import csv from Problem import Problem reader=csv.reader(file("Assignment 3.csv","rb")) problem=Problem() index=0 for row in reader: problem.loadline(row,index) index+=1 # print problem.users # print problem.movies # print problem.matrix #Without Normalization problem.computecorelation() problem.calcuNearneibor() #test 3712(7th) #problem.predict(6) #predict movies for 3867(5th) and 89(14th) problem.predict(4) problem.predict(13) #Normalization problem.predictnormalized(4) problem.predictnormalized(13)
import csv from Problem import Problem reader = csv.reader(open("A1Ratings.csv","rU")) problem=Problem() for row in reader: problem.loadline(row) average_ratings=dict(zip(problem.movienames,[('%.2f')%f for f in problem.meanRatings()])) average_ratings=sorted(average_ratings.iteritems(),key=lambda d:d[1], reverse=True) print average_ratings most_ratings=dict(zip(problem.movienames,[f for f in problem.mostRatings()])) most_ratings=sorted(most_ratings.iteritems(),key=lambda d:d[1],reverse=True) print most_ratings higher_ratings=dict(zip(problem.movienames,[('%.2f')%f for f in problem.rating4()])) higher_ratings=sorted(higher_ratings.iteritems(),key=lambda d:d[1],reverse=True) print higher_ratings rel=dict(zip(problem.movienames[1:],[('%.2f')%f for f in problem.getRelevance()])) rel=sorted(rel.iteritems(),key=lambda d:d[1],reverse=True) print rel