code: def olympic_sprints(data_set='rogers_girolami_data'):
      
      X = np.zeros((0, 2))
      Y = np.zeros((0, 1))
      cats = {}
      for i, dataset in enumerate([olympic_100m_men,
                                olympic_100m_women,
                                olympic_200m_men,
                                olympic_200m_women,
                                olympic_400m_men,
                                olympic_400m_women]):
          data = dataset()
          year = data['X']
          time = data['Y']
          X = np.vstack((X, np.hstack((year, np.ones_like(year)*i))))
          Y = np.vstack((Y, time))
          cats[dataset.__name__] = i
      data['X'] = X
      data['Y'] = Y
      data['info'] = "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning."
      return data_details_return({
          'X': X,
          'Y': Y,
          'covariates' : [decimalyear('year', '%Y'), discrete(cats, 'event')],
          'response' : ['time'],
          'info': "Olympics sprint event winning for men and women to 2008. Data is from Rogers and Girolami's First Course in Machine Learning.",
          'output_info': {
            0:'100m Men',
            1:'100m Women',
            2:'200m Men',
            3:'200m Women',
            4:'400m Men',
            5:'400m Women'}
          }, data_set)
docs: All olympics sprint winning times for multiple output prediction.