基于用户的推荐算法pearson算法实现，算法pearson,pearson.py#-

基于用户的推荐算法pearson算法实现，算法pearson,pearson.py#-

pearson.py

#-*- coding: utf-8 -*-'''Created on 2012-9-3@author: Jekey培生距离如果数据相关性较好，使用该算法'''import codecsfrom math import sqrtusers = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0},         "Bill":{"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0},         "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0},         "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0},         "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0},         "Jordyn":  {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0},         "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0},         "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}        }#pearson 距离def pearson(rate1,rate2):    sum_xy = 0    sum_x=0    sum_y=0    sum_x2=0    sum_y2=0    n=0      for key in rate1:        if key in rate2:            n+=1            x=rate1[key]            y=rate2[key]            sum_xy += x*y            sum_x +=x            sum_y +=y            sum_x2 +=x*x            sum_y2 +=y*y    #计算距离    if n==0:        return 0    else:        sx=sqrt(sum_x2-(pow(sum_x,2)/n))        sy=sqrt(sum_y2-(pow(sum_y,2)/n))        if sx<>0 and sy<>0:            denominator=(sum_xy-sum_x*sum_y/n)/sx/sy        else:            denominator=0    return denominator#返回最近距离用户def computeNearestNeighbor(username,users):    distances = []    for key in users:        if key<>username:            distance = pearson(users[username],users[key])            distances.append((distance,key))     distances.sort()              return distances#推荐def recommend(username,users):    #获得最近用户的name    nearest = computeNearestNeighbor(username,users)[0][1]    recommendations =[]    #得到最近用户的推荐列表    neighborRatings = users[nearest]    for key in neighborRatings:        if not key in users[username]:            recommendations.append((key,neighborRatings[key]))    recommendations.sort(key=lambda rat:rat[1], reverse=True)    return recommendationsif __name__ == '__main__':    #print computeNearestNeighbor('Hailey',users)    print recommend('Hailey', users)