kNN_func.R

#Writing the function to predict kNN
knn_predict <- function(test, train, k_value){
  pred <- c()  
  #LOOP-1
  for(i in c(1:nrow(test))){   
    dist = c()          
    char = c()
    setosa =0              
    versicolor = 0
    virginica = 0
  }
    
    #LOOP-2-looping over train data 
    for(j in c(1:nrow(train))){}
      
      dist <- c(dist, ED(test[i,], train[j,]))
      char <- c(char, as.character(train[j,][[5]]))
    
  
    df <- data.frame(char, dist$SepalLength) 
    df <- df[order(df$dist.SepalLength),]       #sorting dataframe
    df <- df[1:k_value,]               
    
    
    #Loop 3: loops over df and counts classes of neibhors.
    for(k in c(1:nrow(df))){
      if(as.character(df[k,"char"]) == "setosa"){
        setosa = setosa + 1
      }else if(as.character(df[k,"char"]) == "versicolor"){
        versicolor = versicolor + 1
      }else
        virginica = virginica + 1
    }
    
    
    n<-table(df$char)
    pred=names(n)[which(n==max(n))]
    
  return(pred) #return prediction vector
}

#Predicting the value for K=1
K=1
predictions <- knn_predict(test, train, K)