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FIX: two minor modifications in lqcontrol #498

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Merged
merged 2 commits into from
Aug 7, 2019
Merged

FIX: two minor modifications in lqcontrol #498

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86e4338
FIX: bug of computing state values in the last period of `LQMarkov` m…
shizejin Jul 19, 2019
cfd14dc
FIX: avoid solving stationary values for multiple times
shizejin Jul 19, 2019
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8 changes: 5 additions & 3 deletions quantecon/lqcontrol.py
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Original file line number Diff line number Diff line change
Expand Up @@ -298,7 +298,8 @@ def compute_sequence(self, x0, ts_length=None, method='doubling',
# == Preliminaries, infinite horizon case == #
else:
T = ts_length if ts_length else 100
self.stationary_values(method=method)
if self.P is None:
self.stationary_values(method=method)

# == Set up initial condition and arrays to store paths == #
random_state = check_random_state(random_state)
Expand Down Expand Up @@ -570,7 +571,8 @@ def compute_sequence(self, x0, ts_length=None, random_state=None):
"""

# === solve for optimal policies === #
self.stationary_values()
if self.Ps is None:
self.stationary_values()

# === Simplify notation === #
As, Bs, Cs = self.As, self.Bs, self.Cs
Expand Down Expand Up @@ -605,6 +607,6 @@ def compute_sequence(self, x0, ts_length=None, random_state=None):
u_path[:, t] = - (Fs[state[t]] @ x_path[:, t])
Ax = As[state[T]] @ x_path[:, T-1]
Bu = Bs[state[T]] @ u_path[:, T-1]
x_path[:, T] = Ax + Bu + w_path[:, T]
x_path[:, T] = Ax + Bu + Cw_path[:, T]

return x_path, u_path, w_path, state
8 changes: 4 additions & 4 deletions quantecon/tests/test_lqcontrol.py
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Expand Up @@ -95,7 +95,7 @@ class TestLQMarkov(unittest.TestCase):

def setUp(self):

# Markove chain transition matrix
# Markov chain transition matrix
Π = np.array([[0.8, 0.2],
[0.2, 0.8]])

Expand Down Expand Up @@ -153,7 +153,7 @@ def test_scalar_sequences_with_seed(self):
lq_markov_scalar = self.lq_markov_scalar
x0 = 2

expected_x_seq = np.array([[2., 1.15977567, 1.20677567]])
expected_x_seq = np.array([[2., 1.15977567, 0.6725398]])
expected_u_seq = np.array([[1.28044866, 0.7425166]])
expected_w_seq = np.array([[1.3486939, 0.55721062, 0.53423587]])
expected_state = np.array([1, 1, 1])
Expand Down Expand Up @@ -188,8 +188,8 @@ def test_mat_sequences(self):
lq_markov_mat = self.lq_markov_mat1
x0 = np.array([[1000, 1, 25]])

expected_x_seq = np.array([[1.00000000e+03, 1.01490556e+03],
[1.00000000e+00, 2.18454431e+00],
expected_x_seq = np.array([[1.00000000e+03, 1.01372101e+03],
[1.00000000e+00, 1.00000000e+00],
[2.50000000e+01, 2.61845443e+01]])
expected_u_seq = np.array([[1013.72101253]])
expected_w_seq = np.array([[0.41782708, 1.18454431]])
Expand Down