本文整理汇总了Python中birth_death_chain.BirthDeathChain类的典型用法代码示例。如果您正苦于以下问题：Python BirthDeathChain类的具体用法？Python BirthDeathChain怎么用？Python BirthDeathChain使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了BirthDeathChain类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setUp
def setUp(self):
"""Store state of the rng"""
self.state = np.random.mtrand.get_state()
"""Reseed the rng to enforce 'deterministic' behavior"""
np.random.mtrand.seed(42)
"""Meta-stable birth-death chain"""
b = 2
q = np.zeros(7)
p = np.zeros(7)
q[1:] = 0.5
p[0:-1] = 0.5
q[2] = 1.0-10**(-b)
q[4] = 10**(-b)
p[2] = 10**(-b)
p[4] = 1.0-10**(-b)
bdc = BirthDeathChain(q, p)
P = bdc.transition_matrix()
self.dtrajs = [generate_traj(P, 10000, start=0)]
self.tau = 1
"""Estimate MSM"""
self.C_MSM = cmatrix(self.dtrajs, self.tau, sliding=True).toarray()
self.lcc_MSM = largest_connected_set(self.C_MSM)
self.Ccc_MSM = connected_cmatrix(self.C_MSM, lcc=self.lcc_MSM)
self.P_MSM = tmatrix(self.Ccc_MSM, reversible=True)
self.mu_MSM = statdist(self.P_MSM)
self.k = 3
self.ts = timescales(self.P_MSM, k=self.k+1, tau=self.tau)[1:]示例2: TestCommittorDense
class TestCommittorDense(unittest.TestCase):
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)
def tearDown(self):
pass
def test_forward_comittor(self):
P = self.bdc.transition_matrix()
un = committor(P, [0, 1], [8, 9], forward=True)
u = self.bdc.committor_forward(1, 8)
assert_allclose(un, u)
def test_backward_comittor(self):
P = self.bdc.transition_matrix()
un = committor(P, [0, 1], [8, 9], forward=False)
u = self.bdc.committor_backward(1, 8)
assert_allclose(un, u)示例3: TestCommittorSparse
class TestCommittorSparse(unittest.TestCase):
def setUp(self):
p = np.zeros(100)
q = np.zeros(100)
p[0:-1] = 0.5
q[1:] = 0.5
p[49] = 0.01
q[51] = 0.1
self.bdc = BirthDeathChain(q, p)
def tearDown(self):
pass
def test_forward_comittor(self):
P = self.bdc.transition_matrix_sparse()
un = committor(P, range(10), range(90, 100), forward=True)
u = self.bdc.committor_forward(9, 90)
assert_allclose(un, u)
def test_backward_comittor(self):
P = self.bdc.transition_matrix_sparse()
un = committor(P, range(10), range(90, 100), forward=False)
u = self.bdc.committor_backward(9, 90)
assert_allclose(un, u)示例4: TestRelaxationDense
class TestRelaxationDense(unittest.TestCase):
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 test_relaxation(self):
"""k=None"""
relax_amp=np.dot(self.p0, self.R)*np.dot(self.L, self.obs)
relax=np.dot(self.ev_t, relax_amp)
relaxn=relaxation(self.T, self.p0, self.obs, times=self.times)
self.assertTrue(np.allclose(relaxn, relax))
"""k=4"""
k=self.k
relax_amp=np.dot(self.p0, self.R[:,0:k])*np.dot(self.L[0:k,:], self.obs)
relax=np.dot(self.ev_t[:,0:k], relax_amp)
relaxn=relaxation(self.T, self.p0, self.obs, k=k, times=self.times)
self.assertTrue(np.allclose(relaxn, relax))示例5: TestCorrelationSparse
class TestCorrelationSparse(unittest.TestCase):
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, 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_correlation(self):
"""Auto-correlation"""
acorr_amp=np.dot(self.mu*self.obs1, self.R)*np.dot(self.L, self.obs1)
acorr=np.dot(self.ev_t, acorr_amp)
acorrn=correlation(self.T, self.obs1, k=self.k, times=self.times)
self.assertTrue(np.allclose(acorrn, acorr))
"""Cross-correlation"""
corr_amp=np.dot(self.mu*self.obs1, self.R)*np.dot(self.L, self.obs2)
corr=np.dot(self.ev_t, corr_amp)
corrn=correlation(self.T, self.obs1, obs2=self.obs2, k=self.k, times=self.times)
self.assertTrue(np.allclose(corrn, corr)) 示例6: setUp
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()
"""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 示例7: setUp
def setUp(self):
p = np.zeros(100)
q = np.zeros(100)
p[0:-1] = 0.5
q[1:] = 0.5
p[49] = 0.01
q[51] = 0.1
self.bdc = BirthDeathChain(q, p)示例8: setUp
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.T = self.bdc.transition_matrix()
self.mu = self.bdc.stationary_distribution()
self.A = create_rate_matrix()示例9: TestAssessmentDense
class TestAssessmentDense(unittest.TestCase):
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.T = self.bdc.transition_matrix()
self.mu = self.bdc.stationary_distribution()
self.A = create_rate_matrix()
def test_IsRateMatrix(self):
self.assert_(is_rate_matrix(self.A), 'A should be a rate matrix')
# manipulate matrix so it isn't a rate matrix any more
self.A[0][0] = 3
self.assertFalse(is_rate_matrix(self.A), 'matrix is not a rate matrix')
def test_IsReversible(self):
# create a reversible matrix
self.assertTrue(is_reversible(self.T, self.mu),
"T should be reversible")
def test_is_transition_matrix(self):
self.assertTrue(is_transition_matrix(self.T))
"""Larger test-case to prevent too restrictive tolerance settings"""
X = np.random.random((2000, 2000))
Tlarge = X / X.sum(axis=1)[:, np.newaxis]
self.assertTrue(is_transition_matrix(Tlarge))
def test_is_connected(self):
self.assertTrue(is_connected(self.T))
self.assertTrue(is_connected(self.T, directed=False))示例10: TestExpectation
class TestExpectation(unittest.TestCase):
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()
obs1 = np.zeros(10)
obs1[0] = 1
obs1[1] = 1
self.obs1=obs1
def test_expectation(self):
exp=np.dot(self.mu, self.obs1)
expn=expectation(self.T, self.obs1)
self.assertTrue(np.allclose(exp, expn)) 示例11: setUp
def setUp(self):
self.dim=100
self.k=10
"""Set up meta-stable birth-death chain"""
p=np.zeros(self.dim)
p[0:-1]=0.5
q=np.zeros(self.dim)
q[1:]=0.5
p[self.dim/2-1]=0.001
q[self.dim/2+1]=0.001
self.bdc=BirthDeathChain(q, p)示例12: TestAssessmentDense
class TestAssessmentDense(unittest.TestCase):
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.T = self.bdc.transition_matrix()
self.mu = self.bdc.stationary_distribution()
self.A = create_rate_matrix()
def test_IsRateMatrix(self):
self.assert_(is_rate_matrix(self.A), \
'A should be a rate matrix')
# manipulate matrix so it isn't a rate matrix any more
self.A[0][0] = 3
self.assertFalse(is_rate_matrix(self.A), \
'matrix is not a rate matrix')
def test_IsReversible(self):
# create a reversible matrix
self.assertTrue(is_reversible(self.T, self.mu),
"T should be reversible")
def test_is_transition_matrix(self):
self.assertTrue(is_transition_matrix(self.T))
def test_is_connected(self):
self.assertTrue(is_connected(self.T))
self.assertTrue(is_connected(self.T, directed=False))示例13: TestFingerprintSparse
class TestFingerprintSparse(unittest.TestCase):
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 test_fingerprint_correlation(self):
"""Autocorrelation"""
"""k=4, tau=1"""
k=self.k
acorr_amp=np.dot(self.mu*self.obs1, self.R)*np.dot(self.L,self.obs1)
tsn, acorr_ampn=fingerprint_correlation(self.T, self.obs1, k=k)
self.assertTrue(np.allclose(tsn, self.ts))
self.assertTrue(np.allclose(acorr_ampn, acorr_amp))
"""k=4, tau=7.5"""
tau=self.tau
tsn, acorr_ampn=fingerprint_correlation(self.T, self.obs1, k=k, tau=tau)
self.assertTrue(np.allclose(tsn, tau*self.ts))
self.assertTrue(np.allclose(acorr_ampn, acorr_amp))
"""Cross-correlation"""
"""k=4, tau=1"""
k=self.k
corr_amp=np.dot(self.mu*self.obs1, self.R)*np.dot(self.L,self.obs2)
tsn, corr_ampn=fingerprint_correlation(self.T, self.obs1, obs2=self.obs2, k=k)
self.assertTrue(np.allclose(tsn, self.ts))
self.assertTrue(np.allclose(corr_ampn, corr_amp))
"""k=4, tau=7.5"""
tau=self.tau
tsn, corr_ampn=fingerprint_correlation(self.T, self.obs1, obs2=self.obs2, k=k, tau=tau)
self.assertTrue(np.allclose(tsn, tau*self.ts))
self.assertTrue(np.allclose(corr_ampn, corr_amp))
def test_fingerprint_relaxation(self):
one_vec=np.ones(self.T.shape[0])
relax_amp=np.dot(self.p0, self.R)*np.dot(self.L, self.obs1)
tsn, relax_ampn=fingerprint_relaxation(self.T, self.p0, self.obs1, k=self.k)
self.assertTrue(np.allclose(tsn, self.ts))
self.assertTrue(np.allclose(relax_ampn, relax_amp))示例14: TestDecompositionDense
class TestDecompositionDense(unittest.TestCase):
def setUp(self):
self.dim = 100
self.k = 10
"""Set up meta-stable birth-death chain"""
p = np.zeros(self.dim)
p[0:-1] = 0.5
q = np.zeros(self.dim)
q[1:] = 0.5
p[self.dim / 2 - 1] = 0.001
q[self.dim / 2 + 1] = 0.001
self.bdc = BirthDeathChain(q, p)
def test_statdist(self):
P = self.bdc.transition_matrix()
mu = self.bdc.stationary_distribution()
mun = stationary_distribution(P)
assert_allclose(mu, mun)
def test_eigenvalues(self):
P = self.bdc.transition_matrix()
ev = eigvals(P)
"""Sort with decreasing magnitude"""
ev = ev[np.argsort(np.abs(ev))[::-1]]
"""k=None"""
evn = eigenvalues(P)
assert_allclose(ev, evn)
"""k is not None"""
evn = eigenvalues(P, k=self.k)
assert_allclose(ev[0:self.k], evn)
def test_eigenvectors(self):
P = self.bdc.transition_matrix()
# k==None
ev = eigvals(P)
ev = ev[np.argsort(np.abs(ev))[::-1]]
Dn = np.diag(ev)
# right eigenvectors
Rn = eigenvectors(P)
assert_allclose(np.dot(P,Rn),np.dot(Rn,Dn))
# left eigenvectors
Ln = eigenvectors(P, right=False)
assert_allclose(np.dot(Ln.T,P),np.dot(Dn,Ln.T))
# orthogonality
Xn = np.dot(Ln.T, Rn)
di = np.diag_indices(Xn.shape[0])
Xn[di] = 0.0
assert_allclose(Xn,0)
# k!=None
Dnk = Dn[:,0:self.k][0:self.k,:]
# right eigenvectors
Rn = eigenvectors(P, k=self.k)
assert_allclose(np.dot(P,Rn),np.dot(Rn,Dnk))
# left eigenvectors
Ln = eigenvectors(P, right=False, k=self.k)
assert_allclose(np.dot(Ln.T,P),np.dot(Dnk,Ln.T))
# orthogonality
Xn = np.dot(Ln.T, Rn)
di = np.diag_indices(self.k)
Xn[di] = 0.0
assert_allclose(Xn,0)
def test_rdl_decomposition(self):
P = self.bdc.transition_matrix()
mu = self.bdc.stationary_distribution()
"""Non-reversible"""
"""k=None"""
Rn, Dn, Ln = rdl_decomposition(P)
Xn = np.dot(Ln, Rn)
"""Right-eigenvectors"""
assert_allclose(np.dot(P, Rn), np.dot(Rn, Dn))
"""Left-eigenvectors"""
assert_allclose(np.dot(Ln, P), np.dot(Dn, Ln))
"""Orthonormality"""
assert_allclose(Xn, np.eye(self.dim))
"""Probability vector"""
assert_allclose(np.sum(Ln[0, :]), 1.0)
"""k is not None"""
Rn, Dn, Ln = rdl_decomposition(P, k=self.k)
Xn = np.dot(Ln, Rn)
"""Right-eigenvectors"""
assert_allclose(np.dot(P, Rn), np.dot(Rn, Dn))
"""Left-eigenvectors"""
assert_allclose(np.dot(Ln, P), np.dot(Dn, Ln))
"""Orthonormality"""
assert_allclose(Xn, np.eye(self.k))
"""Probability vector"""
assert_allclose(np.sum(Ln[0, :]), 1.0)
#.........这里部分代码省略.........
示例15: TestCorrelationDense
class TestCorrelationDense(unittest.TestCase):
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_correlation(self):
"""Auto-correlation"""
"""k=None"""
acorr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(self.L, self.obs1)
acorr = np.dot(self.ev_t, acorr_amp)
acorrn = correlation(self.T, self.obs1, times=self.times)
assert_allclose(acorrn, acorr)
"""k=4"""
k = self.k
acorr_amp = np.dot(self.mu * self.obs1, self.R[:, 0:k]) * np.dot(self.L[0:k, :], self.obs1)
acorr = np.dot(self.ev_t[:, 0:k], acorr_amp)
acorrn = correlation(self.T, self.obs1, times=self.times, k=k)
assert_allclose(acorrn, acorr)
"""Cross-correlation"""
"""k=None"""
corr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(self.L, self.obs2)
corr = np.dot(self.ev_t, corr_amp)
corrn = correlation(self.T, self.obs1, obs2=self.obs2, times=self.times)
assert_allclose(corrn, corr)
"""k=4"""
k = self.k
corr_amp = np.dot(self.mu * self.obs1, self.R[:, 0:k]) * np.dot(self.L[0:k, :], self.obs2)
corr = np.dot(self.ev_t[:, 0:k], corr_amp)
corrn = correlation(self.T, self.obs1, obs2=self.obs2, times=self.times, k=k)
assert_allclose(corrn, corr)