def setUp(self):
""" Setup a KMRMarkovMatrix and Compute Stationary Values """
self.P = KMR_Markov_matrix_sequential(self.N, self.p, self.epsilon)
self.mc = DMarkov(self.P)
self.stationary = self.mc.mc_compute_stationary()
stat_shape = self.stationary.shape
if len(stat_shape) is 1:
self.n_stat_dists = 1
else:
self.n_stat_dists = stat_shape[1]
def setUp(self):
""" Setup a KMRMarkovMatrix and Compute Stationary Values """
self.P = KMR_Markov_matrix_sequential(self.N, self.p, self.epsilon)
self.mc = DMarkov(self.P)
self.stationary = self.mc.mc_compute_stationary()
stat_shape = self.stationary.shape
if len(stat_shape) is 1:
self.n_stat_dists = 1
else:
self.n_stat_dists = stat_shape[1]
class Test_mc_compute_stationary_KMRMarkovMatrix2():
"""
Test Suite for mc_compute_stationary using KMR Markov Matrix [suitable for nose]
"""
#-Starting Values-#
N = 27
epsilon = 1e-2
p = 1 / 3
TOL = 1e-2
def setUp(self):
""" Setup a KMRMarkovMatrix and Compute Stationary Values """
self.P = KMR_Markov_matrix_sequential(self.N, self.p, self.epsilon)
self.mc = DMarkov(self.P)
self.stationary = self.mc.mc_compute_stationary()
stat_shape = self.stationary.shape
if len(stat_shape) is 1:
self.n_stat_dists = 1
else:
self.n_stat_dists = stat_shape[1]
def test_markov_matrix(self):
"Check that each row of matrix sums to 1"
mc = self.mc
assert_allclose(np.sum(mc.P, axis=1), np.ones(mc.n))
def test_sum_one(self):
"Check each stationary distribution sums to 1"
stationary_distributions = self.stationary
assert_allclose(np.sum(stationary_distributions, axis=0),
np.ones(self.n_stat_dists))
def test_nonnegative(self):
"Check that the stationary distributions are non-negative"
stationary_distributions = self.stationary
assert (np.all(stationary_distributions > -1e-16))
def test_left_eigen_vec(self):
"Check that vP = v for all stationary distributions"
mc = self.mc
stationary = self.stationary
if self.n_stat_dists is 1:
assert_allclose(np.dot(stationary, mc.P),
stationary,
atol=self.TOL)
else:
for i in range(self.n_stat_dists):
curr_v = stationary_distributions[:, i]
assert_allclose(np.dot(curr_v, mc.P), curr_v, atol=self.TOL)
class Test_mc_compute_stationary_KMRMarkovMatrix2():
"""
Test Suite for mc_compute_stationary using KMR Markov Matrix [suitable for nose]
"""
#-Starting Values-#
N = 27
epsilon = 1e-2
p = 1/3
TOL = 1e-2
def setUp(self):
""" Setup a KMRMarkovMatrix and Compute Stationary Values """
self.P = KMR_Markov_matrix_sequential(self.N, self.p, self.epsilon)
self.mc = DMarkov(self.P)
self.stationary = self.mc.mc_compute_stationary()
stat_shape = self.stationary.shape
if len(stat_shape) is 1:
self.n_stat_dists = 1
else:
self.n_stat_dists = stat_shape[1]
def test_markov_matrix(self):
"Check that each row of matrix sums to 1"
mc = self.mc
assert_allclose(np.sum(mc.P, axis=1), np.ones(mc.n))
def test_sum_one(self):
"Check each stationary distribution sums to 1"
stationary_distributions = self.stationary
assert_allclose(np.sum(stationary_distributions, axis=0),
np.ones(self.n_stat_dists))
def test_nonnegative(self):
"Check that the stationary distributions are non-negative"
stationary_distributions = self.stationary
assert(np.all(stationary_distributions > -1e-16))
def test_left_eigen_vec(self):
"Check that vP = v for all stationary distributions"
mc = self.mc
stationary = self.stationary
if self.n_stat_dists is 1:
assert_allclose(np.dot(stationary, mc.P), stationary, atol=self.TOL)
else:
for i in range(self.n_stat_dists):
curr_v = stationary_distributions[:, i]
assert_allclose(np.dot(curr_v, mc.P), curr_v, atol=self.TOL)
