LPWrapper Convex Hull test fixed

Rinitialization removed existing data leading to the problem

src/feature_creation/feature_space/FeatureSpace.cpp

@@ -800,7 +800,6 @@ void FeatureSpace::generate_and_project(std::shared_ptr<LossFunction> loss, std:
     #pragma omp parallel firstprivate(worst_score, worst_score_ind, scores_sel_all)
     {
         std::shared_ptr<LossFunction> loss_copy = loss_function_util::copy(loss);
-        std::cout << prop_sorted_d_mat::N_FEATURES  << '\t' << prop_sorted_d_mat::SORTED_D_MATRIX.size() << '\t' << node_value_arrs::D_MATRIX.size() << std::endl;
         std::vector<node_ptr> phi_sel_private(phi_sel);
         std::vector<double> scores_sel_private(scores_sel);
         int index_base = _phi.size() + _n_sis_select * (omp_get_thread_num() + _mpi_comm->size());

src/loss_function/LossFunctionConvexHull.cpp

@@ -228,12 +228,11 @@ void LossFunctionConvexHull::setup_lp(bool initialize_sorted_d_mat)
     if((omp_get_thread_num() == 0) && initialize_sorted_d_mat)
     {
         prop_sorted_d_mat::initialize_sorted_d_matrix_arr(
-            prop_sorted_d_mat::N_FEATURES, 
-	    _n_task, 
-	    _n_class, 
-	    n_samp_per_class
-	);
-	std::cout << prop_sorted_d_mat::N_TASK << '\t' << prop_sorted_d_mat::N_CLASS << '\t' << prop_sorted_d_mat::N_FEATURES << '\t' << prop_sorted_d_mat::N_SAMPLES << '\t' <<  prop_sorted_d_mat::SORTED_D_MATRIX.size() << std::endl;
+            std::max(prop_sorted_d_mat::N_FEATURES, _n_feat),
+            _n_task,
+            _n_class,
+            n_samp_per_class
+        );
     }
 }
 

tests/googletest/loss_function/test_convex_hull_loss.cc

@@ -124,11 +124,6 @@ namespace
             std::fill_n(_prop_test.begin() +  5, 5, 1.0);
             std::fill_n(_prop_test.begin() + 10, 5, 2.0);
             std::fill_n(_prop_test.begin() + 15, 5, 3.0);
-
-            prop_sorted_d_mat::initialize_sorted_d_matrix_arr(2, _task_sizes_train.size(), 4, std::vector<int>(4, 20));
-            std::copy_n(value_1.data(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(0));
-            std::copy_n(value_2.data(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(1));
-	    std::cout << prop_sorted_d_mat::access_sorted_d_matrix(1)[0] <<'\t' << prop_sorted_d_mat::access_sorted_d_matrix(0)[0] << std::endl;
         }
 
         void TearDown() override
@@ -156,6 +151,8 @@ namespace
             _task_sizes_test,
             2
         );
+        std::copy_n(_phi[0]->value_ptr(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(0));
+        std::copy_n(_phi[1]->value_ptr(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(1));
 
         EXPECT_EQ(std::floor(loss.project(_phi[0])), 80);
         EXPECT_EQ(std::floor(loss.project(_phi[1])), 80);
@@ -176,6 +173,8 @@ namespace
             _task_sizes_test,
             2
         );
+        std::copy_n(_phi[0]->value_ptr(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(0));
+        std::copy_n(_phi[1]->value_ptr(), _task_sizes_train[0], prop_sorted_d_mat::access_sorted_d_matrix(1));
 
         LossFunctionConvexHull loss_copy(std::make_shared<LossFunctionConvexHull>(loss));