magni.cs.reconstruction.it._config module

Module providing configuration options for the magni.cs.reconstruction.it subpackage.

See also

magni.cs.reconstruction._config.Configger

The Configger class used

Notes

This module instantiates the Configger class provided by magni.utils.config. The configuration options are the following:

iterations : int

The maximum number of iterations to do (the default is 300).

kappa : {‘fixed’, ‘adaptive’}

The method used to calculate the step-size (relaxation) parameter kappa.

kappa_fixed : float

The step-size (relaxation parameter) used in the algorithm when a fixed step-size is used (the default is 0.65).

precision_float : {np.float, np.float16, np.float32, np.float64, np.float128}

The floating point precision used for the computations (the default is np.float64).

report_history : bool

The indicator of whether or not to return the progress history along with the result (the default is False).

stop_criterion : str

The stop criterion to use in the iterations (the default is residual_measurements_ratio).

threshold : {‘far’, ‘fixed’}

The method used for calculating the threshold value.

threshold_fixed : int

The number of non-zero coefficients in the signal vector when this number is assumed fixed.

threshold_operator : {‘hard’, ‘soft’, ‘weighted_hard’, ‘weighted_soft’, ‘none’}

The threshold operator used in the backprojection step.

threshold_weights : ndarray

Array of weights to be used in one of the weighted threshold operators (the default is array([[1]]), which implies that all coefficients are weighted equally).

tolerance : float

The least acceptable ratio of residual to measurements (in 2-norm) to break the interations (the default is 0.001).

true_solution : ndarray or None

The true solution to allow for tracking the convergence of the algorithm in the artificial setup where the true solution is known a-priori (the default is None, which implies that no true solution tracking is used).

warm_start : ndarray

The initial guess of the solution vector (the default is None, which implies that a vector of zeros is used).