def test_jump_proposal_call(self):
with mock.patch("numpy.random.normal") as m:
m.return_value = 0.5
sample = proposal.Sample(dict(reflective=0.0, periodic=0.0, default=0.0))
new_sample = self.jump_proposal(sample)
expected = proposal.Sample(dict(reflective=0.5, periodic=0.5, default=0.5))
self.assertDictEqual(expected, new_sample)
def test_jump_proposal_call_case_2(self):
with mock.patch('random.random') as m:
m.return_value = 0.7
sample = proposal.Sample(dict(phase=0.2, psi=0.5))
alpha = 0.7
beta = 3.0 * np.pi * 0.7 - 2 * np.pi
expected = proposal.Sample(dict(phase=0.5*(alpha-beta), psi=0.5*(alpha+beta)))
self.assertEqual(expected, self.jump_proposal(sample))
def test_jump_proposal_update_eigenvectors_1_d(self):
coordinates = [proposal.Sample(dict(periodic=0.3)), proposal.Sample(dict(periodic=0.1))]
with mock.patch('numpy.var') as m:
m.return_value = 1
self.jump_proposal.update_eigenvectors(coordinates)
self.assertTrue(np.equal(np.array([1]), self.jump_proposal.eigen_values))
self.assertTrue(np.equal(np.array([1]), self.jump_proposal.covariance))
self.assertTrue(np.equal(np.array([[1.]]), self.jump_proposal.eigen_vectors))
def test_jump_proposal_call_with_inverse_temperature(self):
with mock.patch("random.gauss") as m:
m.return_value = 1
sample = proposal.Sample(dict(ra=0.2, dec=-0.5))
expected = proposal.Sample(dict(ra=1.2, dec=0.5))
new_sample = self.jump_proposal(sample, inverse_temperature=2.0)
for key, value in new_sample.items():
self.assertAlmostEqual(expected[key], value)
m.assert_called_with(0, np.sqrt(1 / 2.0) / 2 / np.pi)
def test_jump_proposal_call_case_1(self):
with mock.patch("random.random") as m:
m.return_value = 0.3
sample = proposal.Sample(dict(phase=0.2, psi=0.5))
alpha = 3.0 * np.pi * 0.3
beta = 0.3
expected = proposal.Sample(
dict(phase=0.5 * (alpha - beta), psi=0.5 * (alpha + beta)))
self.assertEqual(expected,
self.jump_proposal(sample, coordinates=None))
def test_jump_proposal_call(self):
with mock.patch('random.sample') as m:
self.jump_proposal.random_number_generator = lambda: 2
m.return_value = [proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3)),
proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))]
sample = proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))
new_sample = self.jump_proposal(sample, coordinates=None)
expected = proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))
for key, value in new_sample.items():
self.assertAlmostEqual(expected[key], value)
def test_get_center_of_mass(self):
samples = [
proposal.Sample(
dict(reflective=0.1 * i, periodic=0.1 * i, default=0.1 * i))
for i in range(3)
]
expected = proposal.Sample(
dict(reflective=0.1, periodic=0.1, default=0.1))
actual = self.jump_proposal.get_center_of_mass(samples)
for key in samples[0].keys():
self.assertAlmostEqual(expected[key], actual[key])
def test_jump_proposal_update_eigenvectors_n_d(self):
coordinates = [proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3)),
proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))]
with mock.patch('numpy.cov') as m:
with mock.patch('numpy.linalg.eigh') as n:
m.side_effect = lambda x: x
n.return_value = 1, 2
self.jump_proposal.update_eigenvectors(coordinates)
self.assertTrue(np.array_equal(np.array([[0.3, 0.1], [0.3, 0.1], [0.3, 0.1]]), self.jump_proposal.covariance))
self.assertEqual(1, self.jump_proposal.eigen_values)
self.assertEqual(2, self.jump_proposal.eigen_vectors)
def test_jump_proposal_call(self):
with mock.patch('random.sample') as m:
with mock.patch('random.gauss') as n:
m.return_value = proposal.Sample(dict(periodic=0.2, reflective=0.2, default=0.2)),\
proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3))
n.return_value = 1
sample = proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))
expected = proposal.Sample(dict(periodic=0.2, reflective=0.2, default=0.2))
new_sample = self.jump_proposal(sample, coordinates=None)
for key, value in new_sample.items():
self.assertAlmostEqual(expected[key], value)
def test_log_j_after_call(self):
with mock.patch('random.uniform') as m1:
with mock.patch('numpy.log') as m2:
with mock.patch('numpy.exp') as m3:
m1.return_value = 1
m2.return_value = 1
m3.return_value = 1
coordinates = [proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3)),
proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3))]
sample = proposal.Sample(dict(periodic=0.2, reflective=0.2, default=0.2))
self.jump_proposal(sample=sample,
coordinates=coordinates)
self.assertEqual(3, self.jump_proposal.log_j)
def test_jump_proposal_call(self):
with mock.patch('random.choice') as m:
with mock.patch('random.uniform') as n:
second_sample = proposal.Sample(dict(periodic=0.3, reflective=0.3, default=0.3))
random_number = 0.5
m.return_value = second_sample
n.return_value = random_number
sample = proposal.Sample(dict(periodic=0.1, reflective=0.1, default=0.1))
new_sample = self.jump_proposal(sample, coordinates=None)
coords = 0.3 - 0.2 * np.exp(random_number * np.log(self.jump_proposal.scale))
expected = proposal.Sample(dict(periodic=coords, reflective=coords, default=coords))
for key, value in new_sample.items():
self.assertAlmostEqual(expected[key], value)
def test_jump_proposal_call(self):
self.jump_proposal.update_eigenvectors = lambda x: None
self.jump_proposal.eigen_values = np.array([1, np.nan, np.nan])
self.jump_proposal.eigen_vectors = np.array([[0.1, np.nan, np.nan],
[0.4, np.nan, np.nan],
[0.7, np.nan, np.nan]])
with mock.patch("random.randrange") as m:
with mock.patch("random.gauss") as n:
m.return_value = 0
n.return_value = 1
expected = proposal.Sample()
expected["periodic"] = 0.2
expected["reflective"] = 0.5
expected["default"] = 0.8
sample = proposal.Sample()
sample["periodic"] = 0.1
sample["reflective"] = 0.1
sample["default"] = 0.1
new_sample = self.jump_proposal(sample, coordinates=None)
for key, value in new_sample.items():
self.assertAlmostEqual(expected[key], value)
def test_multiply_sample(self):
other = 2
expected = proposal.Sample(dict(a=2, c=4))
self.assertDictEqual(expected, self.sample * other)
def test_subtract_sample(self):
other = proposal.Sample(dict(a=2, c=5))
expected = proposal.Sample(dict(a=-1, c=-3))
self.assertDictEqual(expected, self.sample - other)
def setUp(self):
self.sample = proposal.Sample(dict(a=1, c=2))
def test_jump_proposal_call(self):
sample = proposal.Sample(dict(phase=0.2, psi=0.5))
expected = proposal.Sample(dict(phase=0.2 + np.pi,
psi=0.5 + np.pi / 2))
self.assertEqual(expected, self.jump_proposal(sample))
def test_jump_proposal_call(self):
with mock.patch("bilby.core.prior.Uniform.sample") as m:
m.return_value = 0.3
sample = proposal.Sample(dict(phase=0.2, psi=0.5))
expected = proposal.Sample(dict(phase=0.3, psi=0.3))
self.assertEqual(expected, self.jump_proposal(sample))
def test_add_sample(self):
other = proposal.Sample(dict(a=2, c=5))
expected = proposal.Sample(dict(a=3, c=7))
self.assertDictEqual(expected, self.sample + other)
