ITMO_FS.filters.univariate.pearson_corr

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

Calculates pearson correlation for each feature. Pearson correleation coeficient is a statistic that measures linear correlation between two variables X and Y. It has a value in interval [-1, +1], where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation

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()

//en.wikipedia.org/wiki/Pearson_correlation_coefficient

Examples

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