# 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 .
#
# 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):
""" 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.
Parameters
----------
a : Operator
Operator which gives the amplitude when evaluated at a position
xi : Operator
Operator which gives the excitation when evaluated at a position
postion : Field, MultiField
Position of the whole problem
samples : Field, MultiField
Residual samples of the whole problem
scaling : Float
Optional rescaling of the Likelihood
ic_samp : Controller
Iteration Controller for Hamiltonian
Returns
-------
Hamiltonian
A Hamiltonian that can be used for further minimization
"""
d = a*xi
d = (d.conjugate()*d).real
n = len(samples)
if n > 0:
d_eval = 0.
for i in range(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)
return Hamiltonian(InverseGammaLikelihood(x, d_eval), ic_samp=ic_samp)