diff --git a/nifty5/library/adjust_variances.py b/nifty5/library/adjust_variances.py index fb5363a1b2a05571e88eabe56911806116def966..7ef27c586fde0381f4e7797f4028db07f338f304 100644 --- a/nifty5/library/adjust_variances.py +++ b/nifty5/library/adjust_variances.py @@ -1,10 +1,10 @@ -from ..operators.energy_operators import InverseGammaLikelihood +from ..operators.energy_operators import InverseGammaLikelihood,Hamiltonian from ..operators.scaling_operator import ScalingOperator -def make_adjust_variances(a,xi,position,samples=[],scaling=None): - """ Creates a Likelihood for constant likelihood optimizations. +def make_adjust_variances(a,xi,position,samples=[],scaling=None,ic_samp=None): + """ Creates a Hamiltonian for constant likelihood optimizations. - Constructs a Likelihood to solve constant likelihood optimizations of the form + Constructs a Hamiltonian to solve constant likelihood optimizations of the form phi = a * xi under the constraint that phi remains constant. @@ -20,11 +20,13 @@ def make_adjust_variances(a,xi,position,samples=[],scaling=None): Residual samples of the whole Problem scaling : Float Optional rescaling of the Likelihood + ic_samp : Controller + Iteration Controller for Hamiltonian Returns ------- - InverseGammaLikelihood - A Likelihood that can be used for further minimization + Hamiltonian + A Hamiltonian that can be used for further minimization """ d = a * xi @@ -42,4 +44,4 @@ def make_adjust_variances(a,xi,position,samples=[],scaling=None): if scaling is not None: x = ScalingOperator(scaling,x.target)(x) - return InverseGammaLikelihood(x,d_eval) \ No newline at end of file + return Hamiltonian(InverseGammaLikelihood(x,d_eval),ic_samp=ic_samp) \ No newline at end of file