# -*- coding:utf-8 -*-

import re
import matplotlib.pyplot as plt
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import cross_validation
import os
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier


def load_kdd99(filename):
 x=[]
 with open(filename) as f:
 for line in f:
 line=line.strip('\n')
 line=line.split(',')
 x.append(line)
 return x

def get_rootkit2andNormal(x):
 v=[]
 w=[]
 y=[]
 for x1 in x:
 if ( x1[41] in ['rootkit.','normal.'] ) and ( x1[2] == 'telnet' ):
 if x1[41] == 'rootkit.':
 y.append(1)
 else:
 y.append(0)

 x1 = x1[9:21]
 v.append(x1)

 for x1 in v :
 v1=[]
 for x2 in x1:
 v1.append(float(x2))
 w.append(v1)
 return w,y

if __name__ == '__main__':
 v=load_kdd99("../data/kddcup99/corrected")
 x,y=get_rootkit2andNormal(v)
 clf = KNeighborsClassifier(n_neighbors=3)
 print cross_validation.cross_val_score(clf, x, y, n_jobs=-1, cv=10)