Python GridCRF.initialize方法代码示例

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_binary_grid_unaries():
    # test handling on unaries for binary grid CRFs
    for ds in binary:
        X, Y = ds(n_samples=1)
        x, y = X[0], Y[0]
        for inference_method in get_installed():
            # dai is to expensive
            crf = GridCRF(inference_method=inference_method)
            crf.initialize(X, Y)
            w_unaries_only = np.zeros(7)
            w_unaries_only[:4] = np.eye(2).ravel()
            # test that inference with unaries only is the
            # same as argmax
            inf_unaries = crf.inference(x, w_unaries_only)

            assert_array_equal(inf_unaries, np.argmax(x, axis=2),
                               "Wrong unary inference for %s"
                               % inference_method)
            try:
                assert(np.mean(inf_unaries == y) > 0.5)
            except:
                print(ds)

            # check that the right thing happens on noise-free data
            X, Y = ds(n_samples=1, noise=0)
            inf_unaries = crf.inference(X[0], w_unaries_only)
            assert_array_equal(inf_unaries, Y[0],
                               "Wrong unary result for %s"
                               % inference_method)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_continuous_y():
    for inference_method in get_installed(["lp", "ad3"]):
        X, Y = generate_blocks(n_samples=1)
        x, y = X[0], Y[0]
        w = np.array([1, 0, 0, 1, 0, -4, 0])  # unary  # pairwise

        crf = GridCRF(inference_method=inference_method)
        crf.initialize(X, Y)
        joint_feature = crf.joint_feature(x, y)
        y_cont = np.zeros_like(x)
        gx, gy = np.indices(x.shape[:-1])
        y_cont[gx, gy, y] = 1
        # need to generate edge marginals
        vert = np.dot(y_cont[1:, :, :].reshape(-1, 2).T, y_cont[:-1, :, :].reshape(-1, 2))
        # horizontal edges
        horz = np.dot(y_cont[:, 1:, :].reshape(-1, 2).T, y_cont[:, :-1, :].reshape(-1, 2))
        pw = vert + horz

        joint_feature_cont = crf.joint_feature(x, (y_cont, pw))
        assert_array_almost_equal(joint_feature, joint_feature_cont)

        const = find_constraint(crf, x, y, w, relaxed=False)
        const_cont = find_constraint(crf, x, y, w, relaxed=True)

        # djoint_feature and loss are equal:
        assert_array_almost_equal(const[1], const_cont[1], 4)
        assert_almost_equal(const[2], const_cont[2], 4)

        # returned y_hat is one-hot version of other
        if isinstance(const_cont[0], tuple):
            assert_array_equal(const[0], np.argmax(const_cont[0][0], axis=-1))

            # test loss:
            assert_almost_equal(crf.loss(y, const[0]), crf.continuous_loss(y, const_cont[0][0]), 4)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_binary_grid_unaries():
    # test handling on unaries for binary grid CRFs
    for ds in binary:
        X, Y = ds(n_samples=1)
        x, y = X[0], Y[0]
        for inference_method in get_installed():
            #NOTE: ad3+ fails because it requires a different data structure
            if inference_method == 'ad3+': continue            
            crf = GridCRF(inference_method=inference_method)
            crf.initialize(X, Y)
            w_unaries_only = np.zeros(7)
            w_unaries_only[:4] = np.eye(2).ravel()
            # test that inference with unaries only is the
            # same as argmax
            inf_unaries = crf.inference(x, w_unaries_only)

            assert_array_equal(inf_unaries, np.argmax(x, axis=2),
                               "Wrong unary inference for %s"
                               % inference_method)
            assert(np.mean(inf_unaries == y) > 0.5)

            # check that the right thing happens on noise-free data
            X, Y = ds(n_samples=1, noise=0)
            inf_unaries = crf.inference(X[0], w_unaries_only)
            assert_array_equal(inf_unaries, Y[0],
                               "Wrong unary result for %s"
                               % inference_method)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_binary_blocks_crf_n8_lp():
    X, Y = generate_blocks(n_samples=1, noise=1)
    x, y = X[0], Y[0]
    w = np.array([1, 0, 0, 1, 1, -1.4, 1])  # unary  # pairwise
    crf = GridCRF(neighborhood=8)
    crf.initialize(X, Y)
    y_hat = crf.inference(x, w)
    assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_max_product_multinomial_crf():
    X, Y = generate_blocks_multinomial(n_samples=1)
    x, y = X[0], Y[0]
    w = np.array([1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.4, -0.3, 0.3, -0.5, -0.1, 0.3])  # unary  # pairwise
    crf = GridCRF(inference_method="max-product")
    crf.initialize(X, Y)
    y_hat = crf.inference(x, w)
    assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_max_product_binary_blocks():
    X, Y = generate_blocks(n_samples=1)
    x, y = X[0], Y[0]
    w = np.array([1, 0, 0, 1, 0, -4, 0])  # unary  # pairwise
    crf = GridCRF(inference_method="max-product")
    crf.initialize(X, Y)
    y_hat = crf.inference(x, w)
    assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_blocks_multinomial_crf():
    X, Y = generate_blocks_multinomial(n_samples=1, size_x=9, seed=0)
    x, y = X[0], Y[0]
    w = np.array([1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.4, -0.3, 0.3, -0.5, -0.1, 0.3])  # unaryA  # pairwise
    for inference_method in get_installed():
        crf = GridCRF(inference_method=inference_method)
        crf.initialize(X, Y)
        y_hat = crf.inference(x, w)
        assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_binary_blocks_crf():
    X, Y = generate_blocks(n_samples=1)
    x, y = X[0], Y[0]
    w = np.array([1, 0, 0, 1, 0, -4, 0])  # unary  # pairwise
    for inference_method in get_installed(["dai", "qpbo", "lp", "ad3", "ogm"]):
        crf = GridCRF(inference_method=inference_method)
        crf.initialize(X, Y)
        y_hat = crf.inference(x, w)
        assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_loss_augmentation():
    X, Y = generate_blocks(n_samples=1)
    x, y = X[0], Y[0]
    w = np.array([1, 0, 0, 1, 0, -4, 0])  # unary  # pairwise
    crf = GridCRF()
    crf.initialize(X, Y)
    y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True)

    assert_almost_equal(energy + crf.loss(y, y_hat), -np.dot(w, crf.joint_feature(x, y_hat)))

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_binary_blocks_crf():
    X, Y = generate_blocks(n_samples=1)
    x, y = X[0], Y[0]
    w = np.array([1, 0,  # unary
                  0, 1,
                  0,     # pairwise
                  -4, 0])
    for inference_method in get_installed(['dai', 'qpbo', 'lp', 'ad3', 'ogm']):
        crf = GridCRF(inference_method=inference_method)
        crf.initialize(X, Y)
        y_hat = crf.inference(x, w)
        assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_blocks_multinomial_crf():
    X, Y = generate_blocks_multinomial(n_samples=1, size_x=9, seed=0)
    x, y = X[0], Y[0]
    w = np.array([1., 0., 0.,  # unaryA
                  0., 1., 0.,
                  0., 0., 1.,
                 .4,           # pairwise
                 -.3, .3,
                 -.5, -.1, .3])
    for inference_method in get_installed():
        #NOTE: ad3+ fails because it requires a different data structure
        if inference_method == 'ad3+': continue
        crf = GridCRF(inference_method=inference_method)
        crf.initialize(X, Y)
        y_hat = crf.inference(x, w)
        assert_array_equal(y, y_hat)

# 需要导入模块: from pystruct.models import GridCRF [as 别名]
# 或者: from pystruct.models.GridCRF import initialize [as 别名]
def test_multinomial_grid_unaries():
    # test handling on unaries for multinomial grid CRFs
    # on multinomial datasets
    for ds in multinomial:
        X, Y = ds(n_samples=1, size_x=9)
        x, y = X[0], Y[0]
        n_labels = len(np.unique(Y))
        crf = GridCRF(n_states=n_labels)
        crf.initialize(X, Y)
        w_unaries_only = np.zeros(crf.size_psi)
        w_unaries_only[:n_labels ** 2] = np.eye(n_labels).ravel()
        # test that inference with unaries only is the
        # same as argmax
        inf_unaries = crf.inference(x, w_unaries_only)

        assert_array_equal(inf_unaries, np.argmax(x, axis=2))
        # check that the right thing happens on noise-free data
        X, Y = ds(n_samples=1, noise=0)
        inf_unaries = crf.inference(X[0], w_unaries_only)
        assert_array_equal(inf_unaries, Y[0])