B | |
Block_diag |
Type of block diagonal matrices
|
C | |
Co_variance_predictor [Interfaces.Sigs.Eval] |
Module for making (co-)variance predictions
|
Cov_const |
Covariance of a constant function
|
Cov_lin_ard |
Covariance of linear functions with Automatic Relevance Determination
|
Cov_lin_one |
Covariance of linear functions with one hyperparameter
|
Cov_sampler [Interfaces.Sigs.Eval] |
Module for sampling (multiple) points from the posterior distribution
accounting for their covariance
|
Cov_se_fat |
Feature-rich ("fat") squared exponential covariance
|
Cov_se_iso |
Isotropic squared exponential covariance
|
Covariances [Interfaces.Sigs.Eval] |
Posterior covariances
|
D | |
Deriv [Cov_se_fat] | |
Deriv [Cov_se_iso] | |
Deriv [Cov_lin_one] | |
Deriv [Cov_lin_ard] | |
Deriv [Cov_const] | |
Deriv [Interfaces.Sigs.Deriv] |
Sub-modules for learning with derivatives.
|
Dim_hyper [Cov_se_fat] | |
E | |
Eval [Cov_se_fat] | |
Eval [Cov_se_iso] | |
Eval [Cov_lin_one] | |
Eval [Cov_lin_ard] | |
Eval [Cov_const] | |
Eval [Interfaces.Sigs.Optimizer] |
Sub-modules for learning without derivatives.
|
Eval [Interfaces.Sigs.Deriv] |
Sub-modules for learning without derivatives.
|
Eval [Interfaces.Specs.Optimizer] |
Derivatives always require evaluation functions
|
Eval [Interfaces.Specs.Deriv] |
Derivatives always require evaluation functions
|
F | |
FIC [Fitc_gp.Make_deriv] | |
FIC [Fitc_gp.Make] | |
FITC [Fitc_gp.Make_deriv] | |
FITC [Fitc_gp.Make] | |
Fitc_gp |
Evaluation
|
G | |
Gpr_utils | |
Gsl [Interfaces.Sigs.Deriv.Deriv.Optim] |
Optimization with the GNU Scientific library (GSL)
|
H | |
Hyper [Interfaces.Specs.Deriv] |
Hyper parameters that have derivatives
|
Hyper_repr [Cov_se_fat] | |
I | |
Inducing [Interfaces.Sigs.Deriv.Deriv] |
Module for inducing inputs with derivatives
|
Inducing [Interfaces.Sigs.Eval] |
Evaluating inducing inputs
|
Inducing [Interfaces.Specs.Deriv] |
Derivatives of the covariance matrix of inducing inputs
|
Inducing [Interfaces.Specs.Eval] |
Signature for evaluating inducing inputs
|
Inducing_hyper [Cov_se_fat] | |
Input [Interfaces.Sigs.Eval] |
Evaluating single inputs
|
Input [Interfaces.Specs.Optimizer] | |
Input [Interfaces.Specs.Eval] |
Signature for evaluating single inputs
|
Inputs [Interfaces.Sigs.Deriv.Deriv] |
Module for inputs with derivatives
|
Inputs [Interfaces.Sigs.Eval] |
Evaluating (multiple) inputs
|
Inputs [Interfaces.Specs.Optimizer] | |
Inputs [Interfaces.Specs.Deriv] |
Derivatives of the (cross-) covariance matrix of inputs.
|
Inputs [Interfaces.Specs.Eval] |
Signature for evaluating multiple inputs
|
Int_vec [Gpr_utils] | |
Interfaces |
Representations of (sparse) derivative matrices
|
K | |
Kernel [Interfaces.Specs.Eval] |
Kernel used for evaluation
|
M | |
Make [Fitc_gp] | |
Make_FIC [Fitc_gp] | |
Make_FIC_deriv [Fitc_gp] | |
Make_FITC [Fitc_gp] | |
Make_FITC_deriv [Fitc_gp] | |
Make_deriv [Fitc_gp] | |
Make_variational_FIC [Fitc_gp] | |
Make_variational_FIC_deriv [Fitc_gp] | |
Make_variational_FITC [Fitc_gp] | |
Make_variational_FITC_deriv [Fitc_gp] | |
Mean [Interfaces.Sigs.Eval] |
Posterior mean for a single input
|
Mean_predictor [Interfaces.Sigs.Eval] |
Module for making mean predictions
|
Means [Interfaces.Sigs.Eval] |
Posterior means for (multiple) inputs
|
Model [Interfaces.Sigs.Deriv.Deriv] |
(Untrained) model with derivative information
|
Model [Interfaces.Sigs.Eval] |
(Untrained) model - does not require targets
|
O | |
Optim [Interfaces.Sigs.Deriv.Deriv] |
Optimization module for evidence maximization
|
Optimizer [Interfaces.Sigs.Optimizer] |
Sub-modules for global optimization.
|
P | |
Params [Cov_se_fat] | |
Params [Cov_se_iso] | |
Params [Cov_lin_one] | |
Params [Cov_lin_ard] | |
Params [Cov_const] | |
Proj_hyper [Cov_se_fat] | |
S | |
SGD [Interfaces.Sigs.Deriv.Deriv.Optim] | |
SMD [Interfaces.Sigs.Deriv.Deriv.Optim] | |
Sampler [Interfaces.Sigs.Eval] |
Module for sampling single points from the posterior distribution
|
Sigs [Interfaces] |
Signatures for learning sparse Gaussian processes with inducing inputs
|
Sparse_indices [Interfaces] |
Representation of indices into sparse matrices
|
Spec [Interfaces.Sigs.Optimizer.Optimizer] | |
Spec [Interfaces.Sigs.Deriv.Deriv] |
Specification of covariance function derivatives
|
Spec [Interfaces.Sigs.Eval] |
Specification of covariance function
|
Specs [Interfaces] |
Specifications of covariance functions (= kernels) and their derivatives
|
Stats [Interfaces.Sigs.Eval] |
Statistics derived from trained models
|
T | |
Test [Interfaces.Sigs.Deriv.Deriv] |
Module for testing derivative code
|
Trained [Interfaces.Sigs.Deriv.Deriv] |
Trained model with derivative information
|
Trained [Interfaces.Sigs.Eval] |
Trained model - requires targets
|
V | |
Var [Interfaces.Specs.Optimizer] |
Input parameters that have derivatives
|
Variance [Interfaces.Sigs.Eval] |
Posterior variance for a single input
|
Variances [Interfaces.Sigs.Eval] |
Posterior variances for (multiple) inputs
|
Variational_FIC [Fitc_gp.Make_deriv] | |
Variational_FIC [Fitc_gp.Make] | |
Variational_FITC [Fitc_gp.Make_deriv] | |
Variational_FITC [Fitc_gp.Make] | |
Version |