#' @include FittedFunctionalModel.R
#' @include tools.R
#' @title Use Powell's BOBYQA Approach to Optimize the Parameters
#'
#' @description Apply Powell's BOBYQA Approach to fit a functional 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 minqa bobyqa newuoa
#' @importFrom learnerSelectoR learning.checkQuality
#' @importClassesFrom regressoR.quality RegressionQualityMetric
#' @importFrom regressoR.functional.models FunctionalModel.par.estimate
#' FunctionalModel.par.check
#' @export FunctionalModel.fit.minqa
FunctionalModel.fit.minqa <- 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);
}
fn <- function(par) metric@quality(model@f, par);
limits <- .fix.boundaries(model, par=par);
if(is.null(limits)) {
lower <- NULL;
upper <- NULL;
} else {
lower <- limits$lower;
upper <- limits$upper;
}
ignoreErrors({
result <- NULL;
ignoreErrors({
if(is.null(lower)) {
if(is.null(upper)) {
result <- bobyqa(par=par, fn=fn);
} else {
result <- bobyqa(par=par, fn=fn, upper=upper);
}
} else {
if(is.null(upper)) {
result <- bobyqa(par=par, fn=fn, lower=lower);
} else {
result <- bobyqa(par=par, fn=fn, lower=lower, upper=upper);
}
}
});
if(!is.null(result)) {
resultpar <- result$par;
if(FunctionalModel.par.check(model, resultpar)) {
resultq <- result$fval;
if(learning.checkQuality(resultq)) {
return(FittedFunctionalModel.new(model, resultpar, resultq));
}
}
}
result <- newuoa(par=par, fn=fn);
if(!is.null(result)) {
resultpar <- result$par;
if(FunctionalModel.par.check(model, resultpar)) {
resultq <- result$fval;
if(learning.checkQuality(resultq)) {
return(FittedFunctionalModel.new(model, resultpar, resultq));
}
}
}
});
return(NULL);
}
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