def test_grad_noreg(self):
X, y = data1()
np.testing.assert_allclose(
solution.grad(np.array([0, 0, 0, 0]), X, y, 0.0),
[-0.2313, 0.1625, -0.6625, -0.2563], rtol=1e-3)
np.testing.assert_allclose(
solution.grad(np.array([0.1, -0.1, 0.1, 0.1]), X, y, 0.0),
[0.5610, 0.5969, -0.1870, -0.1151], rtol=1e-3)
np.testing.assert_allclose(
solution.grad(np.array([-1.06, -5.39, 7.64, 3.79]), X, y, 0.0),
[0., 0., 0., 0.], atol=1e-3)
X, y = data2()
np.testing.assert_allclose(
solution.grad(np.array([0.1, -0.1, 0.1, 0.1]), X, y, 0.0),
[1.3211, 1.0822, 0.1827, -0.0282], rtol=1e-3)
def test_z_grad_cost_compatible(self):
X, y = data1()
thetas = [[-0.231, 0.162, -0.662, -0.256],
[0.561, 0.597, -0.187, -0.115], [-1.06, -5.39, 7.64, 3.79]]
for theta in thetas:
for lambda_ in [0.0, 1.0, 100.0, 0.0001]:
theta = np.array(theta)
an_grad = grad(theta, X, y, lambda_)
n_grad = num_grad(theta, X, y, lambda_)
np.testing.assert_almost_equal(
an_grad,
n_grad,
decimal=5,
err_msg="lambvda: %d theta: %s" % (lambda_, theta))
def test_z_grad_cost_compatible(self):
X, y = data1()
def diff_numberic_analitic_grad(theta, lambda_, analitic_grad):
vec = solution.num_grad(theta, X, y, lambda_)
return np.max(np.abs(analitic_grad-vec))
thetas = [[-0.231, 0.162, -0.662, -0.256],
[0.561, 0.597, -0.187, -0.115],
[-1.06, -5.39, 7.64, 3.79]]
for theta in thetas:
for lambda_ in [0.0, 1.0, 100.0, 0.0001]:
theta = np.array(theta)
an_grad = solution.grad(theta, X, y, lambda_)
self.assertAlmostEqual(
diff_numberic_analitic_grad(theta, lambda_, an_grad),
0.0, places=6)