postprocessor.core.multisignal.mi.miParameters

class miParameters(**kwargs)

Bases: ParametersABC

Parameters for the ‘mi’ process

Parameters for the ‘mi’ process.

Attributes

overtime: boolean (default: True)

If True, calculate the mutual information as a function of the duration of the time series, by finding the mutuation information for all possible sub-time series that start from t= 0.

n_bootstraps: int, optional (default: 100)

The number of bootstraps used to estimate errors.

ci: 1x2 array or list, optional (default: [0.25, 0.75])

The lower and upper confidence intervals.

E.g. [0.25, 0.75] for the interquartile range

Crange: array, optional

An array of potential values for the C parameter of the support vector machine and from which the optimal value of C will be chosen.

If None, np.logspace(-3, 3, 10) is used. This range should be increased if the optimal C is one of the boundary values.

See https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

gammarange: array, optional

An array of potential values for the gamma parameter for the radial basis function kernel of the support vector machine and from which the optimal value of gamma will be chosen.

If None, np.logspace(-3, 3, 10) is used. This range should be increased if the optimal gamma is one of the boundary values.

See https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html

train_test_split_seeding: boolean, optional (default: False)

If True, force a random state for the train-test split in each bootstrap. This is useful in case the user requires reproducibility e.g. code testing.

See https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

Methods

from_yaml(source)	Returns instance from a yaml filename or stdin
to_dict([iterable])	Recursive function to return a nested dictionary of the attributes of the class instance.
to_yaml([path])	Returns a yaml stream of the attributes of the class instance.
update(name, new_value)	Update values recursively if name is a dictionary, replace data where existing found or add if not.

default
from_dict

Defines parameters as attributes

Methods

from_yaml(source)	Returns instance from a yaml filename or stdin
to_dict([iterable])	Recursive function to return a nested dictionary of the attributes of the class instance.
to_yaml([path])	Returns a yaml stream of the attributes of the class instance.
update(name, new_value)	Update values recursively if name is a dictionary, replace data where existing found or add if not.

default
from_dict

__init__(**kwargs)

Defines parameters as attributes

Methods

__init__(**kwargs)	Defines parameters as attributes
default(**kwargs)
from_dict(d)
from_yaml(source)	Returns instance from a yaml filename or stdin
to_dict([iterable])	Recursive function to return a nested dictionary of the attributes of the class instance.
to_yaml([path])	Returns a yaml stream of the attributes of the class instance.
update(name, new_value)	Update values recursively if name is a dictionary, replace data where existing found or add if not.

classmethod from_yaml(source)

Returns instance from a yaml filename or stdin

to_dict(iterable='null')

Recursive function to return a nested dictionary of the attributes of the class instance.

Return type

Dict

to_yaml(path=None)

Returns a yaml stream of the attributes of the class instance. If path is provided, the yaml stream is saved there.

Parameters

pathUnion[Path, str]

Output path.

update(name, new_value)

Update values recursively if name is a dictionary, replace data where existing found or add if not. It warns against type changes.

If the existing structure under name is a dictionary, it looks for the first occurrence and modifies it accordingly.

If a leaf node that is to be changed is a collection, it adds the new elements.