MAINT: Use multivariate_normal via random_state (#581)

* MAINT: Use multivariate_normal via random_state

* MAINT: PEP8 compliance

Author: oyamad

quantecon/lss.py

@@ -10,7 +10,6 @@
 
 from textwrap import dedent
 import numpy as np
-from numpy.random import multivariate_normal
 from scipy.linalg import solve
 from .matrix_eqn import solve_discrete_lyapunov
 from numba import jit
@@ -186,7 +185,8 @@ def simulate(self, ts_length=100, random_state=None):
         """
         random_state = check_random_state(random_state)
 
-        x0 = multivariate_normal(self.mu_0.flatten(), self.Sigma_0)
+        x0 = random_state.multivariate_normal(self.mu_0.flatten(),
+                                              self.Sigma_0)
         w = random_state.randn(self.m, ts_length-1)
         v = self.C.dot(w)  # Multiply each w_t by C to get v_t = C w_t
         # == simulate time series == #
@@ -447,8 +447,8 @@ def __partition(self):
         A_diag = np.diag(A)
         num_const = 0
         for idx in range(n):
-            if (A_diag[idx] == 1) and (C[idx, :] == 0).all() \
-            	and np.linalg.norm(A[idx, :]) == 1:
+            if (A_diag[idx] == 1) and (C[idx, :] == 0).all() and \
+                    np.linalg.norm(A[idx, :]) == 1:
                 sorted_idx.insert(0, idx)
                 num_const += 1
             else:

quantecon/tests/test_lss.py

@@ -52,7 +52,10 @@ def test_stationarity(self):
 
         assert_allclose(ssmux.flatten(), np.array([2.5, 2.5, 1]))
         assert_allclose(ssmuy.flatten(), np.array([2.5]))
-        assert_allclose(sssigx, self.ss2.A @ sssigx @ self.ss2.A.T + self.ss2.C @ self.ss2.C.T)
+        assert_allclose(
+            sssigx,
+            self.ss2.A @ sssigx @ self.ss2.A.T + self.ss2.C @ self.ss2.C.T
+        )
         assert_allclose(sssigy, self.ss2.G @ sssigx @ self.ss2.G.T)
         assert_allclose(sssigyx, self.ss2.G @ sssigx)
 
@@ -61,14 +64,14 @@ def test_simulate(self):
 
         sim = ss.simulate(ts_length=250)
         for arr in sim:
-            self.assertTrue(len(arr[0])==250)
+            self.assertTrue(len(arr[0]) == 250)
 
     def test_simulate_with_seed(self):
         ss = self.ss1
 
         xval, yval = ss.simulate(ts_length=5, random_state=5)
-        expected_output = np.array([0.75 , 0.73456137, 0.6812898, 0.76876387,
-                                    .71772107])
+        expected_output = np.array([0.75, 0.69595649, 0.78269723, 0.73095776,
+                                    0.69989036])
 
         assert_allclose(xval[0], expected_output)
         assert_allclose(yval[0], expected_output)
@@ -82,8 +85,8 @@ def test_replicate(self):
 
     def test_replicate_with_seed(self):
         xval, yval = self.ss1.replicate(T=100, num_reps=5, random_state=5)
-        expected_output = np.array([0.06871204, 0.06937119, -0.1478022,
-                                    0.23841252, -0.06823762])
+        expected_output = np.array([0.10498898, 0.02892168, 0.04915998,
+                                    0.18568489, 0.04541764])
 
         assert_allclose(xval[0], expected_output)
         assert_allclose(yval[0], expected_output)
@@ -101,4 +104,3 @@ def test_non_square_A():
 if __name__ == '__main__':
     suite = unittest.TestLoader().loadTestsFromTestCase(TestLinearStateSpace)
     unittest.TextTestRunner(verbosity=2, stream=sys.stderr).run(suite)
-