parameters: (self, X)
return_statement: return np.where(f_star > 0, self.classes_[1], self.classes_[0])
docstring: Perform classification on an array of test vectors X.
  
          Parameters
          ----------
          X : array-like, shape = (n_samples, n_features)
  
          Returns
          -------
          C : array, shape = (n_samples,)
              Predicted target values for X, values are from ``classes_``
docstring_summary: Perform classification on an array of test vectors X.
docstring_tokens: Perform classification on an array of test vectors X .
function: def predict(self, X):
          """Perform classification on an array of test vectors X.
  
          Parameters
          ----------
          X : array-like, shape = (n_samples, n_features)
  
          Returns
          -------
          C : array, shape = (n_samples,)
              Predicted target values for X, values are from ``classes_``
          """
          check_is_fitted(self, ["X_train_", "y_train_", "pi_", "W_sr_", "L_"])
  
          # As discussed on Section 3.4.2 of GPML, for making hard binary
          # decisions, it is enough to compute the MAP of the posterior and
          # pass it through the link function
          K_star = self.kernel_(self.X_train_, X)  # K_star =k(x_star)
          f_star = K_star.T.dot(self.y_train_ - self.pi_)  # Algorithm 3.2,Line 4
  
          return np.where(f_star > 0, self.classes_[1], self.classes_[0])
function_tokens: def predict ( self , X ) : check_is_fitted ( self , [ "X_train_" , "y_train_" , "pi_" , "W_sr_" , "L_" ] ) # As discussed on Section 3.4.2 of GPML, for making hard binary # decisions, it is enough to compute the MAP of the posterior and # pass it through the link function K_star = self . kernel_ ( self . X_train_ , X ) # K_star =k(x_star) f_star = K_star . T . dot ( self . y_train_ - self . pi_ ) # Algorithm 3.2,Line 4 return np . where ( f_star > 0 , self . classes_ [ 1 ] , self . classes_ [ 0 ] )