Source code for cuqi.implicitprior._regularizedUnboundedUniform

from cuqi.implicitprior import RegularizedGaussian
from cuqi.distribution import Distribution, Gaussian

import numpy as np




class RegularizedUnboundedUniform(RegularizedGaussian):
        """ Implicit Regularized Unbounded Uniform. 

        Defines a so-called implicit prior with implicit regularization on a Gaussian distribution with zero precision.
        The regularization can be defined in the form of a proximal operator or a projector.
        Alternatively, preset constraints and regularization can be used.

        For regularization of the form f(x), provide a single proximal operator.

        Can be used as a prior in a posterior which can be sampled with the RegularizedLinearRTO sampler.

        Alias for :class:`~cuqi.implicitprior.RegularizedGaussian` with zero mean and zero sqrtprec.

        For more details on implicit regularized Gaussian see the following paper:

        [1] Everink, Jasper M., Yiqiu Dong, and Martin S. Andersen. "Sparse Bayesian inference with regularized
        Gaussian distributions." Inverse Problems 39.11 (2023): 115004.

        Parameters
        ----------
        geometry : :class:`~cuqi.geometry.Geometry` or integer
                The geometry of the underlying variable. Defines the dimension of the distribution. 

        proximal : callable f(x, scale) or None
                Euclidean proximal operator f of the regularization function g, that is, a solver for the optimization problem
                min_z 0.5||x-z||_2^2+scale*g(x).

        projector : callable f(x) or None
                Euclidean projection onto the constraint C, that is, a solver for the optimization problem
                min_(z in C) 0.5||x-z||_2^2.

        constraint : string or None
                Preset constraints. Can be set to "nonnegativity" and "box". Required for use in Gibbs.
                For "box", the following additional parameters can be passed:
                lower_bound : array_like or None
                        Lower bound of box, defaults to zero
                upper_bound : array_like
                        Upper bound of box, defaults to one

        regularization : string or None
                Preset regularization. Can be set to "l1". Required for use in Gibbs in future update.
                For "l1" or "tv", the following additional parameters can be passed:
                strength : scalar
                        Regularization parameter, i.e., strength*||Lx||_1, defaults to one

        """


        def __init__(self, geometry, proximal = None, projector = None, constraint = None, regularization = None, **kwargs):
                
                args = {"lower_bound" : kwargs.pop("lower_bound", None),
                        "upper_bound" : kwargs.pop("upper_bound", None),
                        "strength" : kwargs.pop("strength", None)}

                # Underlying explicit Gaussian
                # This line throws a warning due trying to applying get_sqrtprec_from_sqrtprec to an all zero matrix  
                self._gaussian = Gaussian(mean = np.zeros(geometry.par_dim), sqrtprec = np.zeros((geometry.par_dim,geometry.par_dim)), geometry = geometry, **kwargs) 

                # Init from abstract distribution class
                super(Distribution, self).__init__(**kwargs)

                self._force_list = False
                self._parse_regularization_input_arguments(proximal, projector, constraint, regularization, args)