The Laplace approximation FITC inference method with LBFGS class for regression and binary classification.
We use the Limited-memory BFGS (LBFGS) method to obtain the maximum of likelihood. Here L-BFGS only uses the last m (m<<n) function/gradient pairs to find the optimal pointer
For more details of LBFGS, see Nocedal, Jorge, and Stephen J. Wright. "Numerical Optimization 2nd." (2006), Pages 177-180.
This specific implementation was based on the idea from Murphy, Kevin P. "Machine learning: a probabilistic perspective." (2012), Pages 251-252.
在文件 SingleFITCLaplacianInferenceMethodWithLBFGS.h 第 58 行定义.
静态 Public 成员函数 | |
static CSingleFITCLaplacianInferenceMethod * | obtain_from_generic (CInferenceMethod *inference) |
Public 属性 | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
静态 Protected 成员函数 | |
static void * | get_derivative_helper (void *p) |
CSingleFITCLaplacianInferenceMethodWithLBFGS | ( | CKernel * | kernel, |
CFeatures * | features, | ||
CMeanFunction * | mean, | ||
CLabels * | labels, | ||
CLikelihoodModel * | model, | ||
CFeatures * | inducing_features | ||
) |
|
virtual |
|
inherited |
Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
在文件 SGObject.cpp 第 597 行定义.
check the bound constraint is vailid or not
bound | bound constrains of inducing features |
name | the name of the bound |
在文件 SingleSparseInferenceBase.cpp 第 202 行定义.
|
protectedvirtualinherited |
check whether features and inducing features are set
在文件 SparseInferenceBase.cpp 第 49 行定义.
|
protectedvirtualinherited |
check whether the provided kernel can compute the gradient wrt inducing features
Note that currently we check the name of the provided kernel to determine whether the kernel can compute the derivatives wrt inducing_features
The name of a supported Kernel must end with "SparseKernel"
在文件 SingleSparseInferenceBase.cpp 第 96 行定义.
|
protectedvirtualinherited |
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.
|
virtualinherited |
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
在文件 SGObject.cpp 第 714 行定义.
|
protectedvirtualinherited |
efficiently compute the matrix-vector product \(\Sigma \times al$\f, where \)$ is the FITC equivalent covariance n-by-n matrix (prior) of f_n
al | input vector |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 187 行定义.
efficiently compute the matrix-vector product \( \inv{\inv{W}+\Sigma} \times x$\f, where \)$ is the FITC equivalent covariance n-by-n matrix (prior) of f_n
x | input vector |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 172 行定义.
|
protectedvirtualinherited |
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.
|
virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 198 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt param when W has at least one negative element
res | raw derivative |
param | parameter |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 817 行定义.
|
virtualinherited |
whether enable to opitmize inducing features
is_optmization | enable optimization |
在文件 SingleSparseInferenceBase.cpp 第 257 行定义.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
在文件 SGObject.cpp 第 618 行定义.
get 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.
efficiently compute the Cholesky decomposition of inverse of the input matrix chol(inv(mtx))
mtx | symmetric positive definite matrix |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 214 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.
|
staticprotectedinherited |
pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter
在文件 InferenceMethod.cpp 第 255 行定义.
helper function to compute variables which are required to compute negative log marginal likelihood implicit derivatives
d | raw derivative |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 785 行定义.
|
protectedvirtualinherited |
compute variables which are required to compute negative log marginal likelihood derivatives wrt cov-like hyperparameter \(\theta\)
Note that scale, which is a hyperparameter in inference_method, is a cov-like hyperparameter hyperparameters in cov function are cov-like hyperparameters
ddiagKi | \(\textbf{diag}(\frac{\partial {\Sigma_{n}}}{\partial {\theta}})\) |
dKuui | \(\frac{\partial {\Sigma_{m}}}{\partial {\theta}}\) |
dKui | \(\frac{\partial {\Sigma_{m,n}}}{\partial {\theta}}\) |
v | auxiliary variable related to explicit derivative |
R | auxiliary variable related to explicit derivative |
在文件 SingleFITCLaplacianBase.cpp 第 132 行定义.
|
protectedvirtualinherited |
compute variables which are required to compute negative log marginal likelihood full derivatives wrt cov-like hyperparameter \(\theta\)
Note that scale, which is a hyperparameter in inference_method, is a cov-like hyperparameter hyperparameters in cov function are cov-like hyperparameters
ddiagKi | \(\textbf{diag}(\frac{\partial {\Sigma_{n}}}{\partial {\theta}})\) |
dKuui | \(\frac{\partial {\Sigma_{m}}}{\partial {\theta}}\) |
dKui | \(\frac{\partial {\Sigma_{m,n}}}{\partial {\theta}}\) |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 587 行定义.
helper function to compute variables which are required to compute negative log marginal likelihood derivatives wrt the diagonal part of cov-like hyperparameter \(\theta\)
在文件 SingleFITCLaplacianBase.cpp 第 74 行定义.
|
protectedvirtualinherited |
helper function to compute variables which are required to compute negative log marginal likelihood derivatives wrt cov-like hyperparameter \(\theta\)
Note that scale, which is a hyperparameter in inference_method, is a cov-like hyperparameter hyperparameters in cov function are cov-like hyperparameters what is more, derivative wrt inducing_noise will also use this function
dKuui | \(\frac{\partial {\Sigma_{m}}}{\partial {\theta}}\) |
v | auxiliary variable related to explicit derivative |
R | auxiliary variable related to explicit derivative |
在文件 SingleFITCLaplacianBase.cpp 第 87 行定义.
|
protectedvirtualinherited |
helper function to compute variables which are required to compute negative log marginal likelihood derivatives wrt inducing features
Note that the kernel must support to compute the derivatives wrt inducing features
BdK | auxiliary variable related to explicit derivative or implicit derivative |
param | parameter of given kernel |
在文件 SingleFITCLaplacianBase.cpp 第 216 行定义.
helper function to compute variables which are required to compute negative log marginal likelihood derivatives wrt mean \(\lambda\)
dmu | \(\frac{\partial {\mu_{n}}}{\partial {\lambda}}\) |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 770 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt inducing features (input) Note that in order to call this method, kernel must support FITC inference, which means derivatives wrt inducing features can be computed
Note that the kernel must support to compute the derivatives wrt inducing features
param | parameter of given kernel |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 827 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt inducing noise
param | parameter of given inference class |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 874 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class
param | parameter of CInferenceMethod class |
重载 CSingleSparseInferenceBase .
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 631 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt kernel's parameter
param | parameter of given kernel |
重载 CSingleSparseInferenceBase .
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 725 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt parameter of likelihood model
param | parameter of given likelihood model |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 690 行定义.
|
protectedvirtualinherited |
returns derivative of negative log marginal likelihood wrt mean function's parameter
param | parameter of given mean function |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 795 行定义.
get diagonal vector
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 160 行定义.
|
virtualinherited |
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
get the gradient
parameters | parameter's dictionary |
在文件 InferenceMethod.h 第 245 行定义.
|
virtualinherited |
|
virtualinherited |
get the noise for inducing points
在文件 SparseInferenceBase.cpp 第 119 行定义.
|
virtualinherited |
return what type of inference we are
重载 CSparseInferenceBase .
在文件 SingleFITCLaplacianInferenceMethod.h 第 94 行定义.
|
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 126 行定义.
|
virtualinherited |
|
virtualinherited |
get maximum for Brent's minimization method
在文件 SingleFITCLaplacianInferenceMethod.h 第 199 行定义.
|
virtualinherited |
get tolerance for Brent's minimization method
在文件 SingleFITCLaplacianInferenceMethod.h 第 187 行定义.
|
inherited |
|
inherited |
在文件 SGObject.cpp 第 498 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 522 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 535 行定义.
get the E matrix used for multi classification
在文件 InferenceMethod.cpp 第 72 行定义.
|
virtual |
returns the name of the inference method
重载 CSingleFITCLaplacianInferenceMethod .
在文件 SingleFITCLaplacianInferenceMethodWithLBFGS.h 第 87 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 SingleFITCLaplacianInferenceMethod.cpp 第 231 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 185 行定义.
|
virtualinherited |
get max Newton iterations
在文件 SingleFITCLaplacianInferenceMethod.h 第 175 行定义.
|
virtualinherited |
get tolerance for newton iterations
在文件 SingleFITCLaplacianInferenceMethod.h 第 163 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:
Note that the time complexity of this method is O(n^3), where n is the number of samples
\[ p(f_n|y) \approx q(f|y) = \mathcal{N}(f|\mu,\Sigma) \]
实现了 CSparseInferenceBase.
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 928 行定义.
returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:
\[ p(f_n|y) \approx q(f|y) = \mathcal{N}(f|\mu,\Sigma) \]
Mean vector \(\mu\) is evaluated using FITC inference and Newton's method.
实现了 CSparseInferenceBase.
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 903 行定义.
|
virtualinherited |
|
virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
在文件 SGObject.cpp 第 296 行定义.
|
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 |
在文件 SGObject.cpp 第 369 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 426 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 421 行定义.
|
staticinherited |
helper method used to specialize a base class instance
inference | inference method |
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 202 行定义.
|
virtualinherited |
opitmize inducing features
在文件 SingleSparseInferenceBase.cpp 第 262 行定义.
|
virtualinherited |
在文件 SGObject.cpp 第 262 行定义.
|
inherited |
prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 474 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 |
在文件 SGObject.cpp 第 314 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 436 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 431 行定义.
|
virtualinherited |
|
inherited |
在文件 SGObject.cpp 第 41 行定义.
|
inherited |
在文件 SGObject.cpp 第 46 行定义.
|
inherited |
在文件 SGObject.cpp 第 51 行定义.
|
inherited |
在文件 SGObject.cpp 第 56 行定义.
|
inherited |
在文件 SGObject.cpp 第 61 行定义.
|
inherited |
在文件 SGObject.cpp 第 66 行定义.
|
inherited |
在文件 SGObject.cpp 第 71 行定义.
|
inherited |
在文件 SGObject.cpp 第 76 行定义.
|
inherited |
在文件 SGObject.cpp 第 81 行定义.
|
inherited |
在文件 SGObject.cpp 第 86 行定义.
|
inherited |
在文件 SGObject.cpp 第 91 行定义.
|
inherited |
在文件 SGObject.cpp 第 96 行定义.
|
inherited |
在文件 SGObject.cpp 第 101 行定义.
|
inherited |
在文件 SGObject.cpp 第 106 行定义.
|
inherited |
在文件 SGObject.cpp 第 111 行定义.
|
inherited |
set generic type to T
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
|
virtualinherited |
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.
|
virtualinherited |
|
virtualinherited |
|
virtual |
set the lower bound of inducing features
bound | lower bound constrains of inducing features |
Note that if the length of the bound is 1, it means the bound constraint applies to each dimension of all inducing features
Note that if the length of the bound is greater than 1, it means each dimension of the bound constraint applies to the corresponding dimension of inducing features
在文件 SingleSparseInferenceBase.cpp 第 219 行定义.
|
virtualinherited |
set the max number of iterations used in optimization of inducing features
it | max number of iterations |
在文件 SingleSparseInferenceBase.cpp 第 230 行定义.
|
virtualinherited |
|
virtualinherited |
set maximum for Brent's minimization method
max | maximum for Brent's minimization method |
在文件 SingleFITCLaplacianInferenceMethod.h 第 205 行定义.
|
virtualinherited |
set tolerance for Brent's minimization method
tol | tolerance for Brent's minimization method |
在文件 SingleFITCLaplacianInferenceMethod.h 第 193 行定义.
|
virtualinherited |
set likelihood model
mod | model to set |
被 CKLInferenceMethod , 以及 CKLDualInferenceMethod 重载.
在文件 InferenceMethod.h 第 340 行定义.
|
virtualinherited |
set max Newton iterations
iter | max Newton iterations |
在文件 SingleFITCLaplacianInferenceMethod.h 第 181 行定义.
|
virtual |
|
virtualinherited |
set tolerance for newton iterations
tol | tolerance for newton iterations to set |
在文件 SingleFITCLaplacianInferenceMethod.h 第 169 行定义.
|
virtualinherited |
|
virtualinherited |
set the tolearance used in optimization of inducing features
tol | tolearance |
在文件 SingleSparseInferenceBase.cpp 第 235 行定义.
set the upper bound of inducing features
bound | upper bound constrains of inducing features |
Note that if the length of the bound is 1, it means the bound constraint applies to each dimension of all inducing features
Note that if the length of the bound is greater than 1, it means each dimension of the bound constraint applies to the corresponding dimension of inducing features
在文件 SingleSparseInferenceBase.cpp 第 224 行定义.
|
virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 192 行定义.
|
virtualinherited |
重载 CInferenceMethod .
在文件 SingleFITCLaplacianInferenceMethod.h 第 117 行定义.
|
virtualinherited |
whether combination of inference method and given likelihood function supports multiclass classification
在文件 InferenceMethod.h 第 378 行定义.
|
virtualinherited |
重载 CInferenceMethod .
在文件 SingleFITCLaplacianInferenceMethod.h 第 107 行定义.
|
inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 303 行定义.
|
virtualinherited |
update matrices except gradients
实现了 CSparseInferenceBase.
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 146 行定义.
|
protectedvirtual |
update alpha matrix
重载 CSingleFITCLaplacianInferenceMethod .
在文件 SingleFITCLaplacianInferenceMethodWithLBFGS.cpp 第 184 行定义.
|
protectedvirtualinherited |
update covariance matrix of the approximation to the posterior
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 267 行定义.
|
protectedvirtualinherited |
update cholesky matrix
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 458 行定义.
|
protectedvirtualinherited |
update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 555 行定义.
|
protectedvirtualinherited |
pre-compution for Newton's method
在文件 SingleFITCLaplacianInferenceMethod.cpp 第 271 行定义.
|
virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 248 行定义.
|
protectedvirtualinherited |
|
inherited |
io
在文件 SGObject.h 第 369 行定义.
Note that alpha is NOT post.alpha alpha and post.alpha are defined in infFITC.m and infFITC_Laplace.m
在文件 SingleFITCLaplacianBase.h 第 232 行定义.
alpha vector used in process mean calculation
在文件 InferenceMethod.h 第 475 行定义.
在文件 SingleFITCLaplacianBase.h 第 241 行定义.
Cholesky of inverse covariance of inducing features
在文件 SingleFITCLaplacianInferenceMethod.h 第 405 行定义.
second derivative of log likelihood with respect to function location
在文件 SingleFITCLaplacianInferenceMethod.h 第 381 行定义.
third derivative of log likelihood with respect to function location
在文件 SingleFITCLaplacianInferenceMethod.h 第 384 行定义.
derivative of negative log (approximated) marginal likelihood wrt f
在文件 SingleFITCLaplacianInferenceMethod.h 第 408 行定义.
d0 defined in infFITC_Laplace.m
在文件 SingleFITCLaplacianInferenceMethod.h 第 422 行定义.
derivative of log likelihood with respect to function location
在文件 SingleFITCLaplacianInferenceMethod.h 第 387 行定义.
the matrix used for multi classification
在文件 InferenceMethod.h 第 487 行定义.
|
protectedinherited |
features to use
在文件 InferenceMethod.h 第 469 行定义.
|
protectedinherited |
在文件 SingleSparseInferenceBase.h 第 219 行定义.
g defined in infFITC_Laplace.m
在文件 SingleFITCLaplacianInferenceMethod.h 第 411 行定义.
|
inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 384 行定义.
|
protectedinherited |
Whether gradients are updated
在文件 InferenceMethod.h 第 490 行定义.
|
inherited |
Hash of parameter values
在文件 SGObject.h 第 387 行定义.
|
protectedinherited |
tolearance used in optimizing inducing_features
在文件 SingleSparseInferenceBase.h 第 193 行定义.
inducing features for approximation
在文件 SparseInferenceBase.h 第 305 行定义.
|
protectedinherited |
max Newton's iterations
在文件 SingleFITCLaplacianInferenceMethod.h 第 396 行定义.
|
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.
|
protectedinherited |
labels of features
在文件 InferenceMethod.h 第 472 行定义.
|
protectedinherited |
在文件 SingleSparseInferenceBase.h 第 222 行定义.
|
protectedinherited |
noise of the inducing variables
在文件 SparseInferenceBase.h 第 308 行定义.
|
protectedinherited |
kernel scale
在文件 InferenceMethod.h 第 481 行定义.
lower bound of inducing features
在文件 SingleSparseInferenceBase.h 第 184 行定义.
|
protectedinherited |
max number of iterations
在文件 SingleSparseInferenceBase.h 第 190 行定义.
|
protectedinherited |
mean function
在文件 InferenceMethod.h 第 463 行定义.
|
protectedinherited |
likelihood function to use
在文件 InferenceMethod.h 第 466 行定义.
|
inherited |
model selection parameters
在文件 SGObject.h 第 381 行定义.
mean vector of the the posterior Gaussian distribution
在文件 SparseInferenceBase.h 第 320 行定义.
|
protectedinherited |
whether optimize inducing features
在文件 SingleSparseInferenceBase.h 第 196 行定义.
|
protectedinherited |
max iterations for Brent's minimization method
在文件 SingleFITCLaplacianInferenceMethod.h 第 402 行定义.
|
protectedinherited |
amount of tolerance for Brent's minimization method
在文件 SingleFITCLaplacianInferenceMethod.h 第 399 行定义.
|
inherited |
parameters
在文件 SGObject.h 第 378 行定义.
|
protectedinherited |
the negative log likelihood without constant terms of
\[ -log(p(f_n|y)) \]
used in Newton's method
在文件 SingleFITCLaplacianInferenceMethod.h 第 419 行定义.
Rvdd=W where W is defined in infFITC.m and Rvdd is defined in infFITC_Laplace.m Note that W is NOT the diagonal matrix
在文件 SingleFITCLaplacianBase.h 第 250 行定义.
covariance matrix of the the posterior Gaussian distribution
在文件 SparseInferenceBase.h 第 317 行定义.
square root of W
在文件 SingleFITCLaplacianInferenceMethod.h 第 378 行定义.
t=1/g_sn2 in regression, where g_sn2 is defined in infFITC.m t=W.*dd in Laplace for binary classification, where W and dd are defined in infFITC_Laplace.m
在文件 SingleFITCLaplacianBase.h 第 238 行定义.
|
protectedinherited |
amount of tolerance for Newton's iterations
在文件 SingleFITCLaplacianInferenceMethod.h 第 393 行定义.
upper bound of inducing features
在文件 SingleSparseInferenceBase.h 第 187 行定义.
在文件 SingleFITCLaplacianBase.h 第 253 行定义.
在文件 SingleFITCLaplacianBase.h 第 244 行定义.
noise matrix
在文件 SingleFITCLaplacianInferenceMethod.h 第 390 行定义.
|
protectedinherited |
whether W contains negative elements
在文件 SingleFITCLaplacianInferenceMethod.h 第 425 行定义.
|
inherited |
parallel
在文件 SGObject.h 第 372 行定义.
|
inherited |
version
在文件 SGObject.h 第 375 行定义.