集体智慧编程：提供推荐_欧几里得距离

欧几里得距离：m维空间中两个点之间的真实距离。

from math import sqrt

critics = {'Lisa':{'A':2.5,'B':3.5,'C':3.0,'D':3.5,'E':2.5,'F':3.0},
           'Gene':{'A':3.0,'B':3.5,'C':1.5,'D':5.0,'F':3.0,'E':3.5},
           'Michael':{'A':2.5,'B':3.0,'D':3.5,'F':4.0},
           'Claudia':{'B':3.5,'C':3.0,'F':4.5,'D':4.0,'E':2.5},
           'Mick':{'A':3.0,'B':4.0,'C':2.0,'D':3.0,'F':3.0,'E':2.0},
           'Jack':{'A':3.0,'B':4.0,'F':3.0,'D':5.0,'E':3.5},
           'Toby':{'B':4.5,'D':4.0,'E':1.0}}

"""""
欧几里得距离评价
Euclidean Distance Score
"""""
#书本算法
def sim_distance(prefs,person1,person2):

    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1
    if len(si) == 0:
        return 0
    
    sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item],2)
    for item in prefs[person1] if item in prefs[person2]])
    
    return 1/(1+sqrt(sum_of_squares))
    
#自己编写
def sim_distance2(prefs,person1,person2):
    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1
    
    if len(si) == 0:
        return 0
    
    sum = 0
    for item in si:
        sum = sum + pow((prefs[person1][item]-prefs[person2][item]),2)
        
    sim_dis = 1/(1+sqrt(sum))
    return sim_dis