R/simKKDKS.R

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

#'@title SFS similarity measure simKKDKS
#'@description SFS similarity measure values using simKKDKS computation technique with membership,non-membership, and indeterminacy membership values of two objects or set of objects.
#'@param ma SFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function
#'@param na SFS non-membership values for the data set x computed using either Sugeno and Terano's  or Yager's non-membership function
#'@param ia SFS indeterminacy membership values for the data set x
#'@param mb SFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function
#'@param nb SFS non-membership values for the data set y computed using either Sugeno and Terano's  or Yager's non-membership function
#'@param ib SFS indeterminacy membership values for the data set y
#'@param k A constant value, considered as 1
#'@return The SFS similarity values of data set y with data set x
#'@references M. J. Khan, P. Kumam, W. Deebani,W. Kumam, and Z. Shah. Distance and similarity measures for spherical fuzzy sets and their applications in selecting mega projects. Mathematics, 8(4):519, 2020.
#'@examples
#'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)
#'ia<-imemSFS(ma,na)
#'mb<-memG(a1,b1,y)
#'nb<-nonmemS(mb,lam)
#'ib<-imemSFS(mb,nb)
#'k<-1
#'simKKDKS(ma,na,mb,nb,ia,ib,k)
#'#[1] 0.5726216 0.3223250 0.2791418 0.2791418
#'@export
simKKDKS<-function(ma,na,mb,nb,ia,ib,k){
  c<-matrix(0,nrow=nrow(ma),ncol=ncol(ma))
  d<-matrix(0,nrow=nrow(ma),ncol=ncol(ma))
  for (i in 1:nrow(ma)) {
    for(j in 1:ncol(ma))
      c[i,j]<-(ma[i,j]^2)*(mb[k,j]^2)+(na[i,j]^2)*(nb[k,j]^2)+(ia[i,j]^2)*(ib[k,j]^2)
    d[i,j]<-max(ma[i,j]^4,mb[k,j]^4)+max(na[i,j]^4,nb[k,j]^4)+max(ia[i,j]^4,ib[k,j]^4)
  }
  for(j in 1:ncol(c)){
    sum<-1-((1/ncol(c))*(rowSums(c)/rowSums(d)))
  }
  sum
}