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