def test_trunc_log_density(self):
mu = 0
sigma = 1
a = -1
b = 1
data = np.array([-0.5, 0, 0.5])
expected_dist = s.truncnorm(loc=mu, scale=sigma, a=a, b=b)
actual_dist = TruncatedNormalDistribution(mu=mu,
sigma=sigma,
lower=a,
upper=b)
expected = expected_dist.logpdf(data)
actual = actual_dist.log_density(data)
assert (actual == expected).all()
def test_trunc_log_density_shifted(self):
mu = 3
sigma = 2
a = 0
b = 4
data = np.array([0.5, 1, 2, 3, 3.9])
expected_dist = s.truncnorm(loc=mu,
scale=sigma,
a=(a - mu) / sigma,
b=(b - mu) / sigma)
actual_dist = TruncatedNormalDistribution(mu=mu,
sigma=sigma,
lower=a,
upper=b)
expected = expected_dist.logpdf(data)
actual = actual_dist.log_density(data)
for i in range(len(expected)):
assert isclose(actual[i], expected[i], abs_tol=1e-10)
def test_trunc_log_density_shifted_small_sig_outside_range(self):
mu = 3
sigma = 0.5
a = 1
b = 4
data = np.array([-1, 0, 0.5, 5])
expected_dist = s.truncnorm(loc=mu,
scale=sigma,
a=(a - mu) / sigma,
b=(b - mu) / sigma)
actual_dist = TruncatedNormalDistribution(mu=mu,
sigma=sigma,
lower=a,
upper=b)
expected = expected_dist.logpdf(data)
expected = np.where(expected == np.inf, -9999, expected)
expected = np.where(expected == -np.inf, -9999, expected)
actual = actual_dist.log_density(data)
for i in range(len(expected)):
assert isclose(actual[i], expected[i], abs_tol=1e-10)