From bb86637332d49f71c485df34576e464eaf053656 Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Tue, 13 Sep 2011 17:36:23 +0100
Subject: get rid of bad Weights class so it no longer keeps a copy of a vector
 inside it

---
 training/mr_optimize_reduce.cc | 19 +++++--------------
 1 file changed, 5 insertions(+), 14 deletions(-)

(limited to 'training/mr_optimize_reduce.cc')

diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
index b931991d..15e28fa1 100644
--- a/training/mr_optimize_reduce.cc
+++ b/training/mr_optimize_reduce.cc
@@ -88,25 +88,19 @@ int main(int argc, char** argv) {
 
   const bool use_b64 = conf["input_format"].as<string>() == "b64";
 
-  Weights weights;
-  weights.InitFromFile(conf["input_weights"].as<string>());
+  vector<weight_t> lambdas;
+  Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
   const string s_obj = "**OBJ**";
   int num_feats = FD::NumFeats();
   cerr << "Number of features: " << num_feats << endl;
   const bool gaussian_prior = conf.count("gaussian_prior");
-  vector<double> means(num_feats, 0);
+  vector<weight_t> means(num_feats, 0);
   if (conf.count("means")) {
     if (!gaussian_prior) {
       cerr << "Don't use --means without --gaussian_prior!\n";
       exit(1);
     }
-    Weights wm; 
-    wm.InitFromFile(conf["means"].as<string>());
-    if (num_feats != FD::NumFeats()) {
-      cerr << "[ERROR] Means file had unexpected features!\n";
-      exit(1);
-    }
-    wm.InitVector(&means);
+    Weights::InitFromFile(conf["means"].as<string>(), &means);
   }
   shared_ptr<BatchOptimizer> o;
   const string omethod = conf["optimization_method"].as<string>();
@@ -124,8 +118,6 @@ int main(int argc, char** argv) {
       cerr << "No state file found, assuming ITERATION 1\n";
   }
 
-  vector<double> lambdas(num_feats, 0);
-  weights.InitVector(&lambdas);
   double objective = 0;
   vector<double> gradient(num_feats, 0);
   // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
@@ -223,8 +215,7 @@ int main(int argc, char** argv) {
   old.clear();
   SanityCheck(lambdas);
   ShowLargestFeatures(lambdas);
-  weights.InitFromVector(lambdas);
-  weights.WriteToFile(conf["output_weights"].as<string>(), false);
+  Weights::WriteToFile(conf["output_weights"].as<string>(), lambdas, false);
 
   const bool conv = o->HasConverged();
   if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
-- 
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