# predict.frbs: The frbs prediction stage In frbs: Fuzzy Rule-Based Systems for Classification and Regression Tasks

## 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

 1 2 ## S3 method for class '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.

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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 ################################## ## 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) 

### Example output




frbs documentation built on May 29, 2017, 9:08 p.m.