from math import sqrt

def sim_distance(p1,p2):

? ? c=set(p1.keys())&set(p2.keys())

? ? if not c: return 0

? ? sum_of_squares=sum([pow(p1.get(sk)-p2.get(sk), 2 ) for sk in c])

? ? p= 1 /( 1 +sqrt(sum_of_squares))

? ? return p

def sim_distance_pir(p1,p2):

? ? c=set(p1.keys())&set(p2.keys())

? ? if not c: return 0

? ? s1=sum([p1.get(sk) for sk in c])

? ? s2=sum([p2.get(sk) for sk in c])

? ? sq1=sum([pow(p1.get(sk), 2 ) for sk in c])

? ? sq2=sum([pow(p2.get(sk), 2 ) for sk in c])

? ? ss=sum([p1.get(sk)*p2.get(sk) for sk in c])

? ? n=len(c)

? ? num=ss-(s1*s2/n)

? ? den=sqrt((sq1-pow(s1, 2 )/n)*(sq2-pow(s2, 2 )/n))

? ? #print s1,s2,sq1,sq2,ss,n,num,den

? ? if den== 0 : return 0

? ? p=num/den

? ? return p

def sim_distance_jacc(p1,p2):

? ? c=set(p1.keys())&set(p2.keys())

? ? if not c: return 0

? ? ss=sum([p1.get(sk)*p2.get(sk) for sk in c])

? ? sq1=sum([pow(sk, 2 ) for sk in p1.values()])

? ? sq2=sum([pow(sk, 2 ) for sk in p2.values()])

? ? p=float(ss)/(sq1 + sq2 - ss)

? ? return p

?

def sim_distance_cos(p1,p2):

? ? c=set(p1.keys())&set(p2.keys())

? ? if not c: return 0

? ? ss=sum([p1.get(sk)*p2.get(sk) for sk in c])

? ? sq1=sqrt(sum([pow(sk, 2 ) for sk in p1.values()]))

? ? sq2=sqrt(sum([pow(sk, 2 ) for sk in p2.values()]))

? ? p=float(ss )/(sq1*sq2)

? ? return p

#

#a={'a':4.5,'b':1.0,'c':7}

?

from distance import *

def topsimilar(item,data,n= 5 ,sim_func=sim_distance):

? ? score=[(sim_func(data.get(item),data.get(ik)),ik) for ik in data.keys() if ik!=item]

? ? score.sort()

? ? score.reverse()

? ? return score

prefs= {

? ? ? ? "A" : { "1" : 3 , "2" : 4 , "3" : 0 , "4" : 3 , "5" : 3 },

? ? ? ? "B" : { "1" : 2 , "2" : 3 , "3" : 2 },

? ? ? ? "C" : { "1" : 2 , "2" : 4 , "3" : 4 , "4" : 3 , "5" : 0 },

? ? ? ? "D" : { "1" : 0 , "2" : 4 , "3" : 0 , "4" : 2 , "5" : 4 }

}

print topsimilar( 'A' , prefs,)

print topsimilar( 'A' , prefs,sim_func=sim_distance_pir)

print topsimilar( 'A' , prefs,sim_func=sim_distance_cos)

print topsimilar( 'A' , prefs,sim_func=sim_distance_jacc)