Merge 37f3077 into 3ca448c

test/RunTests

@@ -1732,11 +1732,11 @@ printf '3 |f a b c |e x y z\n2 |f a y c |e x\n' | {VW} --oaa 3 -q ef --audit
     train-sets/ref/malformed.stderr
 
 # Test 196: online contextual memory tree
-{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf 1 --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --oas 0 --online 1 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 --passes 1 --loss_function squared --holdout_off
+{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --online --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 --passes 1 --loss_function squared --holdout_off
     train-sets/ref/cmt_rcv1_smaller_online.stderr
 
 # Test 197: offline contextual memory tree
-{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf 1 --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --oas 0 --online 0 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 -c --passes 2 --loss_function squared --holdout_off
+{VW} -d train-sets/rcv1_smaller.dat --memory_tree 10 --learn_at_leaf --max_number_of_labels 2 --dream_at_update 0 --dream_repeats 3 --leaf_example_multiplier 10 --alpha 0.1 -l 0.001 -b 15 -c --passes 2 --loss_function squared --holdout_off
     train-sets/ref/cmt_rcv1_smaller_offline.stderr
 
 # Do not delete this line or the empty line above it

test/train-sets/ref/dictionary_test.stderr

@@ -1,7 +1,4 @@
 ignoring namespaces beginning with: w 
-scanned dictionary 'dictionary_test.dict' from 'train-sets/dictionary_test.dict', hash=3226e82e3d58b6b2
-dictionary dictionary_test.dict contains 4 items
-scanned dictionary 'dictionary_test.dict.gz' from 'train-sets/dictionary_test.dict.gz', hash=3226e82e3d58b6b2
 Num weight bits = 18
 learning rate = 0.5
 initial_t = 0
@@ -10,6 +7,9 @@ decay_learning_rate = 1
 creating cache_file = train-sets/dictionary_test.dat.cache
 Reading datafile = train-sets/dictionary_test.dat
 num sources = 1
+scanned dictionary 'dictionary_test.dict' from 'train-sets/dictionary_test.dict', hash=3226e82e3d58b6b2
+dictionary dictionary_test.dict contains 4 items
+scanned dictionary 'dictionary_test.dict.gz' from 'train-sets/dictionary_test.dict.gz', hash=3226e82e3d58b6b2
 average  since         example        example  current  current  current
 loss     last          counter         weight    label  predict features
 1.000000 1.000000            1            1.0   1.0000  -1.0000        3

vowpalwabbit/accumulate.cc

@@ -144,6 +144,6 @@ void accumulate_weighted_avg(vw& all, parameters& weights)
   if (weights.sparse)
     cout << "sparse parameters not supported with parallel computation!" << endl;
   else
-    all_reduce<float, add_float>(all, weights.dense_weights.first(), ((size_t)length) * (1 << weights.stride_shift()));
+    all_reduce<float, add_float>(all, weights.dense_weights.first(), ((size_t)length) * (1ull << weights.stride_shift()));
   delete[] local_weights;
 }

vowpalwabbit/cb_adf.cc

@@ -39,7 +39,7 @@ struct cb_adf
   action_scores a_s;  // temporary storage for mtr and sm
   action_scores prob_s;  // temporary storage for sm; stores softmax values
   v_array<uint32_t> backup_nf; // temporary storage for sm; backup for numFeatures in examples
-  v_array<uint32_t> backup_weights;  // temporary storage for sm; backup for weights in examples
+  v_array<float> backup_weights;  // temporary storage for sm; backup for weights in examples
 
   uint64_t offset;
   bool no_predict;
@@ -149,10 +149,10 @@ void learn_SM(cb_adf& mydata, multi_learner& base, multi_ex& examples) {
   {
     uint32_t current_action = mydata.prob_s[i].action;
     mydata.backup_weights.push_back(examples[current_action]->weight);
-    mydata.backup_nf.push_back(examples[current_action]->num_features);
+    mydata.backup_nf.push_back((uint32_t)examples[current_action]->num_features);
 
     if (current_action == chosen_action)
-      examples[current_action]->weight = example_weight * (1.0 - mydata.prob_s[i].score);
+      examples[current_action]->weight = example_weight * (1.0f - mydata.prob_s[i].score);
     else
       examples[current_action]->weight = example_weight * mydata.prob_s[i].score;
 

vowpalwabbit/io_buf.h

@@ -379,4 +379,4 @@ inline size_t bin_text_read_write_fixed_validated(
                                     msg,                   \
                                     text);                 \
         }                                                   \
-    while (0);
+    while (0);