from math import sqrtcritics={‘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}}#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]]) #威尔逊相关度  绘制一条尽可能靠近地图上所有的坐标点 称为最佳拟合线def simPerson(prefs, p1, p2):#得到双方都评价过的物品列表    si = {}    for item in prefs[p1]:        if item in prefs[p2]: si[item] = 1    n = len(si)    if n == 0:        return -1    sum1 = sum([prefs[p1][it] for it in si])    sum2 = sum([prefs[p2][it] for it in si])    #求平方和    sum1sq = sum([pow(prefs[p1][it], 2) for it in si])    sum2sq = sum([pow(prefs[p2][it], 2) for it in si])    #求乘积之和    pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si])    #计算皮尔逊评价值    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 rdef topMatches(prefs, person, n=5, similarity=simPerson):    scores = []    # scores=[(similarity(prefs,person,other),other)    #         for other in prefs if other != person]    for other in prefs:        if other != person:            scores.append((similarity(prefs, person, other), other))    scores.sort()    scores.reverse()    print(scores[0:n])    return scores[0:n]topMatches(critics, ‘Toby‘, n=6)def get_recommendation(prefs, person, similarity=simPerson):    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]*simdef transformPrefs(prefs):    result = {}    for person in prefs:        for item in prefs[person]:            result.setdefault(item, {})            #字典中如果有item没有这个key,就插入这个key并赋值，并返回result的值（默认为None）            #如果有这个key则返回相应的value            #作用在于将所有的电影名添加            result[item][person] = prefs[person][item]    return resultimport pydeliciousprint(pydelicious.get_popular(tag=‘programming‘))def calculateSimlarItems(prefs, n = 10):    result = {}    #以物品为中心对偏好矩阵实施倒置处理    itemPrefs = transformPrefs(prefs)    c = 0    for item in itemPrefs:        c += 1        if c % 100 == 0:            d = c / len(itemPrefs)            print(d)        scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance)        result[item] = scores    return resultdef getRcommendeditems(prefs, itemMatch, user):    userRatings = prefs[user]    scores = {}    totalSim = {}    for(item, rating) in userRatings.items(): #循环遍历由当前用户评分的物品        for (similarity, item2) in itemMatch[item]:#循环遍历与当前物品相近的物品            if item2 in userRatings:                continue            scores.setdefault(item2, 0)            scores[item2] += similarity * rating #相似度*当前物品的评分 对某部电影有一个评分，找到相似的并求出相似度，推算出评价分            totalSim.setdefault(item2, 0)            totalSim[item2] += similarity    rankings = [(scores / totalSim[item], item) for item, score in scores.items()] #此时的item为相似的物品，score为加权分    rankings.sort()    rankings.reverse()    return rankings