def test_with_fake_log_prob(self):
np.random.seed(42)
def grad_log_prob(x):
return -(x/2.0 + np.sin(x))*(1.0/2.0 + np.cos(x))
def fake_log_prob(x):
return -(x/5.0 + np.sin(x) )**2.0/2.0
generator = mh_generator(log_density=fake_log_prob,x_start=1.0)
tester = GaussianSteinTest(grad_log_prob,41)
selector = SampleSelector(generator, sample_size=1000,thinning=20,tester=tester, max_iterations=5)
data,converged = selector.points_from_stationary()
assert converged is False
def test_with_fake_log_prob(self):
np.random.seed(42)
def grad_log_prob(x):
return -(x / 2.0 + np.sin(x)) * (1.0 / 2.0 + np.cos(x))
def fake_log_prob(x):
return -(x / 5.0 + np.sin(x))**2.0 / 2.0
generator = mh_generator(log_density=fake_log_prob, x_start=1.0)
tester = GaussianSteinTest(grad_log_prob, 41)
selector = SampleSelector(generator,
sample_size=1000,
thinning=20,
tester=tester,
max_iterations=5)
data, converged = selector.points_from_stationary()
assert converged is False
def test_on_one_dim_gaussian(self):
np.random.seed(42)
def log_normal(x):
return -np.dot(x,x)/2
generator = mh_generator(log_density=log_normal)
def gradient_of_log_of_normal(x):
return -x
tester = GaussianSteinTest(gradient_of_log_of_normal,10)
selector = SampleSelector(generator, sample_size=2000,thinning=15,tester=tester)
data,converged = selector.points_from_stationary()
tester = GaussianSteinTest(gradient_of_log_of_normal,10)
assert tester.compute_pvalue(data)>0.05
assert converged
def test_on_one_dim_gaussian(self):
np.random.seed(42)
def log_normal(x):
return -np.dot(x, x) / 2
generator = mh_generator(log_density=log_normal)
def gradient_of_log_of_normal(x):
return -x
tester = GaussianSteinTest(gradient_of_log_of_normal, 10)
selector = SampleSelector(generator,
sample_size=2000,
thinning=15,
tester=tester)
data, converged = selector.points_from_stationary()
tester = GaussianSteinTest(gradient_of_log_of_normal, 10)
assert tester.compute_pvalue(data) > 0.05
assert converged