'Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested
by Rousseeuw and Van Driessen in [RVD]_.
Parameters
data : array-like, shape (n_samples, n_features)
The data matrix, with p features and n samples.
The data set must be the one which was used to compute
the raw estimates.
References
.. [RVD] `A Fast Algorithm for the Minimum Covariance
Determinant Estimator, 1999, American Statistical Association
and the American Society for Quality, TECHNOMETRICS`
Returns
covariance_corrected : array-like, shape (n_features, n_features)
Corrected robust covariance estimate.'

def correct_covariance(self, data):

    correction = (np.median(self.dist_) / chi2(data.shape[1]).isf(0.5))
    covariance_corrected = (self.raw_covariance_ * correction)
    self.dist_ /= correction
    return covariance_corrected
