def _test_gaussian():
n_observations = 100
x = np.arange(n_observations)
y = make_data(Gaussian(centre=50.0, normalization=25.0, sigma=10.0), x)
prior_model = af.PriorModel(
Gaussian,
# centre=af.GaussianPrior(mean=50, sigma=10),
# normalization=af.GaussianPrior(mean=25, sigma=10),
sigma=af.GaussianPrior(mean=10, sigma=10),
centre=af.UniformPrior(lower_limit=30, upper_limit=70),
normalization=af.UniformPrior(lower_limit=15, upper_limit=35),
# sigma=af.UniformPrior(lower_limit=5, upper_limit=15),
)
factor_model = ep.AnalysisFactor(prior_model, analysis=Analysis(x=x, y=y))
# optimiser = ep.LaplaceOptimiser(
# transform_cls=DiagonalMatrix
# )
optimiser = af.DynestyStatic()
model = factor_model.optimise(optimiser)
assert model.centre.mean == pytest.approx(50, rel=0.1)
assert model.normalization.mean == pytest.approx(25, rel=0.1)
assert model.sigma.mean == pytest.approx(10, rel=0.1)
def make_factor_model(centre: float,
sigma: float,
optimiser=None) -> ep.ModelFactor:
"""
We'll make a LikelihoodModel for each Gaussian we're fitting.
First we'll make the actual data to be fit.
Note that the intensity value is shared.
"""
y = make_data(
Gaussian(centre=centre, intensity=intensity, sigma=sigma), x)
"""
Next we need a prior model.
Note that the intensity prior is shared.
"""
prior_model = af.PriorModel(
Gaussian,
centre=af.GaussianPrior(mean=50, sigma=20),
intensity=intensity_prior,
sigma=af.GaussianPrior(mean=10, sigma=10),
)
"""
Finally we combine the likelihood function with the prior model to produce a likelihood
factor - this will be converted into a ModelFactor which is like any other factor in the
factor graph.
We can also pass a custom optimiser in here that will be used to fit the factor instead
of the default optimiser.
"""
return ep.ModelFactor(prior_model,
analysis=Analysis(x=x, y=y),
optimiser=optimiser)
def make_factor_model(centre: float,
sigma: float,
optimiser=None) -> ep.AnalysisFactor:
y = make_data(
Gaussian(centre=centre, normalization=normalization, sigma=sigma),
x)
prior_model = af.PriorModel(
Gaussian,
centre=af.UniformPrior(lower_limit=10, upper_limit=100),
normalization=normalization_prior,
sigma=af.UniformPrior(lower_limit=0, upper_limit=20),
)
return ep.AnalysisFactor(prior_model,
analysis=Analysis(x=x, y=y),
optimiser=optimiser)
def test_gaussian():
n_observations = 100
x = np.arange(n_observations)
y = make_data(Gaussian(centre=50.0, normalization=25.0, sigma=10.0), x)
prior_model = af.PriorModel(
Gaussian,
centre=af.GaussianPrior(mean=50, sigma=20),
normalization=af.GaussianPrior(mean=25, sigma=10),
sigma=af.GaussianPrior(mean=10, sigma=10),
)
factor_model = ep.AnalysisFactor(prior_model, analysis=Analysis(x=x, y=y))
laplace = ep.LaplaceOptimiser()
model = factor_model.optimise(laplace)
assert model.centre.mean == pytest.approx(50, rel=0.1)
assert model.normalization.mean == pytest.approx(25, rel=0.1)
assert model.sigma.mean == pytest.approx(10, rel=0.1)