def test_ncg_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = NonlinearConjugateGradient(obj.pars, obj.f, obj.fprime, args=args)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_bfgs_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Bfgs(obj.pars, obj.f, obj.fprime, args=args)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_nesterov_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Nesterov(obj.pars, obj.fprime, steprate=0.1, args=args)
for i, info in enumerate(opt):
if i > 750:
break
assert obj.solved(), 'did not find solution'
def test_xnes_lr():
obj = LogisticRegression(seed=10101)
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Xnes(obj.pars, obj.f, args=args)
for i, info in enumerate(opt):
if i > 100:
break
assert obj.solved(), 'did not find solution'
def test_ncg_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = NonlinearConjugateGradient(obj.pars, obj.f, obj.fprime, args=args)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_smd_lr():
obj = LogisticRegression(seed=10101)
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Smd(obj.pars, obj.f, obj.fprime, obj.f_Hp, args=args, eta0=0.1)
for i, info in enumerate(opt):
if i > 150:
break
assert obj.solved(), 'did not find solution'
def test_asgd_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Asgd(obj.pars, obj.fprime, eta0=0.2, lmbd=1e-2, t0=0.1, args=args)
for i, info in enumerate(opt):
if i > 3000:
break
assert obj.solved(0.15), 'did not find solution'
def test_asgd_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Asgd(obj.pars, obj.fprime, eta0=0.2, lmbd=1e-2, t0=0.1, args=args)
for i, info in enumerate(opt):
if i > 3000:
break
assert obj.solved(0.15), 'did not find solution'
def test_sbfgs_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Sbfgs(obj.pars, obj.f, obj.fprime, args=args)
for i, info in enumerate(opt):
if i > 50:
break
assert obj.solved(), 'did not find solution'
def test_xnes_lr():
obj = LogisticRegression(seed=10101)
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Xnes(obj.pars, obj.f, args=args)
for i, info in enumerate(opt):
if i > 100:
break
assert obj.solved(), "did not find solution"
def test_adadelta_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Adadelta(obj.pars, obj.fprime, 0.9, args=args)
for i, info in enumerate(opt):
print obj.f(opt.wrt, obj.X, obj.Z)
if i > 3000:
break
assert obj.solved(0.15), 'did not find solution'
def test_rprop_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Rprop(obj.pars, obj.f, obj.fprime, step_shrink=0.1, step_grow=1.2,
min_step=1e-6, max_step=0.1, args=args)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_gd_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = GradientDescent(
obj.pars, obj.fprime, step_rate=0.01, momentum=.9, args=args)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_gd_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = GradientDescent(obj.pars,
obj.fprime,
step_rate=0.01,
momentum=.9,
args=args)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
def test_rprop_lr():
obj = LogisticRegression()
args = itertools.repeat(((obj.X, obj.Z), {}))
opt = Rprop(obj.pars,
obj.fprime,
step_shrink=0.1,
step_grow=1.2,
min_step=1e-6,
max_step=0.1,
args=args)
for i, info in enumerate(opt):
if i > 500:
break
assert obj.solved(), 'did not find solution'
