udpate test

src/shogun/features/DenseFeatures.cpp

@@ -639,7 +639,6 @@ CFeatures* CDenseFeatures<ST>::copy_dimension_subset(SGVector<index_t> dims)
 template<class ST>
 CFeatures* CDenseFeatures<ST>::shallow_subset_copy()
 {
-	SG_SDEBUG("Entering!\n");
 	CFeatures* shallow_copy_features=nullptr;
 
 	SG_SDEBUG("Using underlying feature matrix with %d dimensions and %d feature vectors!\n", num_features, num_vectors);
@@ -648,7 +647,6 @@ CFeatures* CDenseFeatures<ST>::shallow_subset_copy()
 	if (m_subset_stack->has_subsets())
 		shallow_copy_features->add_subset(m_subset_stack->get_last_subset()->get_subset_idx());
 
-	SG_SDEBUG("Leaving!\n");
 	return shallow_copy_features;
 }
 

src/shogun/labels/MulticlassLabels.cpp

@@ -145,6 +145,5 @@ CLabels* CMulticlassLabels::shallow_subset_copy()
 	if (m_subset_stack->has_subsets())
 		shallow_copy_labels->add_subset(m_subset_stack->get_last_subset()->get_subset_idx());
 
-	SG_SDEBUG("Leaving!\n");
 	return shallow_copy_labels;	
 }

src/shogun/machine/BaggingMachine.cpp

@@ -131,16 +131,16 @@ bool CBaggingMachine::train_machine(CFeatures* data)
 
 	/*
 	  TODO: enable multi-threaded learning. This requires views support
-		on CFeatures
-	#pragma omp parallel for num_threads(parallel->get_num_threads())
-	*/
+		on CFeatures*/
+	#pragma omp parallel for	
 	for (int32_t i = 0; i < m_num_bags; ++i)
 	{
 		CMachine* c=dynamic_cast<CMachine*>(m_machine->clone());
 		ASSERT(c != NULL);
 		SGVector<index_t> idx(get_bag_size());
 		idx.random(0, m_features->get_num_vectors()-1);
-		m_labels->add_subset(idx);
+		CLabels* labels = m_labels->shallow_subset_copy();
+		labels->add_subset(idx);
 		/* TODO:
 		   if it's a binary labeling ensure that
 		   there's always samples of both classes
@@ -157,19 +157,23 @@ bool CBaggingMachine::train_machine(CFeatures* data)
 			}
 		}
 		*/
-		m_features->add_subset(idx);
+		CFeatures* features = m_features->shallow_subset_copy();
+		features->add_subset(idx);
 		set_machine_parameters(c,idx);
-		c->set_labels(m_labels);
-		c->train(m_features);
-		m_features->remove_subset();
-		m_labels->remove_subset();
+		c->set_labels(labels);
+		c->train(features);
+		features->remove_subset();
+		labels->remove_subset();
 
+		#pragma omp critical
+		{		
 		// get out of bag indexes
 		CDynamicArray<index_t>* oob = get_oob_indices(idx);
 		m_oob_indices->push_back(oob);
 
 		// add trained machine to bag array
 		m_bags->push_back(c);
+		}
 
 		SG_UNREF(c);
 	}

src/shogun/multiclass/tree/CARTree.cpp

@@ -603,8 +603,6 @@ int32_t CCARTree::compute_best_attribute(const SGMatrix<float64_t>& mat, const S
 	{
 		SGVector<float64_t> feats(num_vecs);
 		SGVector<index_t> sorted_args(num_vecs);
-		SGVector<int32_t> temp_count_indices(count_indices.size());
-		memcpy(temp_count_indices.vector, count_indices.vector, sizeof(int32_t)*count_indices.size());
 
 		if (m_pre_sort)
 		{
@@ -615,12 +613,13 @@ int32_t CCARTree::compute_best_attribute(const SGMatrix<float64_t>& mat, const S
 			{
 				if (indices_mask[sorted_indices[j]]>=0)
 				{
-					while(temp_count_indices[sorted_indices[j]]>0)
+					int32_t count_idx = count_indices[sorted_indices[j]];
+					while(count_idx>0)
 					{
 						feats[count]=temp_col[j];
 						sorted_args[count]=indices_mask[sorted_indices[j]];
 						++count;
-						--temp_count_indices[sorted_indices[j]];
+						--count_idx;
 					}
 					if (count==num_vecs)
 						break;

tests/unit/features/DenseFeatures_unittest.cc

@@ -119,3 +119,33 @@ TEST(DenseFeaturesTest, copy_dimension_subset_with_subsets)
 	SG_UNREF(features);
 	SG_UNREF(f_reduced);
 }
+
+TEST(DenseFeaturesTest, shallow_copy_subset_data)
+{
+	index_t dim=5;
+	index_t n=10;
+
+	SGMatrix<float64_t> data(dim, n);
+	for (index_t i=0; i<dim; ++i)
+		for (index_t j=0; j<n; ++j)	
+			data(i,j)=CMath::random(0, n-1);
+
+	SGVector<index_t> inds(n/2);
+	for (index_t i=0; i<inds.vlen; ++i)
+		inds[i]=CMath::random(0, n-1);
+
+	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(data);
+	features->add_subset(inds);
+	CFeatures* features_copy = features->shallow_subset_copy();
+
+	SGMatrix<float64_t> orig_matrix=features->get_feature_matrix();
+	SGMatrix<float64_t> copy_matrix=static_cast<CDenseFeatures<float64_t>*>(features_copy)->get_feature_matrix();
+
+
+	for (index_t i=0; i<dim; ++i)
+		for (index_t j=0; j<inds.size(); ++j)	
+			EXPECT_EQ(orig_matrix(i,j), copy_matrix(i,j));
+
+	SG_UNREF(features_copy);
+	SG_UNREF(features);
+}