1 files changed, 4 insertions, 12 deletions

diff --git a/vowpalwabbit/log_multi.cc b/vowpalwabbit/log_multi.cc
index b263d008..1b5eb1a5 100644
--- a/vowpalwabbit/log_multi.cc
+++ b/vowpalwabbit/log_multi.cc
@@ -496,11 +496,7 @@ namespace LOG_MULTI
       }

   }

   

-  void finish_example(vw& all, log_multi&, example& ec)

-  {

-    MULTICLASS::output_example(all, ec);

-    VW::finish_example(all, &ec);

-  }

+  void finish_example(vw& all, log_multi&, example& ec) { MULTICLASS::finish_example(all, ec); }

   

   base_learner* setup(vw& all, po::variables_map& vm)	//learner setup

   {

@@ -513,15 +509,13 @@ namespace LOG_MULTI
     if(!vm.count("log_multi"))

       return NULL;

 

-      log_multi& data = calloc_or_die<log_multi>();
-

+    log_multi& data = calloc_or_die<log_multi>();
     data.k = (uint32_t)vm["log_multi"].as<size_t>();

     data.swap_resist = 4;

 

     if (vm.count("swap_resistance"))

       data.swap_resist = vm["swap_resistance"].as<uint32_t>();

     

-    //append log_multi with nb_actions to options_from_file so it is saved to regressor later

     *all.file_options << " --log_multi " << data.k;

     

     if (vm.count("no_progress"))

@@ -535,14 +529,12 @@ namespace LOG_MULTI
     string loss_function = "quantile"; 

     float loss_parameter = 0.5;

     delete(all.loss);

-    all.loss = getLossFunction(&all, loss_function, loss_parameter);

+    all.loss = getLossFunction(all, loss_function, loss_parameter);

 

     data.max_predictors = data.k - 1;

 

-    learner<log_multi>& l = init_learner(&data, all.l, data.max_predictors);

+    learner<log_multi>& l = init_learner(&data, all.l, learn, predict, data.max_predictors);

     l.set_save_load(save_load_tree);

-    l.set_learn(learn);

-    l.set_predict(predict);

     l.set_finish_example(finish_example);

     l.set_finish(finish);