Perplexity of a context tree

exp(-mean log-likelihood per observation), the standard language-modelling evaluation metric. Lower is better. A perplexity of \(k\) on an alphabet of size \(|S|\) means the model is as predictive as a uniform distribution over \(k\) symbols. \(k = |S|\) is the uniform baseline; \(k = 1\) is perfect deterministic prediction.

Usage

perplexity(tree, newdata = NULL)

Arguments

tree

A transitiontrees.

newdata

Sequence data; NULL (default) returns in-sample perplexity.

Value

Numeric scalar.

Examples

# \donttest{
tree <- context_tree(matrix(sample(c("A","B","C"), 200, TRUE), 20),
                     max_depth = 2, min_count = 2)
perplexity(tree)
#> [1] 2.715389
# }