InceptionTimeClassifer_module

Classes

TimeSeriesStandardScaler	Base class for all estimators in scikit-learn.
InceptionTimeClassifierWrapper
InceptionTimeClassifier_class	Initializes the InceptionTimeClassifier_class.

Module Contents

class InceptionTimeClassifer_module.TimeSeriesStandardScaler(epsilon=1e-06)

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

• setting and getting parameters used by GridSearchCV and friends;

• textual and HTML representation displayed in terminals and IDEs;

• estimator serialization;

• parameters validation;

• data validation;

• feature names validation.

Read more in the User Guide.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples

>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])

scaler

imputer

epsilon = 1e-06

fit(X, y=None)

transform(X)

class InceptionTimeClassifer_module.InceptionTimeClassifierWrapper

Bases: aeon.classification.deep_learning.InceptionTimeClassifier

class InceptionTimeClassifer_module.InceptionTimeClassifier_class(ml_grid_object: ml_grid.pipeline.data.pipe)

Initializes the InceptionTimeClassifier_class.

Parameters:

ml_grid_object (pipe) – An instance of the main data pipeline object.

algorithm_implementation: aeon.classification.deep_learning.InceptionTimeClassifier

method_name: str

parameter_space: Dict[str, List[Any]]