def test_conditional_corrcoeff(corr):
"""
Test whether the conditional correlation coefficient is computed correctly.
"""
d = MultivariateNormal(torch.tensor([0.6, 5.0]),
torch.tensor([[0.1, corr], [corr, 10.0]]))
estimated_corr = conditional_corrcoeff(
density=d,
condition=torch.ones(1, 2),
limits=torch.tensor([[-2.0, 3.0], [-70, 90]]),
resolution=500,
)[0, 1]
assert torch.abs(corr - estimated_corr) < 1e-3
def test_average_cond_coeff_matrix():
d = MultivariateNormal(
torch.tensor([10.0, 5, 1]),
torch.tensor([[100.0, 30.0, 0], [30.0, 10.0, 0], [0, 0, 1.0]]),
)
cond_mat = conditional_corrcoeff(
density=d,
condition=torch.zeros(1, 3),
limits=torch.tensor([[-60.0, 60.0], [-20, 20], [-7, 7]]),
resolution=500,
)
corr_dim12 = torch.sqrt(torch.tensor(30.0**2 / 100.0 / 10.0))
gt_matrix = torch.tensor([[1.0, corr_dim12, 0.0], [corr_dim12, 1.0, 0.0],
[0.0, 0.0, 1.0]])
assert (torch.abs(gt_matrix - cond_mat) < 1e-3).all()