Undo some silly local changes so we can pull

1 files changed, 17 insertions, 6 deletions

diff --git a/training/online_optimizer.h b/training/online_optimizer.h
index 312aabae..28d89344 100644
--- a/training/online_optimizer.h
+++ b/training/online_optimizer.h
@@ -2,6 +2,7 @@
 #define _ONL_OPTIMIZE_H_
 
 #include <tr1/memory>
+#include <set>
 #include <string>
 #include <cmath>
 #include "sparse_vector.h"
@@ -56,8 +57,12 @@ class OnlineOptimizer {
  public:
   virtual ~OnlineOptimizer();
   OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
-                  size_t batch_size)
-    : N_(batch_size),schedule_(s),k_() {}
+                  size_t batch_size,
+                  const std::vector<int>& frozen_feats = std::vector<int>())
+      : N_(batch_size),schedule_(s),k_() {
+    for (int i = 0; i < frozen_feats.size(); ++i)
+      frozen_.insert(frozen_feats[i]);
+  }
   void ResetEpoch() { k_ = 0; ResetEpochImpl(); }
   void UpdateWeights(const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) {
     ++k_;
@@ -69,6 +74,7 @@ class OnlineOptimizer {
   virtual void ResetEpochImpl();
   virtual void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) = 0;
   const size_t N_; // number of training instances per batch
+  std::set<int> frozen_;  // frozen (non-optimizing) features
 
  private:
   std::tr1::shared_ptr<LearningRateSchedule> schedule_;
@@ -78,16 +84,21 @@ class OnlineOptimizer {
 class CumulativeL1OnlineOptimizer : public OnlineOptimizer {
  public:
   CumulativeL1OnlineOptimizer(const std::tr1::shared_ptr<LearningRateSchedule>& s,
-                              size_t training_instances, double C) :
-    OnlineOptimizer(s, training_instances), C_(C), u_() {}
+                              size_t training_instances, double C,
+                              const std::vector<int>& frozen) :
+    OnlineOptimizer(s, training_instances, frozen), C_(C), u_() {}
 
  protected:
   void ResetEpochImpl() { u_ = 0; }
   void UpdateWeightsImpl(const double& eta, const SparseVector<double>& approx_g, int max_feat, SparseVector<double>* weights) {
     u_ += eta * C_ / N_;
-    (*weights) += eta * approx_g;
+    for (SparseVector<double>::const_iterator it = approx_g.begin(); 
+         it != approx_g.end(); ++it) {
+      if (frozen_.count(it->first) == 0)
+        weights->add_value(it->first, eta * it->second);
+    }
     for (int i = 1; i < max_feat; ++i)
-      ApplyPenalty(i, weights);
+      if (frozen_.count(i) == 0) ApplyPenalty(i, weights);
   }
 
  private: