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import re
import ConfigParser
import bisect
import random
import ctypes
import hashlib
import zlib
import binascii
term = {'td_len':(lambda x : len(x)==32),
'data_num':(lambda x : len(x)==4),
'url':(lambda x : x.find['NUll']),
'sfh_len':(lambda x : len(x)>20),
'not_null':(lambda x : len(x)!=0)}
class data_line(object):
"""docstring for ClassName"""
def __init__(self):
super(ClassName, self).__init__()
@staticmethod
def if_error(data_line_str):
data_line_val = re.split(r';',data_line_str)
hashed_len = sfh_fingerprint.get_hashed_len(data_line_val[19])
if(term['data_num'](data_line_val) and term['sfh_len'](data_line_val[19]) and term['td_len'](data_line_val[9])\
and term['td_len'](data_line_val[2]) and term['td_len'](data_line_val[13]) and term['td_len'](data_line_val[15])\
and term['td_len'](data_line_val[17]) and term['not_null'](data_line_val[18]) and term['not_null'](data_line_val[19])\
and hashed_len/float(data_line_val[3])>0.8):
return data_line_val
else:
return -1
class feature_statistics(object):
"""YSP feature_statistics"""
def __init__(self):
super(feature_statistics, self).__init__()
self.meida_len_statistics_set = [0,0,0,0,0,0,0]
self.lost_dict = dict()
def meida_len_statistics(meida_len):
j = bisect.bisect(breakpoints,meida_len)
self.meida_len_statistics_set[j-1]+=1
def data_value_statistics(data_value_dic,data_value):
data_value_str = str()
for x in xrange(0,len(feature_list)):
data_value_str = data_value_str+str(data_value_dic[feature_list[x]])+','
if(self.lost_dict.has_key(data_value_str)==False):
self.lost_dict[data_value_str]=[0,1,0.]
else:
if (int(result[3])==1):
self.lost_dict[data_value_str][0] += 1
self.lost_dict[data_value_str][1] += 1
else:
self.lost_dict[data_value_str][1] += 1
class sfh_fingerprint(object):
def __init__(self,sfh):
self.sfh = sfh
@staticmethod
def get_hashed_len(sfh):
p = r"\[+\d+?:+\d+?\]"
pattern = re.compile(p)
hashed_len_set = pattern.findall(sfh)
if (term['not_null'](hashed_len_set)):
hashed_len = 0
for x in xrange(0,len(hashed_len_set)):
hashed_len_num = re.split(r"\[|\]|:",hashed_len_set[x])
hashed_len = hashed_len + int(hashed_len_num[2]) - int(hashed_len_num[1])
return hashed_len/len(hashed_len_set)
else :
return -1
@staticmethod
def get_base_sfh(data_set):
base_sfh = list()
for x in xrange(0,10):
base_sfh.append(data_set[x])
return base_sfh
class data_value(object):
@staticmethod
def get_data_values(data):
data_set = re.split(r"URL:|ServerIP:|MediaType:|MediaLen:|Etag:|LastModify:",data)
#data_set[0]=null,data_set[1]=url
data_value_dic = dict()
for x in xrange(1,len(feature_list)+1):
if(x==1):
data_value_dic[feature_list[x-1]] = 0 if(term['not_null']==False) else 1
elif(x==2):
data_value_dic[feature_list[x-1]] = 0 if(term['not_null']==False) else 1
elif(x==3):
data_value_dic[feature_list[x-1]] = data_set[x]
elif(x==4):
data_value_dic[feature_list[x-1]] = bisect.bisect(breakpoints,int(data_set[x]))
elif(x==5):
data_value_dic[feature_list[x-1]] = 0 if(term['not_null']==False) else 1
elif(x==6):
data_value_dic[feature_list[x-1]] = 0 if(term['not_null']==False) else 1
return data_value_dic
config = ConfigParser.RawConfigParser()
config.read("feature_statistics.conf")
feature_statistics_type = ("feature","type")
raw_file_address = config.get("file","raw_file_address")
ripe_file_address = config.get("file","ripe_file_address")
if(feature_statistics_type=="meida_len_statistics"):
breakpoints = [int(i) for i in config.get("output","breakpoints").split(",")]
elif(feature_statistics_type=="data_value_statistics"):
feature_list =[i for i in config.get("feature","feature_name").split(",")]
# ll=ctypes.cdll.LoadLibrary
# lib = ll("libmaatframe.so")
i=0
sfh_set = list()
statistic = feature_statistics()
with open(raw_file_address,'r') as infile:
for line in infile:
i+=1
line_return = data_line.if_error(line)
if(line_return != -1):
if(feature_statistics_type=="meida_len_statistics"):
statistic.meida_len_statistics(line_return[3])
elif(feature_statistics_type=="data_value_statistics"):
lost_list = list()
statistic.meida_len_statistics(line_return)
for i in statistic.lost:
(statistic.lost[i])[2] = float((statistic.lost[i])[0])/(statistic.lost[i])[1]
tmp = (i,int((statistic.lost[i])[0]),int((statistic.lost[i])[1]),float((statistic.lost[i])[2]))
lost_list.append(tmp)
print sorted(lost_list,cmp=lambda x,y:cmp(x[2],y[2]))
# if(x == len(feature_list)-1):
# outfile.write(data_value_dic[feature_list[x]]+'\n')
# else:
# print lost
# outfile.write(str(data_value_dic[feature_list[x]])+',')
# outfile.write(result[3])
# sfh_dot=list()
# for x in xrange(0,10):
# #transform sfh to dot
# sfh_dot.append(lib.GIE_sfh_similiarity(result[19],len(result[19]),sfh_set[x],len(sfh_set[x])))
# if(len(data_set)==7):
# outfile.write(str(data_set[0])+','+str(data_set[1])+','+str(data_set[2])\
# +','+str(data_set[3])+','+str(data_set[4])+','+str(data_set[5])+','+result[5]\
# +','+result[7]+','+result[9]+','+result[11]+','+result[13]+','+result[15]+result[17]\
# +','+result[19]+'\n')
# with open(ripe_file_address,'w') as outfile:
# outfile.write(str(lost))
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