Index of types


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.