def test_simple_newton():
startPoint = numpy.zeros(2, numpy.float)
optimi = optimizer.StandardOptimizer(
function=Quadratic(),
step=step.NewtonStep(),
criterion=criterion.RelativeValueCriterion(0.00001),
x0=startPoint,
line_search=line_search.SimpleLineSearch())
assert_almost_equal(optimi.optimize(),
numpy.array([1, 3], dtype=numpy.float))
def test_damped_gradient_relative():
startPoint = numpy.zeros(2, numpy.float)
optimi = optimizer.StandardOptimizer(
function=Quadratic(),
step=step.GradientStep(),
criterion=criterion.RelativeValueCriterion(0.00001),
x0=startPoint,
line_search=line_search.DampedLineSearch(damped_error=0.001,
min_alpha_step=0.0001))
assert_almost_equal(optimi.optimize(),
numpy.array([1, 3], dtype=numpy.float))
def test_simple_newton_relative():
startPoint = numpy.empty(2, numpy.float)
startPoint[0] = -1.01
startPoint[-1] = 1.01
optimi = optimizer.StandardOptimizer(
function=Rosenbrock(2),
step=step.NewtonStep(),
criterion=criterion.RelativeValueCriterion(0.00001),
x0=startPoint,
line_search=line_search.SimpleLineSearch())
assert_almost_equal(optimi.optimize(), numpy.ones(2, numpy.float))
def test_swp_frgradient_relative():
startPoint = numpy.zeros(2, numpy.float)
optimi = optimizer.StandardOptimizer(
function=Quadratic(),
step=step.FRConjugateGradientStep(),
criterion=criterion.criterion(ftol=0.000001,
iterations_max=1000,
gtol=0.0001),
x0=startPoint,
line_search=line_search.StrongWolfePowellRule())
assert_almost_equal(optimi.optimize(),
numpy.array([1, 3], dtype=numpy.float))
def test_simple_marquardt():
startPoint = numpy.zeros(2, numpy.float)
optimi = optimizer.StandardOptimizer(
function=Quadratic(),
step=step.MarquardtStep(),
criterion=criterion.criterion(gtol=0.00001, iterations_max=200),
x0=startPoint,
line_search=line_search.BacktrackingSearch())
opt = optimi.optimize()
assert_almost_equal(optimi.optimize(),
numpy.array([1, 3], dtype=numpy.float),
decimal=5)
def test_wpr_cwgradient():
startPoint = numpy.empty(2, numpy.float)
startPoint[0] = -1.01
startPoint[-1] = 1.01
optimi = optimizer.StandardOptimizer(
function=Rosenbrock(2),
step=step.CWConjugateGradientStep(),
criterion=criterion.criterion(iterations_max=1000,
ftol=0.00000001,
gtol=0.0001),
x0=startPoint,
line_search=line_search.WolfePowellRule())
assert_array_almost_equal(optimi.optimize(), numpy.ones(2, numpy.float))
def test_simple_gradient_monotony():
startPoint = numpy.empty(2, numpy.float)
startPoint[0] = -1.01
startPoint[-1] = 1.01
optimi = optimizer.StandardOptimizer(
function=Rosenbrock(2),
step=step.GradientStep(),
criterion=criterion.OrComposition(criterion.MonotonyCriterion(0.00001),
criterion.IterationCriterion(10000)),
x0=startPoint,
line_search=line_search.SimpleLineSearch(alpha_step=0.001))
assert_almost_equal(optimi.optimize(),
numpy.ones(2, numpy.float),
decimal=1)
def test_simple_newton():
startPoint = numpy.empty(4, numpy.float)
startPoint[0] = 3.
startPoint[1] = -1.
startPoint[2] = 0.
startPoint[3] = 1.
optimi = optimizer.StandardOptimizer(
function=Powell(),
step=step.NewtonStep(),
criterion=criterion.OrComposition(
criterion.RelativeValueCriterion(0.00000001),
criterion.IterationCriterion(1000)),
x0=startPoint,
line_search=line_search.SimpleLineSearch())
assert_array_almost_equal(optimi.optimize(),
numpy.zeros(4, numpy.float),
decimal=2)
