def test_log_prob_normalized(concentration):
grid = torch.arange(0., 2 * math.pi, 1e-4)
prob = VonMises(0.0, concentration).log_prob(grid).exp()
norm = prob.mean().item() * 2 * math.pi
assert abs(norm - 1) < 1e-3, norm
def test_log_prob_normalized(concentration):
grid = torch.arange(0, 2 * math.pi, 1e-4)
prob = VonMises(0.0, concentration).log_prob(grid).exp()
norm = prob.mean().item() * 2 * math.pi
assert abs(norm - 1) < 1e-3, norm
def test_sample(loc, concentration, n_samples=int(1e6), n_iter=50):
prob = VonMises(loc, concentration)
samples = prob.sample((n_samples, ))
mu, kappa = _fit_params_from_samples(samples, n_iter=n_iter)
assert abs(loc - mu) < 0.1
assert abs(concentration - kappa) < concentration * 0.1
def test_von_mises_gof(loc, concentration):
d = VonMises(loc, concentration)
samples = d.sample(torch.Size([100000]))
probs = d.log_prob(samples).exp()
gof = auto_goodness_of_fit(samples, probs, dim=1)
assert gof > TEST_FAILURE_RATE