Gaussian Kernel R6 class
Gaussian Kernel R6 class
Object of R6Class with methods for fitting GP model.
R6Class object.
GauPro::GauPro_kernel -> GauPro::GauPro_kernel_beta -> GauPro_kernel_Gaussian
k()Calculate covariance between two points
Gaussian$k(x, y = NULL, beta = self$beta, s2 = self$s2, params = NULL)
xvector.
yvector, optional. If excluded, find correlation of x with itself.
betaCorrelation parameters.
s2Variance parameter.
paramsparameters to use instead of beta and s2.
dC_dparams()Derivative of covariance with respect to parameters
Gaussian$dC_dparams(params = NULL, X, C_nonug, C, nug)
paramsKernel parameters
Xmatrix of points in rows
C_nonugCovariance without nugget added to diagonal
CCovariance with nugget
nugValue of nugget
C_dC_dparams()Calculate covariance matrix and its derivative with respect to parameters
Gaussian$C_dC_dparams(params = NULL, X, nug)
paramsKernel parameters
Xmatrix of points in rows
nugValue of nugget
dC_dx()Derivative of covariance with respect to X
Gaussian$dC_dx(XX, X, theta, beta = self$beta, s2 = self$s2)
XXmatrix of points
Xmatrix of points to take derivative with respect to
thetaCorrelation parameters
betalog of theta
s2Variance parameter
d2C_dx2()Second derivative of covariance with respect to X
Gaussian$d2C_dx2(XX, X, theta, beta = self$beta, s2 = self$s2)
XXmatrix of points
Xmatrix of points to take derivative with respect to
thetaCorrelation parameters
betalog of theta
s2Variance parameter
d2C_dudv()Second derivative of covariance with respect to X and XX each once.
Gaussian$d2C_dudv(XX, X, theta, beta = self$beta, s2 = self$s2)
XXmatrix of points
Xmatrix of points to take derivative with respect to
thetaCorrelation parameters
betalog of theta
s2Variance parameter
d2C_dudv_ueqvrows()Second derivative of covariance with respect to X and XX when they equal the same value
Gaussian$d2C_dudv_ueqvrows(XX, theta, beta = self$beta, s2 = self$s2)
XXmatrix of points
thetaCorrelation parameters
betalog of theta
s2Variance parameter
clone()The objects of this class are cloneable with this method.
Gaussian$clone(deep = FALSE)
deepWhether to make a deep clone.
# NOT RUN {
k1 <- Gaussian$new(beta=0)
# }
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