debug commit

1 files changed, 19 insertions, 98 deletions

diff --git a/vowpalwabbit/parse_args.cc b/vowpalwabbit/parse_args.cc
index b6afabae..0a4b94da 100644
--- a/vowpalwabbit/parse_args.cc
+++ b/vowpalwabbit/parse_args.cc
@@ -381,11 +381,11 @@ vw* parse_args(int argc, char *argv[])
   msrand48(random_seed);
 
   if (vm.count("active_simulation"))
-      all->active_simulation = true;
-
+    all->active_simulation = true;
+  
   if (vm.count("active_learning") && !all->active_simulation)
     all->active = true;
-
+  
   if (vm.count("no_stdin"))
     all->stdin_off = true;
 
@@ -406,42 +406,6 @@ vw* parse_args(int argc, char *argv[])
       throw exception();
     }
 
-  all->reg.stride_shift = 2; //use stride of 4 for default invariant normalized adaptive updates
-  //if the user specified anything in sgd,adaptive,invariant,normalized, we turn off default update rules and use whatever user specified
-  if( (all->rank > 0 && !vm.count("new_mf")) || !all->training || ( ( vm.count("sgd") || vm.count("adaptive") || vm.count("invariant") || vm.count("normalized") ) && !vm.count("exact_adaptive_norm")) )
-  {
-    all->adaptive = all->training && vm.count("adaptive") && (all->rank == 0 && !vm.count("new_mf"));
-    all->invariant_updates = all->training && vm.count("invariant");
-    all->normalized_updates = all->training && vm.count("normalized") && (all->rank == 0 && !vm.count("new_mf"));
-
-    all->reg.stride_shift = 0;
-
-    if( all->adaptive ) all->reg.stride_shift += 1;
-    else all->normalized_idx = 1; //store per feature norm at 1 index offset from weight value instead of 2
-
-    if( all->normalized_updates ) all->reg.stride_shift += 1;
-
-    if(!vm.count("learning_rate") && !vm.count("l") && !(all->adaptive && all->normalized_updates))
-      if (all->lda == 0)
-        all->eta = 10; //default learning rate to 10 for non default update rule
-
-    //if not using normalized or adaptive, default initial_t to 1 instead of 0
-    if(!all->adaptive && !all->normalized_updates && !vm.count("initial_t")) {
-      all->sd->t = 1.f;
-      all->sd->weighted_unlabeled_examples = 1.f;
-      all->initial_t = 1.f;
-    }
-    if (vm.count("feature_mask")){
-      if(all->reg.stride_shift == 0){
-        all->reg.stride_shift += 1;//if --sgd, stride->2 and use the second position as mask
-        all->feature_mask_idx = 1;
-      }
-      else if(all->reg.stride_shift == 1){
-        all->reg.stride_shift += 1;//if either normalized or adaptive, stride->4, mask_idx is still 3
-      }
-    }
-  }
-
   if (all->l1_lambda < 0.) {
     cerr << "l1_lambda should be nonnegative: resetting from " << all->l1_lambda << " to 0" << endl;
     all->l1_lambda = 0.;
@@ -460,19 +424,12 @@ vw* parse_args(int argc, char *argv[])
 	cerr << "using l2 regularization = " << all->l2_lambda << endl;
     }
 
-  all->l = GD::setup(*all, vm);
-  all->scorer = all->l;
-
-  if (vm.count("bfgs") || vm.count("conjugate_gradient"))
-    all->l = BFGS::setup(*all, to_pass_further, vm, vm_file);
-
   if (vm.count("version") || argc == 1) {
     /* upon direct query for version -- spit it out to stdout */
     cout << version.to_string() << "\n";
     exit(0);
   }
 
-
   if(vm.count("ngram")){
     if(vm.count("sort_features"))
       {
@@ -678,50 +635,29 @@ vw* parse_args(int argc, char *argv[])
 	}
     }
 
-  // (non-reduction) matrix factorization enabled
-  if (!vm.count("new_mf") && all->rank > 0) {
-    // store linear + 2*rank weights per index, round up to power of two
-    float temp = ceilf(logf((float)(all->rank*2+1)) / logf (2.f));
-    all->reg.stride_shift = (size_t) temp;
-    all->random_weights = true;
-
-    if ( vm.count("adaptive") )
-      {
-	cerr << "adaptive is not implemented for matrix factorization" << endl;
-        throw exception();
-      }
-    if ( vm.count("normalized") )
-      {
-	cerr << "normalized is not implemented for matrix factorization" << endl;
-        throw exception();
-      }
-    if ( vm.count("exact_adaptive_norm") )
-      {
-	cerr << "normalized adaptive updates is not implemented for matrix factorization" << endl;
-        throw exception();
-      }
-    if (vm.count("bfgs") || vm.count("conjugate_gradient"))
-      {
-	cerr << "bfgs is not implemented for matrix factorization" << endl;
-	throw exception();
-      }	
-
-    //default initial_t to 1 instead of 0
-    if(!vm.count("initial_t")) {
-      all->sd->t = 1.f;
-      all->sd->weighted_unlabeled_examples = 1.f;
-      all->initial_t = 1.f;
-    }
-  }
-
   if (vm.count("noconstant"))
     all->add_constant = false;
 
   //if (vm.count("nonormalize"))
   //  all->nonormalize = true;
 
-  if (vm.count("lda"))
+  if (!vm.count("bfgs") && !vm.count("conjugate_gradient") && !vm.count("noop") && !vm.count("lda") && !vm.count("print") && !vm.count("new_mf") && !vm.count("sendto"))
+    {
+      all->l = GD::setup(*all, vm);
+      all->scorer = all->l;
+    }
+  else if (vm.count("bfgs") || vm.count("conjugate_gradient"))
+    all->l = BFGS::setup(*all, to_pass_further, vm, vm_file);
+  else if (vm.count("lda"))
     all->l = LDA::setup(*all, to_pass_further, vm);
+  else if (vm.count("noop"))
+    all->l = NOOP::setup(*all);
+  else if (vm.count("print"))
+    all->l = PRINT::setup(*all);
+  else if (!vm.count("new_mf") && all->rank > 0)
+    all->l = GDMF::setup(*all, vm);
+  else if (vm.count("sendto"))
+    all->l = SENDER::setup(*all, vm, all->pairs);
 
   if (!vm.count("lda") && !all->adaptive && !all->normalized_updates)
     all->eta *= powf((float)(all->sd->t), all->power_t);
@@ -757,18 +693,6 @@ vw* parse_args(int argc, char *argv[])
   if(vm.count("quantile_tau"))
     loss_parameter = vm["quantile_tau"].as<float>();
 
-  if (vm.count("noop"))
-    all->l = NOOP::setup(*all);
-
-  if (vm.count("print"))
-    {
-      all->l = PRINT::setup(*all);
-      all->reg.stride_shift = 0;
-    }
-
-  if (!vm.count("new_mf") && all->rank > 0)
-    all->l = GDMF::setup(*all);
-
   all->loss = getLossFunction(all, loss_function, (float)loss_parameter);
 
   if (pow((double)all->eta_decay_rate, (double)all->numpasses) < 0.0001 )
@@ -834,9 +758,6 @@ vw* parse_args(int argc, char *argv[])
     all->audit = true;
   }
 
-  if (vm.count("sendto"))
-    all->l = SENDER::setup(*all, vm, all->pairs);
-
   // Need to see if we have to load feature mask first or second.
   // -i and -mask are from same file, load -i file first so mask can use it
   if (vm.count("feature_mask") && vm.count("initial_regressor")