docs: def predict(self, test_data, **kwargs):
          """
          Adjust new input by the values in the training data
          """
          if test_data.shape[1]!=self.data.shape[1]:
              raise Exception("Test data has different number of columns than training data.")
          for i in xrange(0,test_data.shape[1]):
              test_data.loc[:,i] = test_data.icol(i) - self.column_means[i]
              if int(self.column_stdevs[i])!=0:
                  test_data.loc[:,i] = test_data.icol(i) / self.column_stdevs[i]
          return test_data