SHOGUN
v3.2.0
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Class of the Expectation Propagation (EP) posterior approximation inference method.
For more details, see: Minka, T. P. (2001). A Family of Algorithms for Approximate Bayesian Inference. PhD thesis, Massachusetts Institute of Technology
Definition at line 34 of file EPInferenceMethod.h.
default constructor
Definition at line 44 of file EPInferenceMethod.cpp.
CEPInferenceMethod | ( | CKernel * | kernel, |
CFeatures * | features, | ||
CMeanFunction * | mean, | ||
CLabels * | labels, | ||
CLikelihoodModel * | model | ||
) |
constructor
kernel | covariance function |
features | features to use in inference |
mean | mean function |
labels | labels of the features |
model | likelihood model to use |
Definition at line 49 of file EPInferenceMethod.cpp.
~CEPInferenceMethod | ( | ) | [virtual] |
Definition at line 56 of file EPInferenceMethod.cpp.
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.
dict | dictionary of parameters to be built. |
Definition at line 1156 of file SGObject.cpp.
void check_members | ( | ) | const [protected, virtual, inherited] |
check if members of object are valid for inference
Reimplemented in CFITCInferenceMethod, and CExactInferenceMethod.
Definition at line 263 of file InferenceMethod.cpp.
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 1273 of file SGObject.cpp.
A deep copy. All the instance variables will also be copied.
Definition at line 126 of file SGObject.h.
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) |
Definition at line 1177 of file SGObject.cpp.
returns vector to compute posterior mean of Gaussian Process under EP approximation:
\[ \mathbb{E}_q[f_*|X,y,x_*] = k^T_*\alpha \]
where \(k^T_*\) - covariance between training points \(X\) and test point \(x_*\), and for EP approximation:
\[ \alpha = (K + \tilde{S}^{-1})^{-1}\tilde{S}^{-1}\tilde{\nu} = (I-\tilde{S}^{\frac{1}{2}}B^{-1}\tilde{S}^{\frac{1}{2}}K)\tilde{\nu} \]
where \(K\) is the prior covariance matrix, \(\tilde{S}^{\frac{1}{2}}\) is the diagonal matrix (see description of get_diagonal_vector() method) and \(\tilde{\nu}\) - natural parameter ( \(\tilde{\nu} = \tilde{S}\tilde{\mu}\)).
Implements CInferenceMethod.
Definition at line 75 of file EPInferenceMethod.cpp.
SGMatrix< float64_t > get_cholesky | ( | ) | [virtual] |
returns upper triangular factor \(L^T\) of the Cholesky decomposition ( \(LL^T\)) of the matrix:
\[ B = (\tilde{S}^{\frac{1}{2}}K\tilde{S}^{\frac{1}{2}}+I) \]
where \(\tilde{S}^{\frac{1}{2}}\) is the diagonal matrix (see description of get_diagonal_vector() method) and \(K\) is the prior covariance matrix.
Implements CInferenceMethod.
Definition at line 83 of file EPInferenceMethod.cpp.
void * get_derivative_helper | ( | void * | p | ) | [static, protected, inherited] |
pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
Definition at line 209 of file InferenceMethod.cpp.
SGVector< float64_t > get_derivative_wrt_inference_method | ( | const TParameter * | param | ) | [protected, virtual] |
returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
param | parameter of CInferenceMethod class |
Implements CInferenceMethod.
Definition at line 416 of file EPInferenceMethod.cpp.
SGVector< float64_t > get_derivative_wrt_kernel | ( | const TParameter * | param | ) | [protected, virtual] |
returns derivative of negative log marginal likelihood wrt kernel's parameter
param | parameter of given kernel |
Implements CInferenceMethod.
Definition at line 441 of file EPInferenceMethod.cpp.
SGVector< float64_t > get_derivative_wrt_likelihood_model | ( | const TParameter * | param | ) | [protected, virtual] |
returns derivative of negative log marginal likelihood wrt parameter of likelihood model
param | parameter of given likelihood model |
Implements CInferenceMethod.
Definition at line 434 of file EPInferenceMethod.cpp.
SGVector< float64_t > get_derivative_wrt_mean | ( | const TParameter * | param | ) | [protected, virtual] |
returns derivative of negative log marginal likelihood wrt mean function's parameter
param | parameter of given mean function |
Implements CInferenceMethod.
Definition at line 479 of file EPInferenceMethod.cpp.
SGVector< float64_t > get_diagonal_vector | ( | ) | [virtual] |
returns diagonal vector of the diagonal matrix:
\[ \tilde{S}^{\frac{1}{2}} = \sqrt{\tilde{S}} \]
where \(\tilde{S} = \text{diag}(\tilde{\tau})\), and \(\tilde{\tau}\)
Implements CInferenceMethod.
Definition at line 91 of file EPInferenceMethod.cpp.
virtual CFeatures* get_features | ( | ) | [virtual, inherited] |
SGIO * get_global_io | ( | ) | [inherited] |
Parallel * get_global_parallel | ( | ) | [inherited] |
Version * get_global_version | ( | ) | [inherited] |
virtual CMap<TParameter*, SGVector<float64_t> >* get_gradient | ( | CMap< TParameter *, CSGObject * > * | parameters | ) | [virtual, inherited] |
get the gradient
parameters | parameter's dictionary |
Implements CDifferentiableFunction.
Definition at line 220 of file InferenceMethod.h.
virtual EInferenceType get_inference_type | ( | ) | const [virtual] |
return what type of inference we are
Reimplemented from CInferenceMethod.
Definition at line 57 of file EPInferenceMethod.h.
virtual CKernel* get_kernel | ( | ) | [virtual, inherited] |
virtual CLabels* get_labels | ( | ) | [virtual, inherited] |
float64_t get_marginal_likelihood_estimate | ( | int32_t | num_importance_samples = 1 , |
float64_t | ridge_size = 1e-15 |
||
) | [inherited] |
Computes an unbiased estimate of the log-marginal-likelihood,
\[ log(p(y|X,\theta)), \]
where \(y\) are the labels, \(X\) are the features (omitted from in the following expressions), and \(\theta\) represent hyperparameters.
This is done via an 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).
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 Cholesky factorization fails. |
Definition at line 79 of file InferenceMethod.cpp.
virtual uint32_t get_max_sweep | ( | ) | const [virtual] |
returns maximum number of sweeps over all variables
Definition at line 202 of file EPInferenceMethod.h.
virtual CMeanFunction* get_mean | ( | ) | [virtual, inherited] |
virtual uint32_t get_min_sweep | ( | ) | const [virtual] |
returns minimum number of sweeps over all variables
Definition at line 190 of file EPInferenceMethod.h.
CLikelihoodModel* get_model | ( | ) | [inherited] |
SGStringList< char > get_modelsel_names | ( | ) | [inherited] |
Definition at line 1060 of file SGObject.cpp.
char * get_modsel_param_descr | ( | const char * | param_name | ) | [inherited] |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1084 of file SGObject.cpp.
index_t get_modsel_param_index | ( | const char * | param_name | ) | [inherited] |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 1097 of file SGObject.cpp.
virtual const char* get_name | ( | ) | const [virtual] |
returns the name of the inference method
Implements CSGObject.
Definition at line 63 of file EPInferenceMethod.h.
float64_t get_negative_log_marginal_likelihood | ( | ) | [virtual] |
returns the negative logarithm of the marginal likelihood function:
\[ -log(p(y|X, \theta)) \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
Implements CInferenceMethod.
Definition at line 67 of file EPInferenceMethod.cpp.
CMap< TParameter *, SGVector< float64_t > > * get_negative_log_marginal_likelihood_derivatives | ( | CMap< TParameter *, CSGObject * > * | parameters | ) | [virtual, inherited] |
get log marginal likelihood gradient
\[ -\frac{\partial log(p(y|X, \theta))}{\partial \theta} \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
Definition at line 138 of file InferenceMethod.cpp.
SGMatrix< float64_t > get_posterior_covariance | ( | ) | [virtual] |
returns covariance matrix \(\Sigma=(K^{-1}+\tilde{S})^{-1}\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|X,y) \approx q(f|X,y) = \mathcal{N}(f|\mu,\Sigma) \]
Covariance matrix \(\Sigma\) is evaluated using matrix inversion lemma:
\[ \Sigma = (K^{-1}+\tilde{S})^{-1} = K - K\tilde{S}^{\frac{1}{2}}B^{-1}\tilde{S}^{\frac{1}{2}}K \]
where \(B=(\tilde{S}^{\frac{1}{2}}K\tilde{S}^{\frac{1}{2}}+I)\).
Implements CInferenceMethod.
Definition at line 107 of file EPInferenceMethod.cpp.
SGVector< float64_t > get_posterior_mean | ( | ) | [virtual] |
returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|X,y) \approx q(f|X,y) = \mathcal{N}(f|\mu,\Sigma) \]
Mean vector \(\mu\) is evaluated like:
\[ \mu = \Sigma\tilde{\nu} \]
where \(\Sigma\) - covariance matrix of the posterior approximation and \(\tilde{\nu}\) - natural parameter ( \(\tilde{\nu} = \tilde{S}\tilde{\mu}\)).
Implements CInferenceMethod.
Definition at line 99 of file EPInferenceMethod.cpp.
virtual float64_t get_tolerance | ( | ) | const [virtual] |
returns tolerance of the EP approximation
Definition at line 178 of file EPInferenceMethod.h.
get the function value
Implements CDifferentiableFunction.
Definition at line 230 of file InferenceMethod.h.
bool is_generic | ( | EPrimitiveType * | generic | ) | const [virtual, inherited] |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 228 of file SGObject.cpp.
DynArray< TParameter * > * load_all_file_parameters | ( | int32_t | file_version, |
int32_t | current_version, | ||
CSerializableFile * | file, | ||
const char * | prefix = "" |
||
) | [inherited] |
maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 633 of file SGObject.cpp.
DynArray< TParameter * > * load_file_parameters | ( | const SGParamInfo * | param_info, |
int32_t | file_version, | ||
CSerializableFile * | file, | ||
const char * | prefix = "" |
||
) | [inherited] |
loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 474 of file SGObject.cpp.
bool load_serializable | ( | CSerializableFile * | file, |
const char * | prefix = "" , |
||
int32_t | param_version = Version::get_version_parameter() |
||
) | [virtual, inherited] |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 305 of file SGObject.cpp.
void load_serializable_post | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
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 occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 989 of file SGObject.cpp.
void load_serializable_pre | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
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 occurres. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 984 of file SGObject.cpp.
void map_parameters | ( | DynArray< TParameter * > * | param_base, |
int32_t & | base_version, | ||
DynArray< const SGParamInfo * > * | target_param_infos | ||
) | [inherited] |
Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 671 of file SGObject.cpp.
TParameter * migrate | ( | DynArray< TParameter * > * | param_base, |
const SGParamInfo * | target | ||
) | [protected, virtual, inherited] |
creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 878 of file SGObject.cpp.
void one_to_one_migration_prepare | ( | DynArray< TParameter * > * | param_base, |
const SGParamInfo * | target, | ||
TParameter *& | replacement, | ||
TParameter *& | to_migrate, | ||
char * | old_name = NULL |
||
) | [protected, virtual, inherited] |
This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
Definition at line 818 of file SGObject.cpp.
void print_modsel_params | ( | ) | [inherited] |
prints all parameter registered for model selection and their type
Definition at line 1036 of file SGObject.cpp.
void print_serializable | ( | const char * | prefix = "" | ) | [virtual, inherited] |
prints registered parameters out
prefix | prefix for members |
Definition at line 240 of file SGObject.cpp.
bool save_serializable | ( | CSerializableFile * | file, |
const char * | prefix = "" , |
||
int32_t | param_version = Version::get_version_parameter() |
||
) | [virtual, inherited] |
Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 246 of file SGObject.cpp.
void save_serializable_post | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
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 occurres. |
Reimplemented in CKernel.
Definition at line 999 of file SGObject.cpp.
void save_serializable_pre | ( | ) | throw (ShogunException) [protected, virtual, inherited] |
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 occurres. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 994 of file SGObject.cpp.
virtual void set_features | ( | CFeatures * | feat | ) | [virtual, inherited] |
void set_generic< complex128_t > | ( | ) | [inherited] |
set generic type to T
Definition at line 41 of file SGObject.cpp.
void set_global_io | ( | SGIO * | io | ) | [inherited] |
void set_global_parallel | ( | Parallel * | parallel | ) | [inherited] |
set the parallel object
parallel | parallel object to use |
Definition at line 180 of file SGObject.cpp.
void set_global_version | ( | Version * | version | ) | [inherited] |
set the version object
version | version object to use |
Definition at line 215 of file SGObject.cpp.
virtual void set_kernel | ( | CKernel * | kern | ) | [virtual, inherited] |
virtual void set_labels | ( | CLabels * | lab | ) | [virtual, inherited] |
virtual void set_max_sweep | ( | const uint32_t | max_sweep | ) | [virtual] |
sets maximum number of sweeps over all variables
max_sweep | maximum number of sweeps to set |
Definition at line 208 of file EPInferenceMethod.h.
virtual void set_mean | ( | CMeanFunction * | m | ) | [virtual, inherited] |
virtual void set_min_sweep | ( | const uint32_t | min_sweep | ) | [virtual] |
sets minimum number of sweeps over all variables
min_sweep | minimum number of sweeps to set |
Definition at line 196 of file EPInferenceMethod.h.
virtual void set_model | ( | CLikelihoodModel * | mod | ) | [virtual, inherited] |
virtual void set_tolerance | ( | const float64_t | tol | ) | [virtual] |
sets tolerance of the EP approximation
tol | tolerance to set |
Definition at line 184 of file EPInferenceMethod.h.
virtual CSGObject* shallow_copy | ( | ) | const [virtual, inherited] |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 117 of file SGObject.h.
virtual bool supports_binary | ( | ) | const [virtual] |
Reimplemented from CInferenceMethod.
Definition at line 214 of file EPInferenceMethod.h.
virtual bool supports_multiclass | ( | ) | const [virtual, inherited] |
whether combination of inference method and given likelihood function supports multiclass classification
Definition at line 353 of file InferenceMethod.h.
virtual bool supports_regression | ( | ) | const [virtual, inherited] |
whether combination of inference method and given likelihood function supports regression
Reimplemented in CLaplacianInferenceMethod, CFITCInferenceMethod, and CExactInferenceMethod.
Definition at line 339 of file InferenceMethod.h.
void unset_generic | ( | ) | [inherited] |
unset generic type
this has to be called in classes specializing a template class
Definition at line 235 of file SGObject.cpp.
void update | ( | ) | [virtual] |
update data all matrices
Reimplemented from CInferenceMethod.
Definition at line 115 of file EPInferenceMethod.cpp.
void update_alpha | ( | ) | [protected, virtual] |
update alpha matrix
Implements CInferenceMethod.
Definition at line 260 of file EPInferenceMethod.cpp.
void update_approx_cov | ( | ) | [protected, virtual] |
update covariance matrix of the approximation to the posterior
Definition at line 303 of file EPInferenceMethod.cpp.
void update_approx_mean | ( | ) | [protected, virtual] |
update mean vector of the approximation to the posterior
Definition at line 326 of file EPInferenceMethod.cpp.
void update_chol | ( | ) | [protected, virtual] |
update Cholesky matrix
Implements CInferenceMethod.
Definition at line 283 of file EPInferenceMethod.cpp.
void update_deriv | ( | ) | [protected, virtual] |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
Implements CInferenceMethod.
Definition at line 396 of file EPInferenceMethod.cpp.
void update_negative_ml | ( | ) | [protected, virtual] |
update negative marginal likelihood
Definition at line 340 of file EPInferenceMethod.cpp.
bool update_parameter_hash | ( | ) | [virtual, inherited] |
Updates the hash of current parameter combination.
Definition at line 187 of file SGObject.cpp.
void update_train_kernel | ( | ) | [protected, virtual, inherited] |
update train kernel matrix
Reimplemented in CFITCInferenceMethod.
Definition at line 279 of file InferenceMethod.cpp.
io
Definition at line 473 of file SGObject.h.
alpha vector used in process mean calculation
Definition at line 441 of file InferenceMethod.h.
CFeatures* m_features [protected, inherited] |
features to use
Definition at line 435 of file InferenceMethod.h.
Parameter* m_gradient_parameters [inherited] |
parameters wrt which we can compute gradients
Definition at line 488 of file SGObject.h.
uint32_t m_hash [inherited] |
Hash of parameter values
Definition at line 494 of file SGObject.h.
covariance function
Definition at line 426 of file InferenceMethod.h.
kernel matrix from features (non-scalled by inference scalling)
Definition at line 450 of file InferenceMethod.h.
upper triangular factor of Cholesky decomposition
Definition at line 444 of file InferenceMethod.h.
labels of features
Definition at line 438 of file InferenceMethod.h.
CMeanFunction* m_mean [protected, inherited] |
mean function
Definition at line 429 of file InferenceMethod.h.
CLikelihoodModel* m_model [protected, inherited] |
likelihood function to use
Definition at line 432 of file InferenceMethod.h.
Parameter* m_model_selection_parameters [inherited] |
model selection parameters
Definition at line 485 of file SGObject.h.
ParameterMap* m_parameter_map [inherited] |
map for different parameter versions
Definition at line 491 of file SGObject.h.
Parameter* m_parameters [inherited] |
parameters
Definition at line 482 of file SGObject.h.
kernel scale
Definition at line 447 of file InferenceMethod.h.
parallel
Definition at line 476 of file SGObject.h.
version
Definition at line 479 of file SGObject.h.