dtrain: adadelta, fix output, max input, batch learning

1 files changed, 16 insertions, 20 deletions

diff --git a/training/dtrain/update.h b/training/dtrain/update.h
index f6aa9842..405a3f76 100644
--- a/training/dtrain/update.h
+++ b/training/dtrain/update.h
@@ -20,9 +20,8 @@ updates_multipartite(vector<Hyp>* sample,
                      size_t max_up,
                      weight_t threshold,
                      bool adjust,
-                     bool output=false,
-                     ostream& os=cout,
-                     size_t id=0)
+                     WriteFile& output,
+                     size_t id)
 {
   size_t up = 0;
   size_t sz = sample->size();
@@ -50,7 +49,7 @@ updates_multipartite(vector<Hyp>* sample,
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << id << "\t" << first.f-second.f << endl;
+        *output << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         return up;
@@ -70,7 +69,7 @@ updates_multipartite(vector<Hyp>* sample,
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << id << "\t" << first.f-second.f << endl;
+        *output << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         break;
@@ -91,9 +90,8 @@ updates_all(vector<Hyp>* sample,
             SparseVector<weight_t>& updates,
             size_t max_up,
             weight_t threshold,
-            bool output=false,
-            ostream& os=cout,
-            size_t id=0)
+            WriteFile output,
+            size_t id)
 {
   size_t up = 0;
   size_t sz = sample->size();
@@ -108,7 +106,7 @@ updates_all(vector<Hyp>* sample,
            || (threshold && (first.gold-second.gold < threshold)))
         continue;
       if (output)
-        os << id << "\t" << first.f-second.f << endl;
+        *output << id << "\t" << first.f-second.f << endl;
       updates += first.f-second.f;
       if (++up==max_up)
         break;
@@ -127,9 +125,8 @@ inline size_t
 update_structured(vector<Hyp>* sample,
                   SparseVector<weight_t>& updates,
                   weight_t margin,
-                  bool output=false,
-                  ostream& os=cout,
-                  size_t id=0)
+                  WriteFile output,
+                  size_t id)
 {
   // hope
   sort(sample->begin(), sample->end(), [](Hyp first, Hyp second)
@@ -147,13 +144,13 @@ update_structured(vector<Hyp>* sample,
   if (hope.gold != fear.gold) {
     updates += hope.f - fear.f;
     if (output)
-      os << id << "\t" << hope.f << "\t" << fear.f << endl;
+      *output << id << "\t" << hope.f << "\t" << fear.f << endl;
 
     return 1;
   }
 
   if (output)
-    os << endl;
+    *output << endl;
 
   return 0;
 }
@@ -172,9 +169,8 @@ updates_pro(vector<Hyp>* sample,
            size_t maxs,
            size_t max_up,
            weight_t threshold,
-           bool output=false,
-           ostream& os=cout,
-           size_t id=0)
+           WriteFile& output,
+           size_t id)
 {
 
   size_t sz = sample->size(), s;
@@ -202,7 +198,7 @@ updates_pro(vector<Hyp>* sample,
 
   for (auto i: g) {
     if (output)
-      os << id << "\t" << i.first->f-i.second->f << endl;
+      *output << id << "\t" << i.first->f-i.second->f << endl;
     updates += i.first->f-i.second->f;
   }
 
@@ -215,7 +211,7 @@ updates_pro(vector<Hyp>* sample,
  */
 inline void
 output_sample(vector<Hyp>* sample,
-              ostream& os=cout,
+              WriteFile& output,
               size_t id=0,
               bool sorted=true)
 {
@@ -227,7 +223,7 @@ output_sample(vector<Hyp>* sample,
   }
   size_t j = 0;
   for (auto k: *sample) {
-    os << id << "\t" << j << "\t" << k.gold << "\t" << k.model
+    *output << id << "\t" << j << "\t" << k.gold << "\t" << k.model
        << "\t" << k.f << endl;
     j++;
   }