def test_prior_precision(self):
Q = CovariatePrior(CovariateHyperparameters(-0.5 * numpy.log(1.1)),
7).prior_precision()
self.assertEqual(SPARSEFORMAT, Q.getformat())
self.assertEqual((7, 7), Q.shape)
self.assertEqual(7, Q.nnz)
numpy.testing.assert_almost_equal(
Q.todense(), numpy.diag([1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]))
def test_prior_precision_derivative(self):
dQ = CovariatePrior(CovariateHyperparameters(-0.5 * numpy.log(1.1)),
7).prior_precision_derivative(0)
self.assertEqual(SPARSEFORMAT, dQ.getformat())
self.assertEqual((7, 7), dQ.shape)
self.assertEqual(7, dQ.nnz)
self.assertAlmostEqual(-2.2, dQ[1, 1])
with self.assertRaises(ValueError):
CovariatePrior(CovariateHyperparameters(-0.5 * numpy.log(1.1)),
7).prior_precision_derivative(1)
def test_prior_number_of_state_parameters(self):
h = CombinationHyperparameters(
[CovariateHyperparameters(0.1),
CovariateHyperparameters(0.2)])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=4),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=5)
]
prior = CombinationPrior(h, priorlist)
self.assertEqual(9, prior.prior_number_of_state_parameters())
def test_prior_precision_derivative(self):
h = CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(23.6)),
CovariateHyperparameters(-0.5 * numpy.log(88.9))
])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=2),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=3)
]
prior = CombinationPrior(h, priorlist)
dQ = prior.prior_precision_derivative(1)
self.assertEqual(SPARSEFORMAT, dQ.getformat())
self.assertEqual((5, 5), dQ.shape)
self.assertEqual(3, dQ.nnz)
self.assertAlmostEqual(-2.0 * 88.9, dQ[2, 2])
self.assertAlmostEqual(-2.0 * 88.9, dQ[3, 3])
self.assertAlmostEqual(-2.0 * 88.9, dQ[4, 4])
def test_prior_precision(self):
h = CombinationHyperparameters([
CovariateHyperparameters(-0.5 * numpy.log(23.6)),
CovariateHyperparameters(-0.5 * numpy.log(88.9))
])
priorlist = [
CovariatePrior(h.elementparameters[0],
number_of_state_parameters=2),
CovariatePrior(h.elementparameters[1],
number_of_state_parameters=3)
]
prior = CombinationPrior(h, priorlist)
Q = prior.prior_precision()
self.assertEqual(SPARSEFORMAT, Q.getformat())
numpy.testing.assert_almost_equal(
Q.todense(),
[[23.6, 0.0, 0.0, 0.0, 0.0], [0.0, 23.6, 0.0, 0.0, 0.0],
[0.0, 0.0, 88.9, 0.0, 0.0], [0.0, 0.0, 0.0, 88.9, 0.0],
[0.0, 0.0, 0.0, 0.0, 88.9]])
def element_prior(self, hyperparameters):
return CovariatePrior(hyperparameters,
number_of_state_parameters=2)
def test_prior_number_of_state_parameters(self):
self.assertEqual(
7,
CovariatePrior(CovariateHyperparameters(0.1),
7).prior_number_of_state_parameters())
def test_init(self):
p = CovariatePrior(CovariateHyperparameters(8.8), 7)
self.assertEqual(8.8, p.hyperparameters.value)
self.assertEqual(7, p.number_of_state_parameters)