docstr: Results generated with
      anes_data = sm.datasets.anes96.load()
      anes_exog = anes_data.exog
      anes_exog = sm.add_constant(anes_exog, prepend=False)
      mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
  
      alpha = 10 * np.ones((mlogit_mod.J - 1, mlogit_mod.K))
      alpha[-1, :] = 0
      mlogit_l1_res = mlogit_mod.fit_regularized(
      method='l1', alpha=alpha, trim_mode='auto', auto_trim_tol=0.02,
      acc=1e-10)
docstr_tok: ["Results","generated","with","anes_data","=","sm.datasets.anes96.load","(",")","anes_exog","=","anes_data.exog","anes_exog","=","sm.add_constant","(","anes_exog",",","prepend=False",")","mlogit_mod","=","sm.MNLogit","(","anes_data.endog",",","anes_exog",")","alpha","=","10","*","np.ones","(","(","mlogit_mod.J","-","1",",","mlogit_mod.K",")",")","alpha","[","-1",",",":","]","=","0","mlogit_l1_res","=","mlogit_mod.fit_regularized","(","method='l1","'",",","alpha=alpha",",","trim_mode='auto","'",",","auto_trim_tol=0.02",",","acc=1e-10",")"]
code: def mnlogit():
      """
      Results generated with
          anes_data = sm.datasets.anes96.load()
          anes_exog = anes_data.exog
          anes_exog = sm.add_constant(anes_exog, prepend=False)
          mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
  
          alpha = 10 * np.ones((mlogit_mod.J - 1, mlogit_mod.K))
          alpha[-1, :] = 0
          mlogit_l1_res = mlogit_mod.fit_regularized(
          method='l1', alpha=alpha, trim_mode='auto', auto_trim_tol=0.02,
          acc=1e-10)
      """
      obj = Namespace()
      obj.params = [
          [
              0.00100163,
              -0.05864195,
              -0.06147822,
              -0.04769671,
              -0.05222987,
              -0.09522432,
          ],
          [
              0.0,
              0.03186139,
              0.12048999,
              0.83211915,
              0.92330292,
              1.5680646,
          ],
          [
              -0.0218185,
              -0.01988066,
              -0.00808564,
              -0.00487463,
              -0.01400173,
              -0.00562079,
          ],
          [0.0, 0.03306875, 0.0, 0.02362861, 0.05486435, 0.14656966],
          [
              0.0,
              0.04448213,
              0.03252651,
              0.07661761,
              0.07265266,
              0.0967758,
          ],
          [
              0.90993803,
              -0.50081247,
              -2.08285102,
              -5.26132955,
              -4.86783179,
              -9.31537963,
          ],
      ]
      obj.conf_int = [
          [
              [-0.0646223, 0.06662556],
              [np.nan, np.nan],
              [-0.03405931, -0.00957768],
              [np.nan, np.nan],
              [np.nan, np.nan],
              [0.26697895, 1.55289711],
          ],
          [
              [-0.1337913, 0.01650741],
              [-0.14477255, 0.20849532],
              [-0.03500303, -0.00475829],
              [-0.11406121, 0.18019871],
              [0.00479741, 0.08416684],
              [-1.84626136, 0.84463642],
          ],
          [
              [-0.17237962, 0.04942317],
              [-0.15146029, 0.39244026],
              [-0.02947379, 0.01330252],
              [np.nan, np.nan],
              [-0.02501483, 0.09006785],
              [-3.90379391, -0.26190812],
          ],
          [
              [-0.12938296, 0.03398954],
              [0.62612955, 1.03810876],
              [-0.02046322, 0.01071395],
              [-0.13738534, 0.18464256],
              [0.03017236, 0.12306286],
              [-6.91227465, -3.61038444],
          ],
          [
              [-0.12469773, 0.02023799],
              [0.742564, 1.10404183],
              [-0.02791975, -0.00008371],
              [-0.08491561, 0.19464431],
              [0.0332926, 0.11201273],
              [-6.29331126, -3.44235233],
          ],
          [
              [-0.17165567, -0.01879296],
              [1.33994079, 1.79618841],
              [-0.02027503, 0.00903345],
              [-0.00267819, 0.29581751],
              [0.05343135, 0.14012026],
              [-11.10419107, -7.52656819],
          ],
      ]
  
      obj.bse = [
          [
              0.03348221,
              0.03834221,
              0.05658338,
              0.04167742,
              0.03697408,
              0.03899631,
          ],
          [
              np.nan,
              0.09012101,
              0.13875269,
              0.10509867,
              0.09221543,
              0.11639184,
          ],
          [
              0.00624543,
              0.00771564,
              0.01091253,
              0.00795351,
              0.00710116,
              0.00747679,
          ],
          [
              np.nan,
              0.07506769,
              np.nan,
              0.08215148,
              0.07131762,
              0.07614826,
          ],
          [
              np.nan,
              0.02024768,
              0.02935837,
              0.02369699,
              0.02008204,
              0.02211492,
          ],
          [
              0.32804638,
              0.68646613,
              0.92906957,
              0.84233441,
              0.72729881,
              0.91267567,
          ],
      ]
  
      obj.nnz_params = 32
      obj.aic = 3019.4391360294126
      obj.bic = 3174.6431733460686
      return obj
  
imports: import os
  import numpy
label_desc: Use image node_id 5 for calling the DiscreteL1 obj's underlying member method code with example usage: obj.mnlogit() and returns: obj