SGVector<float64_t> get_log_probability_f ( const CLabels *  lab,
SGVector< float64_t >  func 
) const
pure 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: \( log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)

参数
lablabels \(y_i\)
funcvalues of the function \(f_i\)
返回
logarithm of the point-wise likelihood

在 CSoftMaxLikelihood, CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

SGVector< float64_t > get_log_probability_fmatrix ( const CLabels *  lab,
SGMatrix< float64_t >  F 
) const
virtual

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.

参数
lablabels \(y_i\)
Fvalues of the function \(f_i\) where each column of the matrix is one function \( f \).
返回
log-likelihood for every provided function

在文件 LikelihoodModel.cpp 第 51 行定义.

virtual SGVector<float64_t> get_log_zeroth_moments ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab 
) const
pure 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 \(f_i\).

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
log zeroth moment \(log(Z_i)\)

在 CStudentsTLikelihood, CGaussianLikelihood, CSoftMaxLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

virtual ELikelihoodModelType get_model_type ( ) const
virtual

get model type

返回
model type NONE

被 CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 重载.

在文件 LikelihoodModel.h 第 139 行定义.

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

在文件 SGObject.cpp 第 498 行定义.

char * get_modsel_param_descr ( const char *  param_name)
inherited

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

参数
param_namename of the parameter
返回
description of the parameter

在文件 SGObject.cpp 第 522 行定义.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

在文件 SGObject.cpp 第 535 行定义.

virtual const char* get_name ( ) const
pure virtualinherited

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

返回
name of the SGSerializable

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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, CMinimizerContext, 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, CStringMap< T >, CLMNN, CMMDKernelSelectionMax, CDenseDistance< float64_t >, CStringMap< float64_t >, CStringMap< shogun::SGVector< float64_t > >, CStringMap< int32_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, CLinearOperator< T >, CMultitaskLinearMachine, CLinearOperator< float64_t >, CLinearOperator< complex128_t >, 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.

SGVector< float64_t > get_predictive_log_probabilities ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab = NULL 
)
virtual

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.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)
返回
\(log(p(y_*|X, y, x*))\) for each label \(y_*\)

被 CSoftMaxLikelihood 重载.

在文件 LikelihoodModel.cpp 第 45 行定义.

virtual SGVector<float64_t> get_predictive_means ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab = NULL 
) const
pure virtual

returns mean of the predictive marginal \(p(y_*|X,y,x_*)\)

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)
返回
final means evaluated by likelihood function

在 CStudentsTLikelihood, CGaussianLikelihood, CSoftMaxLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

virtual SGVector<float64_t> get_predictive_variances ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab = NULL 
) const
pure virtual

returns variance of the predictive marginal \(p(y_*|X,y,x_*)\)

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)
返回
final variances evaluated by likelihood function

在 CStudentsTLikelihood, CGaussianLikelihood, CSoftMaxLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

virtual SGVector<float64_t> get_second_derivative ( const CLabels *  lab,
SGVector< float64_t >  func,
const TParameter *  param 
) const
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

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

被 CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h 第 210 行定义.

virtual float64_t get_second_moment ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab,
index_t  i 
) const
pure 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.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
iindex i
返回
the second moment of \(q(f_i)\)

在 CStudentsTLikelihood, CGaussianLikelihood, CSoftMaxLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 内被实现.

SGVector< float64_t > get_second_moments ( SGVector< float64_t >  mu,
SGVector< float64_t >  s2,
const CLabels *  lab 
) const
virtual

returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) 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.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
the second moment of \(q(f_i)\) for each \(f_i\)

在文件 LikelihoodModel.cpp 第 89 行定义.

virtual SGVector<float64_t> get_third_derivative ( const CLabels *  lab,
SGVector< float64_t >  func,
const TParameter *  param 
) const
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

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

被 CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h 第 227 行定义.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

在文件 SGObject.cpp 第 296 行定义.

bool load_serializable ( CSerializableFile *  file,
const char *  prefix = "" 
)
virtualinherited

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

参数
filewhere to load from
prefixprefix for members
返回
TRUE if done, otherwise FALSE

在文件 SGObject.cpp 第 369 行定义.

void load_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

异常
ShogunExceptionwill be thrown if an error occurs.

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

在文件 SGObject.cpp 第 426 行定义.

void load_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

异常
ShogunExceptionwill be thrown if an error occurs.

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 421 行定义.

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

在文件 SGObject.cpp 第 262 行定义.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

在文件 SGObject.cpp 第 474 行定义.

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

prints registered parameters out

参数
prefixprefix for members

在文件 SGObject.cpp 第 308 行定义.

bool save_serializable ( CSerializableFile *  file,
const char *  prefix = "" 
)
virtualinherited

Save this object to file.

参数
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
返回
TRUE if done, otherwise FALSE

在文件 SGObject.cpp 第 314 行定义.

void save_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

异常
ShogunExceptionwill be thrown if an error occurs.

被 CKernel 重载.

在文件 SGObject.cpp 第 436 行定义.

void save_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

异常
ShogunExceptionwill be thrown if an error occurs.

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 431 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 41 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 46 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 51 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 56 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 61 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 66 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 71 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 76 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 81 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 86 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 91 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 96 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 101 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 106 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp 第 111 行定义.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO *  io)
inherited

set the io object

参数
ioio object to use

在文件 SGObject.cpp 第 228 行定义.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

参数
parallelparallel object to use

在文件 SGObject.cpp 第 241 行定义.

void set_global_version ( Version *  version)
inherited

set the version object

参数
versionversion object to use

在文件 SGObject.cpp 第 283 行定义.

CSGObject * shallow_copy ( ) const
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 192 行定义.

virtual bool supports_binary ( ) const
virtual

return whether likelihood function supports binary classification

返回
false

被 CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 重载.

在文件 LikelihoodModel.h 第 329 行定义.

virtual bool supports_multiclass ( ) const
virtual

return whether likelihood function supports multiclass classification

返回
false

被 CSoftMaxLikelihood , 以及 CVariationalLikelihood 重载.

在文件 LikelihoodModel.h 第 335 行定义.

virtual bool supports_regression ( ) const
virtual

return whether likelihood function supports regression

返回
false

被 CStudentsTLikelihood, CGaussianLikelihood , 以及 CVariationalLikelihood 重载.

在文件 LikelihoodModel.h 第 323 行定义.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

在文件 SGObject.cpp 第 303 行定义.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

在文件 SGObject.cpp 第 248 行定义.

类成员变量说明

SGIO* io
inherited

io

在文件 SGObject.h 第 369 行定义.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

在文件 SGObject.h 第 384 行定义.

uint32_t m_hash
inherited

Hash of parameter values

在文件 SGObject.h 第 387 行定义.

Parameter* m_model_selection_parameters
inherited

model selection parameters

在文件 SGObject.h 第 381 行定义.

Parameter* m_parameters
inherited

parameters

在文件 SGObject.h 第 378 行定义.

Parallel* parallel
inherited

parallel

在文件 SGObject.h 第 372 行定义.

Version* version
inherited

version

在文件 SGObject.h 第 375 行定义.


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