R/approach_vaeac_torch_modules.R
mcar_mask_generator.RdA mask generator which masks the entries in the input completely at random.
mcar_mask_generator(masking_ratio = 0.5, paired_sampling = FALSE)Numeric between 0 and 1. The probability for an entry in the generated mask to be 1 (masked).
Boolean. If we are doing paired sampling. So include both S and \(\bar{S}\).
If TRUE, then batch must be sampled using paired_sampler() which ensures that the batch contains
two instances for each original observation. That is, batch \(= [X_1, X_1, X_2, X_2, X_3, X_3, ...]\), where
each entry \(X_j\) is a row of dimension \(p\) (i.e., the number of features).
The mask generator mask each element in the batch (N x p) using a component-wise independent Bernoulli
distribution with probability masking_ratio. Default values for masking_ratio is 0.5, so all
masks are equally likely to be generated, including the empty and full masks.
The function returns a mask of the same shape as the input batch, and the batch can contain
missing values, indicated by the "NaN" token, which will always be masked.
Input: \((N, p)\) where N is the number of observations in the batch and \(p\) is the number of features.
Output: \((N, p)\), same shape as the input
if (FALSE) {
mask_gen <- mcar_mask_generator(masking_ratio = 0.5, paired_sampling = FALSE)
batch <- torch::torch_randn(c(5, 3))
mask_gen(batch)
}