def test_call_gradient(self):
step = FRConjugateGradientStep()
state = {}
function = Function()
assert_equal(
step(function=function, point=numpy.zeros((2)), state=state),
numpy.array((4., -16.)))
def test_call(self):
step = FRConjugateGradientStep()
step_decorated = RestartNotOrthogonalConjugateGradientStep(step, 0.1)
state = {'iteration': 0}
function = Function()
assert_equal(
step_decorated(function=function,
point=numpy.zeros((2)),
state=state), numpy.array((4., -16.)))
def test_call_restart(self):
step = FRConjugateGradientStep()
step_decorated = RestartPeriodicallyConjugateGradientStep(step, 10)
state = {'iteration': 10, 'step': None}
function = Function()
assert_equal(
step_decorated(function=function,
point=numpy.zeros((2)),
state=state), numpy.array((4., -16.)))
def test_call_conjugate_gradient(self):
step = FRConjugateGradientStep()
state = {}
function = Function()
direction = step(function=function,
point=numpy.zeros((2)),
state=state)
origin = 0.178571428571 * direction
newDirection = step(function=function, point=origin, state=state)
assert (function(origin + 0.01 * newDirection) < function(origin))