Rfuzzycoco (version 0.1.0)

fuzzy_coco_systematic_fit: systematic search

Description

This is a R implementation of a systematic search, where the fuzzy set positions are determined by the distribution of the data (cf compute_optimal_quantile_fuzzy_set_positions() and the rules are systematically explored

Usage

fuzzy_coco_systematic_fit(x, y, params, fitter)

Value

a list of the best results (all ties). Each result is also a named list(metric=,fs=) holding the corresponding metric value and the fuzzy system.

Arguments

x

the input variables data (usually to fit) as a dataframe

y

the output variables data (usually to fit) as a dataframe

params

fuzzy coco parameters, as a recursive named list, cf params()

fitter

a function metrics --> fitness value providing the objective/fitness function to optimize TODO: describe the metrics

Details

N.B: this is experimental, only possible for a small number of variables. Not all parameters are used, obviously, and currently fitness_params$output_vars_defuzz_thresholds has to be set explicitly.

Examples

fitter <- function(metrics) 2^-metrics$rms
params <- example_mtcars()$params
params$fitness_params$output_vars_defuzz_thresholds <- 0
params$global_params$nb_rules <- 1
params$global_params$nb_max_var_per_rule <- 2
params$output_vars_params$nb_sets <- 2

x <- mtcars[c("mpg", "hp", "wt")]
y <- mtcars["qsec"]
fit <- fuzzy_coco_systematic_fit(x, y, params, fitter)