The dual KL approximation inference method class.
This inference process is described in the reference paper Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML2013
The idea is to optimize the log marginal likelihood with equality constaints (primal problem) by solving the Lagrangian dual problem. The equality constaints are:
\[ h = \mu, \rho = \sigma^2 = diag(\Sigma) \]
, where h and \(\rho\) are auxiliary variables, \(\mu\) and \(\sigma^2\) are variational variables, and \(\Sigma\) is an approximated posterior covariance matrix. The equality constaints are variational mean constaint ( \(\mu\)) and variational variance constaint ( \(\sigma^2\)).
For detailed information, please refer to the paper.
在文件 KLDualInferenceMethod.h 第 65 行定义.
Public 成员函数 | |
CKLDualInferenceMethod () | |
CKLDualInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model) | |
virtual | ~CKLDualInferenceMethod () |
virtual const char * | get_name () const |
virtual SGVector< float64_t > | get_alpha () |
virtual SGVector< float64_t > | get_diagonal_vector () |
void | set_model (CLikelihoodModel *mod) |
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 | 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="", int32_t param_version=Version::get_version_parameter()) |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
DynArray< TParameter * > * | load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="") |
DynArray< TParameter * > * | load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="") |
void | map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos) |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
SGStringList< char > | get_modelsel_names () |
void | print_modsel_params () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Public 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 |
静态 Protected 成员函数 | |
static void * | get_derivative_helper (void *p) |
default constructor
在文件 KLDualInferenceMethod.cpp 第 55 行定义.
CKLDualInferenceMethod | ( | 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 KLDualInferenceMethod.cpp 第 60 行定义.
|
virtual |
在文件 KLDualInferenceMethod.cpp 第 76 行定义.
|
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 1243 行定义.
|
protectedvirtual |
check the provided likelihood model supports dual variational inference or not
mod | the provided likelihood model |
在文件 KLDualInferenceMethod.cpp 第 80 行定义.
|
protectedvirtualinherited |
check if members of object are valid for inference
被 CFITCInferenceMethod , 以及 CExactInferenceMethod 重载.
在文件 InferenceMethod.cpp 第 275 行定义.
|
protectedvirtualinherited |
check the provided likelihood model supports variational inference
mod | the provided likelihood model |
在文件 KLInferenceMethod.cpp 第 57 行定义.
|
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 1360 行定义.
|
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 1264 行定义.
get alpha vector
\[ \mu = K\alpha+mean_f \]
where \(\mu\) is the mean and \(K\) is the prior covariance matrix.
在文件 KLDualInferenceMethod.cpp 第 67 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 461 行定义.
|
staticprotectedinherited |
pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
在文件 InferenceMethod.cpp 第 221 行定义.
|
protectedvirtual |
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 related to cov |
实现了 CKLInferenceMethod.
在文件 KLDualInferenceMethod.cpp 第 245 行定义.
|
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 410 行定义.
|
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.
|
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 326 行定义.
|
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 342 行定义.
get diagonal vector
\[ Cov = (K^{-1}+sW^{2})^{-1} \]
where \(Cov\) is the posterior covariance matrix, \(K\) is the prior covariance matrix, and \(sW\) is the diagonal vector.
在文件 KLDualInferenceMethod.cpp 第 426 行定义.
|
protectedvirtual |
compute the objective value for LBFGS optimizer
The mathematical equation (equation 24 in the paper) is defined as below
\[ min_{\lambda \in S}{0.5*[(\lambda-y)^TK(\lambda-y)-log(det(A_{\lambda}))]-mean_{f}^T(\lambda-y)+\sum_{i=1}^{n}{Fenchel_i{(\lambda)}}} \]
where S is the feasible set defined for \(\lambda\), K comes from covariance function, \(mean_f\) comes from mean function, \(\lambda\) is the dual parameter, y are data labels, n is the number point, \(A_{\lambda}=K^{-1}+diag(\lambda)\), and \(Fenchel_i{(\lambda)}=Fenchel_i{(\alpha,\lambda)}\) since \(\alpha\) is implicitly defined by \(\lambda\)
Note that S and \(Fenchel_i{(\lambda)}\) are specified by the data modeling distribution, which are implemented in dual variational likelihood class.
在文件 KLDualInferenceMethod.cpp 第 160 行定义.
|
protectedvirtual |
this method is used to dynamic-cast the likelihood model, m_model, to dual variational likelihood model.
在文件 KLDualInferenceMethod.cpp 第 93 行定义.
|
virtualinherited |
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
get the gradient
parameters | parameter's dictionary |
在文件 InferenceMethod.h 第 215 行定义.
|
protectedvirtual |
compute the gradient of the objective function for LBFGS optimizer The mathematical equation (equation 25 in the paper) is defined as below
\[ 0.5*[2*K(\lambda-y)-diag(A_{\lambda}^{-1})]-mean_{f}+\sum_{i=1}^{n}{\nabla Fenchel_i{(\lambda)}} \]
where \(A_{\lambda}=K^{-1}+diag(\lambda)\), K comes from covariance function, \(mean_f\) comes from mean function, \(\lambda\) is the dual parameter, y are data labels, n is the number point, and \(\nabla Fenchel_i{(\lambda)}\) is the gradient of \(Fenchel_i{(\lambda)}\) wrt to \(\lambda\)
Note that \(\nabla Fenchel_i{(\lambda)}\) are specified by the data modeling distribution, which are implemented in dual variational likelihood class.
在文件 KLDualInferenceMethod.cpp 第 181 行定义.
compute the gradient wrt variational parameters given the current variational parameters (mu and s2)
实现了 CKLInferenceMethod.
在文件 KLDualInferenceMethod.h 第 127 行定义.
|
virtualinherited |
|
virtualinherited |
|
virtualinherited |
|
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 91 行定义.
|
virtualinherited |
|
inherited |
|
inherited |
在文件 SGObject.cpp 第 1135 行定义.
|
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 1159 行定义.
|
inherited |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
在文件 SGObject.cpp 第 1172 行定义.
get the E matrix used for multi classification
在文件 InferenceMethod.cpp 第 40 行定义.
|
virtual |
returns the name of the inference method
重载 CKLInferenceMethod .
在文件 KLDualInferenceMethod.h 第 88 行定义.
|
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 318 行定义.
|
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 150 行定义.
|
protectedvirtual |
the helper function to compute the negative log marginal likelihood
实现了 CKLInferenceMethod.
在文件 KLDualInferenceMethod.cpp 第 236 行定义.
|
protectedvirtualinherited |
compute the negative log marginal likelihood given the current variational parameters (mu and s2)
在文件 KLInferenceMethod.cpp 第 275 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 239 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 231 行定义.
|
virtualinherited |
|
protectedvirtualinherited |
this method is used to dynamic-cast the likelihood model, m_model, to variational likelihood model.
在文件 KLInferenceMethod.cpp 第 268 行定义.
|
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 297 行定义.
|
protectedvirtual |
Using L-BFGS to estimate posterior parameters
重载 CKLInferenceMethod .
在文件 KLDualInferenceMethod.cpp 第 396 行定义.
|
protectedvirtual |
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)
实现了 CKLInferenceMethod.
在文件 KLDualInferenceMethod.cpp 第 120 行定义.
|
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 704 行定义.
|
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 545 行定义.
|
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 374 行定义.
|
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 1062 行定义.
|
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 1057 行定义.
|
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 742 行定义.
|
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 949 行定义.
|
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 889 行定义.
|
virtualinherited |
在文件 SGObject.cpp 第 263 行定义.
|
inherited |
prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1111 行定义.
|
virtualinherited |
|
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 315 行定义.
|
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 1072 行定义.
|
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 1067 行定义.
|
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 189 行定义.
|
virtualinherited |
|
inherited |
在文件 SGObject.cpp 第 42 行定义.
|
inherited |
在文件 SGObject.cpp 第 47 行定义.
|
inherited |
在文件 SGObject.cpp 第 52 行定义.
|
inherited |
在文件 SGObject.cpp 第 57 行定义.
|
inherited |
在文件 SGObject.cpp 第 62 行定义.
|
inherited |
在文件 SGObject.cpp 第 67 行定义.
|
inherited |
在文件 SGObject.cpp 第 72 行定义.
|
inherited |
在文件 SGObject.cpp 第 77 行定义.
|
inherited |
在文件 SGObject.cpp 第 82 行定义.
|
inherited |
在文件 SGObject.cpp 第 87 行定义.
|
inherited |
在文件 SGObject.cpp 第 92 行定义.
|
inherited |
在文件 SGObject.cpp 第 97 行定义.
|
inherited |
在文件 SGObject.cpp 第 102 行定义.
|
inherited |
在文件 SGObject.cpp 第 107 行定义.
|
inherited |
在文件 SGObject.cpp 第 112 行定义.
|
inherited |
set generic type to T
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
|
virtualinherited |
|
virtualinherited |
在文件 KLInferenceMethod.cpp 第 282 行定义.
|
virtualinherited |
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 183 行定义.
|
virtualinherited |
|
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 177 行定义.
|
virtual |
set variational likelihood model
mod | model to set |
重载 CKLInferenceMethod .
在文件 KLDualInferenceMethod.cpp 第 87 行定义.
|
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 171 行定义.
|
virtualinherited |
|
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.
|
virtualinherited |
重载 CInferenceMethod .
在文件 KLInferenceMethod.h 第 167 行定义.
|
virtualinherited |
whether combination of inference method and given likelihood function supports multiclass classification
在文件 InferenceMethod.h 第 348 行定义.
|
virtualinherited |
重载 CInferenceMethod .
在文件 KLInferenceMethod.h 第 157 行定义.
|
inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 304 行定义.
|
virtualinherited |
|
protectedvirtual |
|
protectedvirtual |
update covariance matrix of the approximation to the posterior
实现了 CKLInferenceMethod.
在文件 KLDualInferenceMethod.cpp 第 448 行定义.
|
protectedvirtual |
|
protectedvirtual |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
实现了 CInferenceMethod.
在文件 KLDualInferenceMethod.cpp 第 434 行定义.
|
protectedvirtualinherited |
correct the kernel matrix and factorizated the corrected Kernel matrix for update
被 CKLLowerTriangularInferenceMethod 重载.
在文件 KLInferenceMethod.cpp 第 195 行定义.
|
protectedvirtualinherited |
a helper function used to correct the kernel matrix using LDLT factorization
在文件 KLInferenceMethod.cpp 第 200 行定义.
|
virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 250 行定义.
|
protectedvirtualinherited |
|
inherited |
io
在文件 SGObject.h 第 496 行定义.
alpha vector used in process mean calculation
在文件 InferenceMethod.h 第 443 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 437 行定义.
the matrix used for multi classification
在文件 InferenceMethod.h 第 455 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 443 行定义.
|
protectedinherited |
The factor used to exponentially increase noise_factor
在文件 KLInferenceMethod.h 第 294 行定义.
|
protectedinherited |
features to use
在文件 InferenceMethod.h 第 437 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 452 行定义.
|
inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 511 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 458 行定义.
|
inherited |
Hash of parameter values
在文件 SGObject.h 第 517 行定义.
|
protectedinherited |
covariance function
在文件 InferenceMethod.h 第 428 行定义.
kernel matrix from features (non-scalled by inference scalling)
在文件 InferenceMethod.h 第 452 行定义.
upper triangular factor of Cholesky decomposition
在文件 InferenceMethod.h 第 446 行定义.
|
protectedinherited |
labels of features
在文件 InferenceMethod.h 第 440 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 431 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 425 行定义.
|
protectedinherited |
Max number of attempt to correct kernel matrix to be positive definite
在文件 KLInferenceMethod.h 第 297 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 434 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 428 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 449 行定义.
|
protectedinherited |
mean function
在文件 InferenceMethod.h 第 431 行定义.
|
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 288 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 446 行定义.
|
protectedinherited |
likelihood function to use
在文件 InferenceMethod.h 第 434 行定义.
|
inherited |
model selection parameters
在文件 SGObject.h 第 508 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 414 行定义.
|
protectedinherited |
The factor used to ensure kernel matrix to be positive definite
在文件 KLInferenceMethod.h 第 291 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 464 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 470 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 467 行定义.
|
inherited |
map for different parameter versions
在文件 SGObject.h 第 514 行定义.
|
inherited |
parameters
在文件 SGObject.h 第 505 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 440 行定义.
variational parameter sigma2 Note that sigma2 = diag(m_Sigma)
在文件 KLInferenceMethod.h 第 422 行定义.
|
protectedinherited |
kernel scale
在文件 InferenceMethod.h 第 449 行定义.
covariance matrix of the approximation to the posterior
在文件 KLInferenceMethod.h 第 417 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 455 行定义.
|
protectedinherited |
在文件 KLInferenceMethod.h 第 461 行定义.
|
inherited |
parallel
在文件 SGObject.h 第 499 行定义.
|
inherited |
version
在文件 SGObject.h 第 502 行定义.