for newsid in l:
rank = 100 - int(newsid[8::])
if rank < 0:
print newsid
newistimedic[newsid] = (newsid[4:8] + newsid[0:4] + str(rank))
l = []
for newsid,_ in sorted(newistimedic.items(), key=lambda x:x[1]):
l.append(newsid)
"""
from IIalgorithm_model import create_thread, create_toptarget_dic_newl_dic, maketext, maketextdoc,makedocvec,PredictAndAnalyze2,PredictAndAnalyze3
#for folda in ['topnewstextswithtag/']:
thread2014 = create_thread('../getruiternewsfromweb/topnewstextswithtag/')
thread2015 = create_thread('../getruiternewsfromweb/topnewstextswithtag2015/')
thread2013 = create_thread('../getruiternewsfromweb/topnewstextswithtag2013/')
thread = thread2013
thread.update(thread2014)
thread.update(thread2015)
import pandas as pd
import datetime
toptarget_dic, newl_dic = create_toptarget_dic_newl_dic(l,stockvaluedict, thread, thred_value = 0.01)
DimentionN = 500
#DimentionN = 50
word2vecdic = pickle.load(open("../getruiternewsfromweb/word2vecdic_10.dump","r"))
#toptarget.append(stockvaluedict[newsid]['tag'])
try:
#print len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]])
ID = stockvaluedict[newsid]['ID'][0]
if ((".T" in ID) & (newsid not in nikkeiheikinlist) & (len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]]) == 3)):
#if (len(stockvaluedict[newsid]['value'][stockvaluedict[newsid]['ID'][0]]) == 3):
#print newsid
toptarget.append(stockvaluedict[newsid]['tag'][stockvaluedict[newsid]['ID'][0]])
newl.append(newsid)
except:
excpetl.append(ID)
continue
"""
from IIalgorithm_model import create_thread, create_toptarget_dic_newl_dic, maketext, maketextdoc,makedocvec
thread2013 = create_thread('topnewstextswithtag/')
thread2014 = create_thread('topnewstextswithtag2015/')
thread2015 = create_thread('topnewstextswithtag2013/')
thread = thread2013
thread.update(thread2014)
thread.update(thread2015)
toptarget_dic, newl_dic = create_toptarget_dic_newl_dic(l,stockvaluedict, thread, thred_value = 0.01)
dic_key = 'close_previousday_to_close_nextday'
newl = np.array(newl_dic[dic_key])
toptarget = np.array(toptarget_dic[dic_key])
#toptarget = np.array(toptarget)
topvectorMat2 = []
#newl = pickle.load(open("l_balanced_2014.dump","r"))
#for newsid in l:
