R/simNNNG1.R

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

#'@title PFS similarity measure simNNNG1
#'@description PFS similarity measure values using simNNNG1 computation technique with membership, and non-membership values of two objects or set of objects.
#'@param ma PFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function
#'@param na PFS non-membership values for the data set x computed using either Sugeno and Terano's  or Yager's non-membership function
#'@param mb PFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function
#'@param nb PFS non-membership values for the data set y computed using either Sugeno and Terano's  or Yager's non-membership function
#'@param k A constant value, considered as 1
#'@return The PFS similarity values of data set y with data set x
#'@references X. T. Nguyen, V. D. Nguyen, V. H. Nguyen, and H. Garg. Exponential similarity measures for pythagorean fuzzy sets and their applications to pattern recognition and decision-making process. Complex & Intelligent Systems, 5(2):217 - 228, 2019.
#'@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)
#'mb<-memG(a1,b1,y)
#'nb<-nonmemS(mb,lam)
#'k<-1
#'simNNNG1(ma,na,mb,nb,k)
#'#[1] 0.5885775 0.5995230 0.8202927 0.8202927
#'@export
simNNNG1<-function(ma,na,mb,nb,k){
  c<-matrix(0,nrow=nrow(ma),ncol=ncol(ma))
  for (i in 1:nrow(ma)) {
    for(j in 1:ncol(ma))
      c[i,j]<-exp(-(abs(ma[i,j]^2-mb[k,j]^2)))*exp(-(abs(na[i,j]^2-nb[k,j]^2)))
  }
  for(j in 1:ncol(c)){
    sum<-(1/ncol(c))*(rowSums(c))
  }
  sum
}