% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/featured_space.R
\name{maxent_featured_space}
\alias{maxent_featured_space}
\title{Create a FeaturedSpace Object}
\usage{
maxent_featured_space(
  num_points,
  sample_indices,
  features,
  bias_weights = NULL
)
}
\arguments{
\item{num_points}{Integer: total number of background points.}

\item{sample_indices}{Integer vector: 0-based indices (into the background
array) of the occurrence sample locations.}

\item{features}{List of external pointers to Feature objects, as
returned by \code{maxent_linear_feature()}, \code{maxent_hinge_feature()},
\code{maxent_generate_features()}, etc.}

\item{bias_weights}{Optional numeric vector of per-point bias weights
(length \code{num_points}).  When supplied, background density is computed
as \code{bias[i] * exp(lp[i] - lpn)} instead of the standard
\code{exp(lp[i] - lpn)}.  This mirrors Java Maxent's \code{biasFile}
parameter.  Pass \code{NULL} (default) for uniform (unbiased) background.}
}
\value{
External pointer to a FeaturedSpace C++ object.
}
\description{
Constructs a MaxEnt FeaturedSpace from background point data, occurrence
sample indices, and a list of pre-built Feature objects.
}
\examples{
n   <- 100L
idx <- 90:99  # 0-based sample indices
env <- seq(0, 1, length.out = n)
f   <- maxent_linear_feature(env, "env1")
fs  <- maxent_featured_space(n, idx, list(f))
}
