parameters: (self, nk, xk, sk)
docstring: Estimate the spherical Wishart distribution parameters.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          nk : array-like, shape (n_components,)
  
          xk : array-like, shape (n_components, n_features)
  
          sk : array-like, shape (n_components,)
docstring_summary: Estimate the spherical Wishart distribution parameters.
docstring_tokens: Estimate the spherical Wishart distribution parameters .
function: def _estimate_wishart_spherical(self, nk, xk, sk):
          """Estimate the spherical Wishart distribution parameters.
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_features)
  
          nk : array-like, shape (n_components,)
  
          xk : array-like, shape (n_components, n_features)
  
          sk : array-like, shape (n_components,)
          """
          _, n_features = xk.shape
  
          # Warning : in some Bishop book, there is a typo on the formula 10.63
          # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk`
          # is the correct formula
          self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk
  
          diff = xk - self.mean_prior_
          self.covariances_ = (
              self.covariance_prior_ + nk * (
                  sk + self.mean_precision_prior_ / self.mean_precision_ *
                  np.mean(np.square(diff), 1)))
  
          # Contrary to the original bishop book, we normalize the covariances
          self.covariances_ /= self.degrees_of_freedom_
function_tokens: def _estimate_wishart_spherical ( self , nk , xk , sk ) : _ , n_features = xk . shape # Warning : in some Bishop book, there is a typo on the formula 10.63 # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` # is the correct formula self . degrees_of_freedom_ = self . degrees_of_freedom_prior_ + nk diff = xk - self . mean_prior_ self . covariances_ = ( self . covariance_prior_ + nk * ( sk + self . mean_precision_prior_ / self . mean_precision_ * np . mean ( np . square ( diff ) , 1 ) ) ) # Contrary to the original bishop book, we normalize the covariances self . covariances_ /= self . degrees_of_freedom_