The maximum likelihood cost function used for CDEN model fitting. Calculates the negative of the logarithm of the likelihood. A normal distribution prior can be set for the magnitude of the input-hidden layer weights, thus leading to weight penalty regularization.
cadence.cost(weights, x, y, n.hidden, hidden.fcn, distribution, sd.norm,
valid)weight vector of length returned by cadence.initialize.
matrix with number of rows equal to the number of samples and number of columns equal to the number of predictor variables.
column matrix of predictand values with number of rows equal to the number of samples.
number of hidden nodes in the CDEN model.
hidden layer transfer function.
a list that describes the probability density function associated with the predictand.
sd parameter for normal distribution prior for the magnitude of input-hidden layer weights; equivalent to weight penalty regularization.
valid logical vector indicating which weights are non-zero or fixed at zero, i.e., due to use of parameters.fixed in distribution.