def test_regression_linear_noise(self):
"""Check regression works with a linear function with added noise."""
clf = RVR(kernel='linear', alpha=1e11)
x = np.arange(1, 101)
y = x + 5
np.random.seed(1)
y = y + 0.1 * np.random.randn(y.shape[0])
X = x[:, np.newaxis]
clf.fit(X, y)
score = clf.score(X, y)
m_target = np.array([1, 5])
rel_target = np.array([[1]])
self.assertGreater(score, 0.99)
np.testing.assert_allclose(clf.m_, m_target, rtol=1e-2)
np.testing.assert_allclose(clf.relevance_, rel_target)
self.assertAlmostEqual(clf.beta_, 126.583, places=3)
prediction, mse = clf.predict(np.array([[50]]), eval_MSE=True)
self.assertAlmostEqual(prediction[0], 55.006, places=3)
self.assertAlmostEqual(mse[0], 0.00798, places=5)
def test_regression_sinc(self):
"""Check regression works with y=sinc(x)."""
clf = RVR()
x = np.linspace(0, 10, 101)
y = np.sinc(x)
np.random.seed(1)
y = y + 0.1 * np.random.randn(y.shape[0])
X = x[:, np.newaxis]
clf.fit(X, y)
score = clf.score(X, y)
m_target = [
1.117655e+00, -6.334513e-01, 5.868671e-01, -4.370936e-01,
2.320311e-01, -4.638864e-05, -7.505325e-02, 6.133291e-02
]
self.assertGreater(score, 0.85)
np.testing.assert_allclose(clf.m_, m_target, rtol=1e-3)
self.assertEqual(clf.relevance_.shape, (8, 1))
prediction, mse = clf.predict(np.array([[0.5]]), eval_MSE=True)
self.assertAlmostEqual(prediction[0], 0.611, places=3)
self.assertAlmostEqual(mse[0], 0.00930, places=5)
def test_regression_linear(self):
"""Check regression works with a linear function."""
clf = RVR(kernel='linear', alpha=1e11)
x = np.arange(1, 100)
y = x + 5
X = x[:, np.newaxis]
clf.fit(X, y)
score = clf.score(X, y)
m_target = np.array([1, 5])
self.assertGreater(score, 0.99)
np.testing.assert_allclose(clf.m_, m_target)
prediction, mse = clf.predict(np.array([[50]]), eval_MSE=True)
self.assertAlmostEqual(prediction[0], 55, places=3)
self.assertAlmostEqual(mse[0], 6.18e-6, places=3)
