# -*- coding: utf-8 -*-
“””
Created on Tue Sep 29 18:50:33 2015
@author: tanyouwei
“””
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}
}
#pearson 距离
def pearson(rate1,rate2):
sum_xy = 0
sum_x=0
sum_y=0
sum_x2=0
sum_y2=0
n=0
for key in rate1:
if key in rate2:
n+=1
x=rate1[key]
y=rate2[key]
sum_xy += x*y
sum_x +=x
sum_y +=y
sum_x2 +=x*x
sum_y2 +=y*y
#计算距离
if n==0:
return 0
else:
sx=sqrt(sum_x2-(pow(sum_x,2)/n))
sy=sqrt(sum_y2-(pow(sum_y,2)/n))
if sx<>0 and sy<>0:
denominator=(sum_xy-sum_x*sum_y/n)/sx/sy
else:
denominator=0
return denominator
#返回最近距离用户
def computeNearestNeighbor(username,users):
distances = []
for key in users:
if key<>username:
distance = pearson(users[username],users[key])
distances.append((distance,key))
distances.sort()
return distances
”’
#推荐一人
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 computeNearestNeighbor(‘Hailey’,users)
print recommend(‘Hailey’, users)
”’
#推荐多人
def recommend(username,users):
#获得最近用户的name
near=[]
for i in range(3):
nearest = computeNearestNeighbor(username,users)[i][1]
near.append(nearest)
recommendations = []
#得到最近用户的推荐列表
for i in near:
neighborRatings = users[i]
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 computeNearestNeighbor(‘Hailey’,users)
for i in users:
print ‘{username} 的推荐电影是： ‘.format(username=i)
print recommend(i, users)
