def test_errors(self):
with self.assertRaises(TypeError):
LeapFrog(
model=self.test_model,
position=1,
momentum=[1, 1],
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
)
with self.assertRaises(TypeError):
LeapFrog(
model=self.test_model,
position=[1, 1],
momentum=1,
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
)
with self.assertRaises(ValueError):
LeapFrog(
model=self.test_model,
position=[1, 1],
momentum=[1],
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
)
with self.assertRaises(TypeError):
LeapFrog(
model=self.test_model,
position=[1],
momentum=[1],
stepsize=0.1,
grad_log_pdf=1,
)
with self.assertRaises(ValueError):
LeapFrog(
model=self.test_model,
position=[1, 1],
momentum=[1, 1],
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
)
with self.assertRaises(TypeError):
LeapFrog(
model=self.test_model,
position=[1, 1, 1],
momentum=[1, 1, 1],
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
grad_log_position=1,
)
with self.assertRaises(ValueError):
LeapFrog(
model=self.test_model,
position=[1, 1, 1],
momentum=[1, 1, 1],
stepsize=0.1,
grad_log_pdf=GradLogPDFGaussian,
grad_log_position=[1, 1],
)
def setUp(self):
mean = np.array([-1, 1, -1])
covariance = np.array([[1, 0.6, 0.5], [0.6, 2, 0.3], [0.5, 0.3, 1]])
self.test_model = JGD(['x', 'y', 'z'], mean, covariance)
position = [0, 0, 0]
momentum = [-1, -1, -1]
self.test_with_grad_log = LeapFrog(model=self.test_model,
position=position,
momentum=momentum,
stepsize=0.3,
grad_log_pdf=GradLogPDFGaussian,
grad_log_position=None)
grad_log_position, _ = GradLogPDFGaussian(
position, self.test_model).get_gradient_log_pdf()
self.test_without_grad_log = LeapFrog(
model=self.test_model,
position=position,
momentum=momentum,
stepsize=0.4,
grad_log_pdf=GradLogPDFGaussian,
grad_log_position=grad_log_position)