parameters: (self, X)
return_statement: return log_like
docstring: Compute the log-likelihood of each sample
  
          Parameters
          ----------
          X : array, shape (n_samples, n_features)
              The data
  
          Returns
          -------
          ll : array, shape (n_samples,)
              Log-likelihood of each sample under the current model
docstring_summary: Compute the log-likelihood of each sample
docstring_tokens: Compute the log - likelihood of each sample
function: def score_samples(self, X):
          """Compute the log-likelihood of each sample
  
          Parameters
          ----------
          X : array, shape (n_samples, n_features)
              The data
  
          Returns
          -------
          ll : array, shape (n_samples,)
              Log-likelihood of each sample under the current model
          """
          check_is_fitted(self)
  
          Xr = X - self.mean_
          precision = self.get_precision()
          n_features = X.shape[1]
          log_like = -.5 * (Xr * (np.dot(Xr, precision))).sum(axis=1)
          log_like -= .5 * (n_features * log(2. * np.pi)
                            - fast_logdet(precision))
          return log_like
function_tokens: def score_samples ( self , X ) : check_is_fitted ( self ) Xr = X - self . mean_ precision = self . get_precision ( ) n_features = X . shape [ 1 ] log_like = - .5 * ( Xr * ( np . dot ( Xr , precision ) ) ) . sum ( axis = 1 ) log_like -= .5 * ( n_features * log ( 2. * np . pi ) - fast_logdet ( precision ) ) return log_like