From 337ead1bbf4d4f1e6297a6c584a4718f8ba90173 Mon Sep 17 00:00:00 2001
From: Rico Sennrich <rico.sennrich@gmx.ch>
Date: Mon, 17 Nov 2014 11:01:00 +0000
Subject: re-apply 31412f (osx compile)

---
 src/prepareNeuralLM.cpp    | 4 ++--
 src/trainNeuralNetwork.cpp | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/prepareNeuralLM.cpp b/src/prepareNeuralLM.cpp
index 13a534a..adedc72 100644
--- a/src/prepareNeuralLM.cpp
+++ b/src/prepareNeuralLM.cpp
@@ -240,7 +240,7 @@ void writeMmapNgrams(const string &input_filename,
             if (i %500000 == 0) {
               cerr<<"Shuffled "<<num_tokens-1<<" instances...";
             }
-            data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+            data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
             for (int k=0;k<ngram_size;k++) {
               int temp_val = temp.at(i*ngram_size+k);
               temp.at(i*ngram_size+k) =
@@ -263,7 +263,7 @@ void writeMmapNgrams(const string &input_filename,
         if (i %500000 == 0) {
           cerr<<"Shuffled "<<num_tokens-1<<" instances...";
         }
-        data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+        data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
         for (int k=0;k<ngram_size;k++) {
           int temp_val = mMapVec->at(i*ngram_size+k);
           mMapVec->at(i*ngram_size+k) =
diff --git a/src/trainNeuralNetwork.cpp b/src/trainNeuralNetwork.cpp
index e231c20..a4cac12 100644
--- a/src/trainNeuralNetwork.cpp
+++ b/src/trainNeuralNetwork.cpp
@@ -312,7 +312,7 @@ int main(int argc, char** argv)
             if (i %500000 == 0) {
               cerr<<"Shuffled "<<training_data_size-1<<" instances...";
             }
-            data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+            data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
             for (int k=0;k<myParam.ngram_size;k++) {
               int temp_val = training_data_flat_mmap->at(i*myParam.ngram_size+k);
               training_data_flat_mmap->at(i*myParam.ngram_size+k) =
@@ -326,7 +326,7 @@ int main(int argc, char** argv)
             if (i %500000 == 0) {
               cerr<<"Shuffled "<<training_data_size-1<<" instances...";
             }
-            data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+            data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
             for (int k=0;k<myParam.ngram_size;k++) {
               int temp_val = temp.at(i*myParam.ngram_size+k);
               temp.at(i*myParam.ngram_size+k) =
@@ -348,7 +348,7 @@ int main(int argc, char** argv)
           if (i %500000 == 0) {
             cerr<<"Shuffled "<<training_data_size-1<<" instances...";
           }
-          data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+          data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
           for (int k=0;k<myParam.ngram_size;k++) {
             int temp_val = training_data_flat_mmap->at(i*myParam.ngram_size+k);
             training_data_flat_mmap->at(i*myParam.ngram_size+k) =
@@ -396,7 +396,7 @@ int main(int argc, char** argv)
       // Randomly shuffle training data to improve learning
       for (data_size_t i=training_data_size-1; i>0; i--)
       {
-        data_size_t j = uniform_int_distribution<data_size_t>(0, i-1)(rng);
+        data_size_t j = boost::random::uniform_int_distribution<data_size_t>(0, i-1)(rng);
         training_data.col(i).swap(training_data.col(j));
       }
     }
-- 
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