diff --git a/bayesnet/network/Network.cc b/bayesnet/network/Network.cc
index b399782..ceede5b 100644
--- a/bayesnet/network/Network.cc
+++ b/bayesnet/network/Network.cc
@@ -292,17 +292,18 @@ namespace bayesnet {
         std::vector<int> predictions(tsamples[0].size(), 0);
         std::vector<int> sample;
         std::vector<std::thread> threads;
-        std::mutex mtx;
         auto& semaphore = CountingSemaphore::getInstance();
-        auto worker = [&](const std::vector<int>& sample, const int row, std::vector<int>& predictions) {
-            semaphore.acquire();
+        auto worker = [&](const std::vector<int>& sample, const int row, int& prediction) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             auto classProbabilities = predict_sample(sample);
             auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
             int predictedClass = distance(classProbabilities.begin(), maxElem);
-            {
-                std::lock_guard<std::mutex> lock(mtx);
-                predictions[row] = predictedClass;
-            }
+            prediction = predictedClass;
             semaphore.release();
             };
         for (int row = 0; row < tsamples[0].size(); ++row) {
@@ -310,7 +311,8 @@ namespace bayesnet {
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            threads.emplace_back(worker, sample, row, std::ref(predictions));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
         }
         for (auto& thread : threads) {
             thread.join();
@@ -329,14 +331,31 @@ namespace bayesnet {
             throw std::invalid_argument("(V) Sample size (" + std::to_string(tsamples.size()) +
                 ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
         }
-        std::vector<std::vector<double>> predictions;
+        std::vector<std::vector<double>> predictions(tsamples[0].size(), std::vector<double>(classNumStates, 0.0));
         std::vector<int> sample;
+        std::vector<std::thread> threads;
+        auto& semaphore = CountingSemaphore::getInstance();
+        auto worker = [&](const std::vector<int>& sample, int row, std::vector<double>& predictions) {
+            std::string threadName = "(V)PWorker-" + std::to_string(row);
+#if defined(__linux__)
+            pthread_setname_np(pthread_self(), threadName.c_str());
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
+            std::vector<double> classProbabilities = predict_sample(sample);
+            predictions = classProbabilities;
+            semaphore.release();
+            };
         for (int row = 0; row < tsamples[0].size(); ++row) {
             sample.clear();
             for (int col = 0; col < tsamples.size(); ++col) {
                 sample.push_back(tsamples[col][row]);
             }
-            predictions.push_back(predict_sample(sample));
+            semaphore.acquire();
+            threads.emplace_back(worker, sample, row, std::ref(predictions[row]));
+        }
+        for (auto& thread : threads) {
+            thread.join();
         }
         return predictions;
     }
diff --git a/sample/CMakeLists.txt b/sample/CMakeLists.txt
index d50030e..b36cfc7 100644
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -8,7 +8,7 @@ find_package(Torch REQUIRED)
 find_library(BayesNet NAMES BayesNet.a libBayesNet.a REQUIRED)
 
 include_directories(
-    lib/Files
+    ../tests/lib/Files
     lib/mdlp
     lib/json/include
     /usr/local/include
diff --git a/sample/sample.cc b/sample/sample.cc
index 511230f..478ff85 100644
--- a/sample/sample.cc
+++ b/sample/sample.cc
@@ -60,9 +60,9 @@ int main(int argc, char* argv[])
     auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
     std::cout << "Library version: " << clf.getVersion() << std::endl;
     tie(X, y, features, className, states) = loadDataset(file_name, true);
-    clf.fit(X, y, features, className, states);
+    clf.fit(X, y, features, className, states, bayesnet::Smoothing_t::LAPLACE);
     auto score = clf.score(X, y);
-    std::cout << "File: " << file_name << " score: " << score << std::endl;
+    std::cout << "File: " << file_name << " Model: BoostAODE score: " << score << std::endl;
     return 0;
 }