print(r.get_attribute('title'))
else:
if knownRooms[indexChosen2] != "-1":
r = chooseKnowRoom(rooms[knownRoomsMap[knownRooms[indexChosen2]]],
startTime)
print(r.get_attribute('title'))
else:
r = chooseRandomroom(rooms, startTime)
if r != None:
print(r.get_attribute('title') + " Random")
if r != None:
length = len(r.get_attribute('title'))
excelOperator.fillCell(index - startPoint + 1, startTime,
r.get_attribute('title')[length - 4:length])
process(r, driver, login, trueUserName, truePassWord)
login = False
else:
print(startDate, "not exist")
#################################################################
driver.get("https://ufl.libcal.com/reserve/studyMSL")
time.sleep(2)
startDate += 1
if startDate == lastDateThisMonth + 1:
startDate = 1
rooms = []
index += 1
indexChosen2 += 1
#test StartDate
print(startDate)
picture = random.randint(1, 4); #生成随机数
tsk = []; #等待线程结束的
crawler_progress = []; # 爬虫等待线程结束的
if thread > 500:
print '\033[1;31;1m Command parse error ！！！ \033[0m';
exit();
if __name__ == '__main__':
hound_db = DB();
blast = blast();
if Dictionaries: #批量导入字典
function.process(hound_db.Dictionaries,Dictionaries);
elif imurl and len(imurl) == 2: #导入域名
function.im_url(imurl[0],imurl[1])
elif url:
if picture == 1:
function.a1();
elif picture == 2:
function.a2();
elif picture == 3:
function.a3();
elif picture == 4:
function.a4();
lis = hound_db.query_all("select lis from lis"); #获取所有字典数据
print "\033[1;35;1m Dictionary--> %i Tools--> hound version--> 1.7 \033[0m \n" % (len(lis));
#sklearn modules
from sklearn.utils import shuffle #random permutation
from sklearn import preprocessing # Normalize Sparces Matrix
from sklearn.feature_extraction.text import CountVectorizer # create at CSR from data
from sklearn.linear_model import LogisticRegression#Logistic Classifier
from sklearn import svm #SVM Clasiffier
from sklearn.naive_bayes import MultinomialNB#Naive BAIse
from sklearn.ensemble import BaggingClassifier
#utilities
import numpy as np
import function
if __name__ == "__main__":
responce = input("Do you want to repopulate feature vectors?[Y/N]:")
big_feature_list ,label = function.process(responce)
#shuffle data
big_feature_list ,label = shuffle(big_feature_list, label, random_state=5)
label = label.tolist()
#build stopword list Not used
# for document in big_feature_list.tolist():
# corpus += document.split();
# fdist = FreqDist(corpus)
# print(fdist.most_common(200))
# s = open('stopwords.txt' , 'w')
# for pair in fdist.most_common(500):
# s.write(str(pair[0])+'\n')
# s.close()
