movie_id_master = int(Movie_list_master.readline().split()[0])
movie1 = imdb_access.get_movie(movie_id_master)
print 'Processing movie #',i,': ',movie1['title']
top5 = [DEFAULT_MOVIE]
Movie_list_parser = open("title_index.txt", "r")
#movies being compared to the primary movie
for j in range(100):
movie_id_parser = int(Movie_list_parser.readline().split()[0])
# don't compare a movie to itself! not fair
if movie_id_parser == movie_id_master:
continue
movie2 = imdb_access.get_movie(movie_id_parser)
# this is where the magic happens
# get diff
response = Get_Fuzzy.get_closeness(imdb_access, movie1, movie2)
# see if it belongs in the top 5
for m in top5:
if response < m[1]:
top5.append((movie2['title'], response, movie2.movieID))
break
# done: time to cut down the top 5 and print out the recommendations to
# the output file
# movieID title|movieID1 title1|movieID2 title2|movieID3 title3|movieID4 title4|movieID5 title5
# when reading, split around '|'
Movie_list_parser.close()
top5.sort(key=lambda diff: diff[1])
del top5[5:]
Rec_Out.write(str(movie1.movieID) + ' ' + movie1['title'])
for movie in top5:
#!/usr/bin/env python
import string
import imdb
import Get_Fuzzy
URI = 'mysql://*****:*****@localhost/imdb'
id = 'console'
imdb_access = imdb.IMDb('sql', uri=URI)
movie1 = raw_input('Movie 1: ')
movie2 = raw_input('Movie 2: ')
movie1 = imdb_access.search_movie(movie1)
movie2 = imdb_access.search_movie(movie2)
movie1 = Get_Fuzzy.get_movie(movie1)
movie2 = Get_Fuzzy.get_movie(movie2)
response = Get_Fuzzy.get_closeness(imdb_access, movie1, movie2)
print 'Difference factor between ',movie1,
print '\n',' and ',movie2,': ',response,'\n'
