diff --git a/bayesnet/classifiers/.XSPODE.h.swp b/bayesnet/classifiers/.XSPODE.h.swp
deleted file mode 100644
index 024d03c..0000000
Binary files a/bayesnet/classifiers/.XSPODE.h.swp and /dev/null differ
diff --git a/lib/folding b/lib/folding
index 2ac43e3..9652853 160000
--- a/lib/folding
+++ b/lib/folding
@@ -1 +1 @@
-Subproject commit 2ac43e32ac1eac0c986702ec526cf5367a565ef0
+Subproject commit 9652853d692ed3b8a38d89f70559209ffb988020
diff --git a/sample/sample_xspode.cc b/sample/sample_xspode.cc
index a071ae3..48e93c6 100644
--- a/sample/sample_xspode.cc
+++ b/sample/sample_xspode.cc
@@ -43,16 +43,6 @@ tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>,
     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[])
@@ -62,13 +52,11 @@ int main(int argc, char* argv[])
         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
+    bayesnet::BaseClassifier* clf = new bayesnet::XSpode(0);
     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;
diff --git a/tests/lib/catch2 b/tests/lib/catch2
index 506276c..0321d2f 160000
--- a/tests/lib/catch2
+++ b/tests/lib/catch2
@@ -1 +1 @@
-Subproject commit 506276c59217429c93abd2fe9507c7f45eb81072
+Subproject commit 0321d2fce328b5e2ad106a8230ff20e0d5bf5501