library(frbs)
## 1. Preparing data: 
## In this example, we use the gas furnace dataset included in the frbs package. 
## Therefore, we only need to load it.
data(frbsData)
data.train <- frbsData$GasFurnance.dt[1 : 204, ]
data.fit <- data.train[, 1 : 2]
data.tst <- frbsData$GasFurnance.dt[205 : 292, 1 : 2]
real.val <- matrix(frbsData$GasFurnance.dt[205 : 292, 3], ncol = 1)
range.data <- matrix(c(-2.716, 2.834, 45.6, 60.5, 45.6, 60.5), nrow = 2)

## 2. Constructing an FRBS model:
## Set the method and its parameters. See the frbs manual for more detail.
method.type <- "WM"
control <- list(num.labels = 3, type.mf = "GAUSSIAN", type.defuz = "WAM",
           type.tnorm = "MIN", type.implication.func = "ZADEH",
           name = "sim-0")
		   
## Generate fuzzy model
object <- frbs.learn(data.train, range.data, method.type, control)

## 3. Exporting the model to PMML format:
## In this step, we provide two ways as follows.
## a. by calling frbsPMML() function directly.
objPMML <- frbsPMML(object)

## b. by calling write.frbsPMML() function. the result will be saved as a file
## in the working directory.  
write.frbsPMML(objPMML, file = "MAMDANI.GasFur")

## 4. Importing the frbsPMML format to an FRBS model:
## There are two ways: using frbs and frbsJpmml.
## If we want to import frbsPMML format using frbs, we execute the following command in the R environment.
## In case of frbsJpmml, we import and predict at the same time. It will be explained on step 5.
object <- read.frbsPMML("MAMDANI.GasFur.frbsPMML")

## 5. Prediction over data:
## a. Using frbs:
res.test <- predict(object, data.tst)

## b. Using frbsJpmml: 
## It should be noted that the following command is executed on command prompt, and GasFur.tst.txt is a file containing testing data. See the file.
java -jar frbsJpmml.jar "MAMDANI.GasFur.frbsPMML" "GasFur.tst.txt"