def test_sufficient_statistics():
# test all of the sufficient statistics against sklearn and pure python
model = MetastableSwitchingLDS(n_states=N_STATES,
n_features=refmodel.n_features)
model._impl._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
logprob, stats = model._impl.do_estep()
rlogprob, rstats = _sklearn_estep()
yield lambda: np.testing.assert_array_almost_equal(
stats['post'], rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post[1:]'], rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['post[:-1]'], rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs'], rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]'], rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]'], rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs.T'], rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=3)
