def setUpClass(cls):
data = datasets.load_2well_discrete()
cls.obs_micro = data.dtraj_T100K_dt10
# coarse-grain microstates to two metastable states
cg = np.zeros(100, dtype=int)
cg[50:] = 1
cls.obs_macro = cg[cls.obs_micro]
# hidden states
cls.nstates = 2
# samples
cls.nsamples = 10
cls.lag = 100
cls.msm = datasets.load_2well_discrete().msm
cls.bmsm_rev = bayesian_markov_model(cls.obs_macro,
cls.lag,
reversible=True,
nsamples=cls.nsamples)
def setUpClass(cls):
from pyemma.datasets import load_2well_discrete
dw = load_2well_discrete()
v = dw.dtraj_T100K_dt10[:10000]
cls.T = v.size
nstates = 100
b = np.linspace(-1, 1, nstates)
sigma = 0.15
cls.Z = np.zeros((cls.T, nstates))
for t in range(cls.T):
for j in range(nstates):
cls.Z[t, j] = np.exp(-(b[v[t]] - b[j])**2 / (2 * sigma**2))
cls.lag = 10
cls.tica_obj = tica(data=cls.Z, lag=cls.lag)
def test_initialized_bhmm(self):
import pyemma.datasets as d
import pyemma.msm
obs = d.load_2well_discrete().dtraj_T100K_dt10
init_hmm = pyemma.msm.estimate_hidden_markov_model(obs, 2, 10)
bay_hmm = pyemma.msm.estimators.BayesianHMSM(nstates=init_hmm.nstates, lag=init_hmm.lag,
stride=init_hmm.stride, init_hmsm=init_hmm)
bay_hmm.estimate(obs)
assert np.isclose(bay_hmm.stationary_distribution.sum(), 1)
with self.assertRaises(NotImplementedError) as ctx:
obs = np.copy(obs)
assert obs[0] != np.min(obs)
obs[0] = np.min(obs)
bay_hmm.estimate(obs)
self.assertIn('same data', ctx.exception.message)
def setUpClass(cls):
from pyemma import datasets
cls.dtraj = datasets.load_2well_discrete().dtraj_T100K_dt10
