Пример #1
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def test_score_matching_matches_sym():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    a_sym = score_matching_sym(Z, sigma, lmbda)
    a = score_matching(Z, Z, sigma, lmbda)

    assert_allclose(a, a_sym)
Пример #2
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def test_score_matching_objective_matches_sym():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    K = gaussian_kernel(Z, sigma=sigma)
    J_sym = score_matching_sym(Z, sigma, lmbda, K)
    J = score_matching(Z, Z, sigma, lmbda, K)

    assert_allclose(J, J_sym)
Пример #3
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def test_score_matching_sym_low_rank_matches_full():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    a = score_matching_sym(Z, sigma, lmbda)

    R = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"]
    a_cholesky_cg = score_matching_sym_low_rank(Z, sigma, lmbda, L=R.T)
    assert_allclose(a, a_cholesky_cg, atol=3)
Пример #4
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def test_score_matching_sym_execute_only():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    # only run here
    a = score_matching_sym(Z, sigma, lmbda)
    assert type(a) == np.ndarray
    assert len(a.shape) == 1
    assert len(a) == len(Z)
Пример #5
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def test_objective_sym_same_as_from_estimation():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    K = gaussian_kernel(Z, sigma=sigma)
    a = score_matching_sym(Z, sigma, lmbda, K)
    C = _compute_C_sym(Z, K, sigma)
    b = _compute_b_sym(Z, K, sigma)
    J = _objective_sym(Z, sigma, lmbda, a, K, b, C)

    J2 = _objective_sym(Z, sigma, lmbda, a, K)
    assert_almost_equal(J, J2)
Пример #6
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def test_objective_sym_optimum():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    K = gaussian_kernel(Z, sigma=sigma)
    a = score_matching_sym(Z, sigma, lmbda, K)
    J_opt = _objective_sym(Z, sigma, lmbda, a, K)

    for _ in range(10):
        a_random = np.random.randn(len(Z))
        J = _objective_sym(Z, sigma, lmbda, a_random, K)
        assert J >= J_opt
Пример #7
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def test_objective_sym_low_rank_matches_full():
    sigma = 1.
    lmbda = 1.
    Z = np.random.randn(100, 2)

    K = gaussian_kernel(Z, sigma=sigma)
    a_opt = score_matching_sym(Z, sigma, lmbda, K)
    J_opt = _objective_sym(Z, sigma, lmbda, a_opt, K)

    L = incomplete_cholesky_gaussian(Z, sigma, eta=0.01)["R"].T
    a_opt_chol = score_matching_sym_low_rank(Z, sigma, lmbda, L)
    J_opt_chol = _objective_sym_low_rank(Z, sigma, lmbda, a_opt_chol, L)

    assert_almost_equal(J_opt, J_opt_chol, delta=2.)