from math import sqrt critics Lisa Rose Lady in the Water Snakes on Pl

from math import sqrt

critics = {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
    'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
    'The Night Listener': 3.0},
    'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
        'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
        'You, Me and Dupree': 3.5},
    'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
        'Superman Returns': 3.5, 'The Night Listener': 4.0},
    'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
        'The Night Listener': 4.5, 'Superman Returns': 4.0,
        'You, Me and Dupree': 2.5},
    'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
        'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
        'You, Me and Dupree': 2.0},
    'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
        'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
    'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}

def sim_distance(prefs, person1, person2):
    # Get the list of shared items
    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1

    # if they have no rating in common, return 0
    if len(si) == 0:
        return 0

    # Add up the squaresof all the differences
    sum_of_squares = sum(pow(prefs[person1][item] - prefs[person2][item], 2)
            for item in prefs[person1] if item in prefs[person2])

    return 1/(1+sum_of_squares)

def sim_pearson(prefs,p1,p2):
  # Get the list of mutually rated items
  si={}
  for item in prefs[p1]:
    if item in prefs[p2]: si[item]=1
  
  n=len(si)

  if n==0: return 0

  sum1=sum([prefs[p1][it] for it in si])
  sum2=sum([prefs[p2][it] for it in si])

  sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
  sum2Sq=sum([pow(prefs[p2][it],2) for it in si])

  pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])

  num=pSum-(sum1*sum2/n)
  den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
  if den==0: return 0

  r=num/den
  return r

def topMatches(prefs, person, n=5, similarity=sim_pearson):
    scores = [(similarity(prefs, person, other), other)
            for other in prefs if other != person]

    scores.sort()
    scores.reverse()
    return scores[0:n]

def getRecommendations(prefs, person, similarity=sim_pearson):
    totals = {}
    simSums = {}
    for other in prefs:
        if other == person: continue
        sim = similarity(prefs, person, other)

        if sim <= 0: continue
        for item in prefs[other]:
            if item not in prefs[person] or prefs[person][item] == 0:
                totals.setdefault(item, 0)
                totals[item] += prefs[other][item] * sim

                simSums.setdefault(item, 0)
                simSums[item] += sim
    rankings = [(totals/simSums[item], item) for (item, totals) in totals.items()]

    rankings.sort()
    rankings.reverse()
    return rankings

def transformPrefs(prefs):
    result = {}
    for person in prefs:
        for item in prefs[person]:
            result.setdefault(item, {})

            result[item][person] = prefs[person][item]
    return result

def calculateSimiliarItems(prefs, n=10):
    result = {}

    itemPrefs = transformPrefs(prefs)
    c = 0
    for item in itemPrefs:
        c += 1
        if c % 100 == 0:
            print "%d / %d" % (c, len(itemPrefs))
        scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance)
        result[item] = scores
    return result

def getRecommendedItems(prefs, itemMatch, user):
    userRatings = prefs[user]
    scores = {}
    totalSim = {}

    for (item, rating) in userRatings.items():
        for (similarity, item2) in itemMatch[item]:
            if item2 in userRatings: continue

            scores.setdefault(item2, 0)
            scores[item2] += similarity * rating

            totalSim.setdefault(item2, 0)
            totalSim[item2] += similarity

        rankings = [(score/totalSim[item], item) for item,score in scores.items()]
        rankings.sort()
        rankings.reverse()
        return rankings

def loadMovieLens(path='/home/xa4a/merc/c_i/ml-data'):
    movies = {}
    for line in open(path + '/u.item'):
        (id, title) = line.split('|')[0:2]
        movies[id] = title

    prefs = {}
    for line in open(path + '/u.data'):
        (user, movieid, rating, ts) = line.split('\t')
        prefs.setdefault(user,{})
        prefs[user][movies[movieid]] = float(rating)
    return prefs