#基于用户的推荐类算法

from math import sqrt

#计算两个person的欧几里德距离

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  1/(1+sqrt(sum_of_squares))

#计算两个person 的皮尔逊相关系数

def sim_person(prefs,p1,p2,n=5):#n指的时电影评分满分是5

    si = {}

    for item in prefs[p1]:

        if item in prefs[p2]:

            return  1

    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],2] 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

#返回跟输入person的相似排名结果

def topMatches(prefs,person,n=5,similarity = sim_person):

    scores = [(similarity(prefs,person,other,n),other) for other in prefs if other != person]

    scores.sort()

    scores.reverse()

    return scores[0:n]

#针对person进行推荐

def getRecommenddation(prefs,person,similarity = sim_person):

    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

critics = {

    'Jack':{'See You Again':4.5,'Try Everything':3.5,'Let it Go':5.0,'Sugar':3.5,'Sorry':2.5,'Baby':3.0},

    'Michael':{'See You Again':2.5,'Try Everything':3.0,'Let it Go':3.0,'Sorry':3.5},

    'Petter':{'See You Again':2.5,'Try Everything':3.5,'Let it Go':3.0,'Sugar':4.5,'Sorry':4.5,'Animals':2.0},

    'Tom':{'See You Again':4.5,'Try Everything':4.0,'Let it Go':5.0},

}

#w为tom进行电影推荐

print(getRecommenddation(critics,"Tom"))