def test_regularized_least_squares_err(self):
x_train = TEST_DATA['rls']['x_train']
y_train = TEST_DATA['rls']['y_train']
M = TEST_DATA['rls']['M']
err = TEST_DATA['rls']['err']
regularization_lambda = TEST_DATA['rls']['lambda']
_, err_computed = regularized_least_squares(x_train, y_train, M, regularization_lambda)
self.assertAlmostEqual(err, err_computed, 8)
def test_regularized_least_squares_w(self):
x_train = TEST_DATA['rls']['x_train']
y_train = TEST_DATA['rls']['y_train']
M = TEST_DATA['rls']['M']
w = TEST_DATA['rls']['w']
regularization_lambda = TEST_DATA['rls']['lambda']
w_computed, _ = regularized_least_squares(x_train, y_train, M, regularization_lambda)
max_diff = np.max(np.abs(w - w_computed))
self.assertAlmostEqual(max_diff, 0, 6)
def test_regularized_least_squares_w(self):
x_train = TEST_DATA['rls']['x_train']
y_train = TEST_DATA['rls']['y_train']
M = TEST_DATA['rls']['M']
w_expected = TEST_DATA['rls']['w']
regularization_lambda = TEST_DATA['rls']['lambda']
w, _ = regularized_least_squares(x_train, y_train, M, regularization_lambda)
self.assertEqual(np.shape(w), (5, 1))
np.testing.assert_almost_equal(w, w_expected)
