code: def _covariance_matrix(self, type='noise'):
          
          if type == 'sampling':
              return self.sigma**2/(self.n-1)
          elif type == 'noise':
              return 4*self.sigma*N.var(self.rotated(), axis=0)
docs: Constructs the covariance matrix from PCA
          residuals