GauPro_Gauss | R Documentation |
Corr Gauss GP using inherited optim
Corr Gauss GP using inherited optim
R6Class
object.
Object of R6Class
with methods for fitting GP model.
GauPro::GauPro
-> GauPro_Gauss
corr
Name of correlation
theta
Correlation parameters
theta_length
Length of theta
theta_map
Map for theta
theta_short
Short vector for theta
separable
Are the dimensions separable?
GauPro::GauPro$cool1Dplot()
GauPro::GauPro$deviance_searchnug()
GauPro::GauPro$fit()
GauPro::GauPro$grad_norm()
GauPro::GauPro$initialize_GauPr()
GauPro::GauPro$loglikelihood()
GauPro::GauPro$nugget_update()
GauPro::GauPro$optim()
GauPro::GauPro$optimRestart()
GauPro::GauPro$plot()
GauPro::GauPro$plot1D()
GauPro::GauPro$plot2D()
GauPro::GauPro$pred()
GauPro::GauPro$pred_LOO()
GauPro::GauPro$pred_mean()
GauPro::GauPro$pred_meanC()
GauPro::GauPro$pred_one_matrix()
GauPro::GauPro$pred_var()
GauPro::GauPro$predict()
GauPro::GauPro$sample()
GauPro::GauPro$update()
GauPro::GauPro$update_K_and_estimates()
GauPro::GauPro$update_corrparams()
GauPro::GauPro$update_data()
GauPro::GauPro$update_nugget()
new()
Create GauPro object
GauPro_Gauss$new( X, Z, verbose = 0, separable = T, useC = F, useGrad = T, parallel = FALSE, nug = 1e-06, nug.min = 1e-08, nug.est = T, param.est = T, theta = NULL, theta_short = NULL, theta_map = NULL, ... )
X
Matrix whose rows are the input points
Z
Output points corresponding to X
verbose
Amount of stuff to print. 0 is little, 2 is a lot.
separable
Are dimensions separable?
useC
Should C code be used when possible? Should be faster.
useGrad
Should the gradient be used?
parallel
Should code be run in parallel? Make optimization faster but uses more computer resources.
nug
Value for the nugget. The starting value if estimating it.
nug.min
Minimum allowable value for the nugget.
nug.est
Should the nugget be estimated?
param.est
Should the kernel parameters be estimated?
theta
Correlation parameters
theta_short
Correlation parameters, not recommended
theta_map
Correlation parameters, not recommended
...
Not used
corr_func()
Correlation function
GauPro_Gauss$corr_func(x, x2 = NULL, theta = self$theta)
x
First point
x2
Second point
theta
Correlation parameter
deviance_theta()
Calculate deviance
GauPro_Gauss$deviance_theta(theta)
theta
Correlation parameter
deviance_theta_log()
Calculate deviance
GauPro_Gauss$deviance_theta_log(beta)
beta
Correlation parameter on log scale
deviance()
Calculate deviance
GauPro_Gauss$deviance(theta = self$theta, nug = self$nug)
theta
Correlation parameter
nug
Nugget
deviance_grad()
Calculate deviance gradient
GauPro_Gauss$deviance_grad( theta = NULL, nug = self$nug, joint = NULL, overwhat = if (self$nug.est) "joint" else "theta" )
theta
Correlation parameter
nug
Nugget
joint
Calculate over theta and nug at same time?
overwhat
Calculate over theta and nug at same time?
deviance_fngr()
Calculate deviance and gradient at same time
GauPro_Gauss$deviance_fngr( theta = NULL, nug = NULL, overwhat = if (self$nug.est) "joint" else "theta" )
theta
Correlation parameter
nug
Nugget
overwhat
Calculate over theta and nug at same time?
joint
Calculate over theta and nug at same time?
deviance_log()
Calculate deviance gradient
GauPro_Gauss$deviance_log(beta = NULL, nug = self$nug, joint = NULL)
beta
Correlation parameter on log scale
nug
Nugget
joint
Calculate over theta and nug at same time?
deviance_log2()
Calculate deviance on log scale
GauPro_Gauss$deviance_log2(beta = NULL, lognug = NULL, joint = NULL)
beta
Correlation parameter on log scale
lognug
Log of nugget
joint
Calculate over theta and nug at same time?
deviance_log_grad()
Calculate deviance gradient on log scale
GauPro_Gauss$deviance_log_grad( beta = NULL, nug = self$nug, joint = NULL, overwhat = if (self$nug.est) "joint" else "theta" )
beta
Correlation parameter
nug
Nugget
joint
Calculate over theta and nug at same time?
overwhat
Calculate over theta and nug at same time?
deviance_log2_grad()
Calculate deviance gradient on log scale
GauPro_Gauss$deviance_log2_grad( beta = NULL, lognug = NULL, joint = NULL, overwhat = if (self$nug.est) "joint" else "theta" )
beta
Correlation parameter
lognug
Log of nugget
joint
Calculate over theta and nug at same time?
overwhat
Calculate over theta and nug at same time?
deviance_log2_fngr()
Calculate deviance and gradient on log scale
GauPro_Gauss$deviance_log2_fngr( beta = NULL, lognug = NULL, joint = NULL, overwhat = if (self$nug.est) "joint" else "theta" )
beta
Correlation parameter
lognug
Log of nugget
joint
Calculate over theta and nug at same time?
overwhat
Calculate over theta and nug at same time?
get_optim_functions()
Get optimization functions
GauPro_Gauss$get_optim_functions(param_update, nug.update)
param_update
Should the parameters be updated?
nug.update
Should the nugget be updated?
param_optim_lower()
Lower bound of params
GauPro_Gauss$param_optim_lower()
param_optim_upper()
Upper bound of params
GauPro_Gauss$param_optim_upper()
param_optim_start()
Start value of params for optim
GauPro_Gauss$param_optim_start()
param_optim_start0()
Start value of params for optim
GauPro_Gauss$param_optim_start0()
param_optim_jitter()
Jitter value of params for optim
GauPro_Gauss$param_optim_jitter(param_value)
param_value
param value to add jitter to
update_params()
Update value of params after optim
GauPro_Gauss$update_params(restarts, param_update, nug.update)
restarts
Number of restarts
param_update
Are the params being updated?
nug.update
Is the nugget being updated?
grad()
Calculate the gradient
GauPro_Gauss$grad(XX)
XX
Points to calculate grad at
grad_dist()
Calculate the gradient distribution
GauPro_Gauss$grad_dist(XX)
XX
Points to calculate grad at
hessian()
Calculate the hessian
GauPro_Gauss$hessian(XX, useC = self$useC)
XX
Points to calculate grad at
useC
Should C code be used to speed up?
print()
Print this object
GauPro_Gauss$print()
clone()
The objects of this class are cloneable with this method.
GauPro_Gauss$clone(deep = FALSE)
deep
Whether to make a deep clone.
n <- 12
x <- matrix(seq(0,1,length.out = n), ncol=1)
y <- sin(2*pi*x) + rnorm(n,0,1e-1)
gp <- GauPro_Gauss$new(X=x, Z=y, parallel=FALSE)
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