diff --git a/lib/catch2 b/lib/catch2
index cd8f97e..4e8d92b 160000
--- a/lib/catch2
+++ b/lib/catch2
@@ -1 +1 @@
-Subproject commit cd8f97e6c7f6aaf332992f83bf9d7ee7f357f85e
+Subproject commit 4e8d92bf02f7d1c8006a0e7a5ecabd8e62d98502
diff --git a/lib/json b/lib/json
index 377c767..8c391e0 160000
--- a/lib/json
+++ b/lib/json
@@ -1 +1 @@
-Subproject commit 377c767aa19da7159cf537490bc33da96edf8803
+Subproject commit 8c391e04fe4195d8be862c97f38cfe10e2a3472e
diff --git a/lib/mdlp b/lib/mdlp
index 5708dc3..236d1b2 160000
--- a/lib/mdlp
+++ b/lib/mdlp
@@ -1 +1 @@
-Subproject commit 5708dc3de944fc22d61a2dd071b63aa338e04db3
+Subproject commit 236d1b2f8be185039493fe7fce04a83e02ed72e5
diff --git a/pyclfs/PyClassifier.cc b/pyclfs/PyClassifier.cc
index aeb3798..9ab656a 100644
--- a/pyclfs/PyClassifier.cc
+++ b/pyclfs/PyClassifier.cc
@@ -21,10 +21,22 @@ namespace pywrap {
         Xn = Xn.transpose();
         return Xn;
     }
+    np::ndarray tensorInt2numpy(torch::Tensor& X)
+    {
+        int m = X.size(0);
+        int n = X.size(1);
+        auto Xn = np::from_data(X.data_ptr(), np::dtype::get_builtin<int>(), bp::make_tuple(m, n), bp::make_tuple(sizeof(X.dtype()) * 2 * n, sizeof(X.dtype()) * 2), bp::object());
+        Xn = Xn.transpose();
+        //std::cout << "Transposed array:\n" << boost::python::extract<char const*>(boost::python::str(Xn)) << std::endl;
+        return Xn;
+    }
     std::pair<np::ndarray, np::ndarray> tensors2numpy(torch::Tensor& X, torch::Tensor& y)
     {
         int n = X.size(1);
         auto yn = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), bp::make_tuple(n), bp::make_tuple(sizeof(y.dtype()) * 2), bp::object());
+        if (X.dtype() == torch::kInt32) {
+            return { tensorInt2numpy(X), yn };
+        }
         return { tensor2numpy(X), yn };
     }
     std::string PyClassifier::version()
@@ -65,8 +77,14 @@ namespace pywrap {
     torch::Tensor PyClassifier::predict(torch::Tensor& X)
     {
         int dimension = X.size(1);
-        auto Xn = tensor2numpy(X);
-        CPyObject Xp = bp::incref(bp::object(Xn).ptr());
+        CPyObject Xp;
+        if (X.dtype() == torch::kInt32) {
+            auto Xn = tensorInt2numpy(X);
+            Xp = bp::incref(bp::object(Xn).ptr());
+        } else {
+            auto Xn = tensor2numpy(X);
+            Xp = bp::incref(bp::object(Xn).ptr());
+        }
         PyObject* incoming = pyWrap->predict(id, Xp);
         bp::handle<> handle(incoming);
         bp::object object(handle);
diff --git a/tests/TestPythonClassifiers.cc b/tests/TestPythonClassifiers.cc
index e72c4b1..b1ea6c2 100644
--- a/tests/TestPythonClassifiers.cc
+++ b/tests/TestPythonClassifiers.cc
@@ -13,7 +13,7 @@
 #include "pyclfs/ODTE.h"
 #include "TestUtils.h"
 
-const std::string ACTUAL_VERSION = "1.0.4";
+const std::string ACTUAL_VERSION = "1.0.5";
 
 TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
 {
@@ -60,19 +60,27 @@ TEST_CASE("Test Python Classifiers score", "[PyClassifiers]")
 }
 TEST_CASE("Classifiers features", "[PyClassifiers]")
 {
-    auto raw = RawDatasets("iris", true);
+    auto raw = RawDatasets("iris", false);
     auto clf = pywrap::STree();
     clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-    REQUIRE(clf.getNumberOfNodes() == 3);
-    REQUIRE(clf.getNumberOfEdges() == 2);
+    REQUIRE(clf.getNumberOfNodes() == 5);
+    REQUIRE(clf.getNumberOfEdges() == 3);
 }
 TEST_CASE("Get num features & num edges", "[PyClassifiers]")
 {
-    auto raw = RawDatasets("iris", true);
+    auto raw = RawDatasets("iris", false);
     auto clf = pywrap::ODTE();
     clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
-    REQUIRE(clf.getNumberOfNodes() == 10);
-    REQUIRE(clf.getNumberOfEdges() == 10);
+    REQUIRE(clf.getNumberOfNodes() == 50);
+    REQUIRE(clf.getNumberOfEdges() == 30);
+}
+TEST_CASE("Classifier with discretized dataset", "[PyClassifiers]")
+{
+    auto raw = RawDatasets("iris", true);
+    auto clf = pywrap::SVC();
+    clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    auto score = clf.score(raw.Xt, raw.yt);
+    REQUIRE(score == Catch::Approx(0.96667f).epsilon(raw.epsilon));
 }
 // TEST_CASE("XGBoost", "[PyClassifiers]")
 // {