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SHOGUN
4.1.0
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The KL approximation inference method class.
The class is implemented based on the KL method in the Challis's paper, which uses lower triangular represention.
Code adapted from http://hannes.nickisch.org/code/approxXX.tar.gz and Gaussian Process Machine Learning Toolbox http://www.gaussianprocess.org/gpml/code/matlab/doc/ and the reference paper is Challis, Edward, and David Barber. "Concave Gaussian variational approximations for inference in large-scale Bayesian linear models." International conference on Artificial Intelligence and Statistics. 2011.
Note that "lowerTriangular" means lowerTriangular represention of the variational co-variance matrix is explicitly used in inference
在文件 KLLowerTriangularInferenceMethod.h 第 69 行定义.
Public 成员函数 | |
| CKLLowerTriangularInferenceMethod () | |
| CKLLowerTriangularInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model) | |
| virtual | ~CKLLowerTriangularInferenceMethod () |
| virtual const char * | get_name () const |
| virtual SGVector< float64_t > | get_diagonal_vector () |
| virtual EInferenceType | get_inference_type () const |
| virtual float64_t | get_negative_log_marginal_likelihood () |
| virtual SGVector< float64_t > | get_posterior_mean () |
| virtual SGMatrix< float64_t > | get_posterior_covariance () |
| virtual bool | supports_regression () const |
| virtual bool | supports_binary () const |
| virtual void | set_model (CLikelihoodModel *mod) |
| virtual void | update () |
| virtual void | set_lbfgs_parameters (int m=100, int max_linesearch=1000, int linesearch=LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE, int max_iterations=1000, float64_t delta=0.0, int past=0, float64_t epsilon=1e-5, float64_t min_step=1e-20, float64_t max_step=1e+20, float64_t ftol=1e-4, float64_t wolfe=0.9, float64_t gtol=0.9, float64_t xtol=1e-16, float64_t orthantwise_c=0.0, int orthantwise_start=0, int orthantwise_end=1) |
| virtual SGMatrix< float64_t > | get_cholesky () |
| virtual void | set_noise_factor (float64_t noise_factor) |
| virtual void | set_max_attempt (index_t max_attempt) |
| virtual void | set_exp_factor (float64_t exp_factor) |
| virtual void | set_min_coeff_kernel (float64_t min_coeff_kernel) |
| 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 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 float64_t | get_scale () const |
| virtual void | set_scale (float64_t scale) |
| virtual bool | supports_multiclass () const |
| virtual SGMatrix< float64_t > | get_multiclass_E () |
| virtual CSGObject * | shallow_copy () const |
| virtual CSGObject * | deep_copy () const |
| 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 |
静态 Protected 成员函数 | |
| static void * | get_derivative_helper (void *p) |
default constructor
在文件 KLLowerTriangularInferenceMethod.cpp 第 54 行定义.
| CKLLowerTriangularInferenceMethod | ( | 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 |
在文件 KLLowerTriangularInferenceMethod.cpp 第 59 行定义.
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virtual |
在文件 KLLowerTriangularInferenceMethod.cpp 第 87 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 597 行定义.
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protectedvirtualinherited |
check if members of object are valid for inference
被 CSparseInferenceBase, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod , 以及 CMultiLaplacianInferenceMethod 重载.
在文件 InferenceMethod.cpp 第 309 行定义.
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protectedvirtualinherited |
check the provided likelihood model supports variational inference
| mod | the provided likelihood model |
在文件 KLInferenceMethod.cpp 第 57 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 714 行定义.
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protectedvirtualinherited |
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 198 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 618 行定义.
get Cholesky decomposition matrix
\[ L = cholesky(sW*K*sW+I) \]
where \(K\) is the prior covariance matrix, \(sW\) is the vector returned by get_diagonal_vector(), and \(I\) is the identity matrix.
Note that in some sub class L is not the Cholesky decomposition In this case, L will still be used to compute required matrix for prediction see CGaussianProcessMachine::get_posterior_variances()
在文件 KLInferenceMethod.cpp 第 451 行定义.
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staticprotectedinherited |
pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
在文件 InferenceMethod.cpp 第 255 行定义.
compute matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter in cov function Note that get_derivative_wrt_inference_method(const TParameter* param) and get_derivative_wrt_kernel(const TParameter* param) will call this function
| the | gradient wrt hyperparameter related to cov |
实现了 CKLInferenceMethod.
在文件 KLLowerTriangularInferenceMethod.cpp 第 153 行定义.
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protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
| param | parameter of CInferenceMethod class |
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 411 行定义.
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protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt kernel's parameter
| param | parameter of given kernel |
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 427 行定义.
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protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt parameter of likelihood model
| param | parameter of given likelihood model |
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 337 行定义.
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protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt mean function's parameter
| param | parameter of given mean function |
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 353 行定义.
get diagonal vector
Note that this vector is not avaliable for the KL method
The diagonal vector W is NOT used in this KL method Therefore, return empty vector
在文件 KLLowerTriangularInferenceMethod.cpp 第 91 行定义.
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virtualinherited |
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inherited |
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inherited |
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inherited |
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get the gradient
| parameters | parameter's dictionary |
在文件 InferenceMethod.h 第 245 行定义.
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protectedpure virtualinherited |
compute the gradient wrt variational parameters given the current variational parameters (mu and s2)
在 CKLCovarianceInferenceMethod, CKLDualInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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virtualinherited |
return what type of inference we are
重载 CInferenceMethod .
被 CKLApproxDiagonalInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod , 以及 CKLDualInferenceMethod 重载.
在文件 KLInferenceMethod.h 第 99 行定义.
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virtualinherited |
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virtualinherited |
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inherited |
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. |
在文件 InferenceMethod.cpp 第 126 行定义.
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virtualinherited |
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inherited |
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inherited |
在文件 SGObject.cpp 第 498 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 522 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 535 行定义.
get the E matrix used for multi classification
在文件 InferenceMethod.cpp 第 72 行定义.
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virtual |
returns the name of the inference method
重载 CKLInferenceMethod .
被 CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 重载.
在文件 KLLowerTriangularInferenceMethod.h 第 92 行定义.
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virtualinherited |
get negative log marginal likelihood
\[ -log(p(y|X, \theta)) \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 329 行定义.
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virtualinherited |
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.
在文件 InferenceMethod.cpp 第 185 行定义.
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protectedpure virtualinherited |
the helper function to compute the negative log marginal likelihood
在 CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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protectedvirtualinherited |
compute the negative log marginal likelihood given the current variational parameters (mu and s2)
在文件 KLInferenceMethod.cpp 第 286 行定义.
returns covariance matrix \(\Sigma=(K^{-1}+W)^{-1}\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|y) \approx q(f|y) = \mathcal{N}(f|\mu,\Sigma) \]
Covariance matrix is evaluated using matrix inversion lemma:
\[ (K^{-1}+W)^{-1} = K - KW^{\frac{1}{2}}B^{-1}W^{\frac{1}{2}}K \]
where \(B=(W^{frac{1}{2}}*K*W^{frac{1}{2}}+I)\).
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 251 行定义.
returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|y) \approx q(f|y) = \mathcal{N}(f|\mu,\Sigma) \]
实现了 CInferenceMethod.
在文件 KLInferenceMethod.cpp 第 244 行定义.
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virtualinherited |
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protectedvirtualinherited |
this method is used to dynamic-cast the likelihood model, m_model, to variational likelihood model.
在文件 KLInferenceMethod.cpp 第 279 行定义.
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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 296 行定义.
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protectedvirtualinherited |
Using L-BFGS to estimate posterior parameters
被 CKLDualInferenceMethod 重载.
在文件 KLInferenceMethod.cpp 第 382 行定义.
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protectedpure virtualinherited |
pre-compute the information for lbfgs optimization. This function needs to be called before calling get_negative_log_marginal_likelihood_wrt_parameters() and/or get_gradient_of_nlml_wrt_parameters(SGVector<float64_t> gradient)
在 CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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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 |
在文件 SGObject.cpp 第 369 行定义.
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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 426 行定义.
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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 421 行定义.
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virtualinherited |
在文件 SGObject.cpp 第 262 行定义.
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 474 行定义.
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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 |
在文件 SGObject.cpp 第 314 行定义.
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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 436 行定义.
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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 431 行定义.
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virtualinherited |
set exp factor to exponentially increase noise factor
| exp_factor | should be greater than 1.0 default value is 2 |
在文件 KLInferenceMethod.cpp 第 201 行定义.
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inherited |
在文件 SGObject.cpp 第 41 行定义.
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在文件 SGObject.cpp 第 46 行定义.
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在文件 SGObject.cpp 第 51 行定义.
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在文件 SGObject.cpp 第 56 行定义.
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在文件 SGObject.cpp 第 61 行定义.
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在文件 SGObject.cpp 第 66 行定义.
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在文件 SGObject.cpp 第 71 行定义.
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在文件 SGObject.cpp 第 76 行定义.
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在文件 SGObject.cpp 第 81 行定义.
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在文件 SGObject.cpp 第 86 行定义.
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在文件 SGObject.cpp 第 91 行定义.
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在文件 SGObject.cpp 第 96 行定义.
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在文件 SGObject.cpp 第 101 行定义.
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在文件 SGObject.cpp 第 106 行定义.
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在文件 SGObject.cpp 第 111 行定义.
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set generic type to T
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virtualinherited |
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virtualinherited |
在文件 KLInferenceMethod.cpp 第 293 行定义.
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set max attempt to ensure Kernel matrix to be positive definite
| max_attempt | should be non-negative. 0 means infinity attempts default value is 0 |
在文件 KLInferenceMethod.cpp 第 195 行定义.
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virtualinherited |
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virtualinherited |
set minimum coeefficient of kernel matrix used in LDLT factorization
| min_coeff_kernel | should be non-negative default value is 1e-5 |
在文件 KLInferenceMethod.cpp 第 189 行定义.
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virtualinherited |
set variational likelihood model
| mod | model to set |
重载 CInferenceMethod .
被 CKLDualInferenceMethod 重载.
在文件 KLInferenceMethod.cpp 第 67 行定义.
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virtualinherited |
set noise factor to ensure Kernel matrix to be positive definite by adding non-negative noise to diagonal elements of Kernel matrix
| noise_factor | should be non-negative default value is 1e-10 |
在文件 KLInferenceMethod.cpp 第 183 行定义.
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virtualinherited |
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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 192 行定义.
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protected |
compute the inv(corrected_Kernel*sq(m_scale))*A
| A | input matrix |
在文件 KLLowerTriangularInferenceMethod.cpp 第 126 行定义.
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virtualinherited |
重载 CInferenceMethod .
在文件 KLInferenceMethod.h 第 167 行定义.
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virtualinherited |
whether combination of inference method and given likelihood function supports multiclass classification
在文件 InferenceMethod.h 第 378 行定义.
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virtualinherited |
重载 CInferenceMethod .
在文件 KLInferenceMethod.h 第 157 行定义.
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unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 303 行定义.
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protectedpure virtualinherited |
update alpha vector
在 CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethodWithLBFGS, CSingleFITCLaplacianInferenceMethodWithLBFGS, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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protectedvirtual |
update covariance matrix of the approximation to the posterior
update_Sigma() does the similar job Therefore, this function body is empty
实现了 CKLInferenceMethod.
在文件 KLLowerTriangularInferenceMethod.cpp 第 167 行定义.
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protectedvirtual |
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protectedvirtual |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
get_derivative_related_cov() does the similar job Therefore, this function body is empty
实现了 CInferenceMethod.
在文件 KLLowerTriangularInferenceMethod.cpp 第 99 行定义.
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protectedvirtual |
correct the kernel matrix and factorizated the corrected Kernel matrix for update
重载 CKLInferenceMethod .
在文件 KLLowerTriangularInferenceMethod.cpp 第 106 行定义.
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protectedvirtualinherited |
a helper function used to correct the kernel matrix using LDLT factorization
在文件 KLInferenceMethod.cpp 第 212 行定义.
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protectedpure virtual |
compute inv(corrected_Kernel)*Sigma matrix
在 CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 248 行定义.
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protectedpure virtual |
compute posterior Sigma matrix
在 CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.
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protectedvirtualinherited |
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inherited |
io
在文件 SGObject.h 第 369 行定义.
alpha vector used in process mean calculation
在文件 InferenceMethod.h 第 475 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 440 行定义.
the matrix used for multi classification
在文件 InferenceMethod.h 第 487 行定义.
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在文件 KLInferenceMethod.h 第 446 行定义.
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The factor used to exponentially increase noise_factor
在文件 KLInferenceMethod.h 第 297 行定义.
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features to use
在文件 InferenceMethod.h 第 469 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 455 行定义.
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parameters wrt which we can compute gradients
在文件 SGObject.h 第 384 行定义.
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Whether gradients are updated
在文件 InferenceMethod.h 第 490 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 461 行定义.
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Hash of parameter values
在文件 SGObject.h 第 387 行定义.
The K^{-1}Sigma matrix
在文件 KLLowerTriangularInferenceMethod.h 第 128 行定义.
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protectedinherited |
covariance function
在文件 InferenceMethod.h 第 460 行定义.
The L*sqrt(D) matrix, where L and D are defined in LDLT factorization on Kernel*sq(m_scale)
在文件 KLLowerTriangularInferenceMethod.h 第 137 行定义.
The permutation sequence of P, where P are defined in LDLT factorization on Kernel*sq(m_scale)
在文件 KLLowerTriangularInferenceMethod.h 第 140 行定义.
kernel matrix from features (non-scalled by inference scalling)
在文件 InferenceMethod.h 第 484 行定义.
upper triangular factor of Cholesky decomposition
在文件 InferenceMethod.h 第 478 行定义.
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protectedinherited |
labels of features
在文件 InferenceMethod.h 第 472 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 434 行定义.
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protected |
The Log-determinant of Kernel
在文件 KLLowerTriangularInferenceMethod.h 第 134 行定义.
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protectedinherited |
kernel scale
在文件 InferenceMethod.h 第 481 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 428 行定义.
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protectedinherited |
Max number of attempt to correct kernel matrix to be positive definite
在文件 KLInferenceMethod.h 第 300 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 437 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 431 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 452 行定义.
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protectedinherited |
mean function
在文件 InferenceMethod.h 第 463 行定义.
The mean vector generated from mean function
在文件 KLLowerTriangularInferenceMethod.h 第 131 行定义.
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protectedinherited |
The minimum coeefficient of kernel matrix in LDLT factorization used to check whether the kernel matrix is positive definite or not
在文件 KLInferenceMethod.h 第 291 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 449 行定义.
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protectedinherited |
likelihood function to use
在文件 InferenceMethod.h 第 466 行定义.
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inherited |
model selection parameters
在文件 SGObject.h 第 381 行定义.
mean vector of the approximation to the posterior Note that m_mu is also a variational parameter
在文件 KLInferenceMethod.h 第 417 行定义.
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protectedinherited |
The factor used to ensure kernel matrix to be positive definite
在文件 KLInferenceMethod.h 第 294 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 467 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 473 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 470 行定义.
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inherited |
parameters
在文件 SGObject.h 第 378 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 443 行定义.
variational parameter sigma2 Note that sigma2 = diag(m_Sigma)
在文件 KLInferenceMethod.h 第 425 行定义.
covariance matrix of the approximation to the posterior
在文件 KLInferenceMethod.h 第 420 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 458 行定义.
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protectedinherited |
在文件 KLInferenceMethod.h 第 464 行定义.
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parallel
在文件 SGObject.h 第 372 行定义.
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inherited |
version
在文件 SGObject.h 第 375 行定义.
