original_docstring:  Predicts new instances using the trained SST model.
  
              Parameters
              ----------
                  X: pandas.DataFrame or numpy.ndarray
                      The new input values.
              Returns
              -------
                  predictions: numpy.ndarray
                      A matrix with the same number of lines of 'X', where each
                      column represents a target.
          
docstring: Predicts new instances using the trained SST model.
  Parameters
  
  
  
  numpy.ndarray
  A matrix with the same number of lines of 'X', where each
  column represents a target.
docstring_tokens: Predicts new instances using the trained SST model . Parameters numpy . ndarray A matrix with the same number of lines of ' X ' where each column represents a target .
code: def predict(self, X):
          """ Predicts new instances using the trained SST model.
  
              Parameters
              ----------
                  X: pandas.DataFrame or numpy.ndarray
                      The new input values.
              Returns
              -------
                  predictions: numpy.ndarray
                      A matrix with the same number of lines of 'X', where each
                      column represents a target.
          """
          if isinstance(X, pd.core.frame.DataFrame):
              X = X.values
          elif not isinstance(X, np.ndarray):
              raise ValueError('"X" must be a numpy matrix or pandas \
                               DataFrame')
  
          n_rows = X.shape[0]
          base_predictions = np.zeros((n_rows, self.n_targets))
          meta_predictions = np.zeros((n_rows, self.n_targets))
  
          for t in range(self.n_targets):
              base_predictions[:, t] = self._base_models[t].predict(X)
  
          X_aug = np.column_stack((X, base_predictions))
  
          for t in range(self.n_targets):
              meta_predictions[:, t] = self._meta_models[t].predict(X_aug)
  
          return meta_predictions
code_tokens: def predict ( self , X ) : if isinstance ( X , pd . core . frame . DataFrame ) : X = X . values elif not isinstance ( X , np . ndarray ) : raise ValueError ( '"X" must be a numpy matrix or pandas \
                               DataFrame' ) n_rows = X . shape [ 0 ] base_predictions = np . zeros ( ( n_rows , self . n_targets ) ) meta_predictions = np . zeros ( ( n_rows , self . n_targets ) ) for t in range ( self . n_targets ) : base_predictions [ : , t ] = self . _base_models [ t ] . predict ( X ) X_aug = np . column_stack ( ( X , base_predictions ) ) for t in range ( self . n_targets ) : meta_predictions [ : , t ] = self . _meta_models [ t ] . predict ( X_aug ) return meta_predictions
short_docstring: Predicts new instances using the trained SST model.
short_docstring_tokens: Predicts new instances using the trained SST model .
comment: """ Predicts new instances using the trained SST model.
  
              Parameters
              ----------
                  X: pandas.DataFrame or numpy.ndarray
                      The new input values.
              Returns
              -------
                  predictions: numpy.ndarray
                      A matrix with the same number of lines of 'X', where each
                      column represents a target.
          """