diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 2c2142c..c2a109b 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -6,4 +6,4 @@ add_executable(main main.cc STree.cc SVC.cc PyClassifier.cc PyWrap.cc)
 add_executable(example example.cpp PyWrap.cc)
 
 target_link_libraries(main ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)
-target_link_libraries(example ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" Boost::boost Boost::python Boost::numpy ArffFiles)
+target_link_libraries(example ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::boost Boost::python Boost::numpy ArffFiles)
diff --git a/src/PyClassifier.cc b/src/PyClassifier.cc
index 81f7b43..a97033f 100644
--- a/src/PyClassifier.cc
+++ b/src/PyClassifier.cc
@@ -54,10 +54,10 @@ namespace pywrap {
     {
         std::cout << "PyClassifier:fit:Converting X to PyObject" << std::endl;
         auto [Xn, yn] = tensors2numpy(X, y);
-        CPyObject Xp = Xn.ptr();
+        CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
         std::cout << "PyClassifier:fit:Converting y to PyObject" << std::endl;
         print_array(yn);
-        CPyObject yp = yn.ptr();
+        CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
         std::cout << "PyClassifier:fit:Calling fit" << std::endl;
         pyWrap->fit(module, this->className, Xp, yp);
         return *this;
@@ -65,18 +65,18 @@ namespace pywrap {
     torch::Tensor PyClassifier::predict(torch::Tensor& X)
     {
         auto Xn = tensor2numpy(X);
-        print_array(Xn);
-        CPyObject Xp = Xn.ptr();
+        CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
         auto PyResult = pyWrap->predict(module, className, Xp);
         auto result = torch::tensor({ 1,2,3 });
+
         return result;
     }
     double PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
     {
         std::cout << "PyClassifier::Score:Converting X to PyObject" << std::endl;
         auto [Xn, yn] = tensors2numpy(X, y);
-        CPyObject Xp = Xn.ptr();
-        CPyObject yp = yn.ptr();
+        CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
+        CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
         print_array(yn);
         auto result = pyWrap->score(module, className, Xp, yp);
         return result;
diff --git a/src/PyClassifier.h b/src/PyClassifier.h
index bdc6364..c777624 100644
--- a/src/PyClassifier.h
+++ b/src/PyClassifier.h
@@ -1,11 +1,12 @@
 #ifndef PYCLASSIFER_H
 #define PYCLASSIFER_H
+#include "boost/python/detail/wrap_python.hpp"
+#include <boost/python/numpy.hpp>
 #include <string>
 #include <map>
 #include <vector>
 #include <utility>
 #include <torch/torch.h>
-#include <boost/python/numpy.hpp>
 #include "PyWrap.h"
 
 namespace pywrap {
diff --git a/src/PyHelper.hpp b/src/PyHelper.hpp
index 2404f6f..ea3f5df 100644
--- a/src/PyHelper.hpp
+++ b/src/PyHelper.hpp
@@ -3,9 +3,9 @@
 #pragma once
 // Code taken and adapted from 
 // https ://www.codeproject.com/Articles/820116/Embedding-Python-program-in-a-C-Cplusplus-code
-#include <iostream>
-#include <Python.h>
+#include "boost/python/detail/wrap_python.hpp"
 #include <boost/python/numpy.hpp>
+#include <iostream>
 
 namespace pywrap {
     namespace p = boost::python;
diff --git a/src/PyWrap.h b/src/PyWrap.h
index 0dfe02b..96edd79 100644
--- a/src/PyWrap.h
+++ b/src/PyWrap.h
@@ -1,6 +1,6 @@
 #ifndef PYWRAP_H
 #define PYWRAP_H
-#include <Python.h>
+#include "boost/python/detail/wrap_python.hpp"
 #include <string>
 #include <map>
 #include <tuple>
diff --git a/src/example.cpp b/src/example.cpp
index c2ae048..e60b8af 100644
--- a/src/example.cpp
+++ b/src/example.cpp
@@ -1,9 +1,11 @@
+#include "boost/python/detail/wrap_python.hpp"
+#include <boost/python/numpy.hpp>
 #include <torch/torch.h>
+#include <torch/csrc/utils/tensor_numpy.h>
 #include <string>
 #include <iostream>
 #include "ArffFiles.h"
 #include "PyHelper.hpp"
-#include <boost/python/numpy.hpp>
 #include "PyWrap.h"
 
 
@@ -128,24 +130,59 @@ int main(int argc, char** argv)
         {
             cout << "--Fit Phase--" << endl;
             auto [Xn, yn] = getData(dataset);
-            CPyObject Xp = Xn.ptr();
-            CPyObject yp = yn.ptr();
+            auto Xn_shapes = Xn.get_shape();
+            auto yn_shapes = yn.get_shape();
+            CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
+            CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
+            //print_array(yn);
             // Call fit
             cout << "Calling fit" << endl;
             wrapper->fit(moduleName, className, Xp, yp);
             cout << "--Fit Phase end--" << endl;
         }
-        // Call score
+        // Score
         {
             cout << "--Score Phase--" << endl;
             auto [Xn, yn] = getData(dataset);
-            CPyObject Xp = Xn.ptr();
-            CPyObject yp = yn.ptr();
+            auto Xn_shapes = Xn.get_shape();
+            auto yn_shapes = yn.get_shape();
+            CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
+            CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
+            //print_array(yn);
+            // Call score
             cout << "Calling score" << endl;
-            auto score = wrapper->score(moduleName, className, Xp, yp);
-            cout << "Score: " << score << endl;
+            auto result = wrapper->score(moduleName, className, Xp, yp);
+            cout << "Score: " << result << endl;
             cout << "--Score Phase end--" << endl;
         }
+        // Call score
+        // {
+        //     np::initialize();
+        //     cout << "--Score Phase--" << endl;
+        //     auto [X, y, featuresx, classNamex, statesx] = loadDataset(dataset, true);
+        //     auto [Xn, yn] = tensors2numpy(X, y);
+        //     auto Xn_shapes = Xn.get_shape();
+        //     auto yn_shapes = yn.get_shape();
+        //     cout << "Xn_shapes: " << Xn_shapes[0] << ", " << Xn_shapes[1] << endl;
+        //     cout << "yn_shapes: " << yn_shapes[0] << endl;
+        //     cout << "X shapes: " << X.sizes() << endl;
+        //     cout << "y shapes: " << y.sizes() << endl;
+        //     assert(Xn_shapes[0] == X.sizes()[0]);
+        //     assert(Xn_shapes[1] == X.sizes()[1]);
+        //     assert(yn_shapes[0] == y.sizes()[0]);
+        //     CPyObject Xp = Xn.ptr();
+        //     CPyObject yp = yn.ptr();
+        //     print_array(yn);
+        //     cout << "Calling score" << endl;
+        //     auto instance = wrapper->getClass(moduleName, className);
+        //     CPyObject result;
+        //     if (!(result = PyObject_CallMethod(instance, "score", "OO", Xp.getObject(), yp.getObject())))
+        //         errorAbort("Couldn't call method score");
+        //     auto score = PyFloat_AsDouble(result);
+        //     //auto score = wrapper->score(moduleName, className, Xp, yp);
+        //     cout << "Score: " << score << endl;
+        //     cout << "--Score Phase end--" << endl;
+        // }
         // Clean module
         {
             cout << "--Clean Phase--" << endl;
diff --git a/src/main.cc b/src/main.cc
index 977e963..e2d642a 100644
--- a/src/main.cc
+++ b/src/main.cc
@@ -40,27 +40,6 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
 }
 
-// int main(int argc, char* argv[])
-// {
-//     auto [X, y, features, className, states] = loadDataset("iris", true);
-//     auto stree = pywrap::STree();
-//     stree.version();
-//     auto svc = pywrap::SVC();
-//     svc.version();
-//     cout << "Graph: " << stree.graph() << endl;
-//     stree.version();
-//     cout << string(80, '-') << endl;
-//     cout << "X: " << X.sizes() << endl;
-//     cout << "y: " << y.sizes() << endl;
-//     auto result = stree.fit(X, y, features, className, states);
-//     result = svc.fit(X, y, features, className, states);
-//     cout << "Now calling score" << endl;
-//     // auto result1 = stree.score(X, y);
-//     // auto result2 = svc.score(X, y);
-//     // cout << "STree score " << result1 << endl;
-//     // cout << "SVC score " << result2 << endl;
-//     return 0;
-// }
 int main(int argc, char* argv[])
 {
     cout << "* Begin." << endl;
@@ -68,17 +47,16 @@ int main(int argc, char* argv[])
         auto [X, y, features, className, states] = loadDataset("iris", true);
         cout << "X: " << X.sizes() << endl;
         cout << "y: " << y.sizes() << endl;
-        cout << "y: " << y << endl;
         auto clf = pywrap::PyClassifier("stree", "Stree");
         cout << "STree Version: " << clf.version() << endl;
-        // if (true) {
-        //     auto svc = pywrap::PyClassifier("sklearn.svm", "SVC");
-        //     cout << "SVC Version: " << svc.callMethodString("_repr_html_") << endl;
-        //     cout << "Calling fit" << endl;
-        //     svc.fit(X, y, features, className, states);
-        //     cout << "Calling score" << endl;
-        //     cout << "SVC Score: " << svc.score(X, y) << endl;
-        // }
+        if (true) {
+            auto svc = pywrap::PyClassifier("sklearn.svm", "SVC");
+            cout << "SVC Version: " << svc.callMethodString("_repr_html_") << endl;
+            cout << "Calling fit" << endl;
+            svc.fit(X, y, features, className, states);
+            cout << "Calling score" << endl;
+            cout << "SVC Score: " << svc.score(X, y) << endl;
+        }
         cout << "Graph: " << clf.graph() << endl;
         cout << "Calling fit" << endl;
         clf.fit(X, y, features, className, states);