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)
self.assertTrue(np.allclose(ts[1:self.k], tsn[1:]))
"""k is not None, ncv is not None"""
tsn=timescales(P, k=self.k, ncv=self.ncv)
self.assertTrue(np.allclose(ts[1:self.k], tsn[1:]))
"""tau=7"""
"""k is not None"""
tsn=timescales(P, k=self.k, tau=7)
self.assertTrue(np.allclose(7*ts[1:self.k], tsn[1:]))
def test_timescales(self):
P=self.bdc.transition_matrix()
ev=eigvals(P)
"""Sort with decreasing magnitude"""
ev=ev[np.argsort(np.abs(ev))[::-1]]
ts=-1.0/np.log(np.abs(ev))
"""k=None"""
tsn=timescales(P)
self.assertTrue(np.allclose(ts[1:], tsn[1:]))
"""k is not None"""
tsn=timescales(P, k=self.k)
self.assertTrue(np.allclose(ts[1:self.k], tsn[1:]))
"""tau=7"""
"""k=None"""
tsn=timescales(P, tau=7)
self.assertTrue(np.allclose(7*ts[1:], tsn[1:]))
"""k is not None"""
tsn=timescales(P, k=self.k, tau=7)
self.assertTrue(np.allclose(7*ts[1:self.k], tsn[1:]))
def setUp(self):
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=BirthDeathChain(q, p)
self.mu = self.bdc.stationary_distribution()
self.T = self.bdc.transition_matrix()
"""Test matrix-vector product against spectral decomposition"""
R, D, L=rdl_decomposition(self.T)
self.L=L
self.R=R
self.ts=timescales(self.T)
self.times=np.array([1, 5, 10, 20, 100])
ev=np.diagonal(D)
self.ev_t=ev[np.newaxis,:]**self.times[:,np.newaxis]
self.k=4
"""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):
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=BirthDeathChain(q, p)
self.mu = self.bdc.stationary_distribution()
self.T = self.bdc.transition_matrix()
R, D, L=rdl_decomposition(self.T, norm='reversible')
self.L=L
self.R=R
self.ts=timescales(self.T)
self.times=np.array([1, 5, 10, 20, 100])
ev=np.diagonal(D)
self.ev_t=ev[np.newaxis,:]**self.times[:,np.newaxis]
self.k=4
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 setUp(self):
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 = BirthDeathChain(q, p)
self.mu = self.bdc.stationary_distribution()
self.T = self.bdc.transition_matrix()
"""Test matrix-vector product against spectral decomposition"""
R, D, L = rdl_decomposition(self.T)
self.L = L
self.R = R
self.ts = timescales(self.T)
self.times = np.array([1, 5, 10, 20, 100])
ev = np.diagonal(D)
self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis]
self.k = 4
"""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):
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 = BirthDeathChain(q, p)
self.mu = self.bdc.stationary_distribution()
self.T = self.bdc.transition_matrix()
R, D, L = rdl_decomposition(self.T, norm='reversible')
self.L = L
self.R = R
self.ts = timescales(self.T)
self.times = np.array([1, 5, 10, 20, 100])
ev = np.diagonal(D)
self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis]
self.k = 4
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_2(self):
"""Eigenvalues with non-zero imaginary part"""
ts = np.array([np.inf, 0.971044, 0.971044])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
tsn = timescales(0.5 * self.T + 0.5 * self.P)
assert_allclose(tsn, ts)
assert issubclass(w[-1].category, ImaginaryEigenValueWarning)
def test_timescales_2(self):
"""Eigenvalues with non-zero imaginary part"""
ts=np.array([np.inf, 0.971044, 0.971044])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
tsn=timescales(0.5*self.T+0.5*self.P)
self.assertTrue(np.allclose(tsn, ts))
assert issubclass(w[-1].category, ImaginaryEigenValueWarning)
def test_timescales_1(self):
"""Multiple eigenvalues of magnitude one,
eigenvalues with non-zero imaginary part"""
ts = np.array([np.inf, np.inf])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
tsn = timescales(self.W)
assert_allclose(tsn, ts)
assert issubclass(w[-1].category, SpectralWarning)
def test_timescales(self):
P = self.bdc.transition_matrix()
ev = eigvals(P)
"""Sort with decreasing magnitude"""
ev = ev[np.argsort(np.abs(ev))[::-1]]
ts = -1.0 / np.log(np.abs(ev))
"""k=None"""
tsn = timescales(P)
assert_allclose(ts[1:], tsn[1:])
"""k is not None"""
tsn = timescales(P, k=self.k)
assert_allclose(ts[1:self.k], tsn[1:])
"""tau=7"""
"""k=None"""
tsn = timescales(P, tau=7)
assert_allclose(7 * ts[1:], tsn[1:])
"""k is not None"""
tsn = timescales(P, k=self.k, tau=7)
assert_allclose(7 * ts[1:self.k], tsn[1:])
def test_timescales_1(self):
"""Multiple eigenvalues of magnitude one,
eigenvalues with non-zero imaginary part"""
ts=np.array([np.inf, np.inf])
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
tsn=timescales(self.W)
self.assertTrue(np.allclose(tsn, ts))
assert issubclass(w[-1].category, SpectralWarning)
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 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=BirthDeathChain(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
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 = BirthDeathChain(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
