diff --git a/mlpp/kmeans/kmeans.cpp b/mlpp/kmeans/kmeans.cpp
index 0e5d1a1..d0db130 100644
--- a/mlpp/kmeans/kmeans.cpp
+++ b/mlpp/kmeans/kmeans.cpp
@@ -12,16 +12,101 @@
 #include <iostream>
 #include <random>
 
+Ref<MLPPMatrix> MLPPKMeans::get_input_set() {
+	return _input_set;
+}
+void MLPPKMeans::set_input_set(const Ref<MLPPMatrix> &val) {
+	_input_set = val;
+	_initialized = false;
+}
 
-MLPPKMeans::MLPPKMeans(std::vector<std::vector<real_t>> inputSet, int k, std::string init_type) :
-		inputSet(inputSet), k(k), init_type(init_type) {
-	if (init_type == "KMeans++") {
-		kmeansppInitialization(k);
+int MLPPKMeans::get_k() {
+	return _k;
+}
+void MLPPKMeans::set_k(const int val) {
+	_k = val;
+	_initialized = false;
+}
+
+MLPPKMeans::MeanType MLPPKMeans::get_mean_type() {
+	return _mean_type;
+}
+void MLPPKMeans::set_mean_type(const MLPPKMeans::MeanType val) {
+	_mean_type = val;
+	_initialized = false;
+}
+
+void MLPPKMeans::initialize() {
+	if (_mean_type == MEAN_TYPE_KMEANSPP) {
+		_kmeanspp_initialization(_k);
 	} else {
-		centroidInitialization(k);
+		_centroid_initialization(_k);
 	}
 }
 
+Ref<MLPPMatrix> MLPPKMeans::model_set_test(const Ref<MLPPMatrix> &X) {
+	return Ref<MLPPMatrix>();
+}
+Ref<MLPPVector> MLPPKMeans::model_test(const Ref<MLPPVector> &x) {
+	return Ref<MLPPVector>();
+}
+void MLPPKMeans::train(int epoch_num, bool UI) {
+}
+real_t MLPPKMeans::score() {
+	return 0;
+}
+Ref<MLPPVector> MLPPKMeans::silhouette_scores() {
+	return Ref<MLPPVector>();
+}
+
+MLPPKMeans::MLPPKMeans() {
+	_accuracy_threshold = 0;
+	_k = 0;
+	_initialized = false;
+
+	_mean_type = MEAN_TYPE_CENTROID;
+}
+MLPPKMeans::~MLPPKMeans() {
+}
+
+void MLPPKMeans::_evaluate() {
+}
+void MLPPKMeans::_compute_mu() {
+}
+
+void MLPPKMeans::_centroid_initialization(int k) {
+}
+void MLPPKMeans::_kmeanspp_initialization(int k) {
+}
+real_t MLPPKMeans::_cost() {
+	return 0;
+}
+
+void MLPPKMeans::_bind_methods() {
+	ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPKMeans::get_input_set);
+	ClassDB::bind_method(D_METHOD("set_input_set", "value"), &MLPPKMeans::set_input_set);
+	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
+
+	ClassDB::bind_method(D_METHOD("get_k"), &MLPPKMeans::get_k);
+	ClassDB::bind_method(D_METHOD("set_k", "value"), &MLPPKMeans::set_k);
+	ADD_PROPERTY(PropertyInfo(Variant::INT, "k"), "set_k", "get_k");
+
+	ClassDB::bind_method(D_METHOD("get_mean_type"), &MLPPKMeans::get_mean_type);
+	ClassDB::bind_method(D_METHOD("set_mean_type", "value"), &MLPPKMeans::set_mean_type);
+	ADD_PROPERTY(PropertyInfo(Variant::INT, "mean_type", PROPERTY_HINT_ENUM, "Centroid,KMeansPP"), "set_mean_type", "get_mean_type");
+
+	ClassDB::bind_method(D_METHOD("initialize"), &MLPPKMeans::initialize);
+	ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPKMeans::model_set_test);
+	ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPKMeans::model_test);
+	ClassDB::bind_method(D_METHOD("train", "epoch_num", "UI"), &MLPPKMeans::train, false);
+	ClassDB::bind_method(D_METHOD("score"), &MLPPKMeans::score);
+	ClassDB::bind_method(D_METHOD("silhouette_scores"), &MLPPKMeans::silhouette_scores);
+
+	BIND_ENUM_CONSTANT(MEAN_TYPE_CENTROID);
+	BIND_ENUM_CONSTANT(MEAN_TYPE_KMEANSPP);
+}
+
+/*
 std::vector<std::vector<real_t>> MLPPKMeans::modelSetTest(std::vector<std::vector<real_t>> X) {
 	MLPPLinAlg alg;
 	std::vector<std::vector<real_t>> closestCentroids;
@@ -207,8 +292,8 @@ void MLPPKMeans::kmeansppInitialization(int k) {
 		std::vector<real_t> farthestCentroid;
 		for (int j = 0; j < inputSet.size(); j++) {
 			real_t max_dist = 0;
-			/* SUM ALL THE SQUARED DISTANCES, CHOOSE THE ONE THAT'S FARTHEST
-			AS TO SPREAD OUT THE CLUSTER CENTROIDS. */
+			// SUM ALL THE SQUARED DISTANCES, CHOOSE THE ONE THAT'S FARTHEST
+			// AS TO SPREAD OUT THE CLUSTER CENTROIDS.
 			real_t sum = 0;
 			for (int k = 0; k < mu.size(); k++) {
 				sum += alg.euclideanDistance(inputSet[j], mu[k]);
@@ -233,3 +318,4 @@ real_t MLPPKMeans::Cost() {
 	return sum;
 }
 
+*/
\ No newline at end of file
diff --git a/mlpp/kmeans/kmeans.h b/mlpp/kmeans/kmeans.h
index 5a93012..8dbea48 100644
--- a/mlpp/kmeans/kmeans.h
+++ b/mlpp/kmeans/kmeans.h
@@ -10,38 +10,64 @@
 
 #include "core/math/math_defs.h"
 
-#include <string>
-#include <vector>
+#include "core/object/reference.h"
 
+#include "../lin_alg/mlpp_matrix.h"
+#include "../lin_alg/mlpp_vector.h"
+
+class MLPPKMeans : public Reference {
+	GDCLASS(MLPPKMeans, Reference);
 
-class MLPPKMeans {
 public:
-	MLPPKMeans(std::vector<std::vector<real_t>> inputSet, int k, std::string init_type = "Default");
-	std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
-	std::vector<real_t> modelTest(std::vector<real_t> x);
-	void train(int epoch_num, bool UI = 1);
+	enum MeanType {
+		MEAN_TYPE_CENTROID = 0,
+		MEAN_TYPE_KMEANSPP,
+	};
+
+public:
+	Ref<MLPPMatrix> get_input_set();
+	void set_input_set(const Ref<MLPPMatrix> &val);
+
+	int get_k();
+	void set_k(const int val);
+
+	MeanType get_mean_type();
+	void set_mean_type(const MeanType val);
+
+	void initialize();
+
+	Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
+	Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
+	void train(int epoch_num, bool UI = false);
 	real_t score();
-	std::vector<real_t> silhouette_scores();
+	Ref<MLPPVector> silhouette_scores();
 
-private:
-	void Evaluate();
-	void computeMu();
+	MLPPKMeans();
+	~MLPPKMeans();
 
-	void centroidInitialization(int k);
-	void kmeansppInitialization(int k);
-	real_t Cost();
+protected:
+	
 
-	std::vector<std::vector<real_t>> inputSet;
-	std::vector<std::vector<real_t>> mu;
-	std::vector<std::vector<real_t>> r;
+	void _evaluate();
+	void _compute_mu();
 
-	real_t euclideanDistance(std::vector<real_t> A, std::vector<real_t> B);
+	void _centroid_initialization(int k);
+	void _kmeanspp_initialization(int k);
+	real_t _cost();
 
-	real_t accuracy_threshold;
-	int k;
+	static void _bind_methods();
 
-	std::string init_type;
+	Ref<MLPPMatrix> _input_set;
+	Ref<MLPPMatrix> _mu;
+	Ref<MLPPMatrix> _r;
+
+	real_t _accuracy_threshold;
+	int _k;
+	bool _initialized;
+
+	MeanType _mean_type;
 };
 
+VARIANT_ENUM_CAST(MLPPKMeans::MeanType);
 
 #endif /* KMeans_hpp */
diff --git a/register_types.cpp b/register_types.cpp
index 67379ce..e6fd666 100644
--- a/register_types.cpp
+++ b/register_types.cpp
@@ -28,6 +28,7 @@ SOFTWARE.
 #include "mlpp/lin_alg/mlpp_vector.h"
 
 #include "mlpp/knn/knn.h"
+#include "mlpp/kmeans/kmeans.h"
 
 #include "test/mlpp_tests.h"
 
@@ -37,6 +38,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
 		ClassDB::register_class<MLPPMatrix>();
 
 		ClassDB::register_class<MLPPKNN>();
+		ClassDB::register_class<MLPPKMeans>();
 
 		ClassDB::register_class<MLPPDataESimple>();
 		ClassDB::register_class<MLPPDataSimple>();
diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp
index 74eafa7..9822cbb 100644
--- a/test/mlpp_tests.cpp
+++ b/test/mlpp_tests.cpp
@@ -541,6 +541,7 @@ void MLPPTests::test_k_means(bool ui) {
 	MLPPLinAlg alg;
 
 	// KMeans
+	/*
 	std::vector<std::vector<real_t>> inputSet = { { 32, 0, 7 }, { 2, 28, 17 }, { 0, 9, 23 } };
 	MLPPKMeans kmeans(inputSet, 3, "KMeans++");
 	kmeans.train(3, ui);
@@ -548,6 +549,7 @@ void MLPPTests::test_k_means(bool ui) {
 	alg.printMatrix(kmeans.modelSetTest(inputSet)); // Returns the assigned centroids to each of the respective training examples
 	std::cout << std::endl;
 	alg.printVector(kmeans.silhouette_scores());
+	*/
 }
 void MLPPTests::test_knn(bool ui) {
 	MLPPLinAlg alg;