fuzzy-rough-learn API

This is the full API documentation of fuzzy-rough-learn.

Neighbours

The frlearn.neighbours subpackage implements nearest neighbour algorithms.

Classifiers

neighbours.classifiers.FRNN(*[, …])	Implementation of Fuzzy Rough Nearest Neighbour (FRNN) classification (FRNN).
neighbours.classifiers.FROVOCO([nn_search])	Implementation of the Fuzzy Rough OVO COmbination (FROVOCO) ensemble classifier.
neighbours.classifiers.FRONEC([Q_type, …])	Implementation of the Fuzzy ROugh NEighbourhood Consensus (FRONEC) multilabel classifier.

Nearest Neighbour Search

neighbours.neighbour_search.NNSearch(**kwargs)	Abstract base class for nearest neighbour searches.
neighbours.neighbour_search.BallTree(*[, …])	Nearest neighbour search with a Ball tree.
neighbours.neighbour_search.KDTree(*[, …])	Nearest neighbour search with a KD-tree.

Preprocessors

neighbours.preprocessors.FRFS([n_features, …])	Implementation of the Fuzzy Rough Feature Selection (FRFS) preprocessor.
neighbours.preprocessors.FRPS([owa_weights, …])	Implementation of the Fuzzy Rough Prototype Selection (FRPS) preprocessor.

Utils

The frlearn.utils subpackage implements utility functions used in the other modules.

numpy utils

utils.np_utils.first(a, k[, axis])	Returns the k first values of a along the specified axis.
utils.np_utils.last(a, k[, axis])	Returns the k last values of a along the specified axis, in reverse order.
utils.np_utils.least(a, k[, axis])	Returns the k least values of a along the specified axis, in order.
utils.np_utils.greatest(a, k[, axis])	Returns the k greatest values of a along the specified axis, in order.
utils.np_utils.div_or(x, y[, fallback])	Divides x by y, replacing np.nan values with fallback.

OWA Operators

utils.owa_operators.OWAOperator(f, *[, …])

Ordered Weighted Averaging (OWA) operator, which can be applied to an array to obtain its ordered weighted average.