ITMO_FS.filters.univariate.f_ratio_measure

ITMO_FS.filters.univariate.f_ratio_measure(X, y)

Calculates Fisher score for features.

Parameters:

• X (numpy array, shape (n_samples, n_features)) – The input samples.
• y (numpy array, shape (n_samples, )) – The classes for the samples.

Returns:

Return type:

Score for each feature as a numpy array, shape (n_features, )

See also

https()

//papers.nips.cc/paper/2909-laplacian-score-for-feature-selection.pdf

Examples

>>> import sklearn.datasets as datasets
>>> from ITMO_FS.filters.univariate import f_ratio_measure
>>> X, y = datasets.make_classification(n_samples=200, n_features=7, shuffle=False)
>>> scores = f_ratio_measure(X, y)
>>> print(scores)