Python用户推荐系统曼哈顿算法实现完整代码

#-*- coding: utf-8 -*-
 
import codecs
from 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.com
def 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)