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#' Triangle Kernel R6 class
#'
#' @docType class
#' @importFrom R6 R6Class
#' @export
#' @useDynLib GauPro, .registration = TRUE
#' @importFrom Rcpp evalCpp
#' @importFrom stats optim
# @keywords data, kriging, Gaussian process, regression
#' @return Object of \code{\link{R6Class}} with methods for fitting GP model.
#' @format \code{\link{R6Class}} object.
#' @examples
#' k1 <- Triangle$new(beta=0)
#' plot(k1)
#'
#' n <- 12
#' x <- matrix(seq(0,1,length.out = n), ncol=1)
#' y <- sin(2*pi*x) + rnorm(n,0,1e-1)
#' gp <- GauPro_kernel_model$new(X=x, Z=y, kernel=Triangle$new(1),
#' parallel=FALSE)
#' gp$predict(.454)
#' gp$plot1D()
#' gp$cool1Dplot()
Triangle <- R6::R6Class(
classname = "GauPro_kernel_Triangle",
inherit = GauPro_kernel_beta,
public = list(
#' @description Calculate covariance between two points
#' @param x vector.
#' @param y vector, optional. If excluded, find correlation
#' of x with itself.
#' @param beta Correlation parameters.
#' @param s2 Variance parameter.
#' @param params parameters to use instead of beta and s2.
k = function(x, y=NULL, beta=self$beta, s2=self$s2, params=NULL) {
if (!is.null(params)) {
lenparams <- length(params)
if (self$beta_est) {
beta <- params[1:self$beta_length]
} else {
beta <- self$beta
}
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
s2 <- 10^logs2
} else {
if (is.null(beta)) {beta <- self$beta}
if (is.null(s2)) {s2 <- self$s2}
}
theta <- 10^beta
if (is.null(y)) {
if (is.matrix(x)) {
val <- outer(1:nrow(x), 1:nrow(x),
Vectorize(function(i,j){self$kone(x[i,],x[j,],
theta=theta, s2=s2)}))
# val <- s2 * corr_triangle_matrix_symC(x, theta)
return(val)
} else {
return(s2 * 1)
}
}
if (is.matrix(x) & is.matrix(y)) {
outer(1:nrow(x), 1:nrow(y),
Vectorize(function(i,j){self$kone(x[i,],y[j,],
theta=theta, s2=s2)}))
# s2 * corr_triangle_matrixC(x, y, theta)
} else if (is.matrix(x) & !is.matrix(y)) {
apply(x, 1, function(xx) {self$kone(xx, y, theta=theta, s2=s2)})
# s2 * corr_triangle_matvecC(x, y, theta)
} else if (is.matrix(y)) {
apply(y, 1, function(yy) {self$kone(yy, x, theta=theta, s2=s2)})
# s2 * corr_triangle_matvecC(y, x, theta)
} else {
self$kone(x, y, theta=theta, s2=s2)
}
},
#' @description Find covariance of two points
#' @param x vector
#' @param y vector
#' @param beta correlation parameters on log scale
#' @param theta correlation parameters on regular scale
#' @param s2 Variance parameter
kone = function(x, y, beta, theta, s2) {
if (missing(theta)) {theta <- 10^beta}
r <- sqrt(sum(theta * (x-y)^2))
s2 * max(1 - r, 0)
},
#' @description Derivative of covariance with respect to parameters
#' @param params Kernel parameters
#' @param X matrix of points in rows
#' @param C_nonug Covariance without nugget added to diagonal
#' @param C Covariance with nugget
#' @param nug Value of nugget
dC_dparams = function(params=NULL, X, C_nonug, C, nug) {
n <- nrow(X)
lenparams <- length(params)
if (lenparams > 0) {
if (self$beta_est) {
beta <- params[1:self$beta_length]
} else {
beta <- self$beta
}
if (self$s2_est) {
logs2 <- params[lenparams]
} else {
logs2 <- self$logs2
}
} else {
beta <- self$beta
logs2 <- self$logs2
}
# lenparams <- length(params)
# beta <- params[1:(lenparams - 1)]
theta <- 10^beta
log10 <- log(10)
# logs2 <- params[lenparams]
s2 <- 10 ^ logs2
# if (is.null(params)) {params <- c(self$beta, self$logs2)}
if (missing(C_nonug)) { # Assume C missing too, must have nug
C_nonug <- self$k(x=X, params=params)
C <- C_nonug + diag(nug*s2, nrow(C_nonug))
}
lenparams_D <- self$beta_length*self$beta_est + self$s2_est
dC_dparams <- array(dim=c(lenparams_D, n, n)) # Return as array
if (self$s2_est) {
dC_dparams[lenparams_D, , ] <- C * log10 # Deriv for logs2
}
# Loop to set beta derivatives
if (self$beta_est) {
for (i in seq(1, n-1, 1)) {
for (j in seq(i+1, n, 1)) {
r2 <- sum(theta * (X[i,]-X[j,])^2)
if (r2 == 0) { # Corr is 1, not continuous so no deriv
dC_dparams[1:length(beta),i,j] <-
dC_dparams[1:length(beta),j,i] <- 0
} else {
r <- sqrt(r2)
C <- max(1 - r, 0)
for (k in 1:length(beta)) {
if (C > 0) {
dC_dparams[k,i,j] <- dC_dparams[k,j,i] <- (
-1/2/r * (X[i,k]-X[j,k])^2 * s2 * theta[k] * log10)
} else {
dC_dparams[k,i,j] <- dC_dparams[k,j,i] <- 0
}
}
}
}
}
for (i in seq(1, n, 1)) { # Get diagonal set to zero
for (k in 1:length(beta)) {
dC_dparams[k,i,i] <- 0
}
}
}
return(dC_dparams=dC_dparams)
},
#' @description Derivative of covariance with respect to X
#' @param XX matrix of points
#' @param X matrix of points to take derivative with respect to
#' @param theta Correlation parameters
#' @param beta log of theta
#' @param s2 Variance parameter
dC_dx = function(XX, X, theta, beta=self$beta, s2=self$s2) {
# stop("dC_dx not implemented for triangle")
if (missing(theta)) {theta <- 10^beta}
if (!is.matrix(XX)) {stop()}
d <- ncol(XX)
if (ncol(X) != d) {stop()}
n <- nrow(X)
nn <- nrow(XX)
dC_dx <- array(NA, dim=c(nn, d, n))
for (i in 1:nn) {
for (k in 1:n) {
r <- sqrt(sum(theta * (XX[i,]-X[k,])^2))
# s2 * max(1 - r, 0)
for (j in 1:d) {
dC_dx[i, j, k] <- if (r > 1) {0} else if (r==0 || r==1) {NaN} else {
-s2 * theta[j] * (XX[i, j] - X[k, j]) / r
}
}
}
}
dC_dx
},
#' @description Print this object
print = function() {
cat('GauPro kernel: Triangle\n')
cat('\tD =', self$D, '\n')
cat('\tbeta =', signif(self$beta, 3), '\n')
cat('\ts2 =', self$s2, '\n')
}
)
)
#' @rdname Triangle
#' @export
#' @param beta Initial beta value
#' @param s2 Initial variance
#' @param D Number of input dimensions of data
#' @param beta_lower Lower bound for beta
#' @param beta_upper Upper bound for beta
#' @param beta_est Should beta be estimated?
#' @param s2_lower Lower bound for s2
#' @param s2_upper Upper bound for s2
#' @param s2_est Should s2 be estimated?
#' @param useC Should C code used? Much faster.
k_Triangle <- function(beta, s2=1, D,
beta_lower=-8, beta_upper=6, beta_est=TRUE,
s2_lower=1e-8, s2_upper=1e8, s2_est=TRUE,
useC=TRUE) {
Triangle$new(
beta=beta,
s2=s2,
D=D,
beta_lower=beta_lower,
beta_upper=beta_upper,
beta_est=beta_est,
s2_lower=s2_lower,
s2_upper=s2_upper,
s2_est=s2_est,
useC=useC
)
}
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