ossible centering.

Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
    The preprocessed design matrix.

sqrt_sw : ndarray of shape (n_samples,)
    square roots of sample weights

Returns
-------
covariance : ndarray of shape (n_features, n_features)
    The covariance matrix.
X_mean : ndarray of shape (n_feature,)
    The weighted mean of ``X`` for each feature.

Notes
-----
Since X is sparse it has not been centered in preprocessing, but it has
been scaled by sqrt(sample weights).

When self.fit_intercept is False no centering is done.

The centered X is never actually computed because centering would break
the sparsity of X.
r2