MCC_bin_iris.r

M1<-NULL
mc<-function(k)
{
iris_a<-iris[51:150,] #remove 3rd class of iris data set
iris_data<-iris_a[sample(100),] #shuffled data
iris_test<- iris_data[1:20,] #test data set
iris_train<-iris_data[21:100,] #traning data set

dist<-NULL

for (i in 1:20){
  for (j in 1:80){
    d<-sqrt(sum((iris_train[j,-5]-iris_test[i,-5])^2))
    dist<-c(dist,d)
  }
}
dd<- matrix(dist,20,80,byrow=T)

D<-NULL


for(m in 1:20)       #for-loop for finding class
  {
    c   = as.numeric(iris_train$Species[(order(dd[m,]))[1:k]])
    min = as.numeric(median(c)) 
    D   = c(D,min)
  }
  F       = as.numeric(iris_test$Species)    #compare the class of test set and predicted output
mat<-as.matrix(table(F,D))

TP=mat[1,1]
TN=mat[2,2]
FP=mat[1,2]
FN=mat[2,1]
MCC=((TP*TN)-(FP*FN))/sqrt((TP+FN)*(TP+FP)*(TN+FP)*(TN+FN))
return(MCC)
}
z<-c(mc(1),mc(3),mc(5),mc(7),mc(9))
cat(sprintf("The MCC for k =1 is = %03f \n", mc(1)))
cat(sprintf("The MCC for k =3 is = %03f \n", mc(3)))
cat(sprintf("The MCC for k =5 is = %03f \n", mc(5)))
cat(sprintf("The MCC for k =7 is = %03f \n", mc(7)))
cat(sprintf("The MCC for k =9 is = %03f \n", mc(7)))