diff --git a/Makefile b/Makefile
index 58574c7..bf36640 100644
--- a/Makefile
+++ b/Makefile
@@ -6,7 +6,6 @@ f_release = build_release
 f_debug = build_debug
 app_targets = b_best b_list b_main b_manage b_grid
 test_targets = unit_tests_platform
-n_procs = -j 16
 
 define ClearTests
 	@for t in $(test_targets); do \
@@ -56,10 +55,10 @@ dependency: ## Create a dependency graph diagram of the project (build/dependenc
 	cd $(f_debug) && cmake .. --graphviz=dependency.dot && dot -Tpng dependency.dot -o dependency.png
 
 buildd: ## Build the debug targets
-	cmake --build $(f_debug) -t $(app_targets) PlatformSample $(n_procs)
+	cmake --build $(f_debug) -t $(app_targets) PlatformSample --parallel
 
 buildr: ## Build the release targets
-	cmake --build $(f_release) -t $(app_targets) $(n_procs)
+	cmake --build $(f_release) -t $(app_targets) --parallel
 
 clean: ## Clean the tests info
 	@echo ">>> Cleaning Debug Platform tests...";
@@ -87,7 +86,7 @@ opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
 	@echo ">>> Running Platform tests...";
 	@$(MAKE) clean
-	@cmake --build $(f_debug) -t $(test_targets) $(n_procs)
+	@cmake --build $(f_debug) -t $(test_targets) --parallel
 	@for t in $(test_targets); do \
 		if [ -f $(f_debug)/tests/$$t ]; then \
 			cd $(f_debug)/tests ; \
diff --git a/sample/CMakeLists.txt b/sample/CMakeLists.txt
index ca0245a..4c11760 100644
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -3,7 +3,7 @@ include_directories(
     ${Platform_SOURCE_DIR}/src/main
     ${Python3_INCLUDE_DIRS}
     ${Platform_SOURCE_DIR}/lib/Files
-    ${Platform_SOURCE_DIR}/lib/mdlp
+    ${Platform_SOURCE_DIR}/lib/mdlp/src
     ${Platform_SOURCE_DIR}/lib/argparse/include
     ${Platform_SOURCE_DIR}/lib/folding
     ${Platform_SOURCE_DIR}/lib/json/include
diff --git a/sample/sample.cpp b/sample/sample.cpp
index 5842c53..982fefd 100644
--- a/sample/sample.cpp
+++ b/sample/sample.cpp
@@ -161,7 +161,8 @@ int main(int argc, char** argv)
         }
         states[className] = std::vector<int>(maxes[className]);
         auto clf = platform::Models::instance()->create(model_name);
-        clf->fit(Xd, y, features, className, states);
+        bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::ORIGINAL;
+        clf->fit(Xd, y, features, className, states, smoothing);
         if (dump_cpt) {
             std::cout << "--- CPT Tables ---" << std::endl;
             clf->dump_cpt();
@@ -210,14 +211,14 @@ int main(int argc, char** argv)
                 torch::Tensor ytraint = yt.index({ ttrain });
                 torch::Tensor Xtestt = torch::index_select(Xt, 1, ttest);
                 torch::Tensor ytestt = yt.index({ ttest });
-                clf->fit(Xtraint, ytraint, features, className, states);
+                clf->fit(Xtraint, ytraint, features, className, states, smoothing);
                 auto temp = clf->predict(Xtraint);
                 score_train = clf->score(Xtraint, ytraint);
                 score_test = clf->score(Xtestt, ytestt);
             } else {
                 auto [Xtrain, ytrain] = extract_indices(train, Xd, y);
                 auto [Xtest, ytest] = extract_indices(test, Xd, y);
-                clf->fit(Xtrain, ytrain, features, className, states);
+                clf->fit(Xtrain, ytrain, features, className, states, smoothing);
                 std::cout << "Nodes: " << clf->getNumberOfNodes() << std::endl;
                 nodes += clf->getNumberOfNodes();
                 score_train = clf->score(Xtrain, ytrain);