def test_trajectories(self):
P = np.array([[0.9, 0.1], [0.1, 0.9]])
# test number of trajectories
M = 10
N = 10
trajs = msmgen.generate_trajs(P, M, N, start=0)
assert len(trajs) == M
# test statistics of starting state
trajs = msmgen.generate_trajs(P, 1000, 1)
ss = np.concatenate(trajs).astype(int)
pi = msmana.stationary_distribution(P)
piest = msmest.count_states(ss) / 1000.0
assert np.max(np.abs(pi - piest)) < 0.025
# test stopping state = starting state
M = 10
trajs = msmgen.generate_trajs(P, M, N, start=0, stop=0)
for traj in trajs:
assert traj.size == 1
# test if we always stop at stopping state
M = 100
stop = 1
trajs = msmgen.generate_trajs(P, M, N, start=0, stop=stop)
for traj in trajs:
assert traj.size == N or traj[-1] == stop
assert stop not in traj[:-1]
def setUpClass(cls):
P = np.array([[0.5, .25, .25, 0.],
[0., .25, .5, .25],
[.25, .25, .5, 0],
[.25, .25, .25, .25],
])
dtrajs = generate_trajs(P, 5, 1000)
msm_obj = pyerna.msm.MaximumLikelihoodMSM()
msm_obj.estimate(dtrajs)
cls.ck = msm_obj.cktest(3)
def test_trajectories(self):
# test number of trajectories
M = 10
N = 10
trajs = msmgen.generate_trajs(self.P,
M,
N,
start=0,
random_state=self.random_state)
assert len(trajs) == M
def test_plot(self):
P = np.array([[0.5, .25, .25, 0.],
[0., .25, .5, .25],
[.25, .25, .5, 0],
[.25, .25, .25, .25],
])
dtrajs = generate_trajs(P, 5, 1000)
msm_obj = pyemma.msm.MaximumLikelihoodMSM()
msm_obj.estimate(dtrajs)
ck = msm_obj.cktest(3)
plot_cktest(ck)
def test_stop_eq_start(self):
M = 10
N = 10
trajs = msmgen.generate_trajs(self.P,
M,
N,
start=0,
stop=0,
random_state=self.random_state)
for traj in trajs:
assert traj.size == 1
def test_stats(self):
# test statistics of starting state
N = 5000
trajs = msmgen.generate_trajs(self.P,
N,
1,
random_state=self.random_state)
ss = np.concatenate(trajs).astype(int)
pi = msmana.stationary_distribution(self.P)
piest = msmest.count_states(ss) / float(N)
np.testing.assert_allclose(piest, pi, atol=0.025)
def test_stop(self):
# test if we always stop at stopping state
M = 100
N = 10
stop = 1
trajs = msmgen.generate_trajs(self.P,
M,
N,
start=0,
stop=stop,
random_state=self.random_state)
for traj in trajs:
assert traj.size == N or traj[-1] == stop
assert stop not in traj[:-1]
def simulate(self, N, start=None, dt=1, M=1):
"""
ToDo Document HMM or MM?
generates a realization of the Hidden Markov Model
:param N: int trajectory length in steps of the lag time
:param start: int (default=None) - starting hidden state. If not given, will sample from the stationary
distribution of the hidden transition matrix
:param stop: int or int-array-like (default=None) - stopping hidden set. If given, the trajectory will be stopped before
N steps once a hidden state of the stop set is reached
:param dt: int - trajectory will be saved every dt time steps. Internally, the dt'th power of P is taken to ensure a more efficient simulation
:return: ndarray - M discrete trajectories with length N/dt in a ndarray of shape (M, N/dt)
"""
import msmtools.generation as msmgen
# sample hidden trajectory
dtraj = msmgen.generate_trajs(self.transition_matrix, M, N, start=start, stop=None, dt=dt)
dtraj = np.asarray(dtraj)
return dtraj
def generate_trajs(self, M, N, start=None, stop=None, dt=1):
""" Generates M random trajectories of length N each with time step dt """
from msmtools.generation import generate_trajs
return generate_trajs(self._P, M, N, start=start, stop=stop, dt=dt)
