def querydomain(keywords):
f = open('keysimulated.tab', 'a')
f.write('\t'.join(keywords) + '\t\n\n')
f.close()
doc = mylog.datalog('logs.txt')
#queryfile()
#appendtoqueryfile(keywords)
data = Orange.data.Table('keysimulated.tab')
classifier = Orange.classification.bayes.NaiveLearner(data)
#data2 = Orange.data.Table('queried.tab')
c = classifier(data[-1])
val = c.value
appendtoqueryfile(keywords, val)
doc.enterdomainfound(keywords, val)
#logging.info(str(keywords)+" "+val)
doc.close()
res = []
for each in collection.find({
"keywords": {
"$in": keywords
},
"domain": val
}, {
"url": 1,
"_id": 0
}):
res.append(str(each["url"]))
return val, res
def querydomain(keywords):
f = open("keysimulated.tab", "a")
f.write("\t".join(keywords) + "\t\n\n")
f.close()
doc = mylog.datalog("logs.txt")
# queryfile()
# appendtoqueryfile(keywords)
data = Orange.data.Table("keysimulated.tab")
classifier = Orange.classification.bayes.NaiveLearner(data)
# data2 = Orange.data.Table('queried.tab')
c = classifier(data[-1])
val = c.value
appendtoqueryfile(keywords, val)
doc.enterdomainfound(keywords, val)
# logging.info(str(keywords)+" "+val)
doc.close()
res = []
for each in collection.find({"keywords": {"$in": keywords}, "domain": val}, {"url": 1, "_id": 0}):
res.append(str(each["url"]))
return val, res
"keywords": [
"artificial intelligence",
"data mining",
"data collectiont",
"internet shopping",
"electronic commerce",
],
"url": "http://url14.com",
},
{
"domain": "social computing",
"keywords": ["pattern classification", "data mining", "sentiment analysis"],
"url": "http://url15.com",
},
]
if __name__ == "__main__":
# db.drop_collection("url_collection")
# collection.insert(insert_in_database)
doc = mylog.datalog("logs.txt")
queryfile()
queryingdomain()
"""x = raw_input('enter the 8 keywords:') #min 4 for proper classification
words = x.split(',') #list
a,b = querydomain(words)
print a
print b"""
doc.close()
insert_in_database=[{"domain":"cloud computing","keywords":["cloud computing","cloud services","data centers","open systems"],"url":"http://url1.com"}, \
{"domain":"cloud computing","keywords":["virtualisation","cloud services","parallel computing","peer-to-peer computing"],"url":"http://url2.com"}, \
{"domain":"cloud computing","keywords":["data privacy","distributed computing",],"url":"http://url3.com"}, \
{"domain":"audio,speech and language processing","keywords":["speech","microphone","audio coding","distortion","speech enhancement"],"url":"http://url4.com"}, \
{"domain":"audio,speech and language processing","keywords":["noise reduction","dereverberation","videoconferencing"],"url":"http://url5.com"}, \
{"domain":"audio,speech and language processing","keywords":["audio coding","mixed signals","speech","acoustic correlation"],"url":"http://url6.com"}, \
{"domain":"security and privacy","keywords":["cyberattack","software vulnerabilities","identity management","cryptography"],"url":"http://url7.com"},\
{"domain":"security and privacy","keywords":["data privacy","antivirus","cryptography"],"url":"http://url8.com"},\
{"domain":"security and privacy","keywords":["security","virus","computer crime","cryptography","intrusion tolerance"],"url":"http://url9.com"},\
{"domain":"mobile computing","keywords":["mobile computing","wireless communication","long term evolution"],"url":"http://url10.com"},\
{"domain":"mobile computing","keywords":["interference","long term evolution","smart phones","global positioning system","energy consumption"],"url":"http://url11.com"},\
{"domain":"mobile computing","keywords":["Scattering","signal to noise ratio","ad hoc networks","sporadic connectivity"],"url":"http://url12.com"},\
{"domain":"social computing","keywords":["customer loyalty","social behavior","intelligent agents","social informatics","informatics","psychology"],"url":"http://url13.com"},\
{"domain":"social computing","keywords":["artificial intelligence","data mining","data collectiont","internet shopping","electronic commerce"],"url":"http://url14.com"},\
{"domain":"social computing","keywords":["pattern classification","data mining","sentiment analysis"],"url":"http://url15.com"},\
]
if __name__ == "__main__":
#db.drop_collection("url_collection")
#collection.insert(insert_in_database)
doc = mylog.datalog('logs.txt')
queryfile()
queryingdomain()
'''x = raw_input('enter the 8 keywords:') #min 4 for proper classification
words = x.split(',') #list
a,b = querydomain(words)
print a
print b'''
doc.close()
