bin_fold.r

##Generalized Code of KNN/// Currently using Breast Cancer data set:
data<- read.csv("wdbc.csv") #Import the data
data<- data[,-1] #Removed the first column
n <- nrow(data) #no of rows in the data
data_s<- data[sample(1:n),] #shuffled the data

knn_fold<-function(x) #x
{
  n_test<- floor(n*(1/x)); n_train<-n-n_test  #Nuber of rows for test and train
  data_test<-data_s[1:n_test,]
  data_train<-data_s[(n_test+1):n,]
  
  actual_class<-data_s[1:n_test,1]
  
  dist<-NULL
  minimum<-NULL
  acc<-NULL
  accuracy<-NULL
  
  for (i in 1:n_test){
    for (j in 1:n_train){
      d<-sqrt(sum((data_test[i,-1]-data_train[j,-1])^2))
      dist<-c(dist,d)
    }
  }
  distance<-matrix(dist, nrow=n_test, ncol = n_train, byrow = TRUE)
  
  for(k in c(1,3,5,7,9))
  {
    for(z in 1:n_test)
    {
      clas <- as.numeric(data_train$M[(order(distance[z,]))[1:k]])
      min<- as.numeric(median(clas))
      minimum<-c(minimum, min)
    }
    actual<-as.numeric(actual_class)
    check_label<-(minimum==actual)
    acc<-((sum(check_label))/length(minimum))*100
    accuracy<-c(accuracy,acc)
  }
  print(accuracy) 
}