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_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_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_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([[1.0]]), 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_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_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):
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_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_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 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)
def setUp(self):
self.sample = proposal.Sample(dict(a=1, c=2))