def txt_ltn(self, step, req_n):
with open("./data/Text_Only_Ascii_Coll_MWI_NoSem", "r") as f:
start = time.time()
parser = Parser()
parser.parse(f)
print("time execution parsing {}".format(time.time() - start))
start = time.time()
parser.createVector()
print("time execution create vector {}".format(time.time() -
start))
start = time.time()
search = {2009011: ["olive", "oil", "health", "benefit"]}
search2 = {2009036: ["notting", "hill", "film", "actors"]}
search3 = {
2009067:
["probabilistic", "models", "in", "information", "retrieval"]
}
search4 = {2009073: ["web", "link", "network", "analysis"]}
search5 = {2009074: ["web", "ranking", "scoring", "algorithm"]}
search6 = {
2009078: ["supervised", "machine", "learning", "algorithm"]
}
search7 = {
2009085: ["operating", "system", "+mutual", "exclusion"]
}
parser.scoreAndGenerate("GuillaumeBenoitGauthierTheo", step, req_n,
"ltn", "articles", "test", search, search2,
search3, search4, search5, search6,
search7)
print("time execution generate runs {}".format(time.time() -
start))
def run2(re):
with open("../src/data/Text_Only_Ascii_Coll_MWI_NoSem", "r") as f:
parser = Parser()
parser.parse(f)
#print(parser.corpusW)
#[print(i,j) for i,j in parser.corpusW.items()]
parser.createVector()
search = {2009011: ["olive", "oil", "health", "benefit"]}
search2 = {2009036: ["notting", "hill", "film", "actors"]}
search3 = {2009067: ["probabilistic", "models", "in", "information", "retrieval"]}
search4 = {2009073: ["web", "link", "network", "analysis"]}
search5 = {2009074: ["web", "ranking", "scoring", "algorithm"]}
search6 = {2009078: ["supervised", "machine", "learning", "algorithm"]}
search7 = {2009085: ["operating", "system", "+mutual", "exclusion"]}
parser.scoreAndGenerate("GuillaumeBenoitGauthierTheo", "02", re, "ltn", "articles", "sem_test",
search, search2, search3, search4, search5, search6, search7)