def test_2():
# test counts matrix with trimming
model = MarkovStateModel(n_states=2, reversible_type=None, ergodic_trim=True)
model.fit([[1,1,1,1,1,1,1,1,1]])
eq(model.mapping_, {1: 0})
eq(todense(model.countsmat_), np.array([[8]]))
def test_3():
model = MarkovStateModel(n_states=3, reversible_type='mle', ergodic_trim=True)
model.fit([[0,0,0,0,1,1,1,1,0,0,0,0,2,2,2,2,0,0,0]])
counts = np.array([[8, 1, 1], [1, 3, 0], [1, 0, 3]])
eq(todense(model.rawcounts_), counts)
eq(todense(model.countsmat_), counts)
model.timescales_
# test pickleable
try:
dump(model, 'test-msm-temp.npy', compress=1)
model2 = load('test-msm-temp.npy')
eq(model2.timescales_, model.timescales_)
finally:
os.unlink('test-msm-temp.npy')
def test_5():
trjs = load_doublewell(random_state=0)['trajectories']
clusterer = NDGrid(n_bins_per_feature=5)
mle_msm = MarkovStateModel(lag_time=100, verbose=False)
b_msm = BayesianMarkovStateModel(
lag_time=100, n_samples=1000, n_chains=8, n_steps=1000,
random_state=0)
states = clusterer.fit_transform(trjs)
b_msm.fit(states)
mle_msm.fit(states)
# this is a pretty silly test. it checks that the mean transition
# matrix is not so dissimilar from the MLE transition matrix.
# This shouldn't necessarily be the case anyways -- the likelihood is
# not "symmetric". And the cutoff chosen is just heuristic.
assert np.linalg.norm(b_msm.all_transmats_.mean(axis=0) - mle_msm.transmat_) < 1e-3
def test_1():
# test counts matrix without trimming
model = MarkovStateModel(n_states=2, reversible_type=None, ergodic_trim=False)
model.fit([[1,1,1,1,1,1,1,1,1]])
eq(todense(model.countsmat_), np.array([[0, 0], [0, 8]]))