def test_bfgs_madsen(self):
from ch import SumOfSquares
import scipy.optimize
obj = Ch(lambda x : SumOfSquares(Madsen(x = x)) )
def errfunc(x):
obj.x = Ch(x)
return obj.r
def gradfunc(x):
obj.x = Ch(x)
return obj.dr_wrt(obj.x).ravel()
x0 = np.array((3., 1.))
# Optimize with built-in bfgs.
# Note: with 8 iters, this actually requires 14 gradient evaluations.
# This can be verified by setting "disp" to 1.
#tm = time.time()
x1 = scipy.optimize.fmin_bfgs(errfunc, x0, fprime=gradfunc, maxiter=8, disp=0)
#print 'forward: took %.es' % (time.time() - tm,)
self.assertLess(obj.r/2., 0.4)
# Optimize with chumpy's minimize (which uses scipy's bfgs).
obj.x = x0
minimize(fun=obj, x0=[obj.x], method='bfgs', options={'maxiter': 8, 'disp': False})
self.assertLess(obj.r/2., 0.4)
def test_bfgs_madsen(self):
from ch import SumOfSquares
import scipy.optimize
obj = Ch(lambda x: SumOfSquares(Madsen(x=x)))
def errfunc(x):
obj.x = Ch(x)
return obj.r
def gradfunc(x):
obj.x = Ch(x)
return obj.dr_wrt(obj.x).ravel()
x0 = np.array((3., 1.))
# Optimize with built-in bfgs.
# Note: with 8 iters, this actually requires 14 gradient evaluations.
# This can be verified by setting "disp" to 1.
#tm = time.time()
x1 = scipy.optimize.fmin_bfgs(errfunc,
x0,
fprime=gradfunc,
maxiter=8,
disp=0)
#print 'forward: took %.es' % (time.time() - tm,)
self.assertTrue(obj.r / 2. < 0.386599528247)
# Optimize with chumpy's minimize (which uses scipy's bfgs).
obj.x = x0
minimize(fun=obj, x0=[obj.x], method='bfgs', options={'maxiter': 8})
self.assertTrue(obj.r / 2. < 0.386599528247)
def test_nested_select(self):
def beales(x, y):
e1 = 1.5 - x + x * y
e2 = 2.25 - x + x * (y**2)
e3 = 2.625 - x + x * (y**3)
return {'e1': e1, 'e2': e2, 'e3': e3}
x1 = ch.zeros(10)
y1 = ch.zeros(10)
# With a single select this worked
minimize(beales(x1, y1),
x0=[x1[1:4], y1],
method='dogleg',
options={'disp': False})
x2 = ch.zeros(10)
y2 = ch.zeros(10)
# But this used to raise `AttributeError: 'Select' object has no attribute 'x'`
minimize(beales(x2, y2),
x0=[x2[1:8][:3], y2],
method='dogleg',
options={'disp': False})
np.testing.assert_array_equal(x1, x2)
np.testing.assert_array_equal(y1, y2)
def test_dogleg_rosen(self):
obj, freevars = Rosen()
minimize(fun=obj,
x0=freevars,
method='dogleg',
options={'maxiter': 337})
self.assertTrue(freevars[0].r[0] == 1.)
self.assertTrue(freevars[1].r[0] == 1.)
def test_dogleg_madsen(self):
obj = Madsen(x=Ch(np.array((3., 1.))))
minimize(fun=obj,
x0=[obj.x],
method='dogleg',
options={
'maxiter': 34,
'disp': False
})
self.assertTrue(np.sum(obj.r**2) / 2 < 0.386599528247)
def test_nested_select(self):
def beales(x, y):
e1 = 1.5 - x + x*y
e2 = 2.25 - x + x*(y**2)
e3 = 2.625 - x + x*(y**3)
return {'e1': e1, 'e2': e2, 'e3': e3}
x1 = ch.zeros(10)
y1 = ch.zeros(10)
# With a single select this worked
minimize(beales(x1, y1), x0=[x1[1:4], y1], method='dogleg', options={'disp': False})
x2 = ch.zeros(10)
y2 = ch.zeros(10)
# But this used to raise `AttributeError: 'Select' object has no attribute 'x'`
minimize(beales(x2, y2), x0=[x2[1:8][:3], y2], method='dogleg', options={'disp': False})
np.testing.assert_array_equal(x1, x2)
np.testing.assert_array_equal(y1, y2)
def minimize(self, *args, **kwargs):
import optimization
return optimization.minimize(self, *args, **kwargs)
def minimize(self, *args, **kwargs):
import optimization
return optimization.minimize(self, *args, **kwargs)
def test_dogleg_madsen(self):
obj = Madsen(x = Ch(np.array((3.,1.))))
minimize(fun=obj, x0=[obj.x], method='dogleg', options={'maxiter': 34, 'disp': False})
self.assertTrue(np.sum(obj.r**2)/2 < 0.386599528247)
def test_dogleg_rosen(self):
obj, freevars = Rosen()
minimize(fun=obj, x0=freevars, method='dogleg', options={'maxiter': 337, 'disp': False})
self.assertTrue(freevars[0].r[0]==1.)
self.assertTrue(freevars[1].r[0]==1.)
def mg_opt(rf, meshes, current_mesh_idx):
if current_mesh_idx == len(meshes) - 1:
info_green("Solve problem on coarsest grid")
m_h_p1 = minimize(rf)
else:
m_h1 = minimize(rf, options = {"maxiter": 1})
