diff --git a/Makefile b/Makefile
index 836c853..d48d477 100644
--- a/Makefile
+++ b/Makefile
@@ -99,7 +99,15 @@ sample: ## Build sample
 	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
 	@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample
 	sample/build/bayesnet_sample $(fname)
-	@echo ">>> Done";	
+	@echo ">>> Done";
+
+fname = "tests/data/iris.arff"
+sample2: ## Build sample2
+	@echo ">>> Building Sample...";
+	@if [ -d ./sample/build ]; then rm -rf ./sample/build; fi
+	@cd sample && cmake -B build -S . && cmake --build build -t bayesnet_sample_xspode
+	sample/build/bayesnet_sample_xspode $(fname)
+	@echo ">>> Done";		
 
 opt = ""
 test: ## Run tests (opt="-s") to verbose output the tests, (opt="-c='Test Maximum Spanning Tree'") to run only that section
diff --git a/sample/CMakeLists.txt b/sample/CMakeLists.txt
index d3e79a4..b6f78ac 100644
--- a/sample/CMakeLists.txt
+++ b/sample/CMakeLists.txt
@@ -22,4 +22,6 @@ include_directories(
 )
 
 add_executable(bayesnet_sample sample.cc) 
-target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
\ No newline at end of file
+target_link_libraries(bayesnet_sample ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
+add_executable(bayesnet_sample_xspode sample_xspode.cc) 
+target_link_libraries(bayesnet_sample_xspode ${FImdlp} "${TORCH_LIBRARIES}" "${BayesNet}")
\ No newline at end of file
diff --git a/sample/sample_xspode.cc b/sample/sample_xspode.cc
new file mode 100644
index 0000000..a071ae3
--- /dev/null
+++ b/sample/sample_xspode.cc
@@ -0,0 +1,77 @@
+// ***************************************************************
+// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
+// SPDX-FileType: SOURCE
+// SPDX-License-Identifier: MIT
+// ***************************************************************
+
+#include <ArffFiles.hpp>
+#include <CPPFImdlp.h>
+#include <bayesnet/ensembles/BoostAODE.h>
+#include <bayesnet/classifiers/XSPODE.h>
+
+std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
+{
+    std::vector<mdlp::labels_t> Xd;
+    auto fimdlp = mdlp::CPPFImdlp();
+    for (int i = 0; i < X.size(); i++) {
+        fimdlp.fit(X[i], y);
+        mdlp::labels_t& xd = fimdlp.transform(X[i]);
+        Xd.push_back(xd);
+    }
+    return Xd;
+}
+tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last)
+{
+    auto handler = ArffFiles();
+    handler.load(name, class_last);
+    // Get Dataset X, y
+    std::vector<mdlp::samples_t>& X = handler.getX();
+    mdlp::labels_t y = handler.getY();
+    // Get className & Features
+    auto className = handler.getClassName();
+    std::vector<std::string> features;
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
+    torch::Tensor Xd;
+    auto states = map<std::string, std::vector<int>>();
+    auto Xr = discretizeDataset(X, y);
+    for (int i = 0; i < features.size(); ++i) {
+        states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
+        auto item = states.at(features[i]);
+        iota(begin(item), end(item), 0);
+    }
+    states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
+    iota(begin(states.at(className)), end(states.at(className)), 0);
+    return { Xr, y, features, className, states };
+    // Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
+    // for (int i = 0; i < features.size(); ++i) {
+    //     states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
+    //     auto item = states.at(features[i]);
+    //     iota(begin(item), end(item), 0);
+    //     Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
+    // }
+    // states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
+    // iota(begin(states.at(className)), end(states.at(className)), 0);
+    // return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
+}
+
+int main(int argc, char* argv[])
+{
+    if (argc < 2) {
+        std::cerr << "Usage: " << argv[0] << " <file_name>" << std::endl;
+        return 1;
+    }
+    std::string file_name = argv[1];
+    // auto clf = bayesnet::BoostAODE(false); // false for not using voting in predict
+    bayesnet::BaseClassifier* clf = new bayesnet::XSpode(0); // false for not using voting in predict
+    std::cout << "Library version: " << clf->getVersion() << std::endl;
+    auto [X, y, features, className, states] = loadDataset(file_name, true);
+    torch::Tensor weights = torch::full({ static_cast<long>(X[0].size()) }, 1.0 / X[0].size(), torch::kDouble);
+    clf->fit(X, y, features, className, states, bayesnet::Smoothing_t::ORIGINAL);
+    // auto score = clf.score(X, y);
+    auto score = clf->score(X, y);
+    std::cout << "File: " << file_name << " Model: XSpode(0) score: " << score << std::endl;
+    delete clf;
+    return 0;
+}
+