Commit 610a2f7c authored by Martin Reinecke's avatar Martin Reinecke
Browse files


parent 279e18f7
......@@ -41,25 +41,37 @@ class GaussianEnergy2(ift.Operator):
return ift.Linearization(res.val, res.jac, metric)
class PoissonianEnergy2(ift.Operator):
def __init__(self, d):
def __init__(self, op, d):
super(PoissonianEnergy2, self).__init__()
self._op = op
self._d = d
def __call__(self, x):
x = self._op(x)
res = (x - self._d*mylog(x)).sum()
metric = ift.SandwichOperator.make(x.jac, ift.makeOp(1./x.val))
return ift.Linearization(res.val, res.jac, metric)
class MyHamiltonian(ift.Energy):
class MyHamiltonian(ift.Operator):
def __init__(self, lh):
super(MyHamiltonian, self).__init__()
self._lh = lh
self._prior = GaussianEnergy2()
pvar = ift.Linearization.make_var(position)
self._res = self._lh(pvar)+self._prior(pvar)
def __call__(self, x):
return self._lh(x) + self._prior(x)
class EnergyAdapter(ift.Energy):
def __init__(self, position, op):
super(MyHamiltonian, self).__init__(position)
super(EnergyAdapter, self).__init__(position)
self._op = op
prior = GaussianEnergy2()
pvar = ift.Linearization.make_var(position)
self._res = op(pvar)+prior(pvar)
self._res = op(pvar)
def at(self, position):
return MyHamiltonian(position, self._op)
return EnergyAdapter(position, self._op)
def value(self):
......@@ -73,15 +85,6 @@ class MyHamiltonian(ift.Energy):
def metric(self):
return self._res.metric
class OperatorSequence(ift.Operator):
def __init__(self, ops):
super(OperatorSequence, self).__init__()
self._ops = ops
def __call__(self, x):
for op in reversed(self._ops):
x = op(x)
return x
def get_2D_exposure():
x_shape, y_shape = position_space.shape
......@@ -149,14 +152,15 @@ if __name__ == '__main__':
data = ift.Field.from_global_data(d_space, data)
# Compute likelihood and Hamiltonian
likelihood = PoissonianEnergy2(data)
likelihood = PoissonianEnergy2(lamb, data)
ic_cg = ift.GradientNormController(iteration_limit=50)
ic_newton = ift.GradientNormController(name='Newton', iteration_limit=50,
minimizer = ift.RelaxedNewton(ic_newton)
# Minimize the Hamiltonian
H = MyHamiltonian(position, OperatorSequence([likelihood,lamb]))
H = MyHamiltonian(likelihood)
H = EnergyAdapter(position, H)
H = H.make_invertible(ic_cg)
H, convergence = minimizer(H)
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