def main():
concepts = ["IA","IB","II"]
num_answers = -1
avg_concepts_involved = 2
exm = exam.ProblemSet(concepts)
answers = []
# Now we want to get the model to update in real time. Let's do this
# interactively!
p = problem.RandomProblem(concepts, num_answers, avg_concepts_involved)
exm.addProblem(p)
# The initial competence dictionary for all students is initialized to 0.5
# for all concepts
start_dic = {}
for concept in concepts:
start_dic[concept] = 0.5
logistic_guess = start_dic
binary_guess = start_dic
linear_guess = start_dic
print "Estimated Logistic Competence Level:\n" + str(logistic_guess)
print "Estimated Binary Competence Level:\n" + str(binary_guess)
print "Estimated Linear Competence Level:\n" + str(linear_guess)
print p
# interactive sessions are fun!
# this explores how updating could work
while raw_input("Answer Question? (y/n) ")[0] == 'y':
answer = (raw_input("Correctly? (y/n) ")[0] == 'y')
answers.append(answer)
# update logistic model
temp_stud = students.LogisticStudent(concepts)
temp_stud.competences = logistic_guess
logistic_guess = sim_annealing.most_likely_explanation( \
temp_stud, exm, answers, prob_map.MultMap())
# update binary model
temp_stud = students.BinaryStudent(concepts)
temp_stud.competences = binary_guess
binary_guess = sim_annealing.most_likely_explanation( \
temp_stud, exm, answers, prob_map.MultMap())
# update linear model
temp_stud = students.LinearStudent(concepts)
temp_stud.competences = linear_guess
linear_guess = sim_annealing.most_likely_explanation( \
temp_stud, exm, answers, prob_map.MultMap())
print "Estimated Logistic Competence Level:\n" + str(logistic_guess)
print "Estimated Binary Competence Level:\n" + str(binary_guess)
print "Estimated Linear Competence Level:\n" + str(linear_guess)
p = problem.RandomProblem(concepts, num_answers, avg_concepts_involved)
exm.addProblem(p)
print p
log = students.LogisticStudent(cons)
bin = students.BinaryStudent(cons)
lin = students.LinearStudent(cons)
all_students = {log: " log,", lin: " lin,", bin: " bin,"}
for stu in all_students:
for strat in strategies:
for num_p in num_problems:
for i in range(num_iterations):
stu.random_competences()
answers = []
exm = exam.RandomProblemSet(cons, num_p, num_answers, avg_concepts_involved)
for item in exm.problems:
result = random.random() < strat.process(stu.get_prob_correct(item))
answers.append(result)
true_comp = stu.get_competences()
guess = sim_annealing.most_likely_explanation(stu, exm, answers, strat)
f.write("\n")
f.write("\n")
f.write("\n")
f.write(all_students[stu] + strategies[strat] + str(len(cons)) + ", " + str(num_p) + "\n")
f.write(str(exm))
f.write("\n")
f.write(str(answers))
f.write("\n")
f.write(str(true_comp))
f.write("\n")
stu.competences = true_comp
f.write(str(students.get_probability_of_outcome(exm, answers, stu, strat)))
f.write("\n")
f.write(str(guess))
f.write("\n")
