rGNN {gnn} | R Documentation |
Sampling method for objects of S3
class "gnn_GNN"
.
## S3 method for class 'gnn_GNN' rGNN(x, size, prior = NULL, pobs = FALSE, ...)
x |
object of |
size |
sample size, a positive |
prior |
one of |
pobs |
|
... |
additional arguments passed to the underlying
|
(size, dim(x)[1])
-matrix
of samples.
Marius Hofert
if(TensorFlow_available()) { # rather restrictive (due to R-Forge, winbuilder) library(gnn) # for being standalone ## Define dummy model d <- 2 # bivariate case GMMN <- FNN(c(d, 300, d)) # Feedforward NN with MMD loss (a GMMN; random weights) ## Sampling n <- 3 (X1 <- rGNN(GMMN, size = n)) # default (independent N(0,1) samples as prior) (X2 <- rGNN(GMMN, size = n, # passing additional arguments to rPrior() qmargins = qexp, method = "sobol", seed = 271)) (X3 <- rGNN(GMMN, prior = matrix(rexp(n * d), ncol = d))) # providing 'prior' stopifnot(dim(X1) == c(n, d), dim(X2) == c(n, d), dim(X3) == c(n, d)) }