#' Parse Maxent lambdas information
#'
#' Parse Maxent .lambdas files to extract the types, weights, minima and maxima
#' of features, as well as the fitted model's entropy and other values required
#' for predicting to new data.
#'
#' @param lambdas Either a \code{MaxEnt} fitted model object (fitted with the
#' \code{maxent} function in the \code{dismo} package), or a file path to a
#' Maxent .lambdas file.
#' @return A list with five elements:
#' \itemize{
#' \item{\code{lambdas}}{: a \code{data.frame} describing the features used in
#' a Maxent model, including their weights (lambdas), maxima, minima, and
#' type;}
#' \item{\code{linearPredictorNormalizer}}{: a constant that ensures the
#' linear predictor (the sum of clamped features multiplied by their
#' respective feature weights) is always negative (for numerical stability);}
#' \item{\code{densityNormalizer}}{: a scaling constant that ensures Maxent's
#' raw output sums to 1 over background points;}
#' \item{\code{numBackgroundPoints}}{: the number of background points used in
#' model training; and}
#' \item{\code{entropy}}{: the entropy of the fitted model.}
#' }
#' @author John B. Baumgartner, \email{johnbaums@@gmail.com}
#' @section Warning:
#' This function is still in development, and no guarantee is made for the
#' accuracy of its projections.
#' @keywords maxent, predict, project
#' @references
#' \itemize{
#' \item{Wilson, P. W. (2009) \href{http://gsp.humboldt.edu/OLM/GSP_570/Learning Modules/10 BlueSpray_Maxent_Uncertinaty/MaxEnt lambda files.pdf}{\emph{Guidelines for computing MaxEnt model output values from a lambdas file}}.}
#' \item{\emph{Maxent software for species habitat modeling, version 3.3.3k} help file (software freely available \href{https://www.cs.princeton.edu/~schapire/maxent/}{here}).}
#' }
#' @seealso \code{\link{read_mxe}} \code{\link{project_maxent}}
#' @export
#' @examples
#' # Below we use the dismo::maxent example to fit a Maxent model:
#' if (require(dismo) && require(rJava) &&
#' file.exists(file.path(system.file(package='dismo'), 'java/maxent.jar'))) {
#' fnames <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
#' pattern='grd', full.names=TRUE )
#' predictors <- stack(fnames)
#' occurence <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
#' occ <- read.table(occurence, header=TRUE, sep=',')[,-1]
#' me <- maxent(predictors, occ, path=file.path(tempdir(), 'example'),
#' factors='biome')
#'
#' # ... and then parse the lambdas information:
#' parse_lambdas(me)
#' parse_lambdas(file.path(tempdir(), 'example/species.lambdas'))
#'
#' }
parse_lambdas <- function(lambdas) {
if(is(lambdas, 'MaxEnt')) {
lambdas <- lambdas@lambdas
} else {
lambdas <- readLines(lambdas)
}
con <- textConnection(lambdas)
n <- count.fields(con, ',', quote='')
close(con)
meta <- setNames(lapply(strsplit(lambdas[n==2], ', '),
function(x) as.numeric(x[2])),
sapply(strsplit(lambdas[n==2], ', '), '[[', 1))
lambdas <- setNames(data.frame(do.call(rbind, strsplit(lambdas[n==4], ', ')),
stringsAsFactors=FALSE),
c('feature', 'lambda', 'min', 'max'))
lambdas[, -1] <- lapply(lambdas[, -1], as.numeric)
lambdas$feature <- sub('=', '==', lambdas$feature)
lambdas$feature <- sub('<', '<=', lambdas$feature)
lambdas$type <- factor(sapply(lambdas$feature, function(x) {
switch(gsub("\\w|\\.|-|\\(|\\)", "", x),
"==" = 'categorical',
"<=" = "threshold",
"^" = "quadratic",
"*" = "product",
"`" = "reverse_hinge",
"'" = 'forward_hinge',
'linear')
}))
vars <- gsub("\\^2|\\(.*<=|\\((.*)==.*|`|\\'|\\)", "\\1", lambdas$feature)
lambdas$var <- sub('\\*', ',', vars)
c(list(lambdas=lambdas[, c(1, 6, 2:5)]), meta)
}
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