kernel_sum | R Documentation |
Gaussian Kernel R6 class
Gaussian Kernel R6 class
R6Class
object.
Object of R6Class
with methods for fitting GP model.
GauPro::GauPro_kernel
-> GauPro_kernel_sum
k1
kernel 1
k2
kernel 2
k1_param_length
param length of kernel 1
k2_param_length
param length of kernel 2
k1pl
param length of kernel 1
k2pl
param length of kernel 2
s2
variance
s2_est
Is s2 being estimated?
new()
Initialize kernel
kernel_sum$new(k1, k2, useC = TRUE)
k1
Kernel 1
k2
Kernel 2
useC
Should C code used? Not applicable for kernel sum.
k()
Calculate covariance between two points
kernel_sum$k(x, y = NULL, params, ...)
x
vector.
y
vector, optional. If excluded, find correlation of x with itself.
params
parameters to use instead of beta and s2.
...
Not used
param_optim_start()
Starting point for parameters for optimization
kernel_sum$param_optim_start(jitter = F, y)
jitter
Should there be a jitter?
y
Output
param_optim_start0()
Starting point for parameters for optimization
kernel_sum$param_optim_start0(jitter = F, y)
jitter
Should there be a jitter?
y
Output
param_optim_lower()
Lower bounds of parameters for optimization
kernel_sum$param_optim_lower()
param_optim_upper()
Upper bounds of parameters for optimization
kernel_sum$param_optim_upper()
set_params_from_optim()
Set parameters from optimization output
kernel_sum$set_params_from_optim(optim_out)
optim_out
Output from optimization
dC_dparams()
Derivative of covariance with respect to parameters
kernel_sum$dC_dparams(params = NULL, C, X, C_nonug, nug)
params
Kernel parameters
C
Covariance with nugget
X
matrix of points in rows
C_nonug
Covariance without nugget added to diagonal
nug
Value of nugget
C_dC_dparams()
Calculate covariance matrix and its derivative with respect to parameters
kernel_sum$C_dC_dparams(params = NULL, X, nug)
params
Kernel parameters
X
matrix of points in rows
nug
Value of nugget
dC_dx()
Derivative of covariance with respect to X
kernel_sum$dC_dx(XX, X)
XX
matrix of points
X
matrix of points to take derivative with respect to
s2_from_params()
Get s2 from params vector
kernel_sum$s2_from_params(params)
params
parameter vector
s2_est
Is s2 being estimated?
print()
Print this object
kernel_sum$print()
clone()
The objects of this class are cloneable with this method.
kernel_sum$clone(deep = FALSE)
deep
Whether to make a deep clone.
k1 <- Exponential$new(beta=1)
k2 <- Matern32$new(beta=2)
k <- k1 + k2
k$k(matrix(c(2,1), ncol=1))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.