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# Function Contents -----------------------------------------------------------
# External: (see documentation in predict.R file)
# predict.gpvec
# predict.dgp2vec
# predict.dgp3vec
# Predict One Layer Vecchia ---------------------------------------------------
#' @rdname predict
#' @export
predict.gpvec <- function(object, x_new, m = object$m, lite = TRUE,
cores = 1, ...) {
tic <- proc.time()[3]
object <- clean_prediction(object) # remove previous predictions if present
sep <- (is.matrix(object$theta))
if (is.numeric(x_new)) x_new <- as.matrix(x_new)
object$x_new <- x_new
n_new <- nrow(object$x_new)
# Pre-calculate nearest neighbors for x
if (lite) {
NN_x_new <- FNN::get.knnx(object$x, x_new, m)$nn.index
x_approx <- NULL
} else {
NN_x_new <- NULL
x_approx <- add_pred_to_approx(object$x_approx, x_new, m)
}
# Prepare clusters
iters <- 1:object$nmcmc
if (cores == 1) {
chunks <- list(iters)
} else chunks <- split(iters, sort(cut(iters, cores, labels = FALSE)))
if (cores > detectCores()) warning("cores is greater than available nodes")
cl <- makeCluster(cores)
registerDoParallel(cl)
thread <- NULL
result <- foreach(thread = 1:cores) %dopar% {
out <- list()
out$mu_t <- matrix(nrow = n_new, ncol = length(chunks[[thread]]))
if (lite) {
out$s2_sum <- rep(0, times = n_new)
} else out$sigma_sum <- matrix(0, nrow = n_new, ncol = n_new)
# calculate predictions for each candidate MCMC iterations
j <- 1
for (t in chunks[[thread]]) {
if (sep) theta <- object$theta[t, ] else theta <- object$theta[t]
k <- krig_vec(object$y, theta, object$g[t], object$tau2[t],
s2 = lite, sigma = !lite, v = object$v, m = m,
x = object$x, x_new = x_new, NNarray_pred = NN_x_new,
approx = x_approx, sep = sep)
out$mu_t[, j] <- k$mean
if (lite) {
out$s2_sum <- out$s2_sum + k$s2
} else out$sigma_sum <- out$sigma_sum + k$sigma
j <- j + 1
} # end of t for loop
return(out)
} # end of foreach loop
stopCluster(cl)
# Group elements out of the list
mu_t <- do.call(cbind, lapply(result, with, eval(parse(text = "mu_t"))))
if (lite) {
s2_sum <- Reduce("+", lapply(result, with, eval(parse(text = "s2_sum"))))
} else {
sigma_sum <- Reduce("+", lapply(result, with, eval(parse(text = "sigma_sum"))))
}
# Add variables to the output list
mu_cov <- cov(t(mu_t))
object$mean <- rowMeans(mu_t)
if (lite) {
object$s2 <- s2_sum / object$nmcmc + diag(mu_cov)
object$s2_smooth <- object$s2 - mean(object$g * object$tau2)
} else {
object$Sigma <- sigma_sum / object$nmcmc + mu_cov
object$Sigma_smooth <- object$Sigma - diag(mean(object$g * object$tau2), n_new)
}
toc <- proc.time()[3]
object$time <- object$time + (toc - tic)
return(object)
}
# Predict Two Layer Vecchia ---------------------------------------------------
#' @rdname predict
#' @export
#'
predict.dgp2vec <- function(object, x_new, m = object$m, lite = TRUE,
store_latent = FALSE, mean_map = TRUE,
cores = 1, ...) {
tic <- proc.time()[3]
object <- clean_prediction(object) # remove previous predictions if present
if (is.numeric(x_new)) x_new <- as.matrix(x_new)
object$x_new <- x_new
n_new <- nrow(object$x_new)
D <- ncol(object$w[[1]])
if (!mean_map) stop("mean_map = FALSE has not yet been implemented")
# Pre-calculate nearest neighbors for x
if (lite) {
NN_x_new <- FNN::get.knnx(object$x, x_new, m)$nn.index
x_approx <- NULL
} else {
NN_x_new <- NULL
x_approx <- add_pred_to_approx(object$x_approx, x_new, m)
}
# Prepare clusters
iters <- 1:object$nmcmc
if (cores == 1) {
chunks <- list(iters)
} else chunks <- split(iters, sort(cut(iters, cores, labels = FALSE)))
if (cores > detectCores()) warning('cores is greater than available nodes')
cl <- makeCluster(cores)
registerDoParallel(cl)
thread <- NULL
result <- foreach(thread = 1:cores) %dopar% {
out <- list()
if (store_latent) out$w_new <- list()
out$mu_t <- matrix(nrow = n_new, ncol = length(chunks[[thread]]))
if (lite) {
out$s2_sum <- rep(0, times = n_new)
} else out$sigma_sum <- matrix(0, nrow = n_new, ncol = n_new)
j <- 1
for (t in chunks[[thread]]) {
w_t <- object$w[[t]]
# Map x_new to w_new (separately for each dimension)
w_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) {
k <- krig_vec(w_t[, i], object$theta_w[t, i], g = eps, tau2 = 1,
v = object$v, m = m, x = object$x, x_new = x_new,
NNarray_pred = NN_x_new)
w_new[, i] <- k$mean
} # end of i for loop
if (store_latent) out$w_new[[j]] <- w_new
if (lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m)
}
# Map w_new to y
k <- krig_vec(object$y, object$theta_y[t], object$g[t], object$tau2[t],
s2 = lite, sigma = !lite, v = object$v, m = m,
x = w_t, x_new = w_new, approx = w_approx)
out$mu_t[, j] <- k$mean
if (lite) {
out$s2_sum <- out$s2_sum + k$s2
} else out$sigma_sum <- out$sigma_sum + k$sigma
j <- j + 1
} # end of t for loop
return(out)
} # end of foreach statement
stopCluster(cl)
# Group elements out of the list
mu_t <- do.call(cbind, lapply(result, with, eval(parse(text = "mu_t"))))
if (lite) {
s2_sum <- Reduce("+", lapply(result, with, eval(parse(text = "s2_sum"))))
} else {
sigma_sum <- Reduce("+", lapply(result, with, eval(parse(text = "sigma_sum"))))
}
if (store_latent) w_new <- unlist(lapply(result, with, eval(parse(text = "w_new"))),
recursive = FALSE)
# Add variables to the output list
mu_cov <- cov(t(mu_t))
object$mean <- rowMeans(mu_t)
if (store_latent) object$w_new <- w_new
if (lite) {
object$s2 <- s2_sum / object$nmcmc + diag(mu_cov)
object$s2_smooth <- object$s2 - mean(object$g * object$tau2)
} else {
object$Sigma <- sigma_sum / object$nmcmc + mu_cov
object$Sigma_smooth <- object$Sigma - diag(mean(object$g * object$tau2), n_new)
}
toc <- proc.time()[3]
object$time <- object$time + (toc - tic)
return(object)
}
# Predict Three Layer Vecchia -------------------------------------------------
#' @rdname predict
#' @export
predict.dgp3vec <- function(object, x_new, m = object$m, lite = TRUE,
store_latent = FALSE, mean_map = TRUE,
cores = 1, ...) {
tic <- proc.time()[3]
object <- clean_prediction(object) # remove previous predictions if present
if (is.numeric(x_new)) x_new <- as.matrix(x_new)
object$x_new <- x_new
n_new <- nrow(object$x_new)
D <- ncol(object$z[[1]])
if (!mean_map) stop("mean_map = FALSE has not yet been implemented")
# Pre-calculate nearest neighbors for x
if (lite) {
NN_x_new <- FNN::get.knnx(object$x, x_new, m)$nn.index
x_approx <- NULL
} else {
NN_x_new <- NULL
x_approx <- add_pred_to_approx(object$x_approx, x_new, m)
}
# Prepare clusters
iters <- 1:object$nmcmc
if (cores == 1) {
chunks <- list(iters)
} else chunks <- split(iters, sort(cut(iters, cores, labels = FALSE)))
if (cores > detectCores()) warning('cores is greater than available nodes')
cl <- makeCluster(cores)
registerDoParallel(cl)
thread <- NULL
result <- foreach(thread = 1:cores) %dopar% {
out <- list()
if (store_latent) {
out$z_new <- list()
out$w_new <- list()
}
out$mu_t <- matrix(nrow = n_new, ncol = length(chunks[[thread]]))
if (lite) {
out$s2_sum <- vector(length = n_new)
} else out$sigma_sum <- matrix(0, nrow = n_new, ncol = n_new)
j <- 1
for (t in chunks[[thread]]) {
z_t <- object$z[[t]]
w_t <- object$w[[t]]
# Map x_new to z_new (separately for each dimension)
z_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) { # mean_map = TRUE only
k <- krig_vec(z_t[, i], object$theta_z[t, i], g = eps, tau2 = 1,
v = object$v, m = m, x = object$x, x_new = x_new,
NNarray_pred = NN_x_new)
z_new[, i] <- k$mean
} # end of i for loop
if (store_latent) out$z_new[[j]] <- z_new
# Map z_new to w_new (separately for each dimension)
w_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) {
k <- krig_vec(w_t[, i], object$theta_w[t, i], g = eps, tau2 = 1,
v = object$v, m = m, x = z_t, x_new = z_new)
w_new[, i] <- k$mean
} # end of i for loop
if (store_latent) out$w_new[[j]] <- w_new
if (lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m)
}
# Map w_new to y
k <- krig_vec(object$y, object$theta_y[t], object$g[t], object$tau2[t],
s2 = lite, sigma = !lite, v = object$v, m = m,
x = w_t, x_new = w_new, approx = w_approx)
out$mu_t[, j] <- k$mean
if (lite) {
out$s2_sum <- out$s2_sum + k$s2
} else out$sigma_sum <- out$sigma_sum + k$sigma
j <- j + 1
} # end of t for loop
return(out)
} # end of foreach loop
stopCluster(cl)
# Group elements out of the list
mu_t <- do.call(cbind, lapply(result, with, eval(parse(text = "mu_t"))))
if (lite) {
s2_sum <- Reduce("+", lapply(result, with, eval(parse(text = "s2_sum"))))
} else {
sigma_sum <- Reduce("+", lapply(result, with, eval(parse(text = "sigma_sum"))))
}
if (store_latent) {
z_new <- unlist(lapply(result, with, eval(parse(text = "z_new"))), recursive = FALSE)
w_new <- unlist(lapply(result, with, eval(parse(text = "w_new"))), recursive = FALSE)
}
# Add variables to the output list
mu_cov <- cov(t(mu_t))
object$mean <- rowMeans(mu_t)
if (store_latent) {
object$z_new <- z_new
object$w_new <- w_new
}
if (lite) {
object$s2 <- s2_sum / object$nmcmc + diag(mu_cov)
object$s2_smooth <- object$s2 - mean(object$g * object$tau2)
} else {
object$Sigma <- sigma_sum / object$nmcmc + mu_cov
object$Sigma_smooth <- object$Sigma - diag(mean(object$g * object$tau2), n_new)
}
toc <- proc.time()[3]
object$time <- object$time + (toc - tic)
return(object)
}
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