def test_corner():
dom = NormalDomain([5], [[0.1]], truncate=1e-2)
print dom.norm_lb
print dom.norm_ub
print dom._center()
print "lower bound", dom.corner([-1])
print "upper bound", dom.corner([1])
print dom.sample(10)
def test_sampling(m=5):
np.random.seed(0)
mean = np.random.randn(m)
L = np.random.randn(m, m)
cov = L.dot(L.T)
dom = NormalDomain(mean, cov)
X = dom.sample(1e6)
mean_est = np.mean(X, axis=0)
print('mean')
print(mean)
print(mean_est)
print(
np.isclose(mean,
np.mean(X, axis=0),
rtol=50 / np.sqrt(X.shape[0]),
atol=50 / np.sqrt(X.shape[0])))
assert np.all(
np.isclose(mean,
np.mean(X, axis=0),
rtol=50 / np.sqrt(X.shape[0]),
atol=50 / np.sqrt(X.shape[0]))), "Mean not correct"
print("Covariance")
cov_est = np.cov(X.T)
print(cov)
print(cov_est)
print(
np.isclose(cov,
cov_est,
rtol=100 / np.sqrt(X.shape[0]),
atol=100 / np.sqrt(X.shape[0])))
assert np.all(
np.isclose(cov,
cov_est,
rtol=100 / np.sqrt(X.shape[0]),
atol=100 / np.sqrt(X.shape[0]))), "Covariance not correct"
# Now check with a truncation parameter
truncate = 1e-2
dom2 = NormalDomain(mean, cov, truncate=truncate)
print(dom2.clip)
X2 = dom2.sample(1e5)
assert np.all(dom2.isinside(
X2)), "Sampling did not place all points inside the constraints"
frac_inside = np.sum(dom2.isinside(X)) / len(X)
assert np.isclose(
1 - frac_inside, truncate, atol=100 * truncate / np.sqrt(X.shape[0])
), "Constraint doesn't seem to get right fraction included"