Acoular 24.07 documentation


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class acoular.fbeamform.BeamformerGIB

Bases: BeamformerEig

Beamforming GIB methods with different normalizations.

See [15] for details.

unit_mult = Float(1e9, desc='unit multiplier')

Unit multiplier for evaluating, e.g., nPa instead of Pa. Values are converted back before returning. Temporary conversion may be necessary to not reach machine epsilon within fitting method algorithms. Defaults to 1e9.

max_iter = Int(10, desc='maximum number of iterations')

Maximum number of iterations, tradeoff between speed and precision; defaults to 10

method = Trait(

Type of fit method to be used (‘Suzuki’, ‘LassoLars’, ‘LassoLarsCV’, ‘LassoLarsBIC’, ‘OMPCV’ or ‘NNLS’, defaults to ‘Suzuki’). These methods are implemented in the scikit-learn module.

alpha = Range(0.0, 1.0, 0.0, desc='Lasso weight factor')

Weight factor for LassoLars method, defaults to 0.0.

pnorm = Float(1, desc='Norm for regularization')

Norm to consider for the regularization in InverseIRLS and Suzuki methods defaults to L-1 Norm

beta = Float(0.9, desc='fraction of sources maintained')

Beta - Fraction of sources maintained after each iteration defaults to 0.9

eps_perc = Float(0.05, desc='regularization parameter')

eps - Regularization parameter for Suzuki algorithm defaults to 0.05.

r_diag_norm = Enum(

Energy normalization in case of diagonal removal not implemented for inverse methods.

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