0
我有一個默認的示例詞典,看起來像這樣:爲什麼這個功能不能用於我的輸入?
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}}
我使用,使用Pearson相關係數,看起來像這樣返回最相似的人在字典中的功能:
from math import sqrt
def sim_pearson(prefs,p1,p2):
# lista na zaednichki tochki
si={}
for item in prefs[p1]:
if item in prefs[p2]: si[item]=1
# najdi go brojot na elementi
n=len(si)
# ako nemaat zaednichki tochki vrati 0
if n==0: return 0
# dodadi gi site
sum1=sum([prefs[p1][it] for it in si])
sum2=sum([prefs[p2][it] for it in si])
# sumiraj gi kvadratite
sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
sum2Sq=sum([pow(prefs[p2][it],2) for it in si])
# sumiraj gi proizvodite
pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
# presmetka na Pirsonoviot koeficient
num=pSum-(sum1*sum2/n)
den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n))
if den==0: return 0
r=num/den
return r
它的工作原理。例如,對於呼叫print sim_pearson(critics, 'Toby', 'Lisa Rose')
,我得到係數0.991240707162。
然而,當我嘗試用我的字典相同的功能是:
tests = {'dzam': {'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj3AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiMAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiBAgw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjtAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj_AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiIAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj9AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiqAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjzAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxikAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiaAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj1AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjxAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiYAgw': 5.0},
'kex': {'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj3AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiMAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiBAgw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjtAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj_AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiIAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj9AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiqAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjzAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxikAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiaAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj1AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjxAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiYAgw': 5.0},
'rokoko': {'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj3AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiMAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiBAgw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjtAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj_AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiIAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj9AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiqAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjzAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxikAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiaAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj1AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjxAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiYAgw': 5.0},
'[email protected]': {'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj3AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiMAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiBAgw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjtAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj_AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiIAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj9AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiqAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjzAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxikAgw': 3.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiaAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxj1AQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjxAQw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiYAgw': 5.0},
'seljak': {'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxiKAgw': 5.0,
'ag1yYW5kb20tcmFuZG9tcg8LEghib29rbWFyaxjvAQw': 1.0, }}
我總是得到1.0,不管我有匹配的字典,這是爲什麼呢?
順便說一下,我使用散列,所以我的字典必須有這個長字符串。 :)
整數和分組不相處得很好。請我們從__future__進口部門`看看是否是這個問題。 – 2011-02-02 12:32:10
你所有的字符串在你的失敗測試中都是一樣的 - 是你想要的嗎?如果是這樣,這就是爲什麼你得到1.0,表明完美的相關性,因爲一切都是相同的。 – payne 2011-02-02 12:33:29