oceniPoKorisnici = {
    'Lisa Rose': {'Catch Me If You Can': 3.0, 'Snakes on a Plane': 3.5, 'Superman Returns': 3.5,
                  'You, Me and Dupree': 2.5, 'The Night Listener': 3.0, 'Snitch': 3.0},
    'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5, 'Just My Luck': 1.5, 'The Night Listener': 3.0,
                     'You, Me and Dupree': 3.5},
    'Michael Phillips': {'Catch Me If You Can': 2.5, 'Lady in the Water': 2.5, 'Superman Returns': 3.5,
                         'The Night Listener': 4.0, 'Snitch': 2.0},
    '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,
                     'You, Me and Dupree': 2.0},
    'Jack Matthews': {'Catch Me If You Can': 4.5, '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, 'Snitch': 4.5},
    'Toby': {'Snakes on a Plane': 4.5, 'Snitch': 5.0},
    'Michelle Nichols': {'Just My Luck': 1.0, 'The Night Listener': 4.5, 'You, Me and Dupree': 3.5,
                         'Catch Me If You Can': 2.5, 'Snakes on a Plane': 3.0},
    'Gary Coleman': {'Lady in the Water': 1.0, 'Catch Me If You Can': 1.5, 'Superman Returns': 1.5,
                     'You, Me and Dupree': 2.0},
    'Larry': {'Lady in the Water': 3.0, 'Just My Luck': 3.5, 'Snitch': 1.5, 'The Night Listener': 3.5}
}

from math import *
import math


# Vrakja merka za slicnost bazirana na rastojanieto za person1 i person2
def sim_distance(prefs, person1, person2):
    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1
    if len(si) == 0: return 0
    sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item], 2)
                          for item in prefs[person1] if item in prefs[person2]])
    return (round(1 / (1 + math.sqrt(sum_of_squares)), 2))


def sim_pearson(prefs, person1, person2):
    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1
    n = len(si)
    if len(si) == 0: return 0
    sum1 = sum([prefs[person1][item] for item in si])
    sum2 = sum([prefs[person2][item] for item in si])
    sum1Sq = sum([pow(prefs[person1][item], 2) for item in si])
    sum2Sq = sum([pow(prefs[person2][item], 2) for item in si])
    pSum = sum([prefs[person1][item] * prefs[person2][item] for item in si])
    num = pSum - (sum1 * sum2 / n)
    den = math.sqrt((sum1Sq - pow(sum1, 2) / n) * (sum2Sq - pow(sum2, 2) / n))
    if den == 0: return 0
    r = num / den
    return (round(r, 2))


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 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, n=3):
    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 = [(total / simSums[item], item) for item, total in totals.items()]

    rankings.sort()

    rankings.reverse()
    return rankings[0:n]


def getUserBasedRecomendations(oceni, korisnik, similarity=sim_pearson):
    rankings = []
    slicni = getRecommendations(oceni, korisnik)
    for item in slicni:
        rankings.append(item[1])

    rankings = rankings[0:3]
    rankings.sort()
    return rankings


def getItemBasedRecomendations(oceni, korisnik, similarity=sim_pearson):
    rankings = []
    itembased = transformPrefs(oceni)
    movies = oceniPoKorisnici[korisnik]
    preporaka = []
    for movie in movies:
        slicni = topMatches(itembased, movie)
        for slicen in slicni:
            ocenka = slicen[0]

            if slicen[1] not in movies and ocenka >= 0:
                preporaka.append(slicen)
    preporaka.sort()
    preporaka.reverse()
    preporaka2 = []
    for preporaki in preporaka:
        if preporaki[1] not in preporaka2:
            preporaka2.append(preporaki[1])
    rankings = preporaka2[0:3]
    rankings.sort()

    return rankings


#def square_rooted(x):
    #return round(sqrt(sum([a * a for a in x])), 3)


def cosine_similarity(oceni,p1,p2):
    zaednicki = {}
    for item in oceni[p1]:
        if item in oceni[p2]: zaednicki[item] = 1

    numerator = sum([oceni[p1][it] * oceni[p2][it] for it in zaednicki])
    denominator = sqrt(sum([oceni[p1][it]*oceni[p1][it] for it in zaednicki])) * sqrt(sum([oceni[p2][it]*oceni[p2][it] for it in zaednicki]))

    if denominator == 0:
        return -1
    vkupno= round(numerator / float(denominator),2)
    return vkupno


def transformoceni(oceni):
    result={}
    for person in oceni:
        for item in oceni[person]:
            result.setdefault(item,{})
            # Zameni gi mestata na licnosta i predmetot
            result[item][person]=oceni[person][item]

    return result
def similarity(recnik,film):
    total={}
    for item in recnik.keys():
         if item!=film:
            total.setdefault(item,{})
            total[item]=(cosine_similarity(recnik,film,item),sim_pearson(recnik,film,item),sim_distance(recnik,film,item))
    return total

if __name__ == "__main__":
    film = "Catch Me If You Can"

    filmovi=transformoceni(oceniPoKorisnici)
    lista = similarity(filmovi, film)

    lista1 = lista.items()
    lista1.sort()
    for x in lista1:
        print x[0]
        print x[1][0], x[1][1], x[1][2]
        print '\n'