removed precomputed_prediction in favor of base.update

1 files changed, 10 insertions, 9 deletions

diff --git a/vowpalwabbit/parse_args.cc b/vowpalwabbit/parse_args.cc
index e369d975..82809fe7 100644
--- a/vowpalwabbit/parse_args.cc
+++ b/vowpalwabbit/parse_args.cc
@@ -212,6 +212,7 @@ vw* parse_args(int argc, char *argv[])
     ("cubic", po::value< vector<string> > (),
      "Create and use cubic features")
     ("rank", po::value<uint32_t>(&(all->rank)), "rank for matrix factorization.")
+    ("new_mf", "use new, reduction-based matrix factorization")
     ;
 
   po::options_description lrq_opt("Low Rank Quadratic options");
@@ -398,12 +399,12 @@ vw* parse_args(int argc, char *argv[])
     }
 
   all->reg.stride = 4; //use stride of 4 for default invariant normalized adaptive updates
-  //if we are doing matrix factorization, or user specified anything in sgd,adaptive,invariant,normalized, we turn off default update rules and use whatever user specified
-  if( all->rank > 0 || !all->training || ( ( vm.count("sgd") || vm.count("adaptive") || vm.count("invariant") || vm.count("normalized") ) && !vm.count("exact_adaptive_norm")) )
+  //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);
+    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);
+    all->normalized_updates = all->training && vm.count("normalized") && (all->rank == 0 && !vm.count("new_mf"));
 
     all->reg.stride = 1;
 
@@ -665,8 +666,8 @@ vw* parse_args(int argc, char *argv[])
 	}
     }
 
-  // matrix factorization enabled
-  if (all->rank > 0) {
+  // (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 = 1 << (int) temp;
@@ -747,7 +748,7 @@ vw* parse_args(int argc, char *argv[])
   if (vm.count("noop"))
     all->l = NOOP::setup(*all);
 
-  if (all->rank != 0)
+  if (!vm.count("new_mf") && all->rank > 0)
     all->l = GDMF::setup(*all);
 
   all->loss = getLossFunction(all, loss_function, (float)loss_parameter);
@@ -845,8 +846,8 @@ vw* parse_args(int argc, char *argv[])
   if(vm.count("nn") || vm_file.count("nn") )
     all->l = NN::setup(*all, to_pass_further, vm, vm_file);
 
-  // if (all->rank != 0)
-  //   all->l = MF::setup(*all);
+  if (vm.count("new_mf") && all->rank > 0)
+    all->l = MF::setup(*all, vm);
 
   if(vm.count("autolink") || vm_file.count("autolink") )
     all->l = ALINK::setup(*all, to_pass_further, vm, vm_file);