SHOGUN
4.1.0
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The Fully Independent Conditional Training inference base class.
For more details, see Quiñonero-Candela, Joaquin, and Carl Edward Rasmussen. "A unifying view of sparse approximate Gaussian process regression." The Journal of Machine Learning Research 6 (2005): 1939-1959.
The key idea of Sparse inference is to use the following kernel matrix \(\Sigma_{fitc}\) to approximate a kernel matrix, \(\Sigma_{N}\) derived from a GP prior.
\[ *\Sigma_{Sparse}=\textbf{diag}(\Sigma_{N}-\Phi)+\Phi *\]
where \(\Phi=\Sigma_{NM}\Sigma_{M}^{-1}\Sigma_{MN}\) \(\Sigma_{N}\) is the kernel matrix on features \(\Sigma_{M}\) is the kernel matrix on inducing points \(\Sigma_{NM}=\Sigma_{MN}^{T}\) is the kernel matrix between features and inducing features
Note that the number of inducing points (m) is usually far less than the number of input points (n). (the time complexity is computed based on the assumption m < n) The idea of Sparse approximation is to use a lower-ranked matrix plus a diagonal matrix to approximate the full kernel matrix. The time complexity of the main inference process can be reduced from O(n^3) to O(m^2*n).
Since we use \(\Sigma_{Sparse}\) to approximate \(\Sigma_{N}\), the (approximated) negative log marginal likelihood are computed based on \(\Sigma_{Sparse}\).
在文件 SparseInferenceBase.h 第 72 行定义.
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 |
Protected 成员函数 | |
virtual void | convert_features () |
virtual void | check_features () |
virtual void | check_members () const |
virtual void | update_train_kernel () |
virtual SGVector< float64_t > | get_derivative_wrt_inference_method (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_likelihood_model (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_kernel (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_mean (const TParameter *param)=0 |
virtual SGVector< float64_t > | get_derivative_wrt_inducing_noise (const TParameter *param)=0 |
virtual void | update_alpha ()=0 |
virtual void | update_chol ()=0 |
virtual void | update_deriv ()=0 |
virtual void | compute_gradient () |
virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
静态 Protected 成员函数 | |
static void * | get_derivative_helper (void *p) |
Protected 属性 | |
SGMatrix< float64_t > | m_inducing_features |
float64_t | m_log_ind_noise |
SGMatrix< float64_t > | m_kuu |
SGMatrix< float64_t > | m_ktru |
SGMatrix< float64_t > | m_Sigma |
SGVector< float64_t > | m_mu |
SGVector< float64_t > | m_ktrtr_diag |
CKernel * | m_kernel |
CMeanFunction * | m_mean |
CLikelihoodModel * | m_model |
CFeatures * | m_features |
CLabels * | m_labels |
SGVector< float64_t > | m_alpha |
SGMatrix< float64_t > | m_L |
float64_t | m_log_scale |
SGMatrix< float64_t > | m_ktrtr |
SGMatrix< float64_t > | m_E |
bool | m_gradient_update |
default constructor
在文件 SparseInferenceBase.cpp 第 44 行定义.
CSparseInferenceBase | ( | CKernel * | kernel, |
CFeatures * | features, | ||
CMeanFunction * | mean, | ||
CLabels * | labels, | ||
CLikelihoodModel * | model, | ||
CFeatures * | inducing_features | ||
) |
constructor
kernel | covariance function |
features | features to use in inference |
mean | mean function |
labels | labels of the features |
model | likelihood model to use |
inducing_features | features to use |
在文件 SparseInferenceBase.cpp 第 91 行定义.
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virtual |
在文件 SparseInferenceBase.cpp 第 124 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 1244 行定义.
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protectedvirtual |
check whether features and inducing features are set
在文件 SparseInferenceBase.cpp 第 49 行定义.
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protectedvirtual |
check if members of object are valid for inference
重载 CInferenceMethod .
被 CFITCInferenceMethod , 以及 CSparseVGInferenceMethod 重载.
在文件 SparseInferenceBase.cpp 第 128 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 1361 行定义.
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protectedvirtualinherited |
update gradients
被 CKLInferenceMethod, CExactInferenceMethod, CEPInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod, CFITCInferenceMethod , 以及 CLaplacianInferenceBase 重载.
在文件 InferenceMethod.cpp 第 330 行定义.
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protectedvirtual |
convert inducing features and features to the same represention
Note that these two kinds of features can be different types. The reasons are listed below.
在文件 SparseInferenceBase.cpp 第 54 行定义.
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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 1265 行定义.
get alpha vector
\[ \mu = K\alpha \]
where \(\mu\) is the mean and \(K\) is the prior covariance matrix.
在文件 SparseInferenceBase.cpp 第 136 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.
在文件 SparseInferenceBase.cpp 第 145 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.
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pure virtual |
returns derivative of negative log marginal likelihood wrt inducing features (input) Note that in order to call this method, kernel must support Sparse inference
在 CSingleFITCLaplacianInferenceMethod, CSingleFITCLaplacianBase, CSingleSparseInferenceBase , 以及 CSparseVGInferenceMethod 内被实现.
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protectedpure virtual |
returns derivative of negative log marginal likelihood wrt inducing noise (noise from inducing features) parameter
param | parameter of given SparseInferenceBase class |
In order to enforce symmetrc positive definiteness of the kernel matrix on inducing points, \(\Sigma_{M}\), the following ridge trick is used since the matrix is learned from data.
\[ \Sigma_{M'}=\Sigma_{M}+\lambda*I \]
where \(\lambda \ge 0\) is the inducing noise.
In practice, we use the corrected matrix, {M'} in the following approximation.
\[ *\Sigma_{Sparse}=\textbf{diag}(\Sigma_{N}-\Phi)+\Phi *\]
where \(\Phi=\Sigma_{NM}\Sigma_{M'}^{-1}\Sigma_{MN}\)
在 CSingleFITCLaplacianInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianBase , 以及 CSingleSparseInferenceBase 内被实现.
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protectedpure virtual |
returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
param | parameter of CInferenceMethod class |
实现了 CInferenceMethod.
在 CSingleFITCLaplacianInferenceMethod , 以及 CSingleSparseInferenceBase 内被实现.
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protectedpure virtual |
returns derivative of negative log marginal likelihood wrt kernel's parameter
param | parameter of given kernel |
实现了 CInferenceMethod.
在 CSingleFITCLaplacianInferenceMethod , 以及 CSingleSparseInferenceBase 内被实现.
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protectedpure virtual |
returns derivative of negative log marginal likelihood wrt parameter of likelihood model
param | parameter of given likelihood model |
实现了 CInferenceMethod.
在 CSingleFITCLaplacianInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod , 以及 CSparseVGInferenceMethod 内被实现.
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protectedpure virtual |
returns derivative of negative log marginal likelihood wrt mean function's parameter
param | parameter of given mean function |
实现了 CInferenceMethod.
在 CSingleFITCLaplacianInferenceMethod, CSparseVGInferenceMethod , 以及 CSingleFITCLaplacianBase 内被实现.
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virtualinherited |
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inherited |
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inherited |
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virtualinherited |
get the gradient
parameters | parameter's dictionary |
在文件 InferenceMethod.h 第 245 行定义.
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get the noise for inducing points
在文件 SparseInferenceBase.cpp 第 119 行定义.
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return what type of inference we are
重载 CInferenceMethod .
被 CSingleFITCLaplacianInferenceMethod, CFITCInferenceMethod , 以及 CSparseVGInferenceMethod 重载.
在文件 SparseInferenceBase.h 第 97 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 1136 行定义.
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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 1160 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 1173 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
实现了 CSGObject.
被 CSingleFITCLaplacianBase, CSingleFITCLaplacianInferenceMethod, CSingleFITCLaplacianInferenceMethodWithLBFGS, CFITCInferenceMethod, CSparseVGInferenceMethod , 以及 CSingleSparseInferenceBase 重载.
在文件 SparseInferenceBase.h 第 103 行定义.
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pure virtualinherited |
get negative log marginal likelihood
\[ -log(p(y|X, \theta)) \]
where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.
在 CSingleFITCLaplacianInferenceMethod, CMultiLaplacianInferenceMethod, CKLInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CEPInferenceMethod , 以及 CSingleLaplacianInferenceMethod 内被实现.
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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.
returns covariance matrix \(\Sigma\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|y) \approx q(f|y) = \mathcal{N}(\mu,\Sigma) \]
in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.
实现了 CInferenceMethod.
在 CFITCInferenceMethod, CSparseVGInferenceMethod , 以及 CSingleFITCLaplacianInferenceMethod 内被实现.
returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f|y) \approx q(f|y) = \mathcal{N}(\mu,\Sigma) \]
in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.
实现了 CInferenceMethod.
在 CFITCInferenceMethod, CSparseVGInferenceMethod , 以及 CSingleFITCLaplacianInferenceMethod 内被实现.
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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 |
在文件 SGObject.cpp 第 298 行定义.
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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 705 行定义.
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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 546 行定义.
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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 375 行定义.
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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 1063 行定义.
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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 1058 行定义.
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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 743 行定义.
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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 950 行定义.
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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 890 行定义.
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virtualinherited |
在文件 SGObject.cpp 第 264 行定义.
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1112 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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
在文件 SGObject.cpp 第 316 行定义.
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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 1073 行定义.
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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 1068 行定义.
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inherited |
在文件 SGObject.cpp 第 42 行定义.
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在文件 SGObject.cpp 第 47 行定义.
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在文件 SGObject.cpp 第 52 行定义.
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在文件 SGObject.cpp 第 57 行定义.
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在文件 SGObject.cpp 第 62 行定义.
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在文件 SGObject.cpp 第 67 行定义.
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在文件 SGObject.cpp 第 72 行定义.
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在文件 SGObject.cpp 第 77 行定义.
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在文件 SGObject.cpp 第 82 行定义.
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在文件 SGObject.cpp 第 87 行定义.
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在文件 SGObject.cpp 第 92 行定义.
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在文件 SGObject.cpp 第 97 行定义.
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在文件 SGObject.cpp 第 102 行定义.
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在文件 SGObject.cpp 第 107 行定义.
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在文件 SGObject.cpp 第 112 行定义.
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set generic type to T
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set the noise for inducing points
noise | noise for inducing points |
The noise is used to enfore the kernel matrix about the inducing points are positive definite
在文件 SparseInferenceBase.cpp 第 113 行定义.
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set likelihood model
mod | model to set |
被 CKLInferenceMethod , 以及 CKLDualInferenceMethod 重载.
在文件 InferenceMethod.h 第 340 行定义.
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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.
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whether combination of inference method and given likelihood function supports binary classification
被 CEPInferenceMethod, CKLInferenceMethod, CSingleFITCLaplacianInferenceMethod , 以及 CSingleLaplacianInferenceMethod 重载.
在文件 InferenceMethod.h 第 371 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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whether combination of inference method and given likelihood function supports regression
被 CExactInferenceMethod, CKLInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, CSingleFITCLaplacianInferenceMethod , 以及 CSingleLaplacianInferenceMethod 重载.
在文件 InferenceMethod.h 第 364 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 305 行定义.
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update all matrices
重载 CInferenceMethod .
在 CSingleFITCLaplacianInferenceMethod, CFITCInferenceMethod , 以及 CSparseVGInferenceMethod 内被实现.
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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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protectedpure virtualinherited |
update cholesky matrix
在 CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod , 以及 CKLLowerTriangularInferenceMethod 内被实现.
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protectedpure virtualinherited |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
在 CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod , 以及 CKLLowerTriangularInferenceMethod 内被实现.
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Updates the hash of current parameter combination
在文件 SGObject.cpp 第 250 行定义.
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io
在文件 SGObject.h 第 496 行定义.
alpha vector used in process mean calculation
在文件 InferenceMethod.h 第 475 行定义.
the matrix used for multi classification
在文件 InferenceMethod.h 第 487 行定义.
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features to use
在文件 InferenceMethod.h 第 469 行定义.
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parameters wrt which we can compute gradients
在文件 SGObject.h 第 511 行定义.
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protectedinherited |
Whether gradients are updated
在文件 InferenceMethod.h 第 490 行定义.
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Hash of parameter values
在文件 SGObject.h 第 517 行定义.
inducing features for approximation
在文件 SparseInferenceBase.h 第 305 行定义.
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protectedinherited |
covariance function
在文件 InferenceMethod.h 第 460 行定义.
kernel matrix from features (non-scalled by inference scalling)
在文件 InferenceMethod.h 第 484 行定义.
diagonal elements of kernel matrix m_ktrtr
在文件 SparseInferenceBase.h 第 323 行定义.
covariance matrix of inducing features and training features
在文件 SparseInferenceBase.h 第 314 行定义.
covariance matrix of inducing features
在文件 SparseInferenceBase.h 第 311 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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protected |
noise of the inducing variables
在文件 SparseInferenceBase.h 第 308 行定义.
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protectedinherited |
kernel scale
在文件 InferenceMethod.h 第 481 行定义.
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mean function
在文件 InferenceMethod.h 第 463 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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model selection parameters
在文件 SGObject.h 第 508 行定义.
mean vector of the the posterior Gaussian distribution
在文件 SparseInferenceBase.h 第 320 行定义.
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map for different parameter versions
在文件 SGObject.h 第 514 行定义.
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inherited |
parameters
在文件 SGObject.h 第 505 行定义.
covariance matrix of the the posterior Gaussian distribution
在文件 SparseInferenceBase.h 第 317 行定义.
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inherited |
parallel
在文件 SGObject.h 第 499 行定义.
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inherited |
version
在文件 SGObject.h 第 502 行定义.