def test_display(self):
"""Check if something is printed when `display` is True."""
with Capturing() as output:
nonlinear_conjugate_gradients(self.oracle, self.x0, display=True)
self.assertTrue(
len(output) > 0,
'You should print the progress when `display` is True.')
def test_line_search_options(self):
"""Check if argument `line_search_options` is supported."""
nonlinear_conjugate_gradients(self.oracle,
self.x0,
line_search_options={
'method': 'Wolfe',
'c1': 0.01,
'c2': 0.01
})
def test_history(self):
x0 = -np.array([1.3, 2.7])
x_min, message, history = nonlinear_conjugate_gradients(self.oracle, x0, trace=True,
line_search_options={'method': 'Constant', 'c': 0.6},
tolerance=1e-3)
func_steps = [25.635000000000005,
-7.777199999999997,
-7.8463333368073584,
-9.439531896665148,
-9.41251357036308,
-9.4977531453388693]
grad_norm_steps = [11.629703349613008,
2.4586174977006889,
2.5711348318091276,
0.48633577943739081,
0.58855118960609565,
0.092585266951596912]
time_steps = [0.0] * 6 # Dummy values
x_steps = [np.array([-1.3, -2.7]),
np.array([0.08, 4.14]),
np.array([0.93729171, 4.28518501]),
np.array([1.07314317, 2.75959796]),
np.array([1.05960886, 2.7072376]),
np.array([1.02038104, 3.04515707])]
true_history = dict(grad_norm=grad_norm_steps, time=time_steps, x=x_steps, func=func_steps)
check_equal_histories(history, true_history)
def test_default(self):
"""Check if everything works correctly with default parameters."""
with Capturing() as output:
x_min, message, _ = nonlinear_conjugate_gradients(self.oracle, self.x0)
assert_equal(message, 'success')
self.assertEqual(len(output), 0, 'You should not print anything by default.')
def test_quality(self):
x_min, message, _ = nonlinear_conjugate_gradients(self.oracle, self.x0, tolerance=1e-5)
f_min = self.oracle.func(x_min)
g_k_norm_sqr = norm(self.A.dot(x_min) - self.b, 2)**2
g_0_norm_sqr = norm(self.A.dot(self.x0) - self.b, 2)**2
self.assertLessEqual(g_k_norm_sqr, 1e-5 * g_0_norm_sqr)
self.assertLessEqual(abs(f_min - self.f_star), 1e-5 * g_0_norm_sqr)
def test_max_iter(self):
"""Check if argument `max_iter` is supported."""
nonlinear_conjugate_gradients(self.oracle, self.x0, max_iter=15)
def test_tolerance(self):
"""Check if argument `tolerance` is supported."""
nonlinear_conjugate_gradients(self.oracle, self.x0, tolerance=1e-5)
def test_restart_nu(self):
"""Check if argument `restart_nu` is supported."""
nonlinear_conjugate_gradients(self.oracle, self.x0, restart_nu=0.3)
def test_scheme(self):
"""Check if argument `scheme` is supported."""
nonlinear_conjugate_gradients(self.oracle,
self.x0,
scheme='Fletcher-Reeves')
