Commit 5dd286d0 authored by Reimar H Leike's avatar Reimar H Leike
Browse files

added an InverseGammaLikelihood which is the right likelihood for fitting...

added an InverseGammaLikelihood which is the right likelihood for fitting Gaussian diagonal covariances
parent c7e31fc6
......@@ -46,7 +46,7 @@ from .operators.simple_linear_operators import (
VdotOperator, SumReductionOperator, ConjugationOperator, Realizer,
FieldAdapter, GeometryRemover, NullOperator)
from .operators.energy_operators import (
EnergyOperator, GaussianEnergy, PoissonianEnergy, BernoulliEnergy,
EnergyOperator, GaussianEnergy, PoissonianEnergy, InverseGammaLikelihood, BernoulliEnergy,
Hamiltonian, SampledKullbachLeiblerDivergence)
from .probing import probe_with_posterior_samples, probe_diagonal, \
......@@ -86,6 +86,13 @@ class Linearization(object):
def __rsub__(self, other):
return (-self).__add__(other)
def __rtruediv__(self, other):
return (self.inverse()).__mul__(other)
def inverse(self):
return Linearization(1./self._val,
def __mul__(self, other):
from .sugar import makeOp
if isinstance(other, Linearization):
......@@ -110,6 +110,20 @@ class PoissonianEnergy(EnergyOperator):
metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
return res.add_metric(metric)
class InverseGammaLikelihood(EnergyOperator):
def __init__(self, op, d):
self._op, self._d = op, d
self._domain = d.domain
def apply(self, x):
x = self._op(x)
res = 0.5*(x.log().sum() + (0.5/x).vdot(self._d))
if not isinstance(x, Linearization):
return Field.scalar(res)
metric = SandwichOperator.make(x.jac, makeOp(0.5/(x.val**2)))
return res.add_metric(metric)
class BernoulliEnergy(EnergyOperator):
def __init__(self, p, d):
......@@ -56,6 +56,21 @@ class Energy_Tests(unittest.TestCase):
# energy = ift.QuadraticEnergy(s[0], ift.makeOp(s[1]), s[2])
# ift.extra.check_value_gradient_consistency(energy)
ift.RGSpace(64, distances=.789),
ift.RGSpace([32, 32], distances=.789)],
[4, 78, 23]
def testInverseGammaLikelihood(self, space, seed):
model = self.make_model(
space_key='s1', space=space, seed=seed)['s1']
d = np.random.normal(10, size=space.shape)**2
d = ift.Field.from_global_data(space, d)
energy = ift.InverseGammaLikelihood(ift.exp, d)
ift.extra.check_value_gradient_consistency(energy, model, tol=1e-7)
ift.RGSpace(64, distances=.789),
......@@ -65,7 +80,6 @@ class Energy_Tests(unittest.TestCase):
def testPoissonian(self, space, seed):
model = self.make_model(
space_key='s1', space=space, seed=seed)['s1']
model = ift.exp(model)
d = np.random.poisson(120, size=space.shape)
d = ift.Field.from_global_data(space, d)
energy = ift.PoissonianEnergy(ift.exp, d)
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment