<gh_stars>10-100
# A dictionary of movie critics and their ratings of a small set of movies
critics={'<NAME>': {'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},
     '<NAME>': {'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},
     '<NAME>': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
      'Superman Returns': 3.5, 'The Night Listener': 4.0},
     '<NAME>': {'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},
     '<NAME>': {'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},
     '<NAME>': {'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
    # Find the number of elements
    n = len(si)
    # if they are no ratings in common, return 0
    if n == 0:
        return 0
    # Add up all the preferences
    sum1 = sum([prefs[p1][it] for it in si])
    sum2 = sum([prefs[p2][it] for it in si])
    # Sum up 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 up the products
    pSum = sum([prefs[p1][it] * prefs[p2][it] for it in si])
    # Calculate 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]


    # Sort the list so the highest scores appear at the top scores.sort( )
    scores.reverse()
    return scores[0:n]


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

# transpose row and col
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

# item-based collaborative filtering
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