C | |
co_variance_coeffs [Interfaces.Sigs.Eval.Model] |
Type of covariance coefficients
|
common_mat_deriv [Interfaces] |
Derivative representations for both symmetric and unsymmetric matrices.
|
cross [Interfaces.Specs.Deriv.Inputs] |
Representation of precomputed data for calculating the
derivative of the cross-covariance matrix between inputs
and inducing inputs.
|
D | |
diag [Interfaces.Specs.Deriv.Inputs] |
Representation of precomputed data for calculating the
derivative of the diagonal of the covariance matrix of
inputs.
|
diag_deriv [Interfaces] |
Derivatives of diagonal matrices.
|
F | |
fast_float_ref [Gpr_utils] | |
H | |
hyper_t [Interfaces.Sigs.Deriv.Deriv.Trained] |
Type of trained models for general hyper parameters
|
hyper_t [Interfaces.Sigs.Deriv.Deriv.Model] |
Type of models for general hyper parameters
|
I | |
inducing_hyper [Cov_se_iso] | |
M | |
mat_deriv [Interfaces] |
Only general matrices support sparse column representations.
|
P | |
params [Cov_se_fat.Params] | |
params [Interfaces.Specs.Kernel] |
Type of kernel parameters
|
S | |
symm_mat_deriv [Interfaces] |
Only symmetric (square) matrices support diagonal vectors and
diagonal constants as derivatives.
|
T | |
t [Cov_se_fat.Hyper_repr] | |
t [Cov_se_fat.Inducing_hyper] | |
t [Cov_se_fat.Dim_hyper] | |
t [Cov_se_fat.Proj_hyper] | |
t [Cov_se_fat.Params] | |
t [Cov_se_iso.Params] | |
t [Cov_lin_one.Params] | |
t [Cov_lin_ard.Params] | |
t [Cov_const.Params] | |
t [Block_diag] |
Type of block diagonal matrices
|
t [Interfaces.Sigs.Optimizer.Optimizer] | |
t [Interfaces.Sigs.Deriv.Deriv.Optim.SMD] | |
t [Interfaces.Sigs.Deriv.Deriv.Optim.SGD] | |
t [Interfaces.Sigs.Deriv.Deriv.Trained] |
Type of trained models with derivatives
|
t [Interfaces.Sigs.Deriv.Deriv.Model] |
Type of models with derivatives
|
t [Interfaces.Sigs.Deriv.Deriv.Inputs] |
Type of inputs with derivatives
|
t [Interfaces.Sigs.Deriv.Deriv.Inducing] |
Type of inducing inputs with derivatives
|
t [Interfaces.Sigs.Eval.Cov_sampler] |
Type of covariance sampler
|
t [Interfaces.Sigs.Eval.Sampler] |
Type of sampler
|
t [Interfaces.Sigs.Eval.Covariances] |
Type of covariances
|
t [Interfaces.Sigs.Eval.Variances] |
Type of variances
|
t [Interfaces.Sigs.Eval.Variance] |
Type of variance
|
t [Interfaces.Sigs.Eval.Co_variance_predictor] |
Type of (co-)variance predictor
|
t [Interfaces.Sigs.Eval.Means] |
Type of means
|
t [Interfaces.Sigs.Eval.Mean] |
Type of mean
|
t [Interfaces.Sigs.Eval.Mean_predictor] |
Type of mean predictors
|
t [Interfaces.Sigs.Eval.Stats] |
Type of full statistics
|
t [Interfaces.Sigs.Eval.Trained] |
Type of trained models
|
t [Interfaces.Sigs.Eval.Model] |
Type of models
|
t [Interfaces.Sigs.Eval.Inputs] |
Type of (multiple) inputs
|
t [Interfaces.Sigs.Eval.Input] |
Type of single input
|
t [Interfaces.Sigs.Eval.Inducing] |
Type of inducing inputs
|
t [Interfaces.Specs.Optimizer.Var] |
Type of input parameter
|
t [Interfaces.Specs.Deriv.Hyper] |
Type of hyper parameter
|
t [Interfaces.Specs.Eval.Inputs] |
Type of input points
|
t [Interfaces.Specs.Eval.Input] |
Type of input point
|
t [Interfaces.Specs.Eval.Inducing] | |
t [Interfaces.Specs.Kernel] |
Type of kernel
|
t [Gpr_utils.Int_vec] | |
U | |
upper [Interfaces.Specs.Deriv.Inducing] |
Representation of precomputed data for calculating the
upper triangle of the derivative of the covariance matrix
of inducing inputs.
|