Python用户推荐系统minkowski算法的实现，pythonminkowski,Python用户推荐系统

Python用户推荐系统minkowski算法的实现，pythonminkowski,Python用户推荐系统

#-*- coding: utf-8 -*-'''python: www.iplaypy.com@author: Jekey欧几里得距离如果数据很稠密，使用该算法较好'''import codecsfrom math import sqrtusers = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},         "Bill":{"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0},         "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0},         "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0},         "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0},         "Jordyn":  {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0},         "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0},         "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}        }#Minkowski 距离def minkowski(rate1,rate2,r):    distance = 0    commonRating = False    for key in rate1:        if key in rate2:            distance+=pow(abs(rate1[key]-rate2[key]),r)            commonRating=True    if commonRating:        return pow(distance,1/r)    else:        return -1    #返回最近距离用户def computeNearestNeighbor(username,users):    distances = []    for key in users:        if key<>username:            distance = minkowski(users[username],users[key],2)            distances.append((distance,key))     distances.sort()              return distances#推荐def recommend(username,users):    #获得最近用户的name    nearest = computeNearestNeighbor(username,users)[0][1]    recommendations =[]    #得到最近用户的推荐列表    neighborRatings = users[nearest]    for key in neighborRatings:        if not key in users[username]:            recommendations.append((key,neighborRatings[key]))    recommendations.sort(key=lambda rat:rat[1], reverse=True)    return recommendations     if __name__ == '__main__':    print recommend('Hailey', users)

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