#!/usr/bin/env python
from recommender import Recommender
matchmaker = Recommender()
my_new_user_ratings = [('Moana', 4), ('Frying Nemo', 5), ('Pocahontas', 2),
('Full Metal Racket', 9), ('LionKing', 7),
('Frozen', 3)]
my_username = '******'
matchmaker.add_user_ratings(my_username, my_new_user_ratings)
print matchmaker.dataset
reviewer_comparisons = matchmaker.build_distance_array_for(my_username)
print "Reviewer Similarities: %s" % reviewer_comparisons
# Define some test data to act as movies to use
# movies = [ 'Moana', 'Frying Nemo', 'Pocahontas', 'ToyStory', 'LionKing', 'Frozen' ]
# reviewers = {
# "Janice" : [3, 7, 4, 9, 9, 7],
# "Bob" : [7, 5, 5, 3, 8, 8],
# "Joe" : [7,5,5,0,8,4],
# "Claudia" : [5,6,8,5,9,8],
# "Peter" : [5,8,8,8,10,9],
# "Kenneth" : [7,7,8,4,7,8]
# }
# Update test data to match contribution from Shae
# reviewers_ng = {}
# for reviewer in reviewers:
# movie_mashup = []
# for n,movie in enumerate(movies):
