def test_convergence():
'''
Have the same user and problem win and lose over and over again,
and they should "converge" or ocillate around the same value.
'''
user = users[0]
problem = problems[0]
for i in xrange(15):
user, problem = adjust_distribution(user, problem, True)
user, problem = adjust_distribution(user, problem, False)
user1, problem1 = adjust_distribution(user, problem, True)
user2, problem2 = adjust_distribution(user1, problem1, False)
print user, user1, user2
print problem, problem1, problem2
assert abs(user[0] - user2[0]) < 10
assert abs(user1[0] - user2[0]) < 50
assert abs(problem[0] - problem2[0]) < 10
assert abs(problem1[0] - problem2[0]) < 50
def test_noninformative_presequence():
'''
Solving easier problems prior to solving difficult problems should
not change the user rating too much
'''
u1 = users[0]
u2 = users[1]
# user 1 solves all problems, except last one
for p in problems[:-1]:
u1, p2 = adjust_distribution(u1, p, True)
u1, p2 = adjust_distribution(u1, problems[-1], False)
# user 2 solves some prev problems, except last one
for p in problems[5:-1]:
u2, p2 = adjust_distribution(u2, p, True)
u2, p2 = adjust_distribution(u1, problems[-1], False)
print u1[0]
print u2[0]
assert u1[0] >= u2[0] # make sense for the first person to score little higher
assert abs(u1[0] - u2[0]) < 50 # but not too much
