def test_ls_steepest(self):
x = np.ones(self.model.n)
g = self.model.grad(x)
c1model = C1LineModel(self.model, x, -g)
ls = QuadraticCubicLineSearch(c1model)
with pytest.raises(StopIteration):
ls.next()
np.allclose(ls.iterate, np.zeros(self.model.n))
def test_ls(self):
x = np.array([-4.])
g = np.array([1])
c1model = C1LineModel(self.model, x, g)
ls = QuadraticCubicLineSearch(c1model, step=8)
ls.next()
ls.next()
with pytest.raises(StopIteration):
ls.next()
np.allclose(ls.iterate, np.array([1.69059892324]))
def test_ascent(self):
x = np.ones(self.model.n)
g = self.model.grad(x)
c1model = C1LineModel(self.model, x, g)
with pytest.raises(ValueError):
QuadraticCubicLineSearch(c1model)
def run_demo(model, x, p, step0):
"""Create arrays necessary to display steps of linesearch."""
c1model = C1LineModel(model, x, p)
ls = QuadraticCubicLineSearch(c1model, step=step0)
t = np.linspace(-0.2, 1.2 * ls._step0, 1000)
y = np.empty(t.size)
k = 0
for x in t:
y[k] = c1model.obj(x)
k += 1
plt.figure()
plt.ion()
plt.plot(t, y)
t = np.linspace(0, ls._step0, 1000)
x_p = []
y_p = []
x_p.append(0.)
y_p.append(ls._value)
plt.annotate("$t=0$",
xy=(0, ls._value),
xytext=(-5, 5),
textcoords='offset points',
ha='right',
va='bottom')
x_p.append(ls.step)
y_p.append(ls.trial_value)
plt.scatter(x_p, y_p)
plt.annotate("$t_0=" + str(ls.step) + "$",
xy=(ls.step, ls.trial_value),
xytext=(-5, 5),
textcoords='offset points',
ha='right',
va='bottom')
try:
for k, step in enumerate(ls):
print k, step
if k == 0:
phi = quadratic_interpolant(ls, t)
curve, = plt.plot(t, phi)
last_step = ls._last_step
s = ls.step
else:
phi3 = cubic_interpolant(ls, t, last_step, s)
curve, = plt.plot(t, phi3)
last_step = ls._last_step
s = ls.step
x_p.append(ls.step)
y_p.append(ls.trial_value)
plt.annotate("$t_" + str(k + 1) + "=%3.1f" % ls.step + "$",
xy=(ls.step, ls.trial_value),
xytext=(-5, 5),
textcoords='offset points',
ha='right',
va='bottom')
plt.scatter(x_p, y_p)
plt.pause(3)
curve.remove()
except LineSearchFailure:
pass