Commit 48e68395 authored by Philipp Arras's avatar Philipp Arras
Browse files

Cleanup

parent b7bdb43a
from ..operators.energy_operators import InverseGammaLikelihood,Hamiltonian
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from __future__ import absolute_import, division, print_function
from ..compat import *
from ..operators.energy_operators import Hamiltonian, InverseGammaLikelihood
from ..operators.scaling_operator import ScalingOperator
def make_adjust_variances(a,xi,position,samples=[],scaling=None,ic_samp=None):
def make_adjust_variances(a, xi, position, samples=[], scaling=None, ic_samp=None):
""" Creates a Hamiltonian for constant likelihood optimizations.
Constructs a Hamiltonian to solve constant likelihood optimizations of the form
phi = a * xi
under the constraint that phi remains constant.
Constructs a Hamiltonian to solve constant likelihood optimizations of the
form phi = a * xi under the constraint that phi remains constant.
Parameters
----------
a : Operator
......@@ -29,19 +50,19 @@ def make_adjust_variances(a,xi,position,samples=[],scaling=None,ic_samp=None):
A Hamiltonian that can be used for further minimization
"""
d = a * xi
d = a*xi
d = (d.conjugate()*d).real
n = len(samples)
if n>0:
if n > 0:
d_eval = 0.
for i in range(n):
d_eval = d_eval + d(position+samples[i])
d_eval = d_eval / n
d_eval = d_eval + d(position + samples[i])
d_eval = d_eval/n
else:
d_eval = d(position)
x = (a.conjugate()*a).real
if scaling is not None:
x = ScalingOperator(scaling,x.target)(x)
x = ScalingOperator(scaling, x.target)(x)
return Hamiltonian(InverseGammaLikelihood(x,d_eval),ic_samp=ic_samp)
\ No newline at end of file
return Hamiltonian(InverseGammaLikelihood(x, d_eval), ic_samp=ic_samp)
......@@ -56,15 +56,14 @@ class Energy_Tests(unittest.TestCase):
# energy = ift.QuadraticEnergy(s[0], ift.makeOp(s[1]), s[2])
# ift.extra.check_value_gradient_consistency(energy)
@expand(product(
[ift.GLSpace(15),
ift.RGSpace(64, distances=.789),
ift.RGSpace([32, 32], distances=.789)],
[4, 78, 23]
))
@expand(
product([
ift.GLSpace(15),
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']
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)
......
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