This is the function in question. It calculates the Pearson correlation coefficient for p1 and p2, which is supposed to be a number between -1 and 1.
When I use this with real user data, it sometimes returns a number greater than 1, like in this example:
def sim_pearson(prefs,p1,p2):
si={}
for item in prefs[p1]:
if item in prefs[p2]: si[item]=1
if len(si)==0: return 0
n=len(si)
sum1=sum([prefs[p1][it] for it in si])
sum2=sum([prefs[p2][it] for it in si])
sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
sum2Sq=sum([pow(prefs[p2][it],2) for it in si])
pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])
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
critics = {
'user1':{
'item1': 3,
'item2': 5,
'item3': 5,
},
'user2':{
'item1': 4,
'item2': 5,
'item3': 5,
}
}
print sim_pearson(critics, 'user1', 'user2', )
1.15470053838
It looks like you may be unexpectedly using integer division. I made the following change and your function returned 1.0
:
num=pSum-(1.0*sum1*sum2/n)
den=sqrt((sum1Sq-1.0*pow(sum1,2)/n)*(sum2Sq-1.0*pow(sum2,2)/n))
See PEP 238 for more information on the division operator in Python. An alternate way of fixing your above code is:
from __future__ import division
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