## ----include = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
old <- options(width = 200)

## ----setup--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(Rfuzzycoco)

## ----preprocess---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
x <- mtcars[c("mpg", "hp", "wt")]
y <- mtcars["qsec"]

pms <- params(
  nb_rules = 2, nb_max_var_per_rule = 3,        # structural parameters
  rules.pop_size = 100, mfs.pop_size = 100,     # coevolution population sizes
  ivars.nb_sets = 3, , ivars.nb_bits_pos = 8,   # input vars: 3 fuzzy sets, and 8 bits to discretize the values 
  ovars.nb_sets = 3, ovars.nb_bits_pos = 8,     # output vars: 3 fuzzy sets, and 8 bits to discretize the values 
  metricsw.sensitivity = 0, metricsw.specificity = 0, metricsw.rmse = 1, # we just use RMSE (root mean square error)
  output_vars_defuzz_thresholds = 17            # threshold for the qsec output variable
)

# the full list of generated parameters
str(pms)

## ----model--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
model <- fuzzycoco("regression", pms, seed = 123)
class(model)

## ----fit_xy-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# N.B: progress = FALSE because we are in a vignette
fit <- fit_xy(model, x, y, progress = FALSE)

# we get a "fuzzycoco_fit" object
class(fit)

# that has many components
names(fit)

# among which: the model fitting information 
str(fit$fit)

# the (best) fitted Fuzzy System 
str(fit$fuzzy_system)

## ----fit----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
df <- cbind(x, y)
# N.B: we use a different engine, and also overwrite `max_generations` and `seed` from the model parameters
fit <- fit(model, qsec ~ ., df, engine = "rcpp", seed = 456, max_generations = 20)

str(fit$fit)

## ----evaluate-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# evaluating on the same data used to fit it should obviously get the same results
res <- evaluate(fit, df)
str(res)

# let's modify the data
df2 <- df
df2$qsec <- rnorm(nrow(df2), 17)
res2 <- evaluate(fit, df2)
str(res2)

## ----predict------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
y2 <- predict(fit, x)

# recompute rmse
rmse2 <- sqrt(mean((y2[[1]] - y[[1]])^2))
# compare it to what was reported
all.equal(rmse2,  fit$fit$metrics$rmse)

## ----cleanup, include = FALSE-------------------------------------------------
# restoring options
options(old)