def main(): for q in xrange(0, 1): # an array of posts and users content = [0] * num_posts users = [] ranking = [0] * num_posts # ids of posts and users user_id = 0 content_id = 0 # initialize the number of users, create collusions for i in xrange(num_users): # determine user bias if any user_bias = -1 user_bias_prob = random.uniform(0, 1) if (user_bias_prob < user_no_group_prob): user_bias = no_group elif (user_bias_prob < user_no_group_prob + user_first_group_prob): user_bias = first_group else: user_bias = second_group # create a new user users.append(User(user_bias = user_bias, id = user_id, num_posts = num_posts)) user_id += 1 no_bias_sum = 0 first_bias_sum = 0 second_bias_sum = 0 no_bias_sum2 = 0 first_bias_sum2 = 0 second_bias_sum2 = 0 no_bias_sum3 = 0 first_bias_sum3 = 0 second_bias_sum3 = 0 # run simulation by the minute iterate = 60 * 24 * num_days blah = 0 for i in xrange(iterate): # create three new posts every minute number_new_posts = 3 # number of new posts a minute for j in xrange(number_new_posts): # determine post bias if any for new post post_bias = -1 post_bias_prob = random.uniform(0, 1) if (post_bias_prob < no_group_prob): post_bias = no_group elif (post_bias_prob < no_group_prob + first_group_prob): post_bias = first_group else: post_bias = second_group # create a new post content[content_id] = (Post(post_bias = post_bias, id = content_id, time = i)) content_id += 1 # go through all users and see if they will upvote/downvote posts for j in xrange(num_users): var = random.uniform(0, 1) if (var > 0.0): # look at the past hour last_hour = 60 * 3 if (content_id < last_hour): for k in xrange(content_id): users[j].vote(post = content[k]) else: for k in xrange(last_hour): w = content_id - k - 1 users[j].vote(post = content[w]) # call page rank algorithm for reddit if (i % 30 == 0 and not i == 0): posts_ranking = [] for j in xrange(content_id - 1): post_data = [reddit.hot(content[j].ups, content[j].downs, content[j].date), content[j].id] posts_ranking.append(post_data) sorted_by_second = sorted(posts_ranking, key=lambda tup: tup[0], reverse = True) no_bias_number = 0 first_bias_number = 0 second_bias_number = 0 for b in xrange(0, 30): id = sorted_by_second[b][1] if (content[id].post_bias == no_group): no_bias_number += 1 elif (content[id].post_bias == first_group): first_bias_number += 1 else: second_bias_number += 1 no_bias_sum += no_bias_number first_bias_sum += first_bias_number second_bias_sum += second_bias_number posts_ranking = [] for j in xrange(content_id - 1): post_data = [sampling.hot(content[j].ups, content[j].downs, content[j].date), content[j].id] posts_ranking.append(post_data) sorted_by_second = sorted(posts_ranking, key=lambda tup: tup[0], reverse = True) no_bias_number = 0 first_bias_number = 0 second_bias_number = 0 for b in xrange(0, 30): id = sorted_by_second[b][1] if (content[id].post_bias == no_group): no_bias_number += 1 elif (content[id].post_bias == first_group): first_bias_number += 1 else: second_bias_number += 1 no_bias_sum2 += no_bias_number first_bias_sum2 += first_bias_number second_bias_sum2 += second_bias_number posts_ranking = [] for j in xrange(content_id - 1): post_data = [noisy.hot(content[j].ups, content[j].downs, content[j].date), content[j].id] posts_ranking.append(post_data) sorted_by_second = sorted(posts_ranking, key=lambda tup: tup[0], reverse = True) no_bias_number = 0 first_bias_number = 0 second_bias_number = 0 for b in xrange(0, 30): id = sorted_by_second[b][1] if (content[id].post_bias == no_group): no_bias_number += 1 elif (content[id].post_bias == first_group): first_bias_number += 1 else: second_bias_number += 1 no_bias_sum3 += no_bias_number first_bias_sum3 += first_bias_number second_bias_sum3 += second_bias_number total = no_bias_sum + first_bias_sum + second_bias_sum print '-----Reddit-----' print float(no_bias_sum) / total print float(first_bias_sum) / total print float(second_bias_sum) / total print '----Sampling----' print float(no_bias_sum2) / total print float(first_bias_sum2) / total print float(second_bias_sum2) / total print '-----Noisy------' print float(no_bias_sum3) / total print float(first_bias_sum3) / total print float(second_bias_sum3) / total print no_bias_sum + first_bias_sum + second_bias_sum
def main():
# an array of posts and users
content = [0] * num_posts
users = []
nb_users = []
fg_users = []
sg_users = []
ranking = [0] * num_posts
# ids of posts and users
user_id = 0
content_id = 0
# initialize the number of users, create collusions
for i in xrange(num_users):
# determine user bias if any
user_bias = -1
user_bias_prob = random.uniform(0, 1)
if (user_bias_prob < user_no_group_prob):
user_bias = no_group
# create a new user
nb_users.append(User2(user_bias = user_bias, id = user_id, num_posts = num_posts))
user_id += 1
elif (user_bias_prob < user_no_group_prob + user_first_group_prob):
user_bias = first_group
# create a new user
fg_users.append(User2(user_bias = user_bias, id = user_id, num_posts = num_posts))
user_id += 1
else:
user_bias = second_group
# create a new user
sg_users.append(User2(user_bias = user_bias, id = user_id, num_posts = num_posts))
user_id += 1
users.append(User2(user_bias = user_bias, id = user_id, num_posts = num_posts))
no_bias_sum = 0
first_bias_sum = 0
second_bias_sum = 0
no_bias_sum2 = 0
first_bias_sum2 = 0
second_bias_sum2 = 0
no_bias_sum3 = 0
first_bias_sum3 = 0
second_bias_sum3 = 0
# run simulation by the minute
iterate = 60 * 24 * num_days
for i in xrange(iterate):
# create three new posts every minute
# raw_input("Press Enter to continue...")
number_new_posts = 3
# number of new posts a minute
for j in xrange(number_new_posts):
# determine post bias if any for new post
post_bias = -1
post_bias_prob = random.uniform(0, 1)
this_post_poster_id = -1
if (post_bias_prob < no_group_prob):
post_bias = no_group
this_post_poster_id = random.choice(nb_users).id
elif (post_bias_prob < no_group_prob + first_group_prob):
post_bias = first_group
if (not fg_users):
this_post_poster_id = 0
else:
this_post_poster_id = random.choice(fg_users).id
else:
if (not sg_users):
this_post_poster_id = 0
else:
this_post_poster_id = random.choice(sg_users).id
# create a new post
content[content_id] = (Post2(post_bias = post_bias, id = content_id, time = i, poster_id = this_post_poster_id))
content_id += 1
# go through all users and see if they will upvote/downvote posts
for j in xrange(num_users):
var = random.uniform(0, 1)
if (var > 0.0):
# look at the past hour
last_hour = 60 * 3
if (content_id < last_hour):
for k in xrange(content_id):
users[j].vote(post = content[k],poster = users[content[k].poster_id])
else:
for k in xrange(last_hour):
w = content_id - k - 1
users[j].vote(post = content[w], poster = users[content[w].poster_id])
# call page rank algorithm for reddit
if (i % 30 == 0 and not i == 0):
#Detector
posts_ranking = []
different = 0
summation = 0
for j in xrange(content_id - 1):
if (content[j].alt_ups != content[j].ups):
different += 1
summation += 1
post_data = [detector.hot(content[j].alt_ups, content[j].alt_downs, content[j].date), content[j].id]
posts_ranking.append(post_data)
# print "Percent different == %f" % (float(different)/float(summation))
sorted_by_second = sorted(posts_ranking, key=lambda tup: tup[0], reverse = True)
no_bias_number = 0
first_bias_number = 0
second_bias_number = 0
for b in xrange(0, 30):
id = sorted_by_second[b][1]
if (content[id].post_bias == no_group):
no_bias_number += 1
elif (content[id].post_bias == first_group):
first_bias_number += 1
else:
second_bias_number += 1
no_bias_sum += no_bias_number
first_bias_sum += first_bias_number
second_bias_sum += second_bias_number
#Reddit
posts_ranking2 = []
for j in xrange(content_id - 1):
post_data2 = [reddit.hot(content[j].ups, content[j].downs, content[j].date), content[j].id]
posts_ranking2.append(post_data)
sorted_by_second2 = sorted(posts_ranking2, key=lambda tup: tup[0], reverse = True)
no_bias_number2 = 0
first_bias_number2 = 0
second_bias_number2 = 0
for b in xrange(0, 30):
id = sorted_by_second[b][1]
if (content[id].post_bias == no_group):
no_bias_number2 += 1
elif (content[id].post_bias == first_group):
first_bias_number2 += 1
else:
second_bias_number2 += 1
no_bias_sum2 += no_bias_number2
first_bias_sum2 += first_bias_number2
second_bias_sum2 += second_bias_number2
total = no_bias_sum + first_bias_sum + second_bias_sum
print '-------------------------------------'
print '-----Detector------'
print float(no_bias_sum) / total
print float(first_bias_sum) / total
print float(second_bias_sum) / total
total = no_bias_sum2 + first_bias_sum2 + second_bias_sum2
print '-------------------------------------'
print '-----Reddit------'
print float(no_bias_sum2) / total
print float(first_bias_sum2) / total
print float(second_bias_sum2) / total