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.A = [0, 1]
self.B = [8, 9]
self.a = 1
self.b = 8
self.bdc = BirthDeathChain(q, p)
T_dense = self.bdc.transition_matrix()
T_sparse = csr_matrix(T_dense)
self.T = T_sparse
"""Compute mu, qminus, qplus in constructor"""
self.tpt = flux.tpt(self.T, self.A, self.B)
"""Use precomputed mu, qminus, qplus"""
self.mu = self.bdc.stationary_distribution()
self.qminus = self.bdc.committor_backward(self.a, self.b)
self.qplus = self.bdc.committor_forward(self.a, self.b)
self.tpt_fast = flux.tpt(self.T,
self.A,
self.B,
mu=self.mu,
qminus=self.qminus,
qplus=self.qplus)
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.A = [0, 1]
self.B = [8, 9]
self.a = 1
self.b = 8
import msmtools.analysis.dense.birth_death_chain
self.bdc = msmtools.analysis.dense.birth_death_chain.BirthDeathChain(
q, p)
self.T = self.bdc.transition_matrix()
"""Compute mu, qminus, qplus in constructor"""
self.tpt = flux.tpt(self.T, self.A, self.B)
"""Use precomputed mu, qminus, qplus"""
self.mu = self.bdc.stationary_distribution()
self.qminus = self.bdc.committor_backward(self.a, self.b)
self.qplus = self.bdc.committor_forward(self.a, self.b)
self.tpt_fast = flux.tpt(self.T,
self.A,
self.B,
mu=self.mu,
qminus=self.qminus,
qplus=self.qplus)
def test_flux(self):
r = tpt(self.P, self.A, self.B)
fig, pos = plot_flux(r)
assert type(fig) is matplotlib.figure.Figure
# matplotlib.pyplot.show(fig)
# x values should be close to committor
np.testing.assert_allclose(pos[:, 0], r.committor)
def __init__(self, EvoMSM, source, target):
self.msm = EvoMSM
self.source = source
self.target = target
self.ReactiveFlux = tpt(self.msm.transition_matrix, self.source, self.target)
self.net_flux = self.ReactiveFlux.net_flux
self.total_flux = self.ReactiveFlux.total_flux
self.forward_committor = self.ReactiveFlux.forward_committor
self.backward_committor = self.ReactiveFlux.backward_committor
def test_with_almost_converged_stat_dist(self):
""" test for #106 """
from msmtools.analysis import committor, is_reversible
from msmtools.flux import tpt, flux_matrix, to_netflux, ReactiveFlux
T = np.array([[
0.2576419223095193, 0.2254214623509954, 0.248270708174756,
0.2686659071647294
],
[
0.2233847186210225, 0.2130434781715344,
0.2793477268264001, 0.284224076381043
],
[
0.2118717275169231, 0.2405661227681972,
0.2943396213976011, 0.2532225283172787
],
[
0.2328617711043517, 0.2485926610067547,
0.2571819311236834, 0.2613636367652102
]])
mu = np.array([
0.2306979668517676, 0.2328013892993006, 0.2703312416016573,
0.2661694022472743
])
assert is_reversible(T)
np.testing.assert_allclose(mu.dot(T), mu)
np.testing.assert_equal(mu.dot(T), T.T.dot(mu))
A = [0]
B = [1]
# forward committor
qplus = committor(T, A, B, forward=True, mu=mu)
# backward committor
if is_reversible(T, mu=mu):
qminus = 1.0 - qplus
else:
qminus = committor(T, A, B, forward=False, mu=mu)
tpt_obj = tpt(T, A, B)
tpt_obj.major_flux(1.0)
# gross flux
grossflux = flux_matrix(T, mu, qminus, qplus, netflux=False)
# net flux
netflux = to_netflux(grossflux)
F = ReactiveFlux(A,
B,
netflux,
mu=mu,
qminus=qminus,
qplus=qplus,
gross_flux=grossflux)
F.pathways(1.0)
if len(transition) > 1:
transitions.extend(transition)
topic_list = data.topic.unique().tolist()
topic_list = sorted(topic_list)
transition_matrix = np.zeros([len(topic_list), len(topic_list)]).astype(int)
for element in transitions:
state1 = element[0]
state2 = element[1]
state1_index = topic_list.index(state1)
state2_index = topic_list.index(state2)
transition_matrix[state1_index,
state2_index] = transition_matrix[state1_index,
state2_index] + 1
transition_matrix_scaled = (transition_matrix.T /
transition_matrix.sum(axis=1)).T
mm = msm.markov_model(transition_matrix_scaled)
for element in mm.eigenvalues().argsort()[::-1]:
print(topic_list[element])
transition_matrix_scaled = sparse.csr_matrix(transition_matrix_scaled)
flux = tpt(transition_matrix_scaled, [1], [2])
pdb.set_trace()
chain = MarkovChain(transition_matrix=transition_matrix, states=topic_list)
pdb.set_trace()
def test_random(self):
C = np.random.randint(0, 1000, size=(10, 10))
P = msmtools.estimation.transition_matrix(C, reversible=True)
r = tpt(P, [0], [len(C) - 1])
fig, pos = plot_flux(r)
