def test_step_set1TM_0hp(self):
# carry out fit
S = bl.OnlineStudy(storeHistory=True)
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.Static())
data = np.array([1, 2, 3, 4, 5])
for d in data:
S.step(d)
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.0053811, 0.38690331, 0.16329865, 0.04887604, 0.01334921],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[0.96310103, 1.5065597, 2.00218465, 2.500366, 3.],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-16.1946904707,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_dynamichyperparameter(self):
S = bl.OnlineStudy(storeHistory=True)
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.add(
'gradual',
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 5),
target='rate'))
S.add('static', bl.tm.Static())
for d in np.arange(5):
S.step(d)
p = S.eval('exp(0.99*log(sigma@2))+1 > 1.1')
np.testing.assert_almost_equal(
p,
0.61228433813735061,
decimal=5,
err_msg='Erroneous parsing result for inequality.')
S = bl.OnlineStudy(storeHistory=False)
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.add(
'gradual',
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 5),
target='rate'))
S.add('static', bl.tm.Static())
for d in np.arange(3):
S.step(d)
p = S.eval('exp(0.99*log(sigma))+1 > 1.1')
np.testing.assert_almost_equal(
p,
0.61228433813735061,
decimal=5,
err_msg='Erroneous parsing result for inequality.')
def test_step_add2TM_2hp_prior_hyperpriors_TMprior(self):
# carry out fit
S = bl.OnlineStudy(storeHistory=True)
S.setOM(bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma', bl.oint(0, 2, 20), prior=lambda m, s: 1./s))
T1 = bl.tm.CombinedTransitionModel(bl.tm.GaussianRandomWalk('s1', [0.25, 0.5],
target='mean',
prior=stats.Exponential('e', 0.5)),
bl.tm.GaussianRandomWalk('s2', bl.cint(0, 0.2, 2),
target='sigma',
prior=np.array([0.2, 0.8]))
)
T2 = bl.tm.Independent()
S.addTransitionModel('T1', T1)
S.addTransitionModel('T2', T2)
S.setTransitionModelPrior([0.9, 0.1])
data = np.array([1, 2, 3, 4, 5])
for d in data:
S.step(d)
# test transition model distributions
np.testing.assert_allclose(S.getCurrentTransitionModelDistribution(local=False)[1],
[0.49402616, 0.50597384],
rtol=1e-05, err_msg='Erroneous transition model probabilities.')
np.testing.assert_allclose(S.getCurrentTransitionModelDistribution(local=True)[1],
[0.81739495, 0.18260505],
rtol=1e-05, err_msg='Erroneous local transition model probabilities.')
# test hyper-parameter distributions
np.testing.assert_allclose(S.getCurrentHyperParameterDistribution('s2')[1],
[0.19047162, 0.80952838],
rtol=1e-05, err_msg='Erroneous hyper-parameter distribution.')
# test parameter distributions
np.testing.assert_allclose(S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.05825921, 0.20129444, 0.07273516, 0.02125759, 0.0039255],
rtol=1e-05, err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[1.0771838, 1.71494272, 2.45992376, 3.34160617, 4.39337253],
rtol=1e-05, err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -9.46900822686, decimal=5,
err_msg='Erroneous log-evidence value.')