A sampler used to samples the batches where each instances is sampled twice
paired_sampler(vaeac_dataset_object, shuffle = FALSE)A vaeac_dataset() object containing the data.
Boolean. If TRUE, then the data is shuffled. If FALSE,
then the data is returned in chronological order.
A sampler object that allows for paired sampling by always including each observation from the
vaeac_dataset() twice. A torch::sampler() object can be used with torch::dataloader() when creating
batches from a torch dataset torch::dataset(). See https://rdrr.io/cran/torch/src/R/utils-data-sampler.R for
more information. This function does not use batch iterators, which might increase the speed.
if (FALSE) { # \dontrun{
# Example how to use it combined with mask generators with paired sampling activated
batch_size <- 4
if (batch_size %% 2 == 1) batch_size <- batch_size - 1 # Make sure that batch size is even
n_features <- 3
n_observations <- 5
shuffle <- TRUE
data <- torch_tensor(matrix(rep(seq(n_observations), each = n_features),
ncol = n_features, byrow = TRUE
))
data
dataset <- vaeac_dataset(data, rep(1, n_features))
dataload <- torch::dataloader(dataset,
batch_size = batch_size,
sampler = paired_sampler(dataset,
shuffle = shuffle
)
)
dataload$.length() # Number of batches, same as ceiling((2 * n_observations) / batch_size)
mask_generator <- mcar_mask_generator(paired = TRUE)
coro::loop(for (batch in dataload) {
mask <- mask_generator(batch)
obs <- mask * batch
print(torch::torch_cat(c(batch, mask, obs), -1))
})
} # }