python用户推荐系统曼哈顿算法实现

#-*- 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
distances = []
for key in users:
distances.append((distance,key))
distances.sort()
return distances

#推荐python实现
#获得最近用户的name
recommendations =[]
#得到最近用户的推荐列表
neighborRatings = users[nearest]
for key in neighborRatings:
recommendations.append((key,neighborRatings[key]))
recommendations.sort(key=lambda rat:rat[1], reverse=True)
return recommendations

if __name__ == '__main__':
print recommend('Hailey', users)