class TestLinearStateSpace(unittest.TestCase):
def setUp(self):
# Initial Values
A = .95
C = .05
G = 1.
mu_0 = .75
self.ss = LinearStateSpace(A, C, G, mu_0=mu_0)
def tearDown(self):
del self.ss
def test_stationarity(self):
vals = self.ss.stationary_distributions(max_iter=1000, tol=1e-9)
ssmux, ssmuy, sssigx, sssigy = vals
self.assertTrue(abs(ssmux - ssmuy) < 2e-8)
self.assertTrue(abs(sssigx - sssigy) < 2e-8)
self.assertTrue(abs(ssmux) < 2e-8)
self.assertTrue(abs(sssigx - self.ss.C**2/(1 - self.ss.A**2)) < 2e-8)
def test_replicate(self):
xval, yval = self.ss.replicate(T=100, num_reps=5000)
assert_allclose(xval, yval)
self.assertEqual(xval.size, 5000)
self.assertLessEqual(abs(np.mean(xval)), .05)
class TestLinearStateSpace(unittest.TestCase):
def setUp(self):
# Initial Values
A = .95
C = .05
G = 1.
mu_0 = .75
self.ss = LinearStateSpace(A, C, G, mu_0=mu_0)
def tearDown(self):
del self.ss
def test_stationarity(self):
vals = self.ss.stationary_distributions(max_iter=1000, tol=1e-9)
ssmux, ssmuy, sssigx, sssigy = vals
self.assertTrue(abs(ssmux - ssmuy) < 2e-8)
self.assertTrue(abs(sssigx - sssigy) < 2e-8)
self.assertTrue(abs(ssmux) < 2e-8)
self.assertTrue(abs(sssigx - self.ss.C**2/(1 - self.ss.A**2)) < 2e-8)
def test_simulate(self):
ss = self.ss
sim = ss.simulate(ts_length=250)
for arr in sim:
self.assertTrue(len(arr[0])==250)
def test_simulate_with_seed(self):
ss = self.ss
xval, yval = ss.simulate(ts_length=5, random_state=5)
expected_output = np.array([0.75 , 0.73456137, 0.6812898, 0.76876387,
.71772107])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
def test_replicate(self):
xval, yval = self.ss.replicate(T=100, num_reps=5000)
assert_allclose(xval, yval)
self.assertEqual(xval.size, 5000)
self.assertLessEqual(abs(np.mean(xval)), .05)
def test_replicate_with_seed(self):
xval, yval = self.ss.replicate(T=100, num_reps=5, random_state=5)
expected_output = np.array([0.06871204, 0.06937119, -0.1478022,
0.23841252, -0.06823762])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
class TestLinearStateSpace(unittest.TestCase):
def setUp(self):
# Initial Values
A = .95
C = .05
G = 1.
mu_0 = .75
self.ss = LinearStateSpace(A, C, G, mu_0=mu_0)
def tearDown(self):
del self.ss
def test_stationarity(self):
vals = self.ss.stationary_distributions(max_iter=1000, tol=1e-9)
ssmux, ssmuy, sssigx, sssigy = vals
self.assertTrue(abs(ssmux - ssmuy) < 2e-8)
self.assertTrue(abs(sssigx - sssigy) < 2e-8)
self.assertTrue(abs(ssmux) < 2e-8)
self.assertTrue(abs(sssigx - self.ss.C**2/(1 - self.ss.A**2)) < 2e-8)
def test_simulate(self):
ss = self.ss
sim = ss.simulate(ts_length=250)
for arr in sim:
self.assertTrue(len(arr[0])==250)
def test_simulate_with_seed(self):
ss = self.ss
xval, yval = ss.simulate(ts_length=5, random_state=5)
expected_output = np.array([0.75 , 0.73456137, 0.6812898, 0.76876387,
.71772107])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
def test_replicate(self):
xval, yval = self.ss.replicate(T=100, num_reps=5000)
assert_allclose(xval, yval)
self.assertEqual(xval.size, 5000)
self.assertLessEqual(abs(np.mean(xval)), .05)
def test_replicate_with_seed(self):
xval, yval = self.ss.replicate(T=100, num_reps=5, random_state=5)
expected_output = np.array([0.06871204, 0.06937119, -0.1478022,
0.23841252, -0.06823762])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
class TestLinearStateSpace(unittest.TestCase):
def setUp(self):
# Example 1
A = .95
C = .05
G = 1.
mu_0 = .75
self.ss1 = LinearStateSpace(A, C, G, mu_0=mu_0)
# Example 2
ρ1 = 0.5
ρ2 = 0.3
α = 0.5
A = np.array([[ρ1, ρ2, α], [1, 0, 0], [0, 0, 1]])
C = np.array([[1], [0], [0]])
G = np.array([[1, 0, 0]])
mu_0 = [0.5, 0.5, 1]
self.ss2 = LinearStateSpace(A, C, G, mu_0=mu_0)
def tearDown(self):
del self.ss1
del self.ss2
def test_stationarity(self):
vals = self.ss1.stationary_distributions()
ssmux, ssmuy, sssigx, sssigy, sssigyx = vals
self.assertTrue(abs(ssmux - ssmuy) < 2e-8)
self.assertTrue(abs(sssigx - sssigy) < 2e-8)
self.assertTrue(abs(ssmux) < 2e-8)
self.assertTrue(abs(sssigx - self.ss1.C**2/(1 - self.ss1.A**2)) < 2e-8)
self.assertTrue(abs(sssigyx - self.ss1.G @ sssigx) < 2e-8)
vals = self.ss2.stationary_distributions()
ssmux, ssmuy, sssigx, sssigy, sssigyx = vals
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(sssigy, self.ss2.G @ sssigx @ self.ss2.G.T)
assert_allclose(sssigyx, self.ss2.G @ sssigx)
def test_simulate(self):
ss = self.ss1
sim = ss.simulate(ts_length=250)
for arr in sim:
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])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
def test_replicate(self):
xval, yval = self.ss1.replicate(T=100, num_reps=5000)
assert_allclose(xval, yval)
self.assertEqual(xval.size, 5000)
self.assertLessEqual(abs(np.mean(xval)), .05)
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])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
class TestLinearStateSpace:
def setup(self):
# Example 1
A = .95
C = .05
G = 1.
mu_0 = .75
self.ss1 = LinearStateSpace(A, C, G, mu_0=mu_0)
# Example 2
ρ1 = 0.5
ρ2 = 0.3
α = 0.5
A = np.array([[ρ1, ρ2, α], [1, 0, 0], [0, 0, 1]])
C = np.array([[1], [0], [0]])
G = np.array([[1, 0, 0]])
mu_0 = [0.5, 0.5, 1]
self.ss2 = LinearStateSpace(A, C, G, mu_0=mu_0)
def tearDown(self):
del self.ss1
del self.ss2
def test_stationarity(self):
vals = self.ss1.stationary_distributions()
ssmux, ssmuy, sssigx, sssigy, sssigyx = vals
assert_(abs(ssmux - ssmuy) < 2e-8)
assert_(abs(sssigx - sssigy) < 2e-8)
assert_(abs(ssmux) < 2e-8)
assert_(abs(sssigx - self.ss1.C**2/(1 - self.ss1.A**2)) < 2e-8)
assert_(abs(sssigyx - self.ss1.G @ sssigx) < 2e-8)
vals = self.ss2.stationary_distributions()
ssmux, ssmuy, sssigx, sssigy, sssigyx = vals
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(sssigy, self.ss2.G @ sssigx @ self.ss2.G.T)
assert_allclose(sssigyx, self.ss2.G @ sssigx)
def test_simulate(self):
ss = self.ss1
sim = ss.simulate(ts_length=250)
for arr in sim:
assert_(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, 0.69595649, 0.78269723, 0.73095776,
0.69989036])
assert_allclose(xval[0], expected_output)
assert_allclose(yval[0], expected_output)
def test_replicate(self):
xval, yval = self.ss1.replicate(T=100, num_reps=5000)
assert_allclose(xval, yval)
assert_(xval.size == 5000)
assert_(abs(np.mean(xval)) <= .05)
def test_replicate_with_seed(self):
xval, yval = self.ss1.replicate(T=100, num_reps=5, random_state=5)
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)
