torch::nn_module() Representing a gauss_cat_parametersThe gauss_cat_parameters module extracts the parameters from the inferred generative Gaussian and
categorical distributions for the continuous and categorical features, respectively.
If one_hot_max_sizes is \([4, 1, 1, 2]\), then the inferred distribution parameters for one observation is the
vector \([p_{00}, p_{01}, p_{02}, p_{03}, \mu_1, \sigma_1, \mu_2, \sigma_2, p_{30}, p_{31}]\), where
\(\operatorname{Softmax}([p_{00}, p_{01}, p_{02}, p_{03}])\) and \(\operatorname{Softmax}([p_{30}, p_{31}])\)
are probabilities of the first and the fourth feature categories respectively in the model generative distribution,
and Gaussian(\(\mu_1, \sigma_1^2\)) and Gaussian(\(\mu_2, \sigma_2^2\)) are the model generative distributions
on the second and the third features.
gauss_cat_parameters(one_hot_max_sizes, min_sigma = 1e-04, min_prob = 1e-04)A torch tensor of dimension n_features containing the one hot sizes of the n_features
features. That is, if the ith feature is a categorical feature with 5 levels, then one_hot_max_sizes[i] = 5.
While the size for continuous features can either be 0 or 1.
For stability it might be desirable that the minimal sigma is not too close to zero.
For stability it might be desirable that the minimal probability is not too close to zero.
Lars Henry Berge Olsen