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# Function Contents -----------------------------------------------------------
# External: (see documentation in predict.R file)
# predict.gpvec
# predict.dgp2vec
# predict.dgp3vec
# Internal:
# predict_vec (used in all three of the above)
# Predict One Layer Vecchia ---------------------------------------------------
#' @rdname predict
#' @export
predict.gpvec <- function(object, x_new, m = object$m, ordering_new = NULL,
lite = TRUE, return_all = FALSE, EI = FALSE,
entropy_limit = NULL, cores = 1, ...) {
settings <- list(ordering_new = ordering_new, lite = lite,
return_all = return_all, EI = EI,
entropy_limit = entropy_limit, cores = cores)
object <- predict_vec(object, x_new, m, settings, layers = 1)
return(object)
}
# Predict Two Layer Vecchia ---------------------------------------------------
#' @rdname predict
#' @export
#'
predict.dgp2vec <- function(object, x_new, m = object$m, ordering_new = NULL,
lite = TRUE, store_latent = FALSE, mean_map = TRUE,
return_all = FALSE, EI = FALSE, entropy_limit = NULL,
cores = 1, ...) {
settings <- list(ordering_new = ordering_new, lite = lite,
store_latent = store_latent, mean_map = mean_map,
return_all = return_all, EI = EI,
entropy_limit = entropy_limit, cores = cores)
object <- predict_vec(object, x_new, m, settings, layers = 2)
return(object)
}
# Predict Three Layer Vecchia -------------------------------------------------
#' @rdname predict
#' @export
predict.dgp3vec <- function(object, x_new, m = object$m, ordering_new = NULL,
lite = TRUE, store_latent = FALSE, mean_map = TRUE,
return_all = FALSE, EI = FALSE, entropy_limit = NULL,
cores = 1, ...) {
settings <- list(ordering_new = ordering_new, lite = lite,
store_latent = store_latent, mean_map = mean_map,
return_all = return_all, EI = EI,
entropy_limit = entropy_limit, cores = cores)
object <- predict_vec(object, x_new, m, settings, layers = 3)
return(object)
}
# Predict Vecchia -------------------------------------------------------------
predict_vec <- function(object, x_new, m, settings, layers) {
tic <- proc.time()[[3]]
if (is.numeric(x_new)) x_new <- as.matrix(x_new)
monowarp <- (!is.null(object$x_grid))
object$x_new <- x_new
n_new <- nrow(object$x_new)
object$m_pred <- m
if (monowarp) {
grid_index <- fo_approx_init(object$x_grid, object$x)
grid_index_new <- fo_approx_init(object$x_grid, x_new)
}
if (layers >= 2) {
if (monowarp) D <- ncol(object$w_grid[[1]]) else D <- ncol(object$w[[1]])
if (!settings$mean_map)
stop("mean_map = FALSE is not available for `vecchia = TRUE` case")
}
if (layers == 1) {
sep <- is.matrix(object$theta)
} else sep <- monowarp # separable lengthscales only used in monotonic warpings
if (settings$return_all & !settings$lite)
stop("return_all only offered when lite = TRUE")
if (!is.null(settings$entropy_limit) & !is.numeric(settings$entropy_limit))
stop("entropy_limit must be numeric")
if (settings$EI) { # if no noise, use smallest observed value, else estimate f_min
if (all(object$g <= 1e-6)) {
f_min <- FALSE
y_min <- min(object$y)
} else f_min <- TRUE
} else f_min <- FALSE
if (!is.null(settings$ordering_new)) {
if (settings$lite) message("ordering_new is only relevant when lite = FALSE")
test <- check_ordering(settings$ordering_new, nrow(x_new))
}
# If not monotonic, pre-calculate nearest neighbors for x
if (!monowarp) {
if (settings$lite) {
NN_x_new <- FNN::get.knnx(object$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, settings$ordering_new)
}
}
# Initialize prior mean of 0 (these will only be changed if object$settings$pmx = TRUE)
prior_mean_new <- 0
prior_mean <- rep(0, length(object$y))
prior_tau2 <- 1
if (settings$cores == 1) { # run serial for loop
mu_t <- matrix(nrow = n_new, ncol = object$nmcmc)
if (settings$lite) {
s2_sum <- rep(0, times = n_new)
if (settings$return_all) s2_t <- matrix(nrow = n_new, ncol = object$nmcmc)
} else sigma_sum <- matrix(0, nrow = n_new, ncol = n_new)
if (settings$EI) ei_sum <- rep(0, times = n_new)
if (!is.null(settings$entropy_limit)) ent_sum <- rep(0, times = n_new)
if (layers >= 2) {
if (settings$store_latent) {
w_new_list <- list()
if (layers == 3) z_new_list <- list()
}
}
for (t in 1:object$nmcmc) {
if (layers == 3) {
# 3 layers: map x_new to z_new (separately for each dimension)
z_t <- object$z[[t]]
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$x, x_new = x_new,
NNarray_pred = NN_x_new) # lite = TRUE version only
z_new[, i] <- k$mean
} # end of i for loop
if (settings$store_latent) z_new_list[[t]] <- z_new
}
if (layers >= 2) {
# 2 layers: map x_new to w_new (separately for each dimension)
# 3 layers: map z_new to w_new (separately for each dimension)
if (monowarp) { # simply get the monowarped values at x_new locations
w_grid_t <- object$w_grid[[t]]
w_t <- monowarp_ref(object$x, object$x_grid, w_grid_t, grid_index)
w_new <- monowarp_ref(x_new, object$x_grid, w_grid_t, grid_index_new)
if (settings$lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m, settings$ordering_new)
}
} else {
w_t <- object$w[[t]]
w_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) {
if (layers == 2) {
if (object$settings$pmx) { # Optional prior mean of x
prior_mean_new <- x_new[, i]
prior_mean <- object$x[, i]
prior_tau2 <- object$settings$inner_tau2
}
}
k <- krig_vec(w_t[, i], object$theta_w[t, i], g = eps, tau2 = prior_tau2,
v = object$v, m = m, x = ifel(layers == 2, object$x, z_t),
x_new = ifel(layers == 2, x_new, z_new),
NNarray_pred = ifel(layers == 2, NN_x_new, NULL),
prior_mean = prior_mean,
prior_mean_new = prior_mean_new) # lite = TRUE version only
w_new[, i] <- k$mean
} # end of i for loop
if (settings$store_latent) w_new_list[[t]] <- w_new
if (settings$lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m, settings$ordering_new)
}
} # end of monowarp else statement
}
# 1 layer: map x_new to y
# 2 and 3 layers: map w_new to y
if (sep) { # only occurs in one layer or monowarped two-layer
k <- krig_vec(object$y,
ifel(monowarp, object$theta_y[t, ], object$theta[t, ]),
object$g[t],
object$tau2[t],
s2 = settings$lite, sigma = !settings$lite, v = object$v,
m = m,
x = ifel(monowarp, w_t, object$x),
x_new = ifel(monowarp, w_new, x_new),
NNarray_pred = ifel(monowarp, NULL, NN_x_new),
approx = ifel(monowarp, w_approx, x_approx),
sep = TRUE, f_min = f_min)
} else {
k <- krig_vec(object$y, ifel(layers == 1, object$theta[t], object$theta_y[t]),
object$g[t], object$tau2[t],
s2 = settings$lite, sigma = !settings$lite, v = object$v,
m = m,
x = ifel(layers == 1, object$x, w_t),
x_new = ifel(layers == 1, x_new, w_new),
NNarray_pred = ifel(layers == 1, NN_x_new, NULL),
approx = ifel(layers == 1, x_approx, w_approx),
f_min = f_min)
}
mu_t[, t] <- k$mean
if (settings$lite) {
s2_sum <- s2_sum + k$s2
if (settings$return_all) s2_t[, t] <- k$s2
} else sigma_sum <- sigma_sum + k$sigma
if (settings$EI) {
if (settings$lite) {
sig2 <- k$s2 - (object$tau2[t] * object$g[t])
} else sig2 <- diag(k$sigma) - (object$tau2[t] * object$g[t])
ei_sum <- ei_sum + exp_improv(k$mean, sig2, ifel(f_min, k$f_min, y_min))
}
if (!is.null(settings$entropy_limit)) {
if (settings$lite) {
sig2 <- k$s2 - (object$tau2[t] * object$g[t])
} else sig2 <- diag(k$sigma) - (object$tau2[t] * object$g[t])
ent_sum <- ent_sum + calc_entropy(k$mean, sig2, settings$entropy_limit)
}
} # end of t for loop
} else { # run in parallel using foreach
iters <- 1:object$nmcmc
chunks <- split(iters, sort(cut(iters, settings$cores, labels = FALSE)))
if (settings$cores > detectCores())
warning("cores is greater than available nodes")
cl <- makeCluster(settings$cores)
registerDoParallel(cl)
thread <- NULL
result <- foreach(thread = 1:settings$cores) %dopar% {
out <- list()
out$mu_t <- matrix(nrow = n_new, ncol = length(chunks[[thread]]))
if (settings$lite) {
out$s2_sum <- rep(0, times = n_new)
if (settings$return_all)
out$s2_t <- matrix(nrow = n_new, ncol = length(chunks[[thread]]))
} else out$sigma_sum <- matrix(0, nrow = n_new, ncol = n_new)
if (settings$EI) out$ei_sum <- rep(0, times = n_new)
if (!is.null(settings$entropy_limit)) out$ent_sum <- rep(0, times = n_new)
if (layers >= 2) {
if (settings$store_latent) {
out$w_new <- list()
if (layers == 3) out$z_new <- list()
}
}
# calculate predictions for each candidate MCMC iteration
j <- 1
for (t in chunks[[thread]]) {
if (layers == 3) {
# 3 layers: map x_new to z_new (separately for each dimension)
z_t <- object$z[[t]]
z_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) { # mean_map = TRUE only
k <- krig_vec(z_t[, i], object$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) # lite = TRUE version only
z_new[, i] <- k$mean
} # end of i for loop
if (settings$store_latent) out$z_new[[j]] <- z_new
}
if (layers >= 2) {
# 2 layers: map x_new to w_new (separately for each dimension)
# 3 layers: map z_new to w_new (separately for each dimension)
if (monowarp) { # simply get the monowarped values at x_new locations
w_grid_t <- object$w_grid[[t]]
w_t <- monowarp_ref(object$x, object$x_grid, w_grid_t, grid_index)
w_new <- monowarp_ref(x_new, object$x_grid, w_grid_t, grid_index_new)
if (settings$lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m, settings$ordering_new)
}
} else {
w_t <- object$w[[t]]
w_new <- matrix(nrow = n_new, ncol = D)
for (i in 1:D) {
if (layers == 2) {
if (object$settings$pmx) { # Optional prior mean of x
prior_mean_new <- x_new[, i]
prior_mean <- object$x[, i]
prior_tau2 <- object$settings$inner_tau2
}
}
k <- krig_vec(w_t[, i], object$theta_w[t, i], g = eps, tau2 = prior_tau2,
v = object$v, m = m, x = ifel(layers == 2, object$x, z_t),
x_new = ifel(layers == 2, x_new, z_new),
NNarray_pred = ifel(layers == 2, NN_x_new, NULL),
prior_mean = prior_mean,
prior_mean_new = prior_mean_new) # lite = TRUE version only
w_new[, i] <- k$mean
} # end of i for loop
if (settings$store_latent) out$w_new[[j]] <- w_new
if (settings$lite) {
w_approx <- NULL
} else {
w_approx <- update_obs_in_approx(object$w_approx, w_t)
w_approx <- add_pred_to_approx(w_approx, w_new, m, settings$ordering_new)
}
} # end of monowarp else statement
}
# 1 layer: map x_new to y
# 2 and 3 layers: map w_new to y
if (sep) { # only occurs in one layer or monowarped two-layer
k <- krig_vec(object$y,
ifel(monowarp, object$theta_y[t, ], object$theta[t, ]),
object$g[t], object$tau2[t],
s2 = settings$lite, sigma = !settings$lite, v = object$v,
m = m,
x = ifel(monowarp, w_t, object$x),
x_new = ifel(monowarp, w_new, x_new),
NNarray_pred = ifel(monowarp, NULL, NN_x_new),
approx = ifel(monowarp, w_approx, x_approx),
sep = TRUE, f_min = f_min)
} else {
k <- krig_vec(object$y, ifel(layers == 1, object$theta[t], object$theta_y[t]),
object$g[t], object$tau2[t],
s2 = settings$lite, sigma = !settings$lite, v = object$v,
m = m,
x = ifel(layers == 1, object$x, w_t),
x_new = ifel(layers == 1, x_new, w_new),
NNarray_pred = ifel(layers == 1, NN_x_new, NULL),
approx = ifel(layers == 1, x_approx, w_approx),
f_min = f_min)
}
out$mu_t[, j] <- k$mean
if (settings$lite) {
out$s2_sum <- out$s2_sum + k$s2
if (settings$return_all) out$s2_t[, j] <- k$s2
} else out$sigma_sum <- out$sigma_sum + k$sigma
if (settings$EI) {
if (settings$lite) {
sig2 <- k$s2 - (object$tau2[t] * object$g[t])
} else sig2 <- diag(k$sigma) - (object$tau2[t] * object$g[t])
out$ei_sum <- out$ei_sum + exp_improv(k$mean, sig2, ifel(f_min, k$f_min, y_min))
}
if (!is.null(settings$entropy_limit)) {
if (settings$lite) {
sig2 <- k$s2 - (object$tau2[t] * object$g[t])
} else sig2 <- diag(k$sigma) - (object$tau2[t] * object$g[t])
out$ent_sum <- out$ent_sum + calc_entropy(k$mean, sig2, settings$entropy_limit)
}
j <- j + 1
} # end of t for loop
return(out)
} # end of foreach loop
stopCluster(cl)
# Group elements out of the list
mu_t <- do.call(cbind, lapply(result, with, eval(parse(text = "mu_t"))))
if (settings$lite) {
s2_sum <- Reduce("+", lapply(result, with, eval(parse(text = "s2_sum"))))
if (settings$return_all) s2_t <- do.call(cbind, lapply(result, with, eval(parse(text = "s2_t"))))
} else {
sigma_sum <- Reduce("+", lapply(result, with, eval(parse(text = "sigma_sum"))))
}
if (layers >= 2) {
if (settings$store_latent) {
w_new_list <- unlist(lapply(result, with, eval(parse(text = "w_new"))), recursive = FALSE)
if (layers == 3) z_new_list <- unlist(lapply(result, with, eval(parse(text = "z_new"))), recursive = FALSE)
}
}
if (settings$EI) ei_sum <- Reduce("+", lapply(result, with, eval(parse(text = "ei_sum"))))
if (!is.null(settings$entropy_limit)) ent_sum <- Reduce("+", lapply(result, with,
eval(parse(text = "ent_sum"))))
} # end of else statement
# Add variables to the output list
object$mean <- rowMeans(mu_t)
if (layers >= 2) {
if (settings$store_latent) {
object$w_new <- w_new_list
if (layers == 3) object$z_new <- z_new_list
}
}
if (settings$lite) {
object$s2 <- s2_sum / object$nmcmc + apply(mu_t, 1, var)
if (settings$return_all) {
object$mean_all <- mu_t
object$s2_all <- s2_t
}
} else object$Sigma <- sigma_sum / object$nmcmc + cov(t(mu_t))
if (settings$EI) object$EI <- ei_sum / object$nmcmc
if (!is.null(settings$entropy_limit)) object$entropy <- drop(ent_sum / object$nmcmc)
toc <- proc.time()[[3]]
object$time <- object$time + unname(toc - tic)
return(object)
}
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