SHOGUN
3.2.1
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The KL approximation inference method class.
The class is implemented based on the KL method in the Nickisch's paper Note that lambda (m_W) is a diagonal vector defined in the paper. The implementation apply L-BFGS to finding optimal solution of negative log likelihood. Since lambda is always non-positive according to the paper, this implementation uses log(-lambda) as representation, which assumes lambda is always negative.
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 Nickisch, Hannes, and Carl Edward Rasmussen. "Approximations for Binary Gaussian Process Classification." Journal of Machine Learning Research 9.10 (2008).
The adapted Matlab code can be found at https://gist.github.com/yorkerlin/b64a015491833562d11a
Definition at line 74 of file KLCovarianceInferenceMethod.h.
Public Member Functions | |
CKLCovarianceInferenceMethod () | |
CKLCovarianceInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model) | |
virtual | ~CKLCovarianceInferenceMethod () |
virtual const char * | get_name () const |
virtual SGVector< float64_t > | get_alpha () |
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_DEFAULT, 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 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 () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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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 Attributes | |
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 |
Static Protected Member Functions | |
static void * | get_derivative_helper (void *p) |
default constructor
Definition at line 54 of file KLCovarianceInferenceMethod.cpp.
CKLCovarianceInferenceMethod | ( | CKernel * | kernel, |
CFeatures * | features, | ||
CMeanFunction * | mean, | ||
CLabels * | labels, | ||
CLikelihoodModel * | model | ||
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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 59 of file KLCovarianceInferenceMethod.cpp.
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Definition at line 112 of file KLCovarianceInferenceMethod.cpp.
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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 1185 of file SGObject.cpp.
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check if members of object are valid for inference
Reimplemented in CFITCInferenceMethod, and CExactInferenceMethod.
Definition at line 263 of file InferenceMethod.cpp.
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check the provided likelihood model supports variational inference
mod | the provided likelihood model |
Definition at line 56 of file KLInferenceMethod.cpp.
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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 1302 of file SGObject.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 146 of file SGObject.cpp.
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) |
Definition at line 1206 of file SGObject.cpp.
get alpha vector
\[ \mu = K\alpha \]
where \(\mu\) is the mean and \(K\) is the prior covariance matrix.
Note that m_alpha contains not only the alpha vector defined in the reference but also a vector corresponding to the diagonal part of W
Note that alpha=K^{-1}(mu-mean), where mean is generated from mean function, K is generated from cov function and mu is not only the posterior mean but also the variational mean
Implements CInferenceMethod.
Definition at line 89 of file KLCovarianceInferenceMethod.cpp.
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()
Implements CInferenceMethod.
Definition at line 457 of file KLInferenceMethod.cpp.
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pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
Definition at line 209 of file InferenceMethod.cpp.
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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 |
Implements CKLInferenceMethod.
Definition at line 233 of file KLCovarianceInferenceMethod.cpp.
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returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
param | parameter of CInferenceMethod class |
Implements CInferenceMethod.
Definition at line 406 of file KLInferenceMethod.cpp.
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returns derivative of negative log marginal likelihood wrt kernel's parameter
param | parameter of given kernel |
Implements CInferenceMethod.
Definition at line 423 of file KLInferenceMethod.cpp.
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returns derivative of negative log marginal likelihood wrt parameter of likelihood model
param | parameter of given likelihood model |
Implements CInferenceMethod.
Definition at line 322 of file KLInferenceMethod.cpp.
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returns derivative of negative log marginal likelihood wrt mean function's parameter
param | parameter of given mean function |
Implements CInferenceMethod.
Definition at line 338 of file KLInferenceMethod.cpp.
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.
Implements CInferenceMethod.
Definition at line 321 of file KLCovarianceInferenceMethod.cpp.
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get the gradient
parameters | parameter's dictionary |
Implements CDifferentiableFunction.
Definition at line 224 of file InferenceMethod.h.
compute the gradient wrt variational parameters given the current variational parameters (mu and s2)
Implements CKLInferenceMethod.
Definition at line 155 of file KLCovarianceInferenceMethod.cpp.
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return what type of inference we are
Reimplemented from CInferenceMethod.
Definition at line 92 of file KLInferenceMethod.h.
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Computes an unbiased estimate of the marginal-likelihood,
\[ p(y|X,\theta), \]
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).
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.
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Definition at line 1077 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1101 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 1114 of file SGObject.cpp.
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returns the name of the inference method
Reimplemented from CKLInferenceMethod.
Definition at line 97 of file KLCovarianceInferenceMethod.h.
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get negative log marginal likelihood
\[ -log(p(y|X, \theta)) \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
Implements CInferenceMethod.
Definition at line 314 of file KLInferenceMethod.cpp.
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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.
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the helper function to compute the negative log marginal likelihood
Implements CKLInferenceMethod.
Definition at line 206 of file KLCovarianceInferenceMethod.cpp.
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compute the negative log marginal likelihood given the current variational parameters (mu and s2)
Definition at line 271 of file KLInferenceMethod.cpp.
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 \(B=(W^{frac{1}{2}}*K*W^{frac{1}{2}}+I)\).
Implements CInferenceMethod.
Definition at line 238 of file KLInferenceMethod.cpp.
returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|y) \approx q(f|y) = \mathcal{N}(f|\mu,\Sigma) \]
Implements CInferenceMethod.
Definition at line 230 of file KLInferenceMethod.cpp.
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get the function value
Implements CDifferentiableFunction.
Definition at line 234 of file InferenceMethod.h.
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this method is used to dynamic-cast the likelihood model, m_model, to variational likelihood model.
Definition at line 264 of file KLInferenceMethod.cpp.
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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 243 of file SGObject.cpp.
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Using L-BFGS to estimate posterior parameters
Reimplemented in CKLDualInferenceMethod.
Definition at line 377 of file KLInferenceMethod.cpp.
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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)
Implements CKLInferenceMethod.
Definition at line 116 of file KLCovarianceInferenceMethod.cpp.
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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 648 of file SGObject.cpp.
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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 489 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 320 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1004 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 999 of file SGObject.cpp.
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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 686 of file SGObject.cpp.
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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 893 of file SGObject.cpp.
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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 833 of file SGObject.cpp.
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Definition at line 209 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1053 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 255 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 261 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel.
Definition at line 1014 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 1009 of file SGObject.cpp.
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set exp factor to exponentially increase noise factor
exp_factor | should be greater than 1.0 default value is 2 |
Definition at line 188 of file KLInferenceMethod.cpp.
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Definition at line 38 of file SGObject.cpp.
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Definition at line 43 of file SGObject.cpp.
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Definition at line 48 of file SGObject.cpp.
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Definition at line 53 of file SGObject.cpp.
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Definition at line 58 of file SGObject.cpp.
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Definition at line 63 of file SGObject.cpp.
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Definition at line 68 of file SGObject.cpp.
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Definition at line 73 of file SGObject.cpp.
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Definition at line 78 of file SGObject.cpp.
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Definition at line 83 of file SGObject.cpp.
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Definition at line 88 of file SGObject.cpp.
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Definition at line 93 of file SGObject.cpp.
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Definition at line 98 of file SGObject.cpp.
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Definition at line 103 of file SGObject.cpp.
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Definition at line 108 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 189 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 230 of file SGObject.cpp.
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Definition at line 278 of file KLInferenceMethod.cpp.
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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 |
Definition at line 182 of file KLInferenceMethod.cpp.
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set minimum coeefficient of kernel matrix used in LDLT factorization
min_coeff_kernel | should be non-negative default value is 1e-5 |
Definition at line 176 of file KLInferenceMethod.cpp.
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set variational likelihood model
mod | model to set |
Reimplemented from CInferenceMethod.
Reimplemented in CKLDualInferenceMethod.
Definition at line 66 of file KLInferenceMethod.cpp.
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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 |
Definition at line 170 of file KLInferenceMethod.cpp.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 140 of file SGObject.cpp.
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Reimplemented from CInferenceMethod.
Definition at line 160 of file KLInferenceMethod.h.
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whether combination of inference method and given likelihood function supports multiclass classification
Definition at line 357 of file InferenceMethod.h.
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Reimplemented from CInferenceMethod.
Definition at line 150 of file KLInferenceMethod.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 250 of file SGObject.cpp.
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update data all matrices
Reimplemented from CInferenceMethod.
Definition at line 155 of file KLInferenceMethod.cpp.
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update alpha matrix
Implements CInferenceMethod.
Definition at line 257 of file KLCovarianceInferenceMethod.cpp.
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update covariance matrix of the approximation to the posterior
The variational co-variational matrix, which is automatically computed when update_alpha is called, is an approximated posterior co-variance matrix Therefore, this function body is empty
Implements CKLInferenceMethod.
Definition at line 343 of file KLCovarianceInferenceMethod.cpp.
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update cholesky matrix
L is automatically updated when update_alpha is called Therefore, this function body is empty
Implements CInferenceMethod.
Definition at line 336 of file KLCovarianceInferenceMethod.cpp.
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update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
get_derivative_related_cov(MatrixXd eigen_dK) does the similar job Therefore, this function body is empty
Implements CInferenceMethod.
Definition at line 329 of file KLCovarianceInferenceMethod.cpp.
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correct the kernel matrix and factorizated the corrected Kernel matrix for update
Reimplemented in CKLLowerTriangularInferenceMethod.
Definition at line 194 of file KLInferenceMethod.cpp.
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a helper function used to correct the kernel matrix using LDLT factorization
Definition at line 199 of file KLInferenceMethod.cpp.
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Updates the hash of current parameter combination
Definition at line 196 of file SGObject.cpp.
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update train kernel matrix
Reimplemented in CFITCInferenceMethod.
Definition at line 279 of file InferenceMethod.cpp.
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io
Definition at line 461 of file SGObject.h.
alpha vector used in process mean calculation
Definition at line 445 of file InferenceMethod.h.
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Definition at line 429 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 435 of file KLInferenceMethod.h.
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protectedinherited |
The factor used to exponentially increase noise_factor
Definition at line 287 of file KLInferenceMethod.h.
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protectedinherited |
features to use
Definition at line 439 of file InferenceMethod.h.
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protectedinherited |
Definition at line 444 of file KLInferenceMethod.h.
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parameters wrt which we can compute gradients
Definition at line 476 of file SGObject.h.
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protectedinherited |
Definition at line 450 of file KLInferenceMethod.h.
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Hash of parameter values
Definition at line 482 of file SGObject.h.
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covariance function
Definition at line 430 of file InferenceMethod.h.
kernel matrix from features (non-scalled by inference scalling)
Definition at line 454 of file InferenceMethod.h.
upper triangular factor of Cholesky decomposition
Definition at line 448 of file InferenceMethod.h.
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labels of features
Definition at line 442 of file InferenceMethod.h.
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protectedinherited |
Definition at line 423 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 417 of file KLInferenceMethod.h.
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protectedinherited |
Max number of attempt to correct kernel matrix to be positive definite
Definition at line 290 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 426 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 420 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 441 of file KLInferenceMethod.h.
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protectedinherited |
mean function
Definition at line 433 of file InferenceMethod.h.
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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
Definition at line 281 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 438 of file KLInferenceMethod.h.
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protectedinherited |
likelihood function to use
Definition at line 436 of file InferenceMethod.h.
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model selection parameters
Definition at line 473 of file SGObject.h.
mean vector of the approximation to the posterior Note that m_mu is also a variational parameter
Definition at line 406 of file KLInferenceMethod.h.
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protectedinherited |
The factor used to ensure kernel matrix to be positive definite
Definition at line 284 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 456 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 462 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 459 of file KLInferenceMethod.h.
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map for different parameter versions
Definition at line 479 of file SGObject.h.
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parameters
Definition at line 470 of file SGObject.h.
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protectedinherited |
Definition at line 432 of file KLInferenceMethod.h.
variational parameter sigma2 Note that sigma2 = diag(m_Sigma)
Definition at line 414 of file KLInferenceMethod.h.
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kernel scale
Definition at line 451 of file InferenceMethod.h.
covariance matrix of the approximation to the posterior
Definition at line 409 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 447 of file KLInferenceMethod.h.
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protectedinherited |
Definition at line 453 of file KLInferenceMethod.h.
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parallel
Definition at line 464 of file SGObject.h.
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version
Definition at line 467 of file SGObject.h.