View source: R/approach_vaeac_torch_modules.R
vaeac_preprocess_data | R Documentation |
vaeac only supports numerical values. This function converts categorical features to numerics with class labels 1,2,...,K, and keeps track of the map between the original and new class labels. It also computes the one_hot_max_sizes.
vaeac_preprocess_data(
data,
log_exp_cont_feat = FALSE,
normalize = TRUE,
norm_mean = NULL,
norm_std = NULL
)
data |
matrix/data.frame/data.table containing the training data. Only the features and not the response. |
log_exp_cont_feat |
Boolean. If we are to log transform all continuous
features before sending the data to vaeac. vaeac creates unbounded values, so if the continuous
features are strictly positive, as for Burr and Abalone data, it can be advantageous to log-transform
the data to unbounded form before using vaeac. If TRUE, then |
norm_mean |
Torch tensor (optional). A 1D array containing the means of the columns of |
norm_std |
Torch tensor (optional). A 1D array containing the stds of the columns of |
list containing data which can be used in vaeac, maps between original and new class names for categorical features, one_hot_max_sizes, and list of information about the data.
Lars Henry Berge Olsen
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