def boom(self):
"""
Return the boom.MvnModel corresponding to this object's parameters.
"""
import BayesBoom.boom as boom
return boom.MvnModel(boom.Vector(self._mu),
boom.SpdMatrix(self._Sigma))
def _default_initial_state_prior(self, sdy):
"""
The default prior to use for the initial state vector.
"""
dim = self.nseasons - 1
return boom.MvnModel(
boom.Vector(np.zeros(dim).astype(float)),
boom.SpdMatrix(np.diag(np.full(dim, float(sdy)))))
def test_parameters(self):
"""When parameters are modified outside the object, the object properties
should change. This is testing that pointers are being stored.
"""
zeros = boom.Vector(np.array([0, 0, 0]))
model = boom.MvnModel(zeros, self.Sigma)
mu_prm = model.mean_parameter
new_mu = boom.Vector(np.array([3.0, 2.0, 1.0]))
mu_prm.set(new_mu)
self.assertLess((model.mu - new_mu).normsq(), 1e-5)
def test_data(self):
model = boom.MvnModel(self.mu, self.Sigma)
model.set_data(boom.Matrix(self.data))
model.mle()
self.assertLess(model.siginv.Mdist(self.mu, model.mu), .05)
def test_moments(self):
model = boom.MvnModel(self.mu, self.Sigma)
self.assertLess((model.mu - self.mu).normsq(), 1e-5)
self.assertTrue(
np.allclose(model.Sigma.to_numpy(), self.Sigma.to_numpy()))
def slab(self):
return boom.MvnModel(R.to_boom_vector(self._prior_mean),
R.to_boom_spd(np.diag(self._prior_sd**2)))
def slab(self):
return boom.MvnModel(boom.Vector(self._mean),
boom.SpdMatrix(self._precision), True)