def test_regression_simulation(inputs):
init_dict = random_init(inputs)
# Draw parameter
param_1 = np.random.normal(17.5, 2)
param_2 = np.random.normal(21, 2)
param_3 = np.random.normal(-2, 0.1)
param_4 = np.random.normal(0.2, 0.1)
params = np.array([param_1, param_2, param_3, param_4])
disc_fac = init_dict["simulation"]["discount_factor"]
probs = np.array(init_dict["simulation"]["known_trans"])
num_states = 800
trans_mat = create_transition_matrix(num_states, probs)
costs = calc_obs_costs(num_states, cubic_costs, params, 0.00001)
ev = calc_fixp(trans_mat, costs, disc_fac)[0]
df = simulate(init_dict["simulation"], ev, costs, trans_mat)
v_disc = discount_utility(df, disc_fac)
assert_allclose(v_disc / ev[0], 1, rtol=1e-02)
def test_trans_mat_rows_one():
rand_dict = random_init()
control = np.ones(rand_dict["estimation"]["states"])
assert_array_almost_equal(
create_transition_matrix(
rand_dict["estimation"]["states"],
np.array(rand_dict["simulation"]["known_trans"]),
).sum(axis=1),
control,
)
def test_regression_simulation(inputs):
init_dict = random_init(inputs)
df, unobs, utilities, num_states = simulate(init_dict["simulation"])
num_buses = init_dict["simulation"]["buses"]
num_periods = init_dict["simulation"]["periods"]
beta = init_dict["simulation"]["beta"]
params = np.array(init_dict["simulation"]["params"])
probs = np.array(init_dict["simulation"]["known probs"])
v_disc_ = [0.0, 0.0]
v_disc = discount_utility(
v_disc_, num_buses, num_periods, num_periods, utilities, beta
)
trans_mat = create_transition_matrix(num_states, probs)
costs = myopic_costs(num_states, lin_cost, params)
v_calc = calc_fixp(num_states, trans_mat, costs, beta)
un_ob_av = 0
for bus in range(num_buses):
un_ob_av += unobs[bus, 0, 0]
un_ob_av = un_ob_av / num_buses
assert_allclose(v_disc[1] / (v_calc[0] + un_ob_av), 1, rtol=1e-02)
def inputs_sim(inputs):
out = {}
out["init_dict"] = init_dict = random_init(inputs)["simulation"]
# Draw parameter
param1 = np.random.normal(10.0, 2)
param2 = np.random.normal(2.3, 0.5)
params = np.array([param1, param2])
disc_fac = init_dict["discount_factor"]
probs = np.array(init_dict["known_trans"])
num_states = 300
out["trans_mat"] = trans_mat = create_transition_matrix(num_states, probs)
out["costs"] = costs = calc_obs_costs(num_states, lin_cost, params, 0.001)
out["ev"] = ev = calc_fixp(trans_mat, costs, disc_fac)[0]
out["df"] = simulate(
init_dict,
ev,
costs,
trans_mat,
)
return out
