def setUp(self):
self.k = 4
p = np.zeros(10)
q = np.zeros(10)
p[0:-1] = 0.5
q[1:] = 0.5
p[4] = 0.01
q[6] = 0.1
self.bdc = deeptime.data.birth_death_chain(q, p)
self.mu = self.bdc.stationary_distribution
self.T = self.bdc.transition_matrix_sparse
"""Test matrix-vector product against spectral decomposition"""
R, D, L = rdl_decomposition(self.T, k=self.k)
self.L = L
self.R = R
self.ts = timescales(self.T, k=self.k)
self.times = np.array([1, 5, 10, 20, 100])
ev = np.diagonal(D)
self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis]
"""Observable"""
obs1 = np.zeros(10)
obs1[0] = 1
obs1[1] = 1
self.obs = obs1
"""Initial distribution"""
w0 = np.zeros(10)
w0[0:4] = 0.25
self.p0 = w0
def setUp(self):
self.k = 4
p = np.zeros(10)
q = np.zeros(10)
p[0:-1] = 0.5
q[1:] = 0.5
p[4] = 0.01
q[6] = 0.1
self.bdc = deeptime.data.birth_death_chain(q, p)
self.mu = self.bdc.stationary_distribution
self.T = self.bdc.transition_matrix_sparse
R, D, L = rdl_decomposition(self.T, k=self.k)
self.L = L
self.R = R
self.ts = timescales(self.T, k=self.k)
self.times = np.array([1, 5, 10, 20, 100])
ev = np.diagonal(D)
self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis]
obs1 = np.zeros(10)
obs1[0] = 1
obs1[1] = 1
obs2 = np.zeros(10)
obs2[8] = 1
obs2[9] = 1
self.obs1 = obs1
self.obs2 = obs2
self.one_vec = np.ones(10)
def test_timescales(self):
P_dense = self.bdc.transition_matrix
P = self.bdc.transition_matrix_sparse
ev = eigvals(P_dense)
"""Sort with decreasing magnitude"""
ev = ev[np.argsort(np.abs(ev))[::-1]]
ts = -1.0 / np.log(np.abs(ev))
"""k=None"""
with self.assertRaises(ValueError):
tsn = timescales(P)
"""k is not None"""
tsn = timescales(P, k=self.k)
assert_allclose(ts[1:self.k], tsn[1:])
"""k is not None, ncv is not None"""
tsn = timescales(P, k=self.k, ncv=self.ncv)
assert_allclose(ts[1:self.k], tsn[1:])
"""tau=7"""
"""k is not None"""
tsn = timescales(P, k=self.k, tau=7)
assert_allclose(7 * ts[1:self.k], tsn[1:])
def test_timescales_rev(self):
P_dense = self.bdc.transition_matrix
P = self.bdc.transition_matrix_sparse
mu = self.bdc.stationary_distribution
ev = eigvals(P_dense)
"""Sort with decreasing magnitude"""
ev = ev[np.argsort(np.abs(ev))[::-1]]
ts = -1.0 / np.log(np.abs(ev))
"""k=None"""
with self.assertRaises(ValueError):
tsn = timescales(P, reversible=True)
"""k is not None"""
tsn = timescales(P, k=self.k, reversible=True)
assert_allclose(ts[1:self.k], tsn[1:])
"""k is not None, ncv is not None"""
tsn = timescales(P, k=self.k, ncv=self.ncv, reversible=True)
assert_allclose(ts[1:self.k], tsn[1:])
"""k is not None, mu is not None"""
tsn = timescales(P, k=self.k, reversible=True, mu=mu)
assert_allclose(ts[1:self.k], tsn[1:])
"""k is not None, mu is not None, ncv is not None"""
tsn = timescales(P, k=self.k, ncv=self.ncv, reversible=True, mu=mu)
assert_allclose(ts[1:self.k], tsn[1:])
"""tau=7"""
"""k is not None"""
tsn = timescales(P, k=self.k, tau=7, reversible=True)
assert_allclose(7 * ts[1:self.k], tsn[1:])
def setUp(self):
self.k = 4
p = np.zeros(10)
q = np.zeros(10)
p[0:-1] = 0.5
q[1:] = 0.5
p[4] = 0.01
q[6] = 0.1
self.bdc = deeptime.data.birth_death_chain(q, p)
self.mu = self.bdc.stationary_distribution
self.T = self.bdc.transition_matrix_sparse
R, D, L = rdl_decomposition(self.T, k=self.k)
self.L = L
self.R = R
self.ts = timescales(self.T, k=self.k)
self.times = np.array([1, 5, 10, 20])
ev = np.diagonal(D)
self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis]
self.tau = 7.5
"""Observables"""
obs1 = np.zeros(10)
obs1[0] = 1
obs1[1] = 1
obs2 = np.zeros(10)
obs2[8] = 1
obs2[9] = 1
self.obs1 = obs1
self.obs2 = obs2
"""Initial vector for relaxation"""
w0 = np.zeros(10)
w0[0:4] = 0.25
self.p0 = w0