Merge pull request #498 from shizejin/fix_lqcontrol

FIX: two minor modifications in `lqcontrol`

Author: mmcky

quantecon/lqcontrol.py

@@ -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)
@@ -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
@@ -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

quantecon/tests/test_lqcontrol.py

@@ -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]])
 
@@ -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])
@@ -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]])