frbs.learn: The frbs model building function

Description

This is one of the central functions of the package. This function is used to generate/learn the model from numerical data using fuzzy rule-based systems.

Usage

frbs.learn(data.train, range.data, method.type = c("WM"),
    control = list())

Arguments

data.train

a matrix(m x n) of data for the training process, where m is the number of instances and n is the number of variables; the last column is the output variable.

range.data

a matrix(2 x n) containing the range of the data, where n is the number of variables, and first and second rows are the minimum and maximum values, respectively.

method.type

this parameter determines the learning algorithm to use. The following methods are implemented: "WM": Wang and Mendel, "SBC": subtractive clustering, "GFS": genetic fuzzy system, "HYFIS": hybrid neural fuzzy inference systems, "ANFIS": adaptive ne

control

a list containing all arguments, depending on the learning algorithm to use.

WM method

num.labels: a positive integer to determine the number of labels (fuzzy terms). The default value is 7.
type.mf: the type of the membe

Value

The frbs-object.

Details

This function makes accessible all ten learning methods that are implemented in this package. All of the methods use this function as interface for the learning stage, so users do not need to call other functions in the learning phase. In order to obtain good results, users need to adjust some parameters such as the number of labels, the type of the shape of the membership function, the maximal number of iterations, the step size of the gradient descent, or other method-dependent parameters which are collected in the control parameter. After creating the model using this function, it can be used to predict new data with predict.

Examples

Run this code

##################################
## I. Regression Problem
## The data has two input variables and one output variable.
## We separate them into training, fitting, and testing data.
## data.train, data.fit, data.test, and range.data are inputs
## for all methods except the frcs method.
###################################
## The simulation might take a long time dependent on the hardware we use.
## We might get better results if we take other parameters.

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)

data.test <- 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 The example: Implementation of Wang & Mendel
#############################################################
method.type <- "WM"

## collect control parameters into a list
control.WM <- list(num.labels = 3, type.mf = 3, name = "Sim-0")

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

#############################################################
## I.2 The example: Implementation of SBC
#############################################################
method.type <- "SBC"
control.SBC <- list(r.a = 0.5, eps.high = 0.5, eps.low = 0.15, name = "Sim-0")

object.SBC <- frbs.learn(data.train, range.data, method.type, control.SBC)

#############################################################
## I.3 The example: Implementation of HyFIS
#############################################################
method.type <- "HyFIS"

control.HyFIS <- list(num.labels = 5, max.iter = 50, step.size = 0.01,
                 name = "Sim-0")

object.HyFIS <- frbs.learn(data.train, range.data, method.type, control.HyFIS)

#############################################################
## I.4 The example: Implementation of ANFIS
#############################################################
method.type <- "ANFIS"

control.ANFIS <- list(num.labels = 5, max.iter = 100, step.size = 0.01,
                      name = "Sim-0")

object.ANFIS <- frbs.learn(data.train, range.data, method.type, control.ANFIS)

#############################################################
## I.5 The example: Implementation of DENFIS
#############################################################

control.DENFIS <- list(Dthr = 0.1, max.iter = 100, step.size = 0.001, d = 2,
                       name = "Sim-0")
method.type <- "DENFIS"

object.DENFIS <- frbs.learn(data.train, range.data, method.type, control.DENFIS)

#############################################################
## I.6 The example: Implementation of DM
#############################################################
method.type <- "DM"

control.DM <- list(num.labels = 5, max.iter = 100, step.size = 0.01, name = "Sim-0")
object.DM <- frbs.learn(data.train, range.data, method.type, control.DM)

#############################################################
## I.7 The example: Implementation of HGD
#############################################################
method.type <- "HGD"

control.HGD <- list(num.labels = 5, max.iter = 100, step.size = 0.01,
               alpha.heuristic = 1, name = "Sim-0")
object.HGD <- frbs.learn(data.train, range.data, method.type, control.HGD)

#############################################################
## I.8 The example: Implementation of GFS
#############################################################
method.type <- "GFS"

control.GFS <- list(num.labels = 5, popu.size = 5, persen_cross = 0.9,
                    max.iter = 5, persen_mutant = 0.1,
                    classification = FALSE, name="sim-0")
object.GFS <- frbs.learn(data.train, range.data, method.type, control.GFS)

#############################################################
## I.9 The example: Implementation of MSGFS
#############################################################
method.type <- "MSGFS"

control.MSGFS <- list(popu.size = 15, persen_cross = 0.6,
                    max.iter = 20, persen_mutant = 0.3,
                    epsilon = 0.05, name="sim-0")
object.MSGFS <- frbs.learn(data.train, range.data, method.type, control.MSGFS)

#############################################################
## II. Classification Problem
#############################################################
## II.1 The example: Implementation of fuzzy rule-based classification systems (frcs)
## The iris dataset is shuffled and divided into training and
## testing data. Bad results in the predicted values may
## result from casual imbalanced classes in the training data.

data(iris)
irisShuffled <- iris[sample(nrow(iris)),]
irisShuffled[,5] <- unclass(irisShuffled[,5])
tra.iris <- irisShuffled[1:105,]
tst.iris <- irisShuffled[106:nrow(irisShuffled),1:4]
real.iris <- matrix(irisShuffled[106:nrow(irisShuffled),5], ncol = 1)

range.data.input <- matrix(c(4.3, 7.9, 2.0, 4.4, 1.0, 6.9, 0.1, 2.5), nrow=2)

## generate the model
method.type <- "FRCS"
control <- list(num.labels = 7, type.mf = 1)

object <- frbs.learn(tra.iris, range.data.input, method.type, control)

## conduct the prediction process
res.test <- predict(object, tst.iris)

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