def test_xnes_quadratic():
obj = Quadratic()
opt = Xnes(obj.pars, obj.f)
for i, info in enumerate(opt):
if i > 5000:
break
assert obj.solved(), 'did not find solution'
def test_asgd_quadratic():
obj = Quadratic()
opt = Asgd(obj.pars, obj.fprime, eta0=0.01, lmbd=0.2, t0=0.01)
for i, info in enumerate(opt):
if i > 10000:
break
assert obj.solved(0.1), 'did not find solution'
def test_gd_quadratic():
obj = Quadratic()
opt = GradientDescent(obj.pars, obj.fprime, step_rate=0.01, momentum=.9)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_lbfgs_quadratic():
obj = Quadratic()
opt = Lbfgs(obj.pars, obj.f, obj.fprime)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_ncg_quadratic():
obj = Quadratic()
opt = NonlinearConjugateGradient(obj.pars, obj.f, obj.fprime)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_bfgs_quadratic():
obj = Quadratic()
opt = Bfgs(obj.pars, obj.f, obj.fprime)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_cg_explicit_hessian():
obj = Quadratic()
opt = ConjugateGradient(obj.pars, obj.H, obj.b)
for i, info in enumerate(opt):
if i > 10:
break
assert obj.solved()
def test_xnes_quadratic():
obj = Quadratic()
opt = Xnes(obj.pars, obj.f)
for i, info in enumerate(opt):
if i > 5000:
break
assert obj.solved(), "did not find solution"
def test_ncg_quadratic():
obj = Quadratic()
opt = NonlinearConjugateGradient(obj.pars, obj.f, obj.fprime)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_nesterov_quadratic():
obj = Quadratic()
opt = Nesterov(obj.pars, obj.fprime, steprate=0.01)
for i, info in enumerate(opt):
if i > 750:
break
assert obj.solved(), 'did not find solution'
def test_asgd_quadratic():
obj = Quadratic()
opt = Asgd(obj.pars, obj.fprime, eta0=0.01, lmbd=0.2, t0=0.01)
for i, info in enumerate(opt):
if i > 10000:
break
assert obj.solved(0.1), 'did not find solution'
def test_cg_preconditioning():
obj = Quadratic()
precond = np.array([[1,0], [0, 1e-2]])
opt = ConjugateGradient(obj.pars, obj.H, obj.b, precond=precond)
for i, info in enumerate(opt):
if i > 10:
break
assert obj.solved()
def test_rprop_quadratic():
obj = Quadratic()
opt = Rprop(obj.pars, obj.f, obj.fprime, step_shrink=0.1, step_grow=1.2,
min_step=1e-6, max_step=0.1)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_gd_quadratic():
obj = Quadratic()
opt = GradientDescent(
obj.pars, obj.fprime, step_rate=0.01, momentum=.9)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_cg_implicit_hessian():
obj = Quadratic()
def f_Hp(p):
return obj.f_Hp(obj.pars, p)
opt = ConjugateGradient(obj.pars, f_Hp=f_Hp, b=obj.b)
for i, info in enumerate(opt):
if i > 10:
break
assert obj.solved()
def test_smd_quadratic():
obj = Quadratic()
# TODO: I don't know why these parameters work, but they do.
opt = Smd(obj.pars, obj.f, obj.fprime, obj.f_Hp, eta0=1e-1, mu=2e-4,
lmbd=.5)
for i, info in enumerate(opt):
print obj.pars
if i > 100:
break
assert obj.solved(), 'did not find solution'
def test_rprop_quadratic():
obj = Quadratic()
opt = Rprop(obj.pars,
obj.fprime,
step_shrink=0.1,
step_grow=1.2,
min_step=1e-6,
max_step=0.1)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'