Function test_solve_sample

test/test_solvers.py:532–575

(nx)

def test_solve_sample(nx):
    # test solve_sample when is_Lazy = False
    n = 20
    X_s = np.reshape(1.0 * np.arange(n), (n, 1))
    X_t = np.reshape(1.0 * np.arange(0, n), (n, 1))

    a = ot.utils.unif(X_s.shape[0])
    b = ot.utils.unif(X_t.shape[0])

    M = ot.dist(X_s, X_t)

    # solve with ot.solve
    sol00 = ot.solve(M, a, b)

    # solve unif weights
    sol0 = ot.solve_sample(X_s, X_t)

    # solve signe weights
    sol = ot.solve_sample(X_s, X_t, a, b)

    # check some attributes
    sol.potentials
    sol.sparse_plan
    sol.marginals
    sol.status

    assert_allclose_sol(sol0, sol)
    assert_allclose_sol(sol0, sol00)

    # solve in backend
    X_sb, X_tb, ab, bb = nx.from_numpy(X_s, X_t, a, b)
    solb = ot.solve_sample(X_sb, X_tb, ab, bb)

    assert_allclose_sol(sol, solb)

    # test not implemented unbalanced and check raise
    with pytest.raises(NotImplementedError):
        sol0 = ot.solve_sample(
            X_s, X_t, unbalanced=1, unbalanced_type="cryptic divergence"
        )

    # test not implemented reg_type and check raise
    with pytest.raises(NotImplementedError):
        sol0 = ot.solve_sample(X_s, X_t, reg=1, reg_type="cryptic divergence")


def test_solve_sample_lazy(nx):