def create_problem(assignment_id):
questions = request.json['questions']
qdocs = [question.Question(_id=q['_id'], text=q['text']) for q in questions]
pdoc = problem.Problem(
order=int(request.json['order']),
assignment_id=ObjectId(assignment_id),
text=request.json['text'],
questions=qdocs,
visible=request.json['visible'])
pdoc.save()
return jsonify(code=200, data=pdoc)
# Discretized sampled distribution in order to have real q⋆
X, R = l.M_true.sample(l.n_true)
l.M = synth.MarketDiscreteDistribution(X, R)
l.n_experiments = 100
l.λ = 3
l.δ = 0.2
l.ns = np.arange(25, 2025, 25)
l.Rf = 0
β = 1
r_threshold = 60
l.u = ut.LinearPlateauUtility(β, r_threshold)
print('Computing q⋆ for the discretized problem...')
p_star = pr.Problem(X, R, λ=0, u=l.u)
p_star.solver = cvx.SCS
R_star_q_star = p_star.solve()
q_star = p_star.q
R_star = p_star.insample_cost
R_star_q_star = R_star(q_star)
CE_star = p_star.insample_CE
CE_star_q_star = CE_star(q_star)
print('Done.')
qs = np.zeros(shape=(len(l.ns), l.n_experiments, l.p + 1))
Rs_ins = np.empty(shape=(l.n_experiments, len(l.ns)))
Rs_oos = np.empty(shape=(l.n_experiments, len(l.ns)))
# Rs_lb = np.empty(shape=(len(l.ns)))
X, R = M.sample(n_true)
M = synth.MarketDiscreteDistribution(X, R)
X = DiscreteDistribution(X)
R = DiscreteDistribution(R)
n_experiments = 800
λs = np.arange(0, 13.2, 0.2)
n = 200
δ = 0.2
βs = [1, 0.99, 0.5, 0.1, 0.01]
for β in βs:
u = ut.LinearPlateauUtility(β, 60)
p = pr.Problem(X.points, R.points, λ=0, u=u)
print('Computing q⋆ for the discretized problem...')
p.solver = None
p.solve()
q_star = p.q
print('Done.')
def R_star(q, λ=0):
return p.total_cost(X.points, R.points, q, λ)
def CE(q, λ=0):
return u.inverse(-R_star(q, λ))
R_star_q_star = R_star(q_star)