postprocessor.core.multisignal.mi.miParameters¶
- class miParameters(**kwargs)[source]¶
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[PosixPath, 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.