##
##
## Not run:
#We use the iris dataset for this example
#We need to convert the output in a numeric format.
data(iris)

library(fugeR)

## Start input data
load("iris.5f.RData")
tra.iris.1 <- data.frame(iris.5.1.tra)
tra.iris.2 <- data.frame(iris.5.2.tra)
tra.iris.3 <- data.frame(iris.5.3.tra)
tra.iris.4 <- data.frame(iris.5.4.tra)
tra.iris.5 <- data.frame(iris.5.5.tra)


tst.iris.1 <- data.frame(iris.5.1.tst[, 1 : 4])
tst.iris.2 <- data.frame(iris.5.2.tst[, 1 : 4])
tst.iris.3 <- data.frame(iris.5.3.tst[, 1 : 4])
tst.iris.4 <- data.frame(iris.5.4.tst[, 1 : 4])
tst.iris.5 <- data.frame(iris.5.5.tst[, 1 : 4])

real.iris.1 <- data.frame(matrix(iris.5.1.tst[, 5], ncol = 1))
real.iris.2 <- data.frame(matrix(iris.5.2.tst[, 5], ncol = 1))
real.iris.3 <- data.frame(matrix(iris.5.3.tst[, 5], ncol = 1))
real.iris.4 <- data.frame(matrix(iris.5.4.tst[, 5], ncol = 1))
real.iris.5 <- data.frame(matrix(iris.5.5.tst[, 5], ncol = 1))

#Launch the evolution, fugeR.run will return
#the best fuzzy system found during the evolution
## 1
fuzzySystem.1 <- fugeR.run(tra.iris.1[, 1:4],
tra.iris.1[, 5, drop = FALSE],
generation=100, # Increase the number of generation for a better accuracy
population=100,
elitism=20,
verbose=TRUE,
threshold=NA,
sensiW=0.0,
speciW=0.0,
accuW=0.0,
rmseW=1.0,
maxRules=10,
maxVarPerRule=2,
labelsMf=3
)

## 2
fuzzySystem.2 <- fugeR.run( tra.iris.2[, 1:4],
tra.iris.2[, 5, drop = FALSE],
generation=100, # Increase the number of generation for a better accuracy
population=100,
elitism=20,
verbose=TRUE,
threshold=NA,
sensiW=0.0,
speciW=0.0,
accuW=0.0,
rmseW=1.0,
maxRules=10,
maxVarPerRule=2,
labelsMf=3
)

## 3
fuzzySystem.3 <- fugeR.run( tra.iris.3[, 1:4],
tra.iris.3[, 5, drop = FALSE],
generation=100, # Increase the number of generation for a better accuracy
population=100,
elitism=20,
verbose=TRUE,
threshold=NA,
sensiW=0.0,
speciW=0.0,
accuW=0.0,
rmseW=1.0,
maxRules=10,
maxVarPerRule=2,
labelsMf=3
)

## 4
fuzzySystem.4 <- fugeR.run( tra.iris.4[, 1:4],
tra.iris.4[, 5, drop = FALSE],
generation=100, # Increase the number of generation for a better accuracy
population=100,
elitism=20,
verbose=TRUE,
threshold=NA,
sensiW=0.0,
speciW=0.0,
accuW=0.0,
rmseW=1.0,
maxRules=10,
maxVarPerRule=2,
labelsMf=3
)

## 5
fuzzySystem.5 <- fugeR.run( tra.iris.5[, 1:4],
tra.iris.5[, 5, drop = FALSE],
generation=100, # Increase the number of generation for a better accuracy
population=100,
elitism=20,
verbose=TRUE,
threshold=NA,
sensiW=0.0,
speciW=0.0,
accuW=0.0,
rmseW=1.0,
maxRules=10,
maxVarPerRule=2,
labelsMf=3
)

#Plot the predicton given by the best fuzzy system found during the evolution
pred.1 <- round(fugeR.predict(fuzzySystem.1, tst.iris.1))
pred.2 <- round(fugeR.predict(fuzzySystem.2, tst.iris.2))
pred.3 <- round(fugeR.predict(fuzzySystem.3, tst.iris.3))
pred.4 <- round(fugeR.predict(fuzzySystem.4, tst.iris.4))
pred.5 <- round(fugeR.predict(fuzzySystem.5, tst.iris.5))

err.1 = 100*sum(real.iris.1!=pred.1)/nrow(real.iris.1)
err.2 = 100*sum(real.iris.2!=pred.2)/nrow(real.iris.2)
err.3 = 100*sum(real.iris.3!=pred.3)/nrow(real.iris.3)
err.4 = 100*sum(real.iris.4!=pred.4)/nrow(real.iris.4)
err.5 = 100*sum(real.iris.5!=pred.5)/nrow(real.iris.5)

err.aver <- (err.1 + err.2 + err.3 + err.4 +err.5)/5
print(err.aver)