From 6409c2c91b7fd3d3b42c6bb0d3376b83eb4d5611 Mon Sep 17 00:00:00 2001
From: redpony <redpony@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Wed, 22 Sep 2010 18:28:00 +0000
Subject: few fixes

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@655 ec762483-ff6d-05da-a07a-a48fb63a330f
---
 training/mpi_online_optimize.cc | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

(limited to 'training')

diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 95b462bb..62821aa3 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -85,7 +85,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   }
   po::notify(*conf);
 
-  if (conf->count("help") || !conf->count("input_weights") || !conf->count("training_data") || !conf->count("decoder_config")) {
+  if (conf->count("help") || !conf->count("training_data") || !conf->count("decoder_config")) {
     cerr << dcmdline_options << endl;
     MPI::Finalize();
     exit(1);
@@ -201,14 +201,14 @@ int main(int argc, char** argv) {
   cerr << "MPI: I am " << rank << '/' << size << endl;
   register_feature_functions();
   MT19937* rng = NULL;
-  if (rank == 0) rng = new MT19937;
 
   po::variables_map conf;
   InitCommandLine(argc, argv, &conf);
 
   // load initial weights
   Weights weights;
-  weights.InitFromFile(conf["input_weights"].as<string>());
+  if (conf.count("input_weights"))
+    weights.InitFromFile(conf["input_weights"].as<string>());
 
   // freeze feature set
   const bool freeze_feature_set = conf.count("freeze_feature_set");
@@ -228,6 +228,7 @@ int main(int argc, char** argv) {
 
   std::tr1::shared_ptr<OnlineOptimizer> o;
   std::tr1::shared_ptr<LearningRateSchedule> lr;
+  vector<int> order;
   if (rank == 0) {
     // TODO config
     lr.reset(new ExponentialDecayLearningRate(corpus.size(), conf["eta_0"].as<double>()));
@@ -239,12 +240,21 @@ int main(int argc, char** argv) {
     } else {
       assert(!"fail");
     }
+
+    // randomize corpus
+    rng = new MT19937;
+    order.resize(corpus.size());
+    for (unsigned i = 0; i < order.size(); ++i) order[i]=i;
+    Shuffle(&order, rng);
   }
   double objective = 0;
   vector<double> lambdas;
   weights.InitVector(&lambdas);
   bool converged = false;
-
+  const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
+  for (int i = 0; i < size_per_proc; ++i)
+    cerr << "i=" << i << ": " << order[i] << endl;
+  abort();
   TrainingObserver observer;
   while (!converged) {
     observer.Reset();
-- 
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