# 基於物品進行過濾：
#      首先把｛使用者1｛物品A：得分，物品B：得分。。。｝｝轉換為｛物品A｛使用者1：得分，使用者2：得分。。。｝｝
#      根據上面轉化的表格，可以根據歐式距或者皮爾遜來計算出不同物體之間的相似度（具體計算是計算不同物體同一個使用者的得分差值的平方和的根，
#      也可以根據皮爾遜）
#      最後可以根據某一個使用者未評過分的物體根據使用者評過分的物體*使用者對評分過物體的評分 求和來計算

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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}}

from math import sqrt


# Returns a distance-based similarity score for person1 and person2
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 ratings in common, return 0
    if len(si) == 0: return 0

    # Add up the squares of 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)


# Returns the Pearson correlation coefficient for p1 and p2
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

    # if they are no ratings in common, return 0
    if len(si) == 0: return 0

    # Sum calculations
    n = len(si)

    # Sums of all the preferences
    sum1 = sum([prefs[p1][it] for it in si])
    sum2 = sum([prefs[p2][it] for it in si])

    # Sums of the squares
    sum1Sq = sum([pow(prefs[p1][it], 2) for it in si])
    sum2Sq = sum([pow(prefs[p2][it], 2) for it in si])

    # Sum of the products
    pSum = sum([prefs[p1][it] * prefs[p2][it] for it in si])

    # Calculate r (Pearson score)
    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


# Returns the best matches for person from the prefs dictionary.
# Number of results and similarity function are optional params.
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]


# Gets recommendations for a person by using a weighted average
# of every other user's rankings
def getRecommendations(prefs, person, similarity=sim_pearson):
    totals = {}
    simSums = {}
    for other in prefs:
        # don't compare me to myself
        if other == person: continue
        sim = similarity(prefs, person, other)

        # ignore scores of zero or lower
        if sim <= 0: continue
        for item in prefs[other]:

            # only score movies I haven't seen yet
            if item not in prefs[person] or prefs[person][item] == 0:
                # Similarity * Score
                totals.setdefault(item, 0)
                totals[item] += prefs[other][item] * sim
                # Sum of similarities
                simSums.setdefault(item, 0)
                simSums[item] += sim

    # Create the normalized list
    rankings = [(total / simSums[item], item) for item, total in totals.items()]

    # Return the sorted list
    rankings.sort()
    rankings.reverse()
    return rankings


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

            # Flip item and person
            result[item][person] = prefs[person][item]
    return result


def calculateSimilarItems(prefs, n=10):
    # Create a dictionary of items showing which other items they
    # are most similar to.
    result = {}
    # Invert the preference matrix to be item-centric
    itemPrefs = transformPrefs(prefs)
    c = 0
    for item in itemPrefs:
        # Status updates for large datasets
        c += 1
        if c % 100 == 0: print
        "%d / %d" % (c, len(itemPrefs))
        # Find the most similar items to this one
        scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance)
        result[item] = scores
    return result


def getRecommendedItems(prefs, itemMatch, user):
    userRatings = prefs[user]
    scores = {}
    totalSim = {}
    # Loop over items rated by this user
    for (item, rating) in userRatings.items():

        # Loop over items similar to this one
        for (similarity, item2) in itemMatch[item]:

            # Ignore if this user has already rated this item
            if item2 in userRatings: continue
            # Weighted sum of rating times similarity
            scores.setdefault(item2, 0)
            scores[item2] += similarity * rating
            # Sum of all the similarities
            totalSim.setdefault(item2, 0)
            totalSim[item2] += similarity

    # Divide each total score by total weighting to get an average
    rankings = [(score / totalSim[item], item) for item, score in scores.items()]

    # Return the rankings from highest to lowest
    rankings.sort()
    rankings.reverse()
    return rankings


def loadMovieLens(path='/data/movielens'):
    # Get movie titles
    movies = {}
    for line in open(path + '/u.item',encoding='iso-8859-15'):
        (id, title) = line.split('|')[0:2]
        movies[id] = title

    # Load data
    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
print(loadMovieLens('ml-100k')['87'])