def test_core_covariates_node(covs, sigma):
covs_node = core.CovariatesNode(covs, sigma)
assert covs_node.num_covs == 3
assert covs_node.shape == (5, 4)
assert covs_node.covs_mat.shape == (20, 3)
assert np.all(covs_node.weight_mat == 1.0 / sigma**2)
assert np.all(covs_node.alpha == 0.0)
def test_core_covariates_node_update_params(covs, sigma):
covs_node = core.CovariatesNode(covs, sigma)
mat = np.random.randn(*covs_node.shape)
vec = mat.reshape(mat.size)
covs_node.update_params(mat)
c = covs_node.covs_mat.T.dot(covs_node.covs_mat)
y = covs_node.covs_mat.T.dot(vec)
assert np.linalg.norm(c.dot(covs_node.alpha) - y) < 1e-10
def covs_node(shape):
covs = np.ones((1, ) + shape)
sigma = 0.1
return core.CovariatesNode(covs, sigma)
def test_core_covariates_node_predict_mat(covs, sigma):
covs_node = core.CovariatesNode(covs, sigma)
predict_mat = covs_node.predict_mat
assert predict_mat.shape == (5, 4)
assert np.linalg.norm(predict_mat) < 1e-10