Exemple #1
0
def test_model_1d():
    # 10 1d datapoints between -1 and 1
    np.random.seed(0)
    X = np.random.uniform(size=(10, 1))
    # linearly separable labels
    Y = 1 - 2 * (X.ravel() < .5)
    pbl = BinarySVMModel(n_features=2)
    # we have to add a constant 1 feature by hand :-/
    X = np.hstack([X, np.ones((X.shape[0], 1))])
    w = [1, -.5]
    Y_pred = np.hstack([pbl.inference(x, w) for x in X])
    assert_array_equal(Y, Y_pred)

    # check that sign of psi and inference agree
    for x, y in zip(X, Y):
        assert_true(np.dot(w, pbl.psi(x, y)) > np.dot(w, pbl.psi(x, -y)))

    # check that sign of psi and the sign of y correspond
    for x, y in zip(X, Y):
        assert_true(np.dot(w, pbl.psi(x, y)) == -np.dot(w, pbl.psi(x, -y)))
Exemple #2
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def test_blobs_batch():
    # make two gaussian blobs
    X, Y = make_blobs(n_samples=80, centers=2, random_state=1)
    Y = 2 * Y - 1

    pbl = BinarySVMModel(n_features=2)

    # test psi
    psi_mean = pbl.batch_psi(X, Y)
    psi_mean2 = np.sum([pbl.psi(x, y) for x, y in zip(X, Y)], axis=0)
    assert_array_equal(psi_mean, psi_mean2)

    # test inference
    w = np.random.uniform(-1, 1, size=pbl.size_psi)
    Y_hat = pbl.batch_inference(X, w)
    for i, (x, y_hat) in enumerate(zip(X, Y_hat)):
        assert_array_equal(Y_hat[i], pbl.inference(x, w))

    # test inference
    Y_hat = pbl.batch_loss_augmented_inference(X, Y, w)
    for i, (x, y, y_hat) in enumerate(zip(X, Y, Y_hat)):
        assert_array_equal(Y_hat[i], pbl.loss_augmented_inference(x, y, w))