parameters: (self, nk, xk, sk)
docstring: Estimate the full 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, n_features)
docstring_summary: Estimate the full Wishart distribution parameters.
docstring_tokens: Estimate the full Wishart distribution parameters .
function: def _estimate_wishart_full(self, nk, xk, sk):
          """Estimate the full 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, n_features)
          """
          _, 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
  
          self.covariances_ = np.empty((self.n_components, n_features,
                                        n_features))
  
          for k in range(self.n_components):
              diff = xk[k] - self.mean_prior_
              self.covariances_[k] = (self.covariance_prior_ + nk[k] * sk[k] +
                                      nk[k] * self.mean_precision_prior_ /
                                      self.mean_precision_[k] * np.outer(diff,
                                                                         diff))
  
          # Contrary to the original bishop book, we normalize the covariances
          self.covariances_ /= (
              self.degrees_of_freedom_[:, np.newaxis, np.newaxis])
function_tokens: def _estimate_wishart_full ( 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 self . covariances_ = np . empty ( ( self . n_components , n_features , n_features ) ) for k in range ( self . n_components ) : diff = xk [ k ] - self . mean_prior_ self . covariances_ [ k ] = ( self . covariance_prior_ + nk [ k ] * sk [ k ] + nk [ k ] * self . mean_precision_prior_ / self . mean_precision_ [ k ] * np . outer ( diff , diff ) ) # Contrary to the original bishop book, we normalize the covariances self . covariances_ /= ( self . degrees_of_freedom_ [ : , np . newaxis , np . newaxis ] )