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Train a MaxEnt Model

Source: R/featured_space.R
maxent_fit.Rd

Runs the Java-equivalent sequential coordinate-ascent MaxEnt optimizer (density.Sequential) on a FeaturedSpace.

Usage

maxent_fit(
  featured_space,
  max_iter = 500L,
  convergence = 1e-05,
  beta_multiplier = 1,
  min_deviation = 0.001
)

Arguments

External pointer to a FeaturedSpace object (from maxent_featured_space()).

max_iter

Maximum number of training iterations (default 500).

convergence

Convergence threshold: stop when the per-20-iteration loss improvement is below this value (default 1e-5).

beta_multiplier

Regularization multiplier (default 1.0). Higher values increase regularization strength.

min_deviation

Minimum sample deviation floor used in regularization (default 0.001).

Value

Named list with:

loss

Final regularized loss (scalar).

entropy

Shannon entropy of the trained distribution.

iterations

Number of training iterations completed.

converged

Logical: whether the convergence threshold was reached.

lambdas

Numeric vector of final lambda (weight) values.

trajectory

Empty data.frame by default; use maxent_sequential_fit to request per-iteration snapshots.

Examples

if (FALSE) { # \dontrun{
result <- maxent_fit(fs, max_iter = 500, convergence = 1e-5)
cat("Final loss:", result$loss, "\n")
cat("Entropy:",    result$entropy, "\n")
} # }