#!/usr/local/bin/python2.7 # encoding: utf-8
import sys import os
from argparse import ArgumentParser from argparse import RawDescriptionHelpFormatter
from math import sqrt
import moivescore # import module defined by yourself from rope.base.prefs import Prefs from bokeh.models.tools import Scroll
# 利用欧几里德距离评价相关性 def sim_distance(prefs,p1,p2): si = {} # mark the moive name that both appeared p1 and p2 for item in prefs[p1]: # print(item) if item in prefs[p2]: si[item] = 1 # print(si) if len(si)==0: return 0 # have no same moive # 利用欧几里德距离评价相关性 sum_of_squares = sum([pow(prefs[p1][item]-prefs[p2][item], 2) for item in prefs[p1] if item in prefs[p2] ]) # print(sum_of_squares) print(1/(1+sqrt(sum_of_squares))) return 1/(1+sqrt(sum_of_squares)) # 利用皮尔逊相关系数进行评价，可以修正评分中的夸大分值 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) # EX sum1 = sum(prefs[p1][item] for item in si) # EY sum2 = sum(prefs[p2][item] for item in si) # EX2 sqsum1 = sum(pow(prefs[p1][item],2) for item in si) # EY2 sqsum2 = sum(pow(prefs[p2][item],2) for item in si) #EXY psum = sum(prefs[p1][item]*prefs[p2][item] for item in si) #EXY-EX*EY num = psum-(sum1*sum2/n) den = sqrt((sqsum1-pow(sum1,2)/n)*(sqsum2-pow(sum2,2)/n)) if den == 0: return 0 r = num/den # print(r) return r
# print(moivescore.critics[‘Lisa Rose’]) # key and value
# print(moivescore.critics[‘Lisa Rose’][‘Lady in the Water’])
# sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Gene Seymour’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Michael Phillips’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Claudia Puig’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Mick LaSalle’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Jack Matthews’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘Toby’) # sim_distance(moivescore.critics, ‘Lisa Rose’, ‘xiaoYu’) print(‘——————–pearson——————————–‘) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Gene Seymour’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Michael Phillips’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Claudia Puig’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Mick LaSalle’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Jack Matthews’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘Toby’) # sim_pearson(moivescore.critics, ‘Lisa Rose’, ‘xiaoYu’)
# find the person who have the most likely taste with you def topMatches(prefs,person,n=5,similarity=sim_pearson): # scores = [other for other in prefs if person!=other] # print(scores) scores = [(other,similarity(prefs,person,other)) for other in prefs if person!=other] # print(scores) scores.sort() scores.reverse() print(scores) print(scores[0:n]) return scores[0:n] # return existing data from 0 to n # topMatches(moivescore.critics, ‘Lisa Rose’, 1)
# use pearson to provide us a recommendation of the film def getRecommendation(prefs,person,similarity=sim_pearson): totals={} # sum (similarity*score) all the movie which i havent see simSums={} # sum similarity of all the movie which i havent see for other in prefs: if other == person: continue sim = similarity(prefs,person,other) if sim<=0: continue for item in prefs[other]: # only estimate the movie this person never watched before, means he has no score on this movie if item not in prefs[person] or prefs[person][item]==0: totals.setdefault(item,0) # similarity*score totals[item]+=prefs[other][item]*sim simSums.setdefault(item,0) simSums[item]+=sim print(totals) print(simSums) # create a ranking list rankings = [(item,total/simSums[item]) for item,total in totals.items()] rankings.sort() rankings.reverse() print(rankings) return rankings getRecommendation(moivescore.critics, ‘Toby’)
