from critics import *

import recommendations

choice=raw_input('enter your search type\n1.By user\n2.By Book\nChoice:\t')

if choice=='1':

user = raw_input('enter the user ID:\t')

print '\n'

print 'Books till now read by the user are:\n'

print critics[user],'\n'

dischoice=raw_input('enter your choice\n1.euclidian distance\n2.pearson corelation\nchoice:\t')

if dischoice=='1':

print 'top matched user according to the euclidian distance\n'

print recommendations.topMatches(critics,user,3,recommendations.sim_distance),'\n'

print 'Recommended books according to the euclidian distance\n'

print recommendations.getRecommendations(critics,user,similarity=recommendations.sim_distance)[0:3],'\n'

else:

if dischoice=='2':

print 'top matched user according to the pearson corelation\n'

print recommendations.topMatches(critics,user, 3),'\n'

print 'Recommended books according to the pearson corelaton\n'

print recommendations.getRecommendations(critics,user)[0:3],'\n'

else:

print 'invalid choice... try again'

else:

if choice =='2':

critic = recommendations.transformPrefs(critics)

book=raw_input('enter the book name:\t')

print '\n'

print 'Users who have read the book till now are:\n'

print critic[book],'\n'

disch=raw_input('enter your choice\n1.euclidian distance\n2.pearson corelation\nchoice:\t')

if disch=='1':

print 'top matched book according to the euclidian distance\n'

print recommendations.topMatches(critic,book,3,recommendations.sim_distance),'\n'

print 'Recommended user according to the euclidian distance\n'

print recommendations.getRecommendations(critic,book,similarity=recommendations.sim_distance)[0:3],'\n'

else:

if disch=='2':

print 'top matched book according to the pearson corelation\n'

print recommendations.topMatches(critic,book, 3),'\n'

print 'Recommended users according to the pearson corelaton\n'

print recommendations.getRecommendations(critic,book)[0:3],'\n'

else:

print 'invalid choice... try again'

else:

print 'invalid choice... try again'