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R/IFMCDM.r
In IFMCDM: Intuitionistic Fuzzy Multi-Criteria Decision Making Methods
Defines functions
intuitionisticSyntheticMeasure
IFTOPSIS
IFSM
Documented in
IFSM
IFTOPSIS
#' Implementation of the Intuitionistic Fuzzy Synthetic Measure Method for Fuzzy Multi-Criteria Decision Making Problems
#'
#' @description The \code{IFSM} - Intuitionistic Fuzzy Synthetic Measure Method for Fuzzy Multi-Criteria Decision Making Problems. Reference describing the method: Jefmański, Roszkowska, Kusterka-Jefmańska (2021) \doi{10.3390/e23121636}
#' @param data The data matrix (\emph{m} x \emph{n*3}) with the values of \emph{mi} \emph{ni} and \emph{pi} (three columns for each intuitionistic fuzzy representation of criteria for each alternative) where \emph{m} is the number of alternatives and \emph{n} is the number of criteria.
#' @param d Distance "euclidean" or "hamming".
#' @param w A vector of length \emph{n}, containing the crisp weights for the criteria (one value for intuitionistic fuzzy representation).
#' @param z A vector of length \emph{n}, with preferences type for each criterion with "b" (benefit) and "c" (cost).
#' @param p Ideal point calculation type with one of two values: "dataBounds" – ideal point contains max and min values from the dataset – see details; "idealBounds" – ideal point contains 1 and 0’s - see details.
#' @return \code{IFSM} returns a data frame that contains the scores of the Intuitionistic Fuzzy Synthetic Measure (IFSM) and the ranking of the alternatives.
#' @references Jefmański B, Roszkowska E, Kusterka-Jefmańska M. Intuitionistic Fuzzy Synthetic Measure on the Basis of Survey Responses and Aggregated Ordinal Data. Entropy. 2021; 23(12):1636. \doi{10.3390/e23121636}
#'
#' Roszkowska E, Jefmański B, Kusterka-Jefmańska M. On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights. Entropy. 2022; 24(8):1081. \doi{10.3390/e24081081}
#'
#' Xu, Z. Some Similarity Measures of Intuitionistic Fuzzy Sets and Their Applications to Multiple Attribute Decision Making. Fuzzy Optimization and Decision Making. 2007; 6: 109–121. \doi{10.1007/s10700-007-9004-z}
#'
#' @details For p="dataBounds" the actual ideal point is calculated for benefits as maximum from all values for \emph{mi} and min for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}); in the case of costs, minimal value for \emph{mi} and max for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}).
#' For p="idealBounds" for benefitss is 1 for \emph{mi} and 0 for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni} ). In the case of costs it is 0 for \emph{mi} and 1 for \emph{ni} (\emph{pi} = 1- (\emph{mi} - \emph{ni}).
#' @examples
#' set.seed(823)
#' data<-sample(1:7,26*13*8,replace=TRUE)
#' dim(data)<-c(26*13,8)
#' nrColumns<-8
#' primary<-data.frame(name=rep(LETTERS,each=13),data)
#' f<-IFconversion(primary)
#' print(f)
#' m<-IFSM(f)
#' print(m)
#' @export
IFSM<- function(data, #matrix with all the alternatives
d = "e", # distance "euclidean" or "hamming"
w = rep(3/ncol(data),ncol(data)/3), # weights for each criterion (variable)
z = rep("b",ncol(data)/3), # preferences vector for each variable with "b" (benefit) and "c" (cost) values
p = "dataBounds") # Ideal Point object vector or string with one of two values: "dataBounds" - ideal point object contains maximas and minimas from dataset, "idealBounds" - ideal point object contains 1 and 0's - see details.
{
intuitionisticSyntheticMeasure(data,d,w,z,p,ap="dataBounds",type="SM")
}
#' Implementation of the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution for Fuzzy Multi-Criteria Decision Making Problems
#'
#' @description The \code{IFTOPSIS} - Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution for Fuzzy Multi-Criteria Decision Making. Reference describing the method: Roszkowska, Kusterka-Jefmańska, Jefmański (2021) \doi{10.3390/e23050563}
#' @param data The data matrix (\emph{m} x \emph{n*3}) with the values of \emph{mi} \emph{ni} and \emph{pi} (three columns for each intuitionistic fuzzy representation of criteria for each alternative), where \emph{m} is the number of alternatives and \emph{n} is the number of criteria.
#' @param d Distance "euclidean" or "hamming".
#' @param w A vector of length \emph{n}, containing the crisp weights for the criteria (one value for intuitionistic fuzzy representation)
#' @param z A vector of length \emph{n}, with preferences type for each criterion with "b" (benefit) and "c" (cost).
#' @param p Ideal point calculation type with one of two values: "dataBounds" – ideal point contains max and min values from the dataset – see details; "idealBounds" – ideal point contains 1 and 0’s - see details.
#' @param ap Anti-ideal point calculation type with one of two values: "dataBounds" – anti-ideal point contains min and max from the dataset – see details; "idealBounds" – anti-ideal point contains 0 and 1’s - see details.
#' @return \code{IFTOPSIS} returns a data frame that contains the scores of the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IFTOPSIS) and the ranking of the alternatives.
#' @details For p="dataBounds" the actual ideal point is calculated for benefits as maximum from all values for \emph{mi} and min for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}); in the case of costs, minimal value for \emph{mi} and max for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}).
#' For p="idealBounds" for benefitss is 1 for \emph{mi} and 0 for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni} ). In the case of costs it is 0 for \emph{mi} and 1 for \emph{ni} (\emph{pi} = 1- (\emph{mi} - \emph{ni}).
#' For ap="dataBounds" the actual anti-ideal point is calculated for benefit criteria as minimum of all values for \emph{mi}, maximum of all values for \emph{ni} and \emph{pi} = 1- (\emph{mi} + \emph{ni}); in the case of cost criteria, maximum of all values for \emph{mi},
#' minimum of all values for \emph{ni} and \emph{pi} = 1- (\emph{mi} + \emph{ni}).
#' For ap="idealBounds" in the case of benefit criteria it is 0 for \emph{mi},
#' 1 for \emph{ni}, 0 for \emph{pi}; in the case of cost criteria it is 1 for \emph{mi}, 0 for \emph{ni} and 0 for \emph{pi}.
#' @references Roszkowska E, Kusterka-Jefmańska M, Jefmański B. Intuitionistic Fuzzy TOPSIS as a Method for Assessing Socioeconomic Phenomena on the Basis of Survey Data. Entropy. 2021; 23(5):563. \doi{10.3390/e23050563}
#'
#' Xu, Z. Some Similarity Measures of Intuitionistic Fuzzy Sets and Their Applications to Multiple Attribute Decision Making. Fuzzy Optimization and Decision Making. 2007; 6: 109–121. \doi{10.1007/s10700-007-9004-z}
#'
#' @examples
#' set.seed(823)
#' data<-sample(1:7,26*13*8,replace=TRUE)
#' dim(data)<-c(26*13,8)
#' nrColumns<-8
#' primary<-data.frame(name=rep(LETTERS,each=13),data)
#' f<-IFconversion(primary)
#' m<-IFTOPSIS(f)
#' print(m)
#' @export
IFTOPSIS<- function(data, #matrix with all the alternatives
d = "e", # distance "euclidean" or "hamming"
w = rep(3/ncol(data),ncol(data)/3), # weights for each criterion (variable)
z = rep("b",ncol(data)/3), # preferences vector for each variable with "b" (benefit) and "c" (cost) values
p = "dataBounds", # Ideal point object vector or string with one of two values: "dataBounds" - ideal point object contains maximas and minimas from dataset, "idealBounds" - ideal point object contains 1 and 0's - see details.
ap = "dataBounds") # Anti ideal point object vector or string with one of two values: "dataBounds" - anti ideal point object contains minimas and maximas from dataset, "idealBounds" - anti ideal point object contains 0 and 1's - see details.
{
intuitionisticSyntheticMeasure(data,d,w,z,p,ap,type="TOPSIS")
}
#' @keywords internal
intuitionisticSyntheticMeasure<- function(data,d,w,z,p,ap,type)
{
#on macos machines sometimes 0.5+0.5+0.0 isn't equal to 1
tollerance=0.000001
if (!requireNamespace("dplyr", quietly = TRUE)) {
stop(
"Package \"dplyr\" must be installed to use this function.",
call. = FALSE
)
}
if(ncol(data)%%3 !=0){
stop ("For inthuitionistic data datasets should contain three columns for each variable")
}
if(any(data<0)) {
stop ("For intuitionistic data all values should be in range 0-1")
}
if(any(data>1)) {
stop ("For intuitionistic data all values should be in range 0-1")
}
for(i in 1:(ncol(data)%/%3)){
if(any(abs(apply(cbind(data[,3*i-2],data[,3*i-1],data[,3*i]),1,sum)-1)>tollerance)){
stop ("For intuitionistic fuzzy data mi,ni and pi should sum to 1")
}
}
if(length(w)!=ncol(data)%/%3) {
stop ("length of weights vector w should be equal to number of criteria")
}
if(any(w<0)) {
stop ("all weights in vector w should be in range 0-1")
}
if(any(w>1)) {
stop ("all weights in vector w should be in range 0-1")
}
if(abs(sum(w)-1)>tollerance) {
stop ("the weights in vector w should sum to 1")
}
if(!d %in% c("euclidean","e","h","hamming")){
stop ("the distance parameter should be one of the following \"euclidean\" (\"e)\" or \"hamming\" (\"h\")")
}
if(length(z)!=ncol(data)%/%3) {
stop ("length of preferences vector z should be equal to number of variables")
}
if(!all(z %in% c("b","c"))){
stop ("the preferences vector z should contain values \"b\" or \"c\"")
}
if(length(p)==1){
if(!p %in% c("dataBounds","idealBounds")){
stop ("the ideal point parameter p should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with ideal point coordinates ")
}
}
else{
if(length(p)!=ncol(data)){
stop ("the ideal point parameter p should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with ideal point coordinates ")
}
for(i in 1:(length(p)%/%3)){
if(abs(p[,3*i-2]+p[,3*i-1]+p[,3*i]-1)>tollerance){
stop ("For ideal point object each three mi,ni,pi, should sum to 1")
}
}
}
if(length(ap)==1){
if(!ap %in% c("dataBounds","idealBounds")){
stop ("the anti ideal point parameter ap should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with anti ideal point parameters ")
}
}else{
if(length(ap)!=ncol(data)){
stop ("the anti ideal point parameter ap should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with anti ideal point parameters ")
}
for(i in 1:(length(ap)%/%3)){
if(abs(ap[,3*i-2]+ap[,3*i-1]+ap[,3*i]-1)>tollerance){
stop ("For anti ideal point object each three mi,ni,pi, should sum to 1")
}
}
}
if(length(p)==1){
pattern<-NULL
apattern<-NULL
for(i in 1:(ncol(data)%/%3)){
if(p=="dataBounds"){
if(z[i]=="b"){
mi=max(data[,3*i-2])
ni=min(data[,3*i-1])
pi=1-mi-ni
ami=min(data[,3*i-2])
ani=max(data[,3*i-1])
api=1-ami-ani
} else{
mi=min(data[,3*i-2])
ni=max(data[,3*i-1])
pi=1-mi-ni
ami=max(data[,3*i-2])
ani=min(data[,3*i-1])
api=1-ami-ani
}
pattern<-c(pattern,mi,ni,pi)
apattern<-c(apattern,ami,ani,api)
}
else {
if(z[i]=="b"){
pattern<-c(pattern,1,0,0)
apattern<-c(apattern,0,1,0)
} else {
pattern<-c(pattern,0,1,0)
apattern<-c(apattern,1,0,0)
}
}
}
}
adists<-dists<-array(0,c(nrow(data),1))
rownames(adists)<-rownames(dists)<-rownames(data)
# colnames(adists)<-colnames(adists)<-"Dio_plus"
fseq<-seq(3,ncol(data),by=3)
# print(fseq)
for(i in 1:nrow(data)){
if(substr(d,1,1)=="e" ){
dists[i,]<-sqrt(1/2*sum(w*(data[i,fseq-2]-pattern[fseq-2])^2,
w*(data[i,fseq-1]-pattern[fseq-1])^2,
w*(data[i,fseq]-pattern[fseq])^2))
adists[i,]<-sqrt(1/2*sum(w*(data[i,fseq-2]-apattern[fseq-2])^2,
w*(data[i,fseq-1]-apattern[fseq-1])^2,
w*(data[i,fseq]-apattern[fseq])^2))
}
else{
dists[i,]<-1/2*sum(w*abs(data[i,fseq-2]-pattern[fseq-2]),
w*abs(data[i,fseq-1]-pattern[fseq-1]),
w*abs(data[i,fseq]-pattern[fseq]))
adists[i,]<-1/2*sum(w*abs(data[i,fseq-2]-apattern[fseq-2]),
w*abs(data[i,fseq-1]-apattern[fseq-1]),
w*abs(data[i,fseq]-apattern[fseq]))
}
}
final<-array(0,c(nrow(data),2))
if(type=="SM"){ #synthetic
d0mean<-mean(dists[,1])
sd0<-sqrt(1/nrow(data)*sum((dists[,1]-d0mean)^2))
rownames(final)<-rownames(data)
colnames(final)<-c("IFSM","ranking")
final[,1]<-1-dists/(d0mean+2*sd0)
}
if(type=="TOPSIS"){ #TOPSIS
d0mean<-mean(dists[,1])
sd0<-sqrt(1/nrow(data)*sum((dists[,1]-d0mean)^2))
rownames(final)<-rownames(data)
colnames(final)<-c("IFTOPSIS","ranking")
final[,1]<-adists/(dists+adists)
}
final[,2]<-nrow(data)+1-as.vector(rank((final[,1]),ties.method = "max"))
attr(final,"ideal-point")<-pattern
if(type=="TOPSIS"){
attr(final,"anti-ideal-point")<-apattern
}
attr(final,"dists")<-dists
if(type=="TOPSIS"){
attr(final,"adists")<-adists
}
final
}
# dane<-read.csv2("dane_Ewa.csv",row.names = 1)
# wynik<-IFTOPSIS(dane, d="h", w=c(0.4, 0.3, 0.3), z=c("c", "b", "c"),p="idealBounds")
# print(wynik)
# (rank(wynik[,1]))
# dane<-read.csv2("dane_Ewa.csv",header=TRUE,row.names=1)
# options(OutDec=",")
#
# # Obliczenie IFTOPSIS
# wynik<-IFSM(dane, d="h", w=c(0.4, 0.3, 0.3), z=c("b", "b", "c"),p="dataBounds")
# print(wynik)
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Defines functions intuitionisticSyntheticMeasure IFTOPSIS IFSM
Documented in IFSM IFTOPSIS
#' Implementation of the Intuitionistic Fuzzy Synthetic Measure Method for Fuzzy Multi-Criteria Decision Making Problems
#'
#' @description The \code{IFSM} - Intuitionistic Fuzzy Synthetic Measure Method for Fuzzy Multi-Criteria Decision Making Problems. Reference describing the method: Jefmański, Roszkowska, Kusterka-Jefmańska (2021) \doi{10.3390/e23121636}
#' @param data The data matrix (\emph{m} x \emph{n*3}) with the values of \emph{mi} \emph{ni} and \emph{pi} (three columns for each intuitionistic fuzzy representation of criteria for each alternative) where \emph{m} is the number of alternatives and \emph{n} is the number of criteria.
#' @param d Distance "euclidean" or "hamming".
#' @param w A vector of length \emph{n}, containing the crisp weights for the criteria (one value for intuitionistic fuzzy representation).
#' @param z A vector of length \emph{n}, with preferences type for each criterion with "b" (benefit) and "c" (cost).
#' @param p Ideal point calculation type with one of two values: "dataBounds" – ideal point contains max and min values from the dataset – see details; "idealBounds" – ideal point contains 1 and 0’s - see details.
#' @return \code{IFSM} returns a data frame that contains the scores of the Intuitionistic Fuzzy Synthetic Measure (IFSM) and the ranking of the alternatives.
#' @references Jefmański B, Roszkowska E, Kusterka-Jefmańska M. Intuitionistic Fuzzy Synthetic Measure on the Basis of Survey Responses and Aggregated Ordinal Data. Entropy. 2021; 23(12):1636. \doi{10.3390/e23121636}
#'
#' Roszkowska E, Jefmański B, Kusterka-Jefmańska M. On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights. Entropy. 2022; 24(8):1081. \doi{10.3390/e24081081}
#'
#' Xu, Z. Some Similarity Measures of Intuitionistic Fuzzy Sets and Their Applications to Multiple Attribute Decision Making. Fuzzy Optimization and Decision Making. 2007; 6: 109–121. \doi{10.1007/s10700-007-9004-z}
#'
#' @details For p="dataBounds" the actual ideal point is calculated for benefits as maximum from all values for \emph{mi} and min for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}); in the case of costs, minimal value for \emph{mi} and max for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}).
#' For p="idealBounds" for benefitss is 1 for \emph{mi} and 0 for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni} ). In the case of costs it is 0 for \emph{mi} and 1 for \emph{ni} (\emph{pi} = 1- (\emph{mi} - \emph{ni}).
#' @examples
#' set.seed(823)
#' data<-sample(1:7,26*13*8,replace=TRUE)
#' dim(data)<-c(26*13,8)
#' nrColumns<-8
#' primary<-data.frame(name=rep(LETTERS,each=13),data)
#' f<-IFconversion(primary)
#' print(f)
#' m<-IFSM(f)
#' print(m)
#' @export
IFSM<- function(data, #matrix with all the alternatives
d = "e", # distance "euclidean" or "hamming"
w = rep(3/ncol(data),ncol(data)/3), # weights for each criterion (variable)
z = rep("b",ncol(data)/3), # preferences vector for each variable with "b" (benefit) and "c" (cost) values
p = "dataBounds") # Ideal Point object vector or string with one of two values: "dataBounds" - ideal point object contains maximas and minimas from dataset, "idealBounds" - ideal point object contains 1 and 0's - see details.
{
intuitionisticSyntheticMeasure(data,d,w,z,p,ap="dataBounds",type="SM")
}
#' Implementation of the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution for Fuzzy Multi-Criteria Decision Making Problems
#'
#' @description The \code{IFTOPSIS} - Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution for Fuzzy Multi-Criteria Decision Making. Reference describing the method: Roszkowska, Kusterka-Jefmańska, Jefmański (2021) \doi{10.3390/e23050563}
#' @param data The data matrix (\emph{m} x \emph{n*3}) with the values of \emph{mi} \emph{ni} and \emph{pi} (three columns for each intuitionistic fuzzy representation of criteria for each alternative), where \emph{m} is the number of alternatives and \emph{n} is the number of criteria.
#' @param d Distance "euclidean" or "hamming".
#' @param w A vector of length \emph{n}, containing the crisp weights for the criteria (one value for intuitionistic fuzzy representation)
#' @param z A vector of length \emph{n}, with preferences type for each criterion with "b" (benefit) and "c" (cost).
#' @param p Ideal point calculation type with one of two values: "dataBounds" – ideal point contains max and min values from the dataset – see details; "idealBounds" – ideal point contains 1 and 0’s - see details.
#' @param ap Anti-ideal point calculation type with one of two values: "dataBounds" – anti-ideal point contains min and max from the dataset – see details; "idealBounds" – anti-ideal point contains 0 and 1’s - see details.
#' @return \code{IFTOPSIS} returns a data frame that contains the scores of the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IFTOPSIS) and the ranking of the alternatives.
#' @details For p="dataBounds" the actual ideal point is calculated for benefits as maximum from all values for \emph{mi} and min for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}); in the case of costs, minimal value for \emph{mi} and max for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni}).
#' For p="idealBounds" for benefitss is 1 for \emph{mi} and 0 for \emph{ni} (\emph{pi} = 1- \emph{mi} - \emph{ni} ). In the case of costs it is 0 for \emph{mi} and 1 for \emph{ni} (\emph{pi} = 1- (\emph{mi} - \emph{ni}).
#' For ap="dataBounds" the actual anti-ideal point is calculated for benefit criteria as minimum of all values for \emph{mi}, maximum of all values for \emph{ni} and \emph{pi} = 1- (\emph{mi} + \emph{ni}); in the case of cost criteria, maximum of all values for \emph{mi},
#' minimum of all values for \emph{ni} and \emph{pi} = 1- (\emph{mi} + \emph{ni}).
#' For ap="idealBounds" in the case of benefit criteria it is 0 for \emph{mi},
#' 1 for \emph{ni}, 0 for \emph{pi}; in the case of cost criteria it is 1 for \emph{mi}, 0 for \emph{ni} and 0 for \emph{pi}.
#' @references Roszkowska E, Kusterka-Jefmańska M, Jefmański B. Intuitionistic Fuzzy TOPSIS as a Method for Assessing Socioeconomic Phenomena on the Basis of Survey Data. Entropy. 2021; 23(5):563. \doi{10.3390/e23050563}
#'
#' Xu, Z. Some Similarity Measures of Intuitionistic Fuzzy Sets and Their Applications to Multiple Attribute Decision Making. Fuzzy Optimization and Decision Making. 2007; 6: 109–121. \doi{10.1007/s10700-007-9004-z}
#'
#' @examples
#' set.seed(823)
#' data<-sample(1:7,26*13*8,replace=TRUE)
#' dim(data)<-c(26*13,8)
#' nrColumns<-8
#' primary<-data.frame(name=rep(LETTERS,each=13),data)
#' f<-IFconversion(primary)
#' m<-IFTOPSIS(f)
#' print(m)
#' @export
IFTOPSIS<- function(data, #matrix with all the alternatives
d = "e", # distance "euclidean" or "hamming"
w = rep(3/ncol(data),ncol(data)/3), # weights for each criterion (variable)
z = rep("b",ncol(data)/3), # preferences vector for each variable with "b" (benefit) and "c" (cost) values
p = "dataBounds", # Ideal point object vector or string with one of two values: "dataBounds" - ideal point object contains maximas and minimas from dataset, "idealBounds" - ideal point object contains 1 and 0's - see details.
ap = "dataBounds") # Anti ideal point object vector or string with one of two values: "dataBounds" - anti ideal point object contains minimas and maximas from dataset, "idealBounds" - anti ideal point object contains 0 and 1's - see details.
{
intuitionisticSyntheticMeasure(data,d,w,z,p,ap,type="TOPSIS")
}
#' @keywords internal
intuitionisticSyntheticMeasure<- function(data,d,w,z,p,ap,type)
{
#on macos machines sometimes 0.5+0.5+0.0 isn't equal to 1
tollerance=0.000001
if (!requireNamespace("dplyr", quietly = TRUE)) {
stop(
"Package \"dplyr\" must be installed to use this function.",
call. = FALSE
)
}
if(ncol(data)%%3 !=0){
stop ("For inthuitionistic data datasets should contain three columns for each variable")
}
if(any(data<0)) {
stop ("For intuitionistic data all values should be in range 0-1")
}
if(any(data>1)) {
stop ("For intuitionistic data all values should be in range 0-1")
}
for(i in 1:(ncol(data)%/%3)){
if(any(abs(apply(cbind(data[,3*i-2],data[,3*i-1],data[,3*i]),1,sum)-1)>tollerance)){
stop ("For intuitionistic fuzzy data mi,ni and pi should sum to 1")
}
}
if(length(w)!=ncol(data)%/%3) {
stop ("length of weights vector w should be equal to number of criteria")
}
if(any(w<0)) {
stop ("all weights in vector w should be in range 0-1")
}
if(any(w>1)) {
stop ("all weights in vector w should be in range 0-1")
}
if(abs(sum(w)-1)>tollerance) {
stop ("the weights in vector w should sum to 1")
}
if(!d %in% c("euclidean","e","h","hamming")){
stop ("the distance parameter should be one of the following \"euclidean\" (\"e)\" or \"hamming\" (\"h\")")
}
if(length(z)!=ncol(data)%/%3) {
stop ("length of preferences vector z should be equal to number of variables")
}
if(!all(z %in% c("b","c"))){
stop ("the preferences vector z should contain values \"b\" or \"c\"")
}
if(length(p)==1){
if(!p %in% c("dataBounds","idealBounds")){
stop ("the ideal point parameter p should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with ideal point coordinates ")
}
}
else{
if(length(p)!=ncol(data)){
stop ("the ideal point parameter p should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with ideal point coordinates ")
}
for(i in 1:(length(p)%/%3)){
if(abs(p[,3*i-2]+p[,3*i-1]+p[,3*i]-1)>tollerance){
stop ("For ideal point object each three mi,ni,pi, should sum to 1")
}
}
}
if(length(ap)==1){
if(!ap %in% c("dataBounds","idealBounds")){
stop ("the anti ideal point parameter ap should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with anti ideal point parameters ")
}
}else{
if(length(ap)!=ncol(data)){
stop ("the anti ideal point parameter ap should contain one of the following: \"dataBounds\", \"idealBounds\" or vector with anti ideal point parameters ")
}
for(i in 1:(length(ap)%/%3)){
if(abs(ap[,3*i-2]+ap[,3*i-1]+ap[,3*i]-1)>tollerance){
stop ("For anti ideal point object each three mi,ni,pi, should sum to 1")
}
}
}
if(length(p)==1){
pattern<-NULL
apattern<-NULL
for(i in 1:(ncol(data)%/%3)){
if(p=="dataBounds"){
if(z[i]=="b"){
mi=max(data[,3*i-2])
ni=min(data[,3*i-1])
pi=1-mi-ni
ami=min(data[,3*i-2])
ani=max(data[,3*i-1])
api=1-ami-ani
} else{
mi=min(data[,3*i-2])
ni=max(data[,3*i-1])
pi=1-mi-ni
ami=max(data[,3*i-2])
ani=min(data[,3*i-1])
api=1-ami-ani
}
pattern<-c(pattern,mi,ni,pi)
apattern<-c(apattern,ami,ani,api)
}
else {
if(z[i]=="b"){
pattern<-c(pattern,1,0,0)
apattern<-c(apattern,0,1,0)
} else {
pattern<-c(pattern,0,1,0)
apattern<-c(apattern,1,0,0)
}
}
}
}
adists<-dists<-array(0,c(nrow(data),1))
rownames(adists)<-rownames(dists)<-rownames(data)
# colnames(adists)<-colnames(adists)<-"Dio_plus"
fseq<-seq(3,ncol(data),by=3)
# print(fseq)
for(i in 1:nrow(data)){
if(substr(d,1,1)=="e" ){
dists[i,]<-sqrt(1/2*sum(w*(data[i,fseq-2]-pattern[fseq-2])^2,
w*(data[i,fseq-1]-pattern[fseq-1])^2,
w*(data[i,fseq]-pattern[fseq])^2))
adists[i,]<-sqrt(1/2*sum(w*(data[i,fseq-2]-apattern[fseq-2])^2,
w*(data[i,fseq-1]-apattern[fseq-1])^2,
w*(data[i,fseq]-apattern[fseq])^2))
}
else{
dists[i,]<-1/2*sum(w*abs(data[i,fseq-2]-pattern[fseq-2]),
w*abs(data[i,fseq-1]-pattern[fseq-1]),
w*abs(data[i,fseq]-pattern[fseq]))
adists[i,]<-1/2*sum(w*abs(data[i,fseq-2]-apattern[fseq-2]),
w*abs(data[i,fseq-1]-apattern[fseq-1]),
w*abs(data[i,fseq]-apattern[fseq]))
}
}
final<-array(0,c(nrow(data),2))
if(type=="SM"){ #synthetic
d0mean<-mean(dists[,1])
sd0<-sqrt(1/nrow(data)*sum((dists[,1]-d0mean)^2))
rownames(final)<-rownames(data)
colnames(final)<-c("IFSM","ranking")
final[,1]<-1-dists/(d0mean+2*sd0)
}
if(type=="TOPSIS"){ #TOPSIS
d0mean<-mean(dists[,1])
sd0<-sqrt(1/nrow(data)*sum((dists[,1]-d0mean)^2))
rownames(final)<-rownames(data)
colnames(final)<-c("IFTOPSIS","ranking")
final[,1]<-adists/(dists+adists)
}
final[,2]<-nrow(data)+1-as.vector(rank((final[,1]),ties.method = "max"))
attr(final,"ideal-point")<-pattern
if(type=="TOPSIS"){
attr(final,"anti-ideal-point")<-apattern
}
attr(final,"dists")<-dists
if(type=="TOPSIS"){
attr(final,"adists")<-adists
}
final
}
# dane<-read.csv2("dane_Ewa.csv",row.names = 1)
# wynik<-IFTOPSIS(dane, d="h", w=c(0.4, 0.3, 0.3), z=c("c", "b", "c"),p="idealBounds")
# print(wynik)
# (rank(wynik[,1]))
# dane<-read.csv2("dane_Ewa.csv",header=TRUE,row.names=1)
# options(OutDec=",")
#
# # Obliczenie IFTOPSIS
# wynik<-IFSM(dane, d="h", w=c(0.4, 0.3, 0.3), z=c("b", "b", "c"),p="dataBounds")
# print(wynik)
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