Python LatentGridCRF.initialize方法代码示例

# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import initialize [as 别名]
def test_switch_to_ad3():
    # smoketest only
    # test if switching between qpbo and ad3 works inside latent svm
    # use less perfect initialization

    if not get_installed(['qpbo']) or not get_installed(['ad3']):
        return
    X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
    X_test, Y_test = X[10:], Y[10:]
    X, Y = X[:10], Y[:10]

    crf = LatentGridCRF(n_states_per_label=2,
                        inference_method='qpbo')
    crf.initialize(X, Y)
    H_init = crf.init_latent(X, Y)

    np.random.seed(0)
    mask = np.random.uniform(size=H_init.shape) > .7
    H_init[mask] = 2 * (H_init[mask] / 2)

    base_ssvm = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
                             inference_cache=50, max_iter=10000,
                             switch_to=('ad3', {'branch_and_bound': True}),
                             C=10. ** 3)
    clf = LatentSSVM(base_ssvm)

    clf.fit(X, Y, H_init=H_init)
    assert_equal(clf.model.inference_method[0], 'ad3')

    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)
    # test that score is not always 1
    assert_true(.98 < clf.score(X_test, Y_test) < 1)

# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import initialize [as 别名]
def test_switch_to_ad3():
    # smoketest only
    # test if switching between qpbo and ad3 works inside latent svm
    # use less perfect initialization

    if not get_installed(["qpbo"]) or not get_installed(["ad3"]):
        return
    X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
    X_test, Y_test = X[10:], Y[10:]
    X, Y = X[:10], Y[:10]

    crf = LatentGridCRF(n_states_per_label=2, inference_method="qpbo")
    crf.initialize(X, Y)
    H_init = crf.init_latent(X, Y)

    np.random.seed(0)
    mask = np.random.uniform(size=H_init.shape) > 0.7
    H_init[mask] = 2 * (H_init[mask] / 2)

    base_ssvm = OneSlackSSVM(
        crf,
        inactive_threshold=1e-8,
        cache_tol=0.0001,
        inference_cache=50,
        max_iter=10000,
        switch_to=("ad3", {"branch_and_bound": True}),
        C=10.0 ** 3,
    )
    clf = LatentSSVM(base_ssvm)

    # evil hackery to get rid of ad3 output
    try:
        devnull = open("/dev/null", "w")
        oldstdout_fno = os.dup(sys.stdout.fileno())
        os.dup2(devnull.fileno(), 1)
        replaced_stdout = True
    except:
        replaced_stdout = False

    clf.fit(X, Y, H_init=H_init)

    if replaced_stdout:
        os.dup2(oldstdout_fno, 1)
    assert_equal(clf.model.inference_method[0], "ad3")

    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)
    # test that score is not always 1
    assert_true(0.98 < clf.score(X_test, Y_test) < 1)

# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import initialize [as 别名]
def test_blocks_crf_directional():
    # test latent directional CRF on blocks
    # test that all results are the same as equivalent LatentGridCRF
    X, Y = generate_blocks(n_samples=1)
    x, y = X[0], Y[0]
    pairwise_weights = np.array([0,
                                 0,   0,
                                -4, -4,  0,
                                -4, -4,  0, 0])
    unary_weights = np.repeat(np.eye(2), 2, axis=0)
    w = np.hstack([unary_weights.ravel(), pairwise_weights])
    pw_directional = np.array([0,   0, -4, -4,
                               0,   0, -4, -4,
                               -4, -4,  0,  0,
                               -4, -4,  0,  0,
                               0,   0, -4, -4,
                               0,   0, -4, -4,
                               -4, -4,  0,  0,
                               -4, -4,  0,  0])
    w_directional = np.hstack([unary_weights.ravel(), pw_directional])
    crf = LatentGridCRF(n_states_per_label=2, inference_method='lp')
    crf.initialize(X, Y)
    directional_crf = LatentDirectionalGridCRF(n_states_per_label=2,
                                               inference_method='lp')
    directional_crf.initialize(X, Y)
    h_hat = crf.inference(x, w)
    h_hat_d = directional_crf.inference(x, w_directional)
    assert_array_equal(h_hat, h_hat_d)

    h = crf.latent(x, y, w)
    h_d = directional_crf.latent(x, y, w_directional)
    assert_array_equal(h, h_d)

    h_hat = crf.loss_augmented_inference(x, y, w)
    h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional)
    assert_array_equal(h_hat, h_hat_d)

    psi = crf.psi(x, h_hat)
    psi_d = directional_crf.psi(x, h_hat)
    assert_array_equal(np.dot(psi, w), np.dot(psi_d, w_directional))

# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import initialize [as 别名]
def test_objective():
    # test that SubgradientLatentSSVM does the same as SubgradientSVM,
    # in particular that it has the same loss, if there are no latent states.
    X, Y = generate_blocks_multinomial(n_samples=10, noise=.3, seed=1)
    inference_method = get_installed(["qpbo", "ad3", "lp"])[0]
    n_labels = 3
    crfl = LatentGridCRF(n_labels=n_labels, n_states_per_label=1,
                         inference_method=inference_method)
    clfl = SubgradientLatentSSVM(model=crfl, max_iter=20, C=10.,
                                 learning_rate=0.001, momentum=0.98)
    crfl.initialize(X, Y)
    clfl.w = np.zeros(crfl.size_joint_feature)  # this disables random init
    clfl.fit(X, Y)

    crf = GridCRF(n_states=n_labels, inference_method=inference_method)
    clf = SubgradientSSVM(model=crf, max_iter=20, C=10., learning_rate=0.001,
                          momentum=0.98)
    clf.fit(X, Y)
    assert_array_almost_equal(clf.w, clfl.w)
    assert_almost_equal(clf.objective_curve_[-1], clfl.objective_curve_[-1])
    assert_array_equal(clf.predict(X), clfl.predict(X))
    assert_array_equal(clf.predict(X), Y)

# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import initialize [as 别名]
def test_with_crosses_bad_init():
    # use less perfect initialization
    rnd = np.random.RandomState(0)
    X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
    X_test, Y_test = X[10:], Y[10:]
    X, Y = X[:10], Y[:10]
    crf = LatentGridCRF(n_states_per_label=2)
    crf.initialize(X, Y)
    H_init = crf.init_latent(X, Y)

    mask = rnd.uniform(size=H_init.shape) > 0.7
    H_init[mask] = 2 * (H_init[mask] / 2)

    one_slack_ssvm = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=0.0001, inference_cache=50, C=100)
    clf = LatentSSVM(one_slack_ssvm)

    clf.fit(X, Y, H_init=H_init)
    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)
    # test that score is not always 1
    assert_true(0.98 < clf.score(X_test, Y_test) < 1)