Merge 0d6cc4d into 538e9bb

vowpalwabbit/memory_tree.cc

@@ -356,7 +356,7 @@ namespace memory_tree_ns
 
     //train the node with id cn, using the statistics stored in the node to
     //formulate a binary classificaiton example.
-    float train_node(memory_tree& b, single_learner& base, example& ec, const uint32_t cn)
+    float train_node(memory_tree& b, single_learner& base, example& ec, const uint64_t cn)
     {
         //predict, learn and predict
         //note: here we first train the router and then predict.
@@ -406,7 +406,7 @@ namespace memory_tree_ns
 
     //turn a leaf into an internal node, and create two children
     //when the number of examples is too big
-    void split_leaf(memory_tree& b, single_learner& base, const uint32_t cn)
+    void split_leaf(memory_tree& b, single_learner& base, const uint64_t cn)
     {
         //create two children
         b.nodes[cn].internal = 1; //swith to internal node.
@@ -526,7 +526,7 @@ namespace memory_tree_ns
 	    return (uint32_t)(array_1.size() + array_2.size() - 2*overlap);
     }
 
-    void collect_labels_from_leaf(memory_tree& b, const uint32_t cn, v_array<uint32_t>& leaf_labs){
+    void collect_labels_from_leaf(memory_tree& b, const uint64_t cn, v_array<uint32_t>& leaf_labs){
         if (b.nodes[cn].internal != -1)
             cout<<"something is wrong, it should be a leaf node"<<endl;
 
@@ -540,7 +540,7 @@ namespace memory_tree_ns
         }
     }
 
-    inline void train_one_against_some_at_leaf(memory_tree& b, single_learner& base, const uint32_t cn, example& ec){
+    inline void train_one_against_some_at_leaf(memory_tree& b, single_learner& base, const uint64_t cn, example& ec){
         v_array<uint32_t> leaf_labs = v_init<uint32_t>();
         collect_labels_from_leaf(b, cn, leaf_labs); //unique labels from the leaf.
         MULTILABEL::labels multilabels = ec.l.multilabels;
@@ -557,7 +557,7 @@ namespace memory_tree_ns
     }
 
      inline uint32_t compute_hamming_loss_via_oas(memory_tree& b, single_learner& base,
-		    const uint32_t cn, example& ec, v_array<uint32_t>& selected_labs)
+		    const uint64_t cn, example& ec, v_array<uint32_t>& selected_labs)
     {
 	    selected_labs.delete_v();
     	v_array<uint32_t> leaf_labs = v_init<uint32_t>();
@@ -579,7 +579,7 @@ namespace memory_tree_ns
 
 
     //pick up the "closest" example in the leaf using the score function.
-    int64_t pick_nearest(memory_tree& b, single_learner& base, const uint32_t cn, example& ec)
+    int64_t pick_nearest(memory_tree& b, single_learner& base, const uint64_t cn, example& ec)
     {
         if (b.nodes[cn].examples_index.size() > 0)
         {
@@ -769,7 +769,7 @@ namespace memory_tree_ns
         return;
     }
 
-    void route_to_leaf(memory_tree& b, single_learner& base, const uint32_t & ec_array_index, uint64_t cn, v_array<uint32_t>& path, bool insertion){
+    void route_to_leaf(memory_tree& b, single_learner& base, const uint32_t & ec_array_index, uint64_t cn, v_array<uint64_t>& path, bool insertion){
 		example& ec = *b.examples[ec_array_index];
 
         MULTICLASS::label_t mc;
@@ -817,13 +817,13 @@ namespace memory_tree_ns
 
     //we roll in, then stop at a random step, do exploration. //no real insertion happens in the function.
     void single_query_and_learn(memory_tree& b, single_learner& base, const uint32_t& ec_array_index, example& ec){
-        v_array<uint32_t> path_to_leaf = v_init<uint32_t>();
+        v_array<uint64_t> path_to_leaf = v_init<uint64_t>();
 		route_to_leaf(b, base, ec_array_index, 0, path_to_leaf, false); //no insertion happens here.
 
         if (path_to_leaf.size() > 1){
             //uint32_t random_pos = merand48(b.all->random_state)*(path_to_leaf.size()-1);
           uint32_t random_pos = (uint32_t)(merand48(b.all->random_state)*(path_to_leaf.size())); //include leaf
-            uint32_t cn = path_to_leaf[random_pos];
+            uint64_t cn = path_to_leaf[random_pos];
 
             if (b.nodes[cn].internal != -1){ //if it's an internal node:'
                 float objective = 0.f;
@@ -927,7 +927,7 @@ namespace memory_tree_ns
             	insert_example(b, base, ec_id); //unsupervised learning
             else{
 		if (b.dream_at_update == false){
-	        	v_array<uint32_t> tmp_path = v_init<uint32_t>();
+	        	v_array<uint64_t> tmp_path = v_init<uint64_t>();
 			route_to_leaf(b, base, ec_id, 0, tmp_path, true);
 			tmp_path.delete_v();
 		}