def test_inequality(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.setTM(bl.tm.Static())
S.fit()
S2 = bl.Study()
S2.loadData(np.array([1, 2, 3, 4, 5]))
S2.setOM(bl.om.Poisson('rate2', bl.oint(0, 6, 50)))
S2.setTM(bl.tm.GaussianRandomWalk('sigma', 0.2, target='rate2'))
S2.fit()
P = bl.Parser(S, S2)
P('log(rate2*2*1.2) + 4 + rate^2 > 20', t=3)
np.testing.assert_almost_equal(
P('log(rate2@1*2*1.2) + 4 + rate@2^2 > 20'),
0.162262091093,
decimal=5,
err_msg='Erroneous parsing result for inequality.')
np.testing.assert_almost_equal(
P('log(rate2*2*1.2) + 4 + rate^2 > 20', t=3),
0.163699467863,
decimal=5,
err_msg=
'Erroneous parsing result for inequality with fixed timestamp.')
def test_scaledar1(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.ScaledAR1('rho', bl.oint(-1, 1, 100), 'sigma', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.4178639067800738, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_gaussian(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Gaussian('mu', bl.oint(0, 1, 100), 'std', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -12.430583625665736, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_bivariaterandomwalk(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Gaussian('mu', bl.oint(0, 6, 20), 'sigma', bl.oint(0, 2, 20))
T = bl.tm.BivariateRandomWalk('sigma1', 1., 'sigma2', 0.1, 'rho', 0.5)
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-7.330706514472251,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_array(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=np.ones((20, 20))))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.04317995, 0.04296549, 0.04275526, 0.04262151, 0.04262491],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.66415455, 2.66519273, 2.66664847, 2.66788051, 2.66828383],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.9827282104,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_sympy(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian(
'mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=[stats.Uniform('u', 0, 6),
stats.Exponential('e', 2.)]))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.00909976, 0.0089861, 0.00887967, 0.00881235, 0.00880499],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.9942575, 2.99646768, 3., 3.00353232, 3.0057425],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-12.4324853153,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_optimize(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma', bl.oint(0, 2, 20)))
T = bl.tm.CombinedTransitionModel(bl.tm.GaussianRandomWalk('sigma', 1.07, target='mean'),
bl.tm.RegimeSwitch('log10pMin', -3.90))
S.setTM(T)
S.optimize()
# test parameter distributions
np.testing.assert_allclose(S.getParameterDistributions('mean', density=False)[1][:, 5],
[4.52572851e-04, 1.67790320e-03, 2.94525791e-07, 1.49841548e-08, 1.10238422e-09],
rtol=1e-05, err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[0.95899404, 1.93816557, 2.99999968, 4.06183394, 5.04100612],
rtol=1e-05, err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -8.01030368139, decimal=5,
err_msg='Erroneous log-evidence value.')
# test optimized hyper-parameter values
np.testing.assert_almost_equal(S.getHyperParameterValue('sigma'), 1.06576569677, decimal=5,
err_msg='Erroneous log-evidence value.')
np.testing.assert_almost_equal(S.getHyperParameterValue('log10pMin'), -4.04001476542, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_optimize(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 1000), prior=stats.Exponential('expon', 1.)))
T = bl.tm.CombinedTransitionModel(bl.tm.GaussianRandomWalk('sigma', 2.1, target='rate'),
bl.tm.RegimeSwitch('log10pMin', -3))
S.setTM(T)
S.optimize()
# test parameter distributions
np.testing.assert_allclose(S.getParameterDistributions('rate', density=False)[1][:, 250],
[0.00181567, 0.00213315, 0.00091028, 0.00041154, 0.00090885],
rtol=1e-05, err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('rate'),
[1.01204314, 2.25763551, 3.24176817, 3.74634864, 3.12632199],
rtol=1e-05, err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -9.47362827569, decimal=5,
err_msg='Erroneous log-evidence value.')
# test optimized hyper-parameter values
np.testing.assert_almost_equal(S.getHyperParameterValue('sigma'), 2.11216289063, decimal=5,
err_msg='Erroneous log-evidence value.')
np.testing.assert_almost_equal(S.getHyperParameterValue('log10pMin'), -3.0, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_0hp(self):
# carry out fit (this test is designed to fall back on the fit method of the Study class)
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
S.setTM(bl.tm.Static())
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.00707902, 0.00707902, 0.00707902, 0.00707902, 0.00707902],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[3., 3., 3., 3., 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_fit_2hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
T = bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'),
bl.tm.RegimeSwitch('log10pMin', -3))
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.02976422, 0.15404218, 0.10859567, 0.02553673, 0.00054109],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[1.08288559, 2.24388932, 2.38033179, 2.98934128, 4.64547841],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-14.3305753098,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_function(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1. / s))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.01591204, 0.01579036, 0.01567361, 0.01559665, 0.01558591],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.99576496, 2.99741879, 3., 3.00258121, 3.00423504],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.9842221343,
decimal=5,
err_msg='Erroneous log-evidence value.')
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_fit_prior_function(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Poisson('rate', bl.oint(0, 6, 1000), prior=lambda x: 1. / x))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.0007529, 0.00070742, 0.00066273, 0.00063262, 0.00062968],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.77252114, 2.78251864, 2.79475018, 2.80541289, 2.81072838],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.3966589329,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_array(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 1000),
prior=np.ones(1000)))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.00048679, 0.00045124, 0.00041767, 0.00039717, 0.00039976],
rtol=1e-04,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.81716591, 2.82337653, 2.83204058, 2.83944083, 2.84187612],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.0866227472,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_prior_sympy(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Poisson('rate',
bl.oint(0, 6, 1000),
prior=stats.Exponential('expon', 1.)))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='rate'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('rate')[1][:, 250],
[0.0020607, 0.0019692, 0.00187339, 0.0018053, 0.00179584],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('rate'),
[2.25427356, 2.26949283, 2.28527551, 2.29704214, 2.30024139],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-11.1819034242,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_0hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.Static())
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.00707902, 0.00707902, 0.00707902, 0.00707902, 0.00707902],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(S.getParameterMeanValues('mean'),
[3., 3., 3., 3., 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_fit_1hp(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.GaussianRandomWalk('sigma', 0.1, target='mean'))
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean')[1][:, 5],
[0.00722368, 0.00712209, 0.00702789, 0.00696926, 0.00696322],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[2.99313985, 2.99573566, 3., 3.00426434, 3.00686015],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-16.1865343702,
decimal=5,
err_msg='Erroneous log-evidence value.')
def test_fit_2hp(self):
# carry out fit
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
T = bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean'),
bl.tm.RegimeSwitch('log10pMin', [-3, -1]))
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5], [
5.80970506e-03, 1.12927905e-01, 4.44501254e-02, 1.00250119e-02,
1.72751309e-05
],
rtol=1e-05,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[0.96492471, 2.09944204, 2.82451616, 3.72702495, 5.0219119],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-10.7601875492,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.48943645, 0.51056355]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
# test joint hyper-parameter distribution
x, y, p = S.getJointHyperParameterDistribution(['log10pMin', 'sigma'])
np.testing.assert_allclose(
np.array([x, y]), [[-3., -1.], [0., 0.2]],
rtol=1e-05,
err_msg='Erroneous parameter values in joint hyper-parameter '
'distribution.')
np.testing.assert_allclose(
p, [[0.00701834, 0.0075608], [0.48241812, 0.50300274]],
rtol=1e-05,
err_msg='Erroneous probability values in joint hyper-parameter '
'distribution.')
def test_fit_1cp_1bp_2hp(self):
# carry out fit
S = bl.ChangepointStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean',
bl.cint(0, 6, 20),
'sigma',
bl.oint(0, 2, 20),
prior=lambda m, s: 1 / s**3))
T = bl.tm.SerialTransitionModel(
bl.tm.Static(), bl.tm.ChangePoint('ChangePoint', [0, 1]),
bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma',
bl.cint(0, 0.2, 2),
target='mean'),
bl.tm.RegimeSwitch('log10pMin', [-3, -1])),
bl.tm.BreakPoint('BreakPoint', 'all'), bl.tm.Static())
S.setTM(T)
S.fit()
# test parameter distributions
np.testing.assert_allclose(
S.getParameterDistributions('mean', density=False)[1][:, 5],
[0.01243717, 0.03016095, 0.016939, 0.00024909, 0.00024909],
rtol=1e-04,
err_msg='Erroneous posterior distribution values.')
# test parameter mean values
np.testing.assert_allclose(
S.getParameterMeanValues('mean'),
[0.96802204, 1.95705078, 3.47078681, 4.22225665, 4.22225665],
rtol=1e-05,
err_msg='Erroneous posterior mean values.')
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence,
-15.072007461556161,
decimal=5,
err_msg='Erroneous log-evidence value.')
# test hyper-parameter distribution
x, p = S.getHyperParameterDistribution('sigma')
np.testing.assert_allclose(
np.array([x, p]), [[0., 0.2], [0.4963324, 0.5036676]],
rtol=1e-05,
err_msg='Erroneous values in hyper-parameter distribution.')
# test duration distribution
d, p = S.getDurationDistribution(['ChangePoint', 'BreakPoint'])
np.testing.assert_allclose(
np.array([d, p]),
[[1., 2., 3.], [0.01039273, 0.49395867, 0.49564861]],
rtol=1e-05,
err_msg='Erroneous values in duration distribution.')
def test_distribution(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(bl.om.Poisson('rate', bl.oint(0, 6, 50)))
S.setTM(bl.tm.Static())
S.fit()
S2 = bl.Study()
S2.loadData(np.array([1, 2, 3, 4, 5]))
S2.setOM(bl.om.Poisson('rate2', bl.oint(0, 6, 50)))
S2.setTM(bl.tm.GaussianRandomWalk('sigma', 0.2, target='rate2'))
S2.fit()
P = bl.Parser(S, S2)
x, p = P('log(rate2@1*2*1.2)+ 4 + rate@2^2')
np.testing.assert_allclose(p[100:105],
[0.00643873, 0.00618468, 0.00466452, 0.00314371, 0.00365816],
rtol=1e-05, err_msg='Erroneous derived probability distribution.')
def test_poisson(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Poisson('rate', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.433708287229158, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_scipy_2p(self):
# carry out fit
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
def likelihood(data, mu, std):
x = data
pdf = np.exp((x - mu) ** 2. / (2 * std ** 2.)) / np.sqrt(2 * np.pi * std ** 2.)
return pdf
L = bl.om.NumPy(likelihood, 'mu', bl.oint(0, 7, 100), 'std', bl.oint(1, 2, 100))
S.setOM(L)
S.setTM(bl.tm.Static())
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, 29.792823521784587, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_save_load(self):
S = bl.HyperStudy()
S.loadData(np.array([1, 2, 3, 4, 5]))
S.setOM(
bl.om.Gaussian('mean', bl.cint(0, 6, 20), 'sigma',
bl.oint(0, 2, 20)))
S.setTM(bl.tm.Static())
S.fit()
bl.save('study.bl', S)
S = bl.load('study.bl')
def test_bernoulli(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.Bernoulli('p', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -4.3494298741972859, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_gaussianmean(self):
S = bl.Study()
S.loadData(np.array([[1, 0.5], [0, 0.4], [1, 0.3], [0, 0.2], [0, 0.1]]))
L = bl.om.GaussianMean('mu', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -6.3333705075036226, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_whitenoise(self):
S = bl.Study()
S.loadData(np.array([1, 0, 1, 0, 0]))
L = bl.om.WhiteNoise('std', bl.oint(0, 1, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
np.testing.assert_almost_equal(S.logEvidence, -6.8161638661444073, 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_static(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.Static()
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.372209708143769, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_regimeswitch(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.RegimeSwitch('p_min', -3)
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -10.372866559561402, decimal=5,
err_msg='Erroneous log-evidence value.')
def test_independent(self):
S = bl.Study()
S.loadData(np.array([1, 2, 3, 4, 5]))
L = bl.om.Poisson('rate', bl.oint(0, 6, 100))
T = bl.tm.Independent()
S.set(L, T)
S.fit()
# test model evidence value
np.testing.assert_almost_equal(S.logEvidence, -11.087360077190617, decimal=5,
err_msg='Erroneous log-evidence value.')