class V_TV():
""" Definite Vectorial Total Variation regularization from Total Variation class """
def __init__(self, Vm, parameters=[]):
""" Vm = FunctionSpace for the parameters m1, and m2 """
self.parameters = {}
self.parameters['k'] = 1.0
self.parameters['eps'] = 1e-2
self.parameters['amg'] = 'default'
self.parameters['nb_param'] = 2
self.parameters['use_i'] = False
self.parameters['print'] = False
self.parameters.update(parameters)
n = self.parameters['nb_param']
use_i = self.parameters['use_i']
assert not ((not use_i) * (n > 2))
if not use_i:
VmVm = createMixedFS(Vm, Vm)
else:
if self.parameters['print']:
print '[V_TV] Using createMixedFSi'
Vms = []
for ii in range(n):
Vms.append(Vm)
VmVm = createMixedFSi(Vms)
self.parameters['Vm'] = VmVm
self.regTV = TV(self.parameters)
if not use_i:
self.m1, self.m2 = Function(Vm), Function(Vm)
self.m = Function(VmVm)
def isTV(self):
return True
def isPD(self):
return False
def costab(self, m1, m2):
assign(self.m.sub(0), m1)
assign(self.m.sub(1), m2)
return self.regTV.cost(self.m)
def costabvect(self, m1, m2):
setfct(self.m1, m1)
setfct(self.m2, m2)
return self.costab(self.m1, self.m2)
def costabvecti(self, m):
return self.regTV.cost(m)
def gradab(self, m1, m2):
assign(self.m.sub(0), m1)
assign(self.m.sub(1), m2)
return self.regTV.grad(self.m)
def gradabvect(self, m1, m2):
setfct(self.m1, m1)
setfct(self.m2, m2)
return self.gradab(self.m1, self.m2)
def gradabvecti(self, m):
return self.regTV.grad(m)
def assemble_hessianab(self, m1, m2):
setfct(self.m1, m1)
setfct(self.m2, m2)
assign(self.m.sub(0), self.m1)
assign(self.m.sub(1), self.m2)
self.regTV.assemble_hessian(self.m)
def assemble_hessianabi(self, m):
self.regTV.assemble_hessian(m)
def hessianab(self, m1h, m2h):
""" m1h, m2h = Vector(V) """
setfct(self.m1, m1h)
setfct(self.m2, m2h)
assign(self.m.sub(0), self.m1)
assign(self.m.sub(1), self.m2)
return self.regTV.hessian(self.m.vector())
def hessianabi(self, mh):
return self.regTV.hessian(mh)
def getprecond(self):
return self.regTV.getprecond()
