def METHOD_NAME(self, uniform_dist):
    pdfshape, gridshape, limits = uniform_dist
    # Define grid
    g = grid.randomgrid(pdfshape, gridshape, limits, 50)
    # Shifted Legendre Polynomials
    data = np.vstack(
        [2 * g.coords[:, 0],
         3 * g.coords[:, 1],
         2 * g.coords[:, 0] * g.coords[:, 1]]).T
    reg = adaptive.RegularizedRegression(
        pdfshape, gridshape, limits, [1, 1], 10, 2, g)
    coeffs, err = reg.expand(data)
    d_approx = reg.evaluate(coeffs, g.coords_norm)
    assert np.allclose(
        np.linalg.norm(data - d_approx, axis=1) / np.linalg.norm(data, axis=1),
        0, atol=1e-3)
    assert np.allclose(err, 0, atol=1e-3)