code: def decision_function(self, X, **kwargs):
          "Decision function i.e. the raw data of the prediction"
          if X is None:
              return self._hy_test
          X = self.convert_features(X)
          if len(X) < self.nvar:
              _ = 'Number of variables differ, trained with %s given %s' % (self.nvar, len(X))
              raise RuntimeError(_)
          hist = self._hist
          for node in hist:
              if node.height:
                  node.eval(hist)
              else:
                  node.eval(X)
          node.normalize()
          r = node.hy
          for i in hist[:-1]:
              i.hy = None
              i.hy_test = None
          gc.collect()
          return f decision_function(self, X, **kwargs):
          "Decision function i.e. the raw data of the prediction"
          if X is None:
              return self._hy_test
          X = self.convert_features(X)
          if len(X) < self.nvar:
              _ = 'Number of variables differ, trained with %s given %s' % (self.nvar, len(X))
              raise RuntimeError(_)
          hist = self._hist
          for node in hist:
              if node.height:
                  node.eval(hist)
              else:
                  node.eval(X)
          node.normalize()
          r = node.hy
          for i in hist[:-1]:
              i.hy = None
              i.hy_test = None
          gc.collect()
          return r
docs: Decision function i.e. the raw data of the prediction