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