Give error messages for invalid inputs in exported functions.
validate_inputs(
num_items,
num_skills,
Q_matrix,
model_type = 2,
mean_vector = rep(0, num_skills),
covariance_matrix = diag(num_skills),
enc_hid_arch = c(ceiling((num_items + num_skills)/2)),
hid_enc_activations = rep("sigmoid", length(enc_hid_arch)),
output_activation = "sigmoid",
kl_weight = 1,
learning_rate = 0.001
)the number of items on the assessment; also the number of nodes in the input/output layers of the VAE
the number of skills being evaluated; also the size of the distribution learned by the VAE
a binary, num_skills by num_items matrix relating the assessment items with skills
either 1 or 2, specifying a 1 parameter (1PL) or 2 parameter (2PL) model
a vector of length num_skills specifying the mean of each latent trait
a symmetric, positive definite, num_skills by num_skills, matrix giving the covariance of the latent traits
a vector detailing the number an size of hidden layers in the encoder
a vector specifying the activation function in each hidden layer in the encoder; must be the same length as enc_hid_arch
a string specifying the activation function in the output of the decoder; the ML2P model alsways used 'sigmoid'
an optional weight for the KL divergence term in the loss function
an optional parameter for the adam optimizer