frbs (version 3.2-0)

predict.frbs: The frbs prediction stage

Description

This is the main function to obtain a final result as predicted values for all methods in this package. In order to get predicted values, this function is run using an frbs-object, which is typically generated using frbs.learn.

Usage

# S3 method for frbs
predict(object, newdata, ...)

Arguments

object

an frbs-object.

newdata

a data frame or matrix (\(m \times n\)) of data for the prediction process, where \(m\) is the number of instances and \(n\) is the number of input variables. It should be noted that the testing data must be expressed in numbers (numerical data).

...

the other parameters (not used)

Value

The predicted values.

See Also

frbs.learn and frbs.gen for learning and model generation, and the internal main functions of each method for the theory: WM, SBC, HyFIS, ANFIS, FIR.DM, DENFIS, FS.HGD, FRBCS.W, GFS.FR.MOGUL, GFS.Thrift, GFS.GCCL, FRBCS.CHI, FH.GBML, GFS.LT.RS, and SLAVE.

Examples

# NOT RUN {
##################################
## I. Regression Problem
###################################
## In this example, we just show how to predict using Wang and Mendel's technique but
## users can do it in the same way for other methods.
data.train <- matrix(c(5.2, -8.1, 4.8, 8.8, -16.1, 4.1, 10.6, -7.8, 5.5, 10.4, -29.0, 
                       5.0, 1.8, -19.2, 3.4, 12.7, -18.9, 3.4, 15.6, -10.6, 4.9, 1.9, 
                       -25.0, 3.7, 2.2, -3.1, 3.9, 4.8, -7.8, 4.5, 7.9, -13.9, 4.8, 
                       5.2, -4.5, 4.9, 0.9, -11.6, 3.0, 11.8, -2.1, 4.6, 7.9, -2.0, 
                       4.8, 11.5, -9.0, 5.5, 10.6, -11.2, 4.5, 11.1, -6.1, 4.7, 12.8, 
                       -1.0, 6.6, 11.3, -3.6, 5.1, 1.0, -8.2, 3.9, 14.5, -0.5, 5.7, 
                       11.9, -2.0, 5.1, 8.1, -1.6, 5.2, 15.5, -0.7, 4.9, 12.4, -0.8, 
                       5.2, 11.1, -16.8, 5.1, 5.1, -5.1, 4.6, 4.8, -9.5, 3.9, 13.2, 
                       -0.7, 6.0, 9.9, -3.3, 4.9, 12.5, -13.6, 4.1, 8.9, -10.0, 
                       4.9, 10.8, -13.5, 5.1), ncol = 3, byrow = TRUE)

data.fit <- matrix(c(10.5, -0.9, 5.2, 5.8, -2.8, 5.6, 8.5, -0.2, 5.3, 13.8, -11.9,
                     3.7, 9.8, -1.2, 4.8, 11.0, -14.3, 4.4, 4.2, -17.0, 5.1, 6.9, 
                     -3.3, 5.1, 13.2, -1.9, 4.6), ncol = 3, byrow = TRUE)

newdata <- matrix(c(10.5, -0.9, 5.8, -2.8, 8.5, -0.2, 13.8, -11.9, 9.8, -1.2, 11.0,
                      -14.3, 4.2, -17.0, 6.9, -3.3, 13.2, -1.9), ncol = 2, byrow = TRUE)

range.data<-matrix(c(0.9, 15.6, -29, -0.2, 3, 6.6), ncol=3, byrow = FALSE)
#############################################################
## I.1 Example: Implementation of Wang & Mendel
#############################################################
method.type <- "WM" 

## collect control parameters into a list
## num.labels = 3 means we define 3 as the number of linguistic terms
control.WM <- list(num.labels = 3, type.mf = "GAUSSIAN", type.tnorm = "MIN", 
               type.snorm = "MAX", type.defuz = "WAM", 
               type.implication.func = "ZADEH", name = "Sim-0") 

## generate the model and save it as object.WM
object.WM <- frbs.learn(data.train, range.data, method.type, control.WM)

## the prediction process
## The following code can be used for all methods
res <- predict(object.WM, newdata)

# }