changed pointers to references

1 files changed, 28 insertions, 28 deletions

diff --git a/vowpalwabbit/mf.cc b/vowpalwabbit/mf.cc
index ab6b8988..bbeedb6f 100644
--- a/vowpalwabbit/mf.cc
+++ b/vowpalwabbit/mf.cc
@@ -53,7 +53,7 @@ struct mf {
 };
 
 template <bool cache_sub_predictions>
-void predict(mf *data, learner& base, example* ec) {
+void predict(mf *data, learner& base, example& ec) {
   vw* all = data->all;
 
   float prediction = 0;
@@ -65,15 +65,15 @@ void predict(mf *data, learner& base, example* ec) {
 
   // store linear prediction
   if (cache_sub_predictions)
-    data->sub_predictions[0] = ec->partial_prediction;
-  prediction += ec->partial_prediction;
+    data->sub_predictions[0] = ec.partial_prediction;
+  prediction += ec.partial_prediction;
 
   // store namespace indices
-  copy_array(data->indices, ec->indices);
+  copy_array(data->indices, ec.indices);
 
   // erase indices
-  ec->indices.erase();
-  ec->indices.push_back(0);
+  ec.indices.erase();
+  ec.indices.push_back(0);
 
   // add interaction terms to prediction
   for (vector<string>::iterator i = data->pairs.begin(); i != data->pairs.end(); i++) {
@@ -81,23 +81,23 @@ void predict(mf *data, learner& base, example* ec) {
     int left_ns = (int) (*i)[0];
     int right_ns = (int) (*i)[1];
 
-    if (ec->atomics[left_ns].size() > 0 && ec->atomics[right_ns].size() > 0) {
+    if (ec.atomics[left_ns].size() > 0 && ec.atomics[right_ns].size() > 0) {
       for (size_t k = 1; k <= all->rank; k++) {
 
-	ec->indices[0] = left_ns;
+	ec.indices[0] = left_ns;
 
 	// compute l^k * x_l using base learner
 	base.predict(ec, k);
-	float x_dot_l = ec->partial_prediction;
+	float x_dot_l = ec.partial_prediction;
 	if (cache_sub_predictions)
 	  data->sub_predictions[2*k-1] = x_dot_l;
 
 	// set example to right namespace only
-	ec->indices[0] = right_ns;
+	ec.indices[0] = right_ns;
 
 	// compute r^k * x_r using base learner
 	base.predict(ec, k + all->rank);
-	float x_dot_r = ec->partial_prediction;
+	float x_dot_r = ec.partial_prediction;
 	if (cache_sub_predictions)
 	  data->sub_predictions[2*k] = x_dot_r;
 
@@ -107,14 +107,14 @@ void predict(mf *data, learner& base, example* ec) {
     }
   }
   // restore namespace indices and label
-  copy_array(ec->indices, data->indices);
+  copy_array(ec.indices, data->indices);
 
   // finalize prediction
-  ec->partial_prediction = prediction;
-  ec->final_prediction = GD::finalize_prediction(*(data->all), ec->partial_prediction);
+  ec.partial_prediction = prediction;
+  ec.final_prediction = GD::finalize_prediction(*(data->all), ec.partial_prediction);
 }
 
-void learn(mf* data, learner& base, example* ec) {
+void learn(mf* data, learner& base, example& ec) {
   vw* all = data->all;
 
   // predict with current weights
@@ -124,11 +124,11 @@ void learn(mf* data, learner& base, example* ec) {
   base.update(ec);
 
   // store namespace indices
-  copy_array(data->indices, ec->indices);
+  copy_array(data->indices, ec.indices);
 
   // erase indices
-  ec->indices.erase();
-  ec->indices.push_back(0);
+  ec.indices.erase();
+  ec.indices.push_back(0);
 
   // update interaction terms
   // looping over all pairs of non-empty namespaces
@@ -137,49 +137,49 @@ void learn(mf* data, learner& base, example* ec) {
     int left_ns = (int) (*i)[0];
     int right_ns = (int) (*i)[1];
 
-    if (ec->atomics[left_ns].size() > 0 && ec->atomics[right_ns].size() > 0) {
+    if (ec.atomics[left_ns].size() > 0 && ec.atomics[right_ns].size() > 0) {
 
       // set example to left namespace only
-      ec->indices[0] = left_ns;
+      ec.indices[0] = left_ns;
 
       // store feature values in left namespace
-      copy_array(data->temp_features, ec->atomics[left_ns]);
+      copy_array(data->temp_features, ec.atomics[left_ns]);
 
       for (size_t k = 1; k <= all->rank; k++) {
 
 	// multiply features in left namespace by r^k * x_r
-	for (feature* f = ec->atomics[left_ns].begin; f != ec->atomics[left_ns].end; f++)
+	for (feature* f = ec.atomics[left_ns].begin; f != ec.atomics[left_ns].end; f++)
 	  f->x *= data->sub_predictions[2*k];
 
 	// update l^k using base learner
 	base.update(ec, k);
 
 	// restore left namespace features (undoing multiply)
-	copy_array(ec->atomics[left_ns], data->temp_features);
+	copy_array(ec.atomics[left_ns], data->temp_features);
       }
 
       // set example to right namespace only
-      ec->indices[0] = right_ns;
+      ec.indices[0] = right_ns;
 
       // store feature values for right namespace
-      copy_array(data->temp_features, ec->atomics[right_ns]);
+      copy_array(data->temp_features, ec.atomics[right_ns]);
 
       for (size_t k = 1; k <= all->rank; k++) {
 
 	// multiply features in right namespace by l^k * x_l
-	for (feature* f = ec->atomics[right_ns].begin; f != ec->atomics[right_ns].end; f++)
+	for (feature* f = ec.atomics[right_ns].begin; f != ec.atomics[right_ns].end; f++)
 	  f->x *= data->sub_predictions[2*k-1];
 
 	// update r^k using base learner
 	base.update(ec, k + all->rank);
 
 	// restore right namespace features
-	copy_array(ec->atomics[right_ns], data->temp_features);
+	copy_array(ec.atomics[right_ns], data->temp_features);
       }
     }
   }
   // restore namespace indices
-  copy_array(ec->indices, data->indices);
+  copy_array(ec.indices, data->indices);
 }
 
 void finish(mf* o) {