reductions now have predict functions, but tests break

1 files changed, 8 insertions, 14 deletions

diff --git a/vowpalwabbit/mf.cc b/vowpalwabbit/mf.cc
index a7972726..da87b812 100644
--- a/vowpalwabbit/mf.cc
+++ b/vowpalwabbit/mf.cc
@@ -50,17 +50,15 @@ struct mf {
   vw* all;
 };
 
-void inline_predict(mf* data, vw* all, learner& base, example* &ec) {
+void predict(void *d, learner& base, example* ec) {
+  mf* data = (mf*) d;
+  vw* all = data->all;
 
   float prediction = 0;
   data->sub_predictions.resize(2*all->rank+1, true);
 
-  // set weight to 0 to indicate test example (predict only)
-  float weight = ((label_data*) ec->ld)->weight;
-  ((label_data*) ec->ld)->weight = 0;
-
   // predict from linear terms
-  base.learn(ec);
+  base.predict(ec);
 
   // store linear prediction
   data->sub_predictions[0] = ec->partial_prediction;
@@ -79,7 +77,7 @@ void inline_predict(mf* data, vw* all, learner& base, example* &ec) {
 
       for (size_t k = 1; k <= all->rank; k++) {
 	// compute l^k * x_l using base learner
-	base.learn(ec, k);
+	base.predict(ec, k);
 	data->sub_predictions[2*k-1] = ec->partial_prediction;
       }
 
@@ -89,7 +87,7 @@ void inline_predict(mf* data, vw* all, learner& base, example* &ec) {
 
       for (size_t k = 1; k <= all->rank; k++) {
 	// compute r^k * x_r using base learner
-	base.learn(ec, k + all->rank);
+	base.predict(ec, k + all->rank);
 	data->sub_predictions[2*k] = ec->partial_prediction;
       }
 
@@ -101,21 +99,17 @@ void inline_predict(mf* data, vw* all, learner& base, example* &ec) {
   // restore namespace indices and label
   copy_array(ec->indices, data->indices);
 
-  ((label_data*) ec->ld)->weight = weight;
-
   // finalize prediction
   ec->partial_prediction = prediction;
   ec->final_prediction = GD::finalize_prediction(*(data->all), ec->partial_prediction);
-
 }
 
-
 void learn(void* d, learner& base, example* ec) {
   mf* data = (mf*) d;
   vw* all = data->all;
 
   // predict with current weights
-  inline_predict(data, all, base, ec);
+  predict(d, base, ec);
 
   // force base learner to use precomputed prediction
   ec->precomputed_prediction = true;
@@ -208,7 +202,7 @@ learner* setup(vw& all, po::variables_map& vm) {
       for (size_t j = 0; j < (all.reg.weight_mask + 1) / all.reg.stride; j++)
 	all.reg.weight_vector[j*all.reg.stride] = (float) (0.1 * frand48());
     }
-  learner* l = new learner(data, learn, all.l, 2*data->rank+1);
+  learner* l = new learner(data, learn, predict, all.l, 2*data->rank+1);
   l->set_finish(finish);
   return l;
 }