text: # -*- coding: utf-8 -*-
  """
  Created on Sat Dec 30 21:59:11 2017
  
  @author: Bedirhan
  """
  import statistics
  import numpy as np
  
  def zscore(data):
      zscoredata=[]
      standart_sapma=statistics.stdev(data)
      ortalama=statistics.mean(data)
      for d in data:
          z=(d-ortalama)/standart_sapma
          zscoredata.append(z)
      return zscoredata
      
  def minmax(data):
      minmaxscoredata=[]
      maxData=max(data)
      minData=min(data)
      for d in data:
          m=(d-minData)/(maxData-minData)
          minmaxscoredata.append(m)
      return minmaxscoredata
  
  def median(data):
      medianscore=[]
      med=statistics.median(data)
      for d in data:
          m=d/med
          medianscore.append(m)
      return medianscore
  
  def normalizasyon(filename,currentIndex):
      f = open(filename)
      X=[]
      for i,row in enumerate(f.readlines()):
          
          currentline = row.split(",")   
          temp=[]
          for column_value in currentline:
              temp.append(column_value)
  
          X.append(temp)
  
      X=np.array(X)
  
  
      norm_veri=[]
  
      if len(X[0])!=len(X[len(X)-1]): #eger son satir ile ilk satirin degeri esit degilse son satiri siliyoruz
          X=np.delete(X,[len(X)-1]) 
      
      for a in range(0,len(X[0])-1):
          datalist=[]
          for b in range(0,len(X)):
              p=float(X[b][a])
              datalist.append(p)
          if currentIndex==0:
              norm_veri.append(minmax(datalist))
          elif currentIndex==1:
              norm_veri.append(zscore(datalist))
          elif currentIndex==2:
              norm_veri.append(median(datalist))
  
  
      y_list=[]
      for y in X:
          y_list.append(y[len(y)-1])
          
      norm_veri.append(y_list)
      norm_veri=np.array(norm_veri)
      norm_veri=np.transpose(norm_veri)
      return norm_veri
  