simBA: IFS similarity measure simBA

In ipsfs: Intuitionistic, Pythagorean, and Spherical Fuzzy Similarity Measure

IFS similarity measure simBA

Description

IFS similarity measure values using simBA computation technique with membership, and non-membership of two objects or set of objects.

Usage

simBA(ma, na, mb, nb, p, t, k)

Arguments

ma	IFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function
na	IFS non-membership values for the data set x computed using either Sugeno and Terano's or Yager's non-membership function
mb	IFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function
nb	IFS non-membership values for the data set y computed using either Sugeno and Terano's or Yager's non-membership function
p	Lp norm values for measuring the p-norm distance between x and y, values range from 1 to 5
t	Level of uncertainty values, values range from 1 to 10
k	A constant value depends upon the number of rows in the y data set.

Value

The IFS similarity values of data set y with data set x

References

F. E. Boran and D. Akay. A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition. Information sciences, 255:45 - 57, 2014.

Examples

#When data set y consist of only one row use k=1
x<-matrix(c(12,9,14,11,21,16,15,24,20,17,14,11),nrow=4)
y<-matrix(c(11,21,6),nrow=1)
a<-mn(x)
b<-std(x)
a1<-mn(y)
b1<-std(y)
lam<-0.5
ma<-memG(a,b,x)
na<-nonmemS(ma,lam)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
p<-2
t<-2
k<-1
simBA(ma,na,mb,nb,p,t,k)
#0.7072291 0.6947466 0.8919850 0.8919850

#When data set y having more than one rows
#use k = the number of rows of data set y
x<-matrix(c(12,9,14,11,21,16,15,24,20,17,14,11),nrow=4)
y<-matrix(c(11,24,21,12,6,11),nrow=2)
a<-mn(x)
b<-std(x)
a1<-mn(y)
b1<-std(y)
lam<-0.5
ma<-memG(a,b,x)
na<-nonmemS(ma,lam)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
p<-2
t<-2
sim<-c()
for(k in 1:nrow(y)){sim<-rbind(sim,simBA(ma,na,mb,nb,p,t,k))}
sim
#       [,1]      [,2]      [,3]      [,4]
#[1,] 0.7072291 0.6947466 0.8919850 0.8919850
#[2,] 0.9410582 0.9843247 0.7380007 0.7380007

ipsfs documentation built on June 21, 2022, 5:07 p.m.