def mean_theta(self) -> List[np.ndarray]:
"""Mean estimate of model parameters
Posterior if model is fitted, otherwise prior.
"""
return utils.listmap(np.array)(utils.unflatten(
self.theta.get_moments()[0], self.formula.terms))
def theta_marginal(self, i):
"""Extract marginal distribution for a specific term
"""
mus = utils.unflatten(self.theta.get_moments()[0], self.formula.bases)
covs = utils.extract_diag_blocks(utils.solve_covariance(self.theta),
self.formula.bases)
return bp.nodes.Gaussian(mu=mus[i],
Lambda=bp.utils.linalg.inv(covs[i]))
def theta_marginal(self, i: int) -> Gaussian:
"""Extract marginal distribution for a specific term
"""
u = self.theta.get_moments()
mus = utils.unflatten(u[0], self.formula.terms)
covs = utils.extract_diag_blocks(utils.solve_covariance(u),
self.formula.terms)
return Gaussian(mu=mus[i], Lambda=np.linalg.inv(covs[i]))
def theta_marginals(self):
"""Nodes for the basis specific marginal distributions
"""
# TODO: Test that the marginal distributions are correct
mus = utils.unflatten(self.theta.get_moments()[0], self.formula.bases)
covs = utils.extract_diag_blocks(utils.solve_covariance(self.theta),
self.formula.bases)
return [
bp.nodes.Gaussian(mu=mu, Lambda=bp.utils.linalg.inv(cov))
for mu, cov in zip(mus, covs)
]
def theta_marginals(self) -> List[Gaussian]:
"""Marginal distributions of model parameters
"""
u = self.theta.get_moments()
mus = utils.unflatten(u[0], self.formula.terms)
covs = utils.extract_diag_blocks(utils.solve_covariance(u),
self.formula.terms)
return [
Gaussian(mu=mu, Lambda=np.linalg.inv(cov))
for (mu, cov) in zip(mus, covs)
]
def mean_theta(self):
return pipe(self.theta.get_moments()[0],
lambda x: utils.unflatten(x, self.formula.bases),
listmap(np.array))
def test_unflatten(x, y, expected):
assert utils.unflatten(x, y) == expected
return