Python用户推荐系统曼哈顿算法实现完整代码

#-*- coding: utf-8 -*- import codecsfrom math import sqrt users = {"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}    } # Python计算曼哈顿距离 www.iplaypy.comdef manhattan(rate1,rate2):  distance = 0  commonRating = False  for key in rate1:    if key in rate2:      distance+=abs(rate1[key]-rate2[key])      commonRating=True  if commonRating:    return distance  else:    return -1 # python/83030.html">python/57556.html">python返回最近距离用户def computeNearestNeighbor(username,users):  distances = []  for key in users:    if key<>username:      distance = manhattan(users[username],users[key])      distances.append((distance,key))   distances.sort()       return distances #推荐python实现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)