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)

def test_with_crosses_bad_init():
    # use less perfect initialization
    X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                total_size=8)
    n_labels = 2
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
                        inference_method='lp')
    H_init = crf.init_latent(X, Y)

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

    one_slack = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
                             inference_cache=50, max_iter=10000)
    n_slack = StructuredSVM(crf)
    subgradient = SubgradientSSVM(crf, max_iter=150, learning_rate=5)

    for base_ssvm in [one_slack, n_slack, subgradient]:
        base_ssvm.C = 10. ** 3
        base_ssvm.n_jobs = -1
        clf = LatentSSVM(base_ssvm)

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

        assert_array_equal(np.array(Y_pred), Y)

def test_with_crosses_bad_init():
    # use less perfect initialization
    rnd = np.random.RandomState(0)
    X, Y = toy.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]
    n_labels = 2
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2)
    H_init = crf.init_latent(X, Y)

    mask = rnd.uniform(size=H_init.shape) > .7
    H_init[mask] = 2 * (H_init[mask] / 2)

    one_slack = OneSlackSSVM(crf, inactive_threshold=1e-8, cache_tol=.0001,
                             inference_cache=50, max_iter=10000)
    n_slack = NSlackSSVM(crf)
    subgradient = SubgradientSSVM(crf, max_iter=150, learning_rate=.01,
                                  momentum=0)

    for base_ssvm in [one_slack, n_slack, subgradient]:
        base_ssvm.C = 10. ** 2
        clf = LatentSSVM(base_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(.98 < clf.score(X_test, Y_test) < 1)

def test_directional_bars():
    X, Y = generate_easy(n_samples=10, noise=5, box_size=2, total_size=6, seed=1)
    n_labels = 2
    crf = LatentDirectionalGridCRF(n_labels=n_labels, n_states_per_label=[1, 4])
    clf = LatentSSVM(OneSlackSSVM(model=crf, max_iter=500, C=10.0, inference_cache=50, tol=0.01))
    clf.fit(X, Y)
    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)

def main():
    X, Y = toy.generate_crosses(n_samples=40, noise=8, n_crosses=2,
                                total_size=10)
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
                        inference_method='lp')
    clf = LatentSSVM(problem=crf, max_iter=50, C=1000., verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True,
                     plot=True)
    clf.fit(X_train, Y_train)

    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        i = 0
        loss = 0
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            loss += np.sum(y != y_pred / crf.n_states_per_label)
            fig, ax = plt.subplots(3, 2)
            ax[0, 0].matshow(y * crf.n_states_per_label,
                             vmin=0, vmax=crf.n_states - 1)
            ax[0, 0].set_title("ground truth")
            unary_params = np.repeat(np.eye(2), 2, axis=1)
            pairwise_params = np.zeros(10)
            w_unaries_only = np.hstack([unary_params.ravel(),
                                        pairwise_params.ravel()])
            unary_pred = crf.inference(x, w_unaries_only)
            ax[0, 1].matshow(unary_pred, vmin=0, vmax=crf.n_states - 1)
            ax[0, 1].set_title("unaries only")
            if h_init is None:
                ax[1, 0].set_visible(False)
            else:
                ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                ax[1, 0].set_title("latent initial")
            ax[1, 1].matshow(crf.latent(x, y, clf.w),
                             vmin=0, vmax=crf.n_states - 1)
            ax[1, 1].set_title("latent final")
            ax[2, 0].matshow(y_pred, vmin=0, vmax=crf.n_states - 1)
            ax[2, 0].set_title("prediction")
            ax[2, 1].matshow((y_pred // crf.n_states_per_label)
                             * crf.n_states_per_label,
                             vmin=0, vmax=crf.n_states - 1)
            ax[2, 1].set_title("prediction")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
            i += 1
        print("loss %s set: %f" % (name, loss))
    print(clf.w)

def main():
    # get some data
    X, Y = toy.generate_bars(n_samples=40, noise=10, total_size=10, separate_labels=False)
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5)

    # train a latent grid crf
    n_labels = len(np.unique(Y_train))
    crf = LatentDirectionalGridCRF(n_labels=n_labels, n_states_per_label=[1, 6], inference_method="lp")
    clf = LatentSSVM(
        problem=crf,
        max_iter=50,
        C=10.0,
        verbose=2,
        check_constraints=True,
        n_jobs=-1,
        break_on_bad=False,
        tol=-10,
        base_svm="1-slack",
    )
    clf.fit(X_train, Y_train)

    # the rest is plotting
    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"], [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        i = 0
        loss = 0
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            loss += np.sum(y != y_pred)
            fig, ax = plt.subplots(3, 2)
            ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
            ax[0, 0].set_title("ground truth")
            unary_pred = np.argmax(x, axis=-1)
            ax[0, 1].matshow(unary_pred, vmin=0, vmax=crf.n_labels - 1)
            ax[0, 1].set_title("unaries only")
            if h_init is None:
                ax[1, 0].set_visible(False)
            else:
                ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                ax[1, 0].set_title("latent initial")
            ax[1, 1].matshow(crf.latent(x, y, clf.w), vmin=0, vmax=crf.n_states - 1)
            ax[1, 1].set_title("latent final")
            ax[2, 0].matshow(crf.inference(x, clf.w), vmin=0, vmax=crf.n_states - 1)
            ax[2, 0].set_title("prediction")
            ax[2, 1].matshow(y_pred, vmin=0, vmax=crf.n_labels - 1)
            ax[2, 1].set_title("prediction")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
            i += 1
        print("loss %s set: %f" % (name, loss))

def test_states(states, x, y, x_t, y_t, i, jobs):
    latent_pbl = GraphLDCRF(n_states_per_label=states, inference_method="qpbo")

    base_ssvm = NSlackSSVM(latent_pbl, C=1, tol=0.01, inactive_threshold=1e-3, batch_size=10, verbose=0, n_jobs=jobs)
    latent_svm = LatentSSVM(base_ssvm=base_ssvm, latent_iter=3)
    latent_svm.fit(x, y)

    test = latent_svm.score(x_t, y_t)
    train = latent_svm.score(x, y)

    plot_cm(latent_svm, y_t, x_t, str(states), i)

    print states, "Test:", test, "Train:", train
    return test, train

def test_with_crosses_base_svms():
    # very simple dataset. k-means init is perfect
    for base_svm in ['1-slack', 'n-slack', 'subgradient']:
        X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                    total_size=8)
        n_labels = 2
        crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                            inference_method='lp')
        clf = LatentSSVM(problem=crf, max_iter=150, C=10. ** 5, verbose=2,
                         check_constraints=True, n_jobs=-1, break_on_bad=True,
                         base_svm=base_svm, learning_rate=5)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)

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)

def test_directional_bars():
    for inference_method in ['lp']:
        X, Y = toy.generate_easy(n_samples=10, noise=5, box_size=2,
                                 total_size=6, seed=1)
        n_labels = 2
        crf = LatentDirectionalGridCRF(n_labels=n_labels,
                                       n_states_per_label=[1, 4],
                                       inference_method=inference_method)
        clf = LatentSSVM(problem=crf, max_iter=500, C=10. ** 5, verbose=2,
                         check_constraints=True, n_jobs=-1, break_on_bad=True,
                         base_svm='1-slack')
        clf.fit(X, Y)
        Y_pred = clf.predict(X)

        assert_array_equal(np.array(Y_pred), Y)

def test_with_crosses_perfect_init():
    # very simple dataset. k-means init is perfect
    for n_states_per_label in [2, [1, 2]]:
        # test with 2 states for both foreground and background,
        # as well as with single background state
        X, Y = generate_crosses(n_samples=10, noise=5, n_crosses=1, total_size=8)
        n_labels = 2
        crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=n_states_per_label)
        clf = LatentSSVM(
            OneSlackSSVM(model=crf, max_iter=500, C=10, check_constraints=False, break_on_bad=False, inference_cache=50)
        )
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)
        assert_equal(clf.score(X, Y), 1)

def test_with_crosses_bad_init():
    # use less perfect initialization
    X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                total_size=8)
    n_labels = 2
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
                        inference_method='lp')
    clf = LatentSSVM(problem=crf, max_iter=50, C=10. ** 3, verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True)
    H_init = crf.init_latent(X, Y)

    mask = np.random.uniform(size=H_init.shape) > .7
    H_init[mask] = 2 * (H_init[mask] / 2)
    clf.fit(X, Y, H_init=H_init)
    Y_pred = clf.predict(X)

    assert_array_equal(np.array(Y_pred), Y)

def test_with_crosses_base_svms():
    # very simple dataset. k-means init is perfect
    n_labels = 2
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2])
    one_slack = OneSlackSSVM(crf, inference_cache=50)
    n_slack = NSlackSSVM(crf)
    subgradient = SubgradientSSVM(crf, max_iter=400, learning_rate=0.01, decay_exponent=0, decay_t0=10)

    X, Y = generate_crosses(n_samples=10, noise=5, n_crosses=1, total_size=8)

    for base_ssvm in [one_slack, n_slack, subgradient]:
        base_ssvm.C = 100.0
        clf = LatentSSVM(base_ssvm=base_ssvm)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)
        assert_equal(clf.score(X, Y), 1)

def test_with_crosses_base_svms():
    # very simple dataset. k-means init is perfect
    n_labels = 2
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                        inference_method='lp')
    one_slack = OneSlackSSVM(crf)
    n_slack = StructuredSVM(crf)
    subgradient = SubgradientSSVM(crf, max_iter=150, learning_rate=5)

    for base_ssvm in [one_slack, n_slack, subgradient]:
        base_ssvm.C = 10. ** 5
        base_ssvm.n_jobs = -1
        X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                    total_size=8)
        clf = LatentSSVM(base_ssvm=base_ssvm)
        clf.fit(X, Y)
        Y_pred = clf.predict(X)
        assert_array_equal(np.array(Y_pred), Y)

def test_binary_blocks_cutting_plane_latent_node():
    #testing cutting plane ssvm on easy binary dataset
    # we use the LatentNodeCRF without latent nodes and check that it does the
    # same as GraphCRF
    X, Y = toy.generate_blocks(n_samples=3)
    crf = GraphCRF(inference_method='lp')
    clf = StructuredSVM(model=crf, max_iter=20, C=100, verbose=0,
                        check_constraints=True, break_on_bad=False,
                        n_jobs=1)
    x1, x2, x3 = X
    y1, y2, y3 = Y
    n_states = len(np.unique(Y))
    # delete some rows to make it more fun
    x1, y1 = x1[:, :-1], y1[:, :-1]
    x2, y2 = x2[:-1], y2[:-1]
    # generate graphs
    X_ = [x1, x2, x3]
    G = [make_grid_edges(x) for x in X_]

    # reshape / flatten x and y
    X_ = [x.reshape(-1, n_states) for x in X_]
    Y = [y.ravel() for y in [y1, y2, y3]]

    X = zip(X_, G)

    clf.fit(X, Y)
    Y_pred = clf.predict(X)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    latent_crf = LatentNodeCRF(n_labels=2, inference_method='lp',
                               n_hidden_states=0)
    latent_svm = LatentSSVM(StructuredSVM(model=latent_crf, max_iter=20,
                                          C=100, verbose=0,
                                          check_constraints=True,
                                          break_on_bad=False, n_jobs=1),
                            latent_iter=3)
    X_latent = zip(X_, G, np.zeros(len(X_)))
    latent_svm.fit(X_latent, Y, H_init=Y)
    Y_pred = latent_svm.predict(X_latent)
    for y, y_pred in zip(Y, Y_pred):
        assert_array_equal(y, y_pred)

    assert_array_almost_equal(latent_svm.w, clf.w)

def main():
    X, Y = toy.generate_crosses(n_samples=20, noise=5, n_crosses=1,
                                total_size=8)
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
    n_labels = len(np.unique(Y_train))
    crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
                        inference_method='lp')
    clf = LatentSSVM(problem=crf, max_iter=50, C=1000., verbose=2,
                     check_constraints=True, n_jobs=-1, break_on_bad=True)
    clf.fit(X_train, Y_train)

    i = 0
    for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
                            [X_test, Y_test, [None] * len(X_test), "test"]]:
        Y_pred = clf.predict(X_)
        score = clf.score(X_, Y_)
        for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
            fig, ax = plt.subplots(4, 1)
            ax[0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
            ax[0].set_title("Ground truth")
            ax[1].matshow(np.argmax(x, axis=-1), vmin=0, vmax=crf.n_labels - 1)
            ax[1].set_title("Unaries only")
            #if h_init is None:
                #ax[1, 0].set_visible(False)
            #else:
                #ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
                #ax[1, 0].set_title("latent initial")
            #ax[2].matshow(crf.latent(x, y, clf.w),
                          #vmin=0, vmax=crf.n_states - 1)
            #ax[2].set_title("latent final")
            ax[2].matshow(crf.inference(x, clf.w), vmin=0, vmax=crf.n_states
                          - 1)
            ax[2].set_title("Prediction for h")
            ax[3].matshow(y_pred, vmin=0, vmax=crf.n_labels - 1)
            ax[3].set_title("Prediction for y")
            for a in ax.ravel():
                a.set_xticks(())
                a.set_yticks(())
            plt.subplots_adjust(hspace=.5)
            fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight",
                        dpi=400)
            i += 1
        print("score %s set: %f" % (name, score))
    print(clf.w)

def test_with_crosses():
    # very simple dataset. k-means init is perfect
    for n_states_per_label in [2, [1, 2]]:
        # test with 2 states for both foreground and background,
        # as well as with single background state
        #for inference_method in ['ad3', 'qpbo', 'lp']:
        for inference_method in ['lp']:
            X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
                                        total_size=8)
            n_labels = 2
            crf = LatentGridCRF(n_labels=n_labels,
                                n_states_per_label=n_states_per_label,
                                inference_method=inference_method)
            clf = LatentSSVM(problem=crf, max_iter=50, C=10. ** 5, verbose=2,
                             check_constraints=True, n_jobs=-1,
                             break_on_bad=True)
            clf.fit(X, Y)
            Y_pred = clf.predict(X)
            assert_array_equal(np.array(Y_pred), Y)

def test_latent_node_boxes_edge_features():
    # learn the "easy" 2x2 boxes dataset.
    # smoketest using a single constant edge feature

    X, Y = make_simple_2x2(seed=1, n_samples=40)
    latent_crf = EdgeFeatureLatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
    base_svm = OneSlackSSVM(latent_crf)
    base_svm.C = 10
    latent_svm = LatentSSVM(base_svm,
                            latent_iter=10)

    G = [make_grid_edges(x) for x in X]

    # make edges for hidden states:
    edges = make_edges_2x2()

    G = [np.vstack([make_grid_edges(x), edges]) for x in X]

    # reshape / flatten x and y
    X_flat = [x.reshape(-1, 1) for x in X]
    Y_flat = [y.ravel() for y in Y]

    #X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
    # add edge features
    X_ = [(x, g, np.ones((len(g), 1)), 4) for x, g in zip(X_flat, G)]
    latent_svm.fit(X_[:20], Y_flat[:20])

    assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
    assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)

    # test that score is not always 1
    assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)

def test_latent_node_boxes_standard_latent():
    # learn the "easy" 2x2 boxes dataset.
    # a 2x2 box is placed randomly in a 4x4 grid
    # we add a latent variable for each 2x2 patch
    # that should make the model fairly simple

    X, Y = make_simple_2x2(seed=1, n_samples=40)
    latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
    one_slack = OneSlackSSVM(latent_crf)
    n_slack = NSlackSSVM(latent_crf)
    subgradient = SubgradientSSVM(latent_crf, max_iter=100)
    for base_svm in [one_slack, n_slack, subgradient]:
        base_svm.C = 10
        latent_svm = LatentSSVM(base_svm,
                                latent_iter=10)

        G = [make_grid_edges(x) for x in X]

        # make edges for hidden states:
        edges = make_edges_2x2()

        G = [np.vstack([make_grid_edges(x), edges]) for x in X]

        # reshape / flatten x and y
        X_flat = [x.reshape(-1, 1) for x in X]
        Y_flat = [y.ravel() for y in Y]

        X_ = list(zip(X_flat, G, [2 * 2 for x in X_flat]))
        latent_svm.fit(X_[:20], Y_flat[:20])

        assert_array_equal(latent_svm.predict(X_[:20]), Y_flat[:20])
        assert_equal(latent_svm.score(X_[:20], Y_flat[:20]), 1)

        # test that score is not always 1
        assert_true(.98 < latent_svm.score(X_[20:], Y_flat[20:]) < 1)

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)