BCT (version 1.2)

log_loss: Calculating the log-loss incurred in prediction

Description

Compute the log-loss incurred in BCT prediction with memory length D. Given an initial context and training data , the log-loss is computed in sequentially predicting the test data . The function outputs the cummulative, normalized (per-sample) log-loss, at each prediction step; for more information see Kontoyiannis et al.(2020).

Usage

log_loss(input_data, depth, train_size, beta = NULL)

Arguments

input_data

the sequence to be analysed. The sequence needs to be a "character" object. See the examples section of BCT/kBCT functions on how to transform any dataset to a "character" object.

depth

maximum memory length.

train_size

number of samples used in the training set. The training set size should be at least equal to the depth.

beta

hyper-parameter of the model prior. Takes values between 0 and 1. If not initialised in the call function, the default value is , where is the size of the alphabet; for more information see Kontoyiannis et al. (2020).

Value

returns a vector containing the averaged log-loss incurred in the sequential prediction at each time-step.

See Also

prediction, zero_one_loss

Examples

# NOT RUN {
# Compute the log-loss in the prediction of the last 10 elements 
# of a dataset. 
log_loss(pewee, 5, nchar(pewee) - 10)

# For custom beta (e.g. 0.7):
log_loss(pewee, 5, nchar(pewee) - 10, 0.7)
# }