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)
class TestLeapFrog(unittest.TestCase):
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)
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 test_leapfrog_methods(self):
new_pos, new_momentum, new_grad = self.test_with_grad_log.get_proposed_values()
np.testing.assert_almost_equal(new_pos, np.array([-0.35634146, -0.25609756, -0.33]))
np.testing.assert_almost_equal(new_momentum, np.array([-1.3396624, -0.70344884, -1.16963415]))
np.testing.assert_almost_equal(new_grad, np.array([-1.0123835, 1.00139798, -0.46422764]))
new_pos, new_momentum, new_grad = self.test_without_grad_log.get_proposed_values()
np.testing.assert_almost_equal(new_pos, np.array([-0.5001626, -0.32195122, -0.45333333]))
np.testing.assert_almost_equal(new_momentum, np.array([-1.42947981, -0.60709102, -1.21246612]))
np.testing.assert_almost_equal(new_grad, np.array([-0.89536651, 0.98893516, -0.39566396]))
def tearDown(self):
del self.test_model
del self.test_with_grad_log
del self.test_without_grad_log
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)
class TestLeapFrog(unittest.TestCase):
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)
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 test_leapfrog_methods(self):
new_pos, new_momentum, new_grad = self.test_with_grad_log.get_proposed_values(
)
np.testing.assert_almost_equal(
new_pos, np.array([-0.35634146, -0.25609756, -0.33]))
np.testing.assert_almost_equal(
new_momentum, np.array([-1.3396624, -0.70344884, -1.16963415]))
np.testing.assert_almost_equal(
new_grad, np.array([-1.0123835, 1.00139798, -0.46422764]))
new_pos, new_momentum, new_grad = self.test_without_grad_log.get_proposed_values(
)
np.testing.assert_almost_equal(
new_pos, np.array([-0.5001626, -0.32195122, -0.45333333]))
np.testing.assert_almost_equal(
new_momentum, np.array([-1.42947981, -0.60709102, -1.21246612]))
np.testing.assert_almost_equal(
new_grad, np.array([-0.89536651, 0.98893516, -0.39566396]))
def tearDown(self):
del self.test_model
del self.test_with_grad_log
del self.test_without_grad_log