#' @include FittedFunctionalModel.R
#' @include tools.R
#' @title Use the \code{\link{psoptim}} Method from the \code{pso} Package for
#' Fitting a Model
#'
#' @description Apply the Particle Swarm Optimization algorithm to fit the
#' parameters of a model.
#'
#' @param metric an instance of
#' \code{regressoR.quality::RegressionQualityMetric}
#' @param model an instance of \code{\link{FunctionalModel}}
#' @param par the initial starting point
#' @param q the effort to spent in learning, a value between 0 (min) and 1
#' (max). Higher values may lead to much more computational time, lower values
#' to potentially lower result quality.
#' @return On success, an instance of \code{\link{FittedFunctionalModel}}.
#' \code{NULL} on failure.
#' @importFrom pso psoptim
#' @importFrom learnerSelectoR learning.checkQuality
#' @importClassesFrom regressoR.quality RegressionQualityMetric
#' @importFrom regressoR.functional.models FunctionalModel.par.estimate
#' FunctionalModel.par.check
#' @export FunctionalModel.fit.pso
#' @importFrom utilizeR ignoreErrors
FunctionalModel.fit.pso <- function(metric, model, par=NULL, q=0.75) {
if(is.null(metric) || is.null(model) ) { return(NULL); }
if(is.null(par)) {
par <- FunctionalModel.par.estimate(model, metric@x, metric@y);
}
limits <- .fix.boundaries(model, par=par, need=TRUE);
if(is.null(limits)) {
lower <- NULL;
upper <- NULL;
} else {
lower <- limits$lower;
upper <- limits$upper;
}
fn <- function(par) metric@quality(model@f, par);
# for some reason, I cannot get this to work with using a gradient
ignoreErrors({
result <- NULL;
if(is.null(lower)) {
if(is.null(upper)) {
ignoreErrors({ result <- psoptim(par=par, fn=fn) });
} else {
ignoreErrors({ result <- psoptim(par=par, fn=fn, upper=upper) });
}
} else {
if(is.null(upper)) {
ignoreErrors({ result <- psoptim(par=par, fn=fn, lower=lower) });
} else {
ignoreErrors({ result <- psoptim(par=par, fn=fn, lower=lower, upper=upper) });
}
}
if((!(is.null(result))) &&
FunctionalModel.par.check(model, result$par) &&
learning.checkQuality(result$value)) {
return(FittedFunctionalModel.new(model, result$par, result$value));
}
});
return(NULL);
}
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