fit the FuzzyCoco model using the dataframe interface — fit_xy.fuzzycoco

N.B: the underlying C++ implementation is able to automatically set some missing parameters (NA). The final parameters are those returned by the function, is the params slot.

# S3 method for class 'fuzzycoco_model'
fit_xy(
  object,
  x,
  y,
  engine = FUZZY_COCO_HYBRID_ENGINE,
  max_generations = object$params$global_params$max_generations,
  max_fitness = object$params$global_params$max_fitness,
  seed = object$seed,
  verbose = object$verbose,
  ...
)

Arguments

object

the fuzzycoco_model object to fit

x

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

y

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

engine

the fuzzy coco fit engine to use, one of rcpp and hybrid

max_generations

The maximum number of iterations of the algorithm. Each iteration produces a new generation of the rules and membership functions populations.

max_fitness

a stop condition: the iterations stop as soon as a generated fuzzy system fitness reaches that threshold.

seed

the RNG seed to use (to fit the model)

verbose

whether to be verbose

...

Arguments passed on to fuzzycoco_fit_df_hybrid

model: a Fuzzy Coco model as a fuzzy_coco object
progress: whether to display the computation progress (using progressr, if available)
until: function that takes an engine and returns TRUE if and only if the evolution must stop. It is a way for the user to customize the stop conditions of the algorithm.

Value

the fit as a named list

Examples

model <- fuzzycoco("regression", example_mtcars()$params, seed = 123)
x <- mtcars[c("mpg", "hp", "wt")]
y <- mtcars["qsec"]
fit <- fit_xy(model, x, y, progress = FALSE)
print(names(fit))
#> [1] "fit"          "fuzzy_system" "params"       "engine"       "seed"        
#> [6] "mode"         "infos"