gauss_cat_parameters function

A torch::nn_module() Representing a gauss_cat_parameters

The 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)

Arguments

• one_hot_max_sizes: 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.

• min_sigma: For stability it might be desirable that the minimal sigma is not too close to zero.

• min_prob: For stability it might be desirable that the minimal probability is not too close to zero.

Author(s)

Lars Henry Berge Olsen