1 critics={‘Lisa Rose‘: {‘Lady in the Water‘: 2.5, ‘Snakes on a Plane‘: 3.5,‘Just My Luck‘: 3.0, ‘Superman Returns‘: 3.5, ‘You, Me and Dupree‘: 2.5, ‘The Night Listener‘: 3.0},
 2          ‘Gene Seymour‘: {‘Lady in the Water‘: 3.0, ‘Snakes on a Plane‘: 3.5, ‘Just My Luck‘: 1.5, ‘Superman Returns‘: 5.0, ‘The Night Listener‘: 3.0, ‘You, Me and Dupree‘: 3.5}, 
 3          ‘Michael Phillips‘: {‘Lady in the Water‘: 2.5, ‘Snakes on a Plane‘: 3.0,‘Superman Returns‘: 3.5, ‘The Night Listener‘: 4.0},
 4          ‘Claudia Puig‘: {‘Snakes on a Plane‘: 3.5, ‘Just My Luck‘: 3.0, ‘The Night Listener‘: 4.5, ‘Superman Returns‘: 4.0, ‘You, Me and Dupree‘: 2.5},
 5          ‘Mick LaSalle‘: {‘Lady in the Water‘: 3.0, ‘Snakes on a Plane‘: 4.0, ‘Just My Luck‘: 2.0, ‘Superman Returns‘: 3.0, ‘The Night Listener‘: 3.0,‘You, Me and Dupree‘: 2.0}, 
 6          ‘Jack Matthews‘: {‘Lady in the Water‘: 3.0, ‘Snakes on a Plane‘: 4.0,‘The Night Listener‘: 3.0, ‘Superman Returns‘: 5.0, ‘You, Me and Dupree‘: 3.5},
 7          ‘Toby‘: {‘Snakes on a Plane‘:4.5,‘You, Me and Dupree‘:1.0,‘Superman Returns‘:4.0}}
 8 
 9 critics[‘Lisa Rose‘][‘Lady in the Water‘]
10 critics[‘Toby‘][‘Snakes on a Plane‘] = 4.5
11 critics[‘Toby‘]

 1 from math import sqrt
 2 def sim_distance(prefs,person1,person2):
 3     si = {}
 4     for item in prefs[person1]:
 5         if item in prefs[person2]:
 6             si[item] = 1
 7     if len(si) == 0:return 0
 8     
 9     sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item],2)
10     for item in prefs[person1] if item in prefs[person2]])
11     
12     return 1/(1+sqrt(sum_of_squares))
13 
14 sim_distance(critics,‘Lisa Rose‘,‘Gene Seymour‘)

 1 def sim_pearson(prefs,p1,p2):
 2     si = {}
 3     for item in prefs[p1]:
 4         if item in prefs[p2]:
 5             si[item] = 1
 6             
 7     n = len(si)
 8     
 9     if n==0:
10         return 0
11     
12     sum1=sum([prefs[p1][it] for it in si])
13     sum2=sum([prefs[p2][it] for it in si])
14     
15     sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
16     sum2Sq=sum([pow(prefs[p2][it],2) for it in si])
17     
18     pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
19     
20     num=pSum-(sum1*sum2/n)
21     den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
22     
23     if den==0:
24         return 0
25     
26     r = num/den
27     return r
28 
29 sim_pearson(critics,‘Lisa Rose‘,‘Gene Seymour‘)
30 
31 
32 def topMatchs(prefs,person,n=5,similarity=sim_pearson):
33     scores=[(similarity(prefs,person,other),other)
34     for other in prefs if other !=person]
35     
36     scores.sort()
37     scores.reverse()
38     
39     return scores[0:n]
40 
41 topMatchs(critics,‘Toby‘,n=3)