parameters: (self, data)
return_statement: return covariance_corrected
docstring: Apply a correction to raw Minimum Covariance Determinant estimates.
  
          Correction using the empirical correction factor suggested
          by Rousseeuw and Van Driessen in [Rouseeuw1984]_.
  
          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.
  
          Returns
          -------
          covariance_corrected : array-like, shape (n_features, n_features)
              Corrected robust covariance estimate.
docstring_summary: Apply a correction to raw Minimum Covariance Determinant estimates.
docstring_tokens: Apply a correction to raw Minimum Covariance Determinant estimates .
function: def correct_covariance(self, data):
          """Apply a correction to raw Minimum Covariance Determinant estimates.
  
          Correction using the empirical correction factor suggested
          by Rousseeuw and Van Driessen in [Rouseeuw1984]_.
  
          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.
  
          Returns
          -------
          covariance_corrected : array-like, shape (n_features, n_features)
              Corrected robust covariance estimate.
  
          """
          correction = np.median(self.dist_) / chi2(data.shape[1]).isf(0.5)
          covariance_corrected = self.raw_covariance_ * correction
          self.dist_ /= correction
          return covariance_corrected
function_tokens: 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