http://www.oreilly.com/catalog/9780596529321
# A dictionary of movie critics and their ratings of a small
# set of movies
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},
'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},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'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},
'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},
'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},
'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}
>>> from recommendations import critics
>>> critics['Lisa Rose']['Lady in the Water']
2.5
>>> critics['Toby']['Snakes on a Plane']=4
>>> critics['Toby']
{'Snakes on a Plane': 4, 'Superman Returns': 4.0, 'You, Me and Dupree': 1.0}
>>> critics['Toby']['Snakes on a Plane']=4.5
相似度评价体系
- 欧几里得距离
- 皮尔逊相关系数
欧几里得距离
>>> from recommendations import sim_distance，critics
>>> sim_distance(critics,'Lisa Rose','Gene Seymour')
0.14814814814814814
皮尔逊相关系数
>>>from recommendations import critics, sim_pearson
>>>sim_pearson(critics,'Lisa Rose','Gene Seymour')
0.39605901719066977
>>> from recommendations import critics, sim_pearson,topMatches
>>> topMatches(critics,'Toby',n=3)
[(0.9912407071619299, 'Lisa Rose'), (0.9244734516419049, 'Mick LaSalle'), (0.8934051474415647, 'Claudia Puig')]
