diff --git a/mlpp/regularization/reg_old.cpp b/mlpp/regularization/reg_old.cpp
index 514a432..3d85658 100644
--- a/mlpp/regularization/reg_old.cpp
+++ b/mlpp/regularization/reg_old.cpp
@@ -14,258 +14,6 @@
 #include <iostream>
 #include <random>
 
-real_t MLPPRegOld::reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType p_reg) {
-	int size = weights->size();
-	const real_t *weights_ptr = weights->ptr();
-
-	if (p_reg == REGULARIZATION_TYPE_RIDGE) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			real_t wi = weights_ptr[i];
-			reg += wi * wi;
-		}
-		return reg * lambda / 2;
-	} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			reg += ABS(weights_ptr[i]);
-		}
-		return reg * lambda;
-	} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			real_t wi = weights_ptr[i];
-			reg += alpha * ABS(wi); // Lasso Reg
-			reg += ((1 - alpha) / 2) * wi * wi; // Ridge Reg
-		}
-		return reg * lambda;
-	}
-
-	return 0;
-}
-real_t MLPPRegOld::reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType p_reg) {
-	int size = weights->data_size();
-	const real_t *weights_ptr = weights->ptr();
-
-	if (p_reg == REGULARIZATION_TYPE_RIDGE) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			real_t wi = weights_ptr[i];
-			reg += wi * wi;
-		}
-		return reg * lambda / 2;
-	} else if (p_reg == REGULARIZATION_TYPE_LASSO) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			reg += ABS(weights_ptr[i]);
-		}
-		return reg * lambda;
-	} else if (p_reg == REGULARIZATION_TYPE_ELASTIC_NET) {
-		real_t reg = 0;
-		for (int i = 0; i < size; ++i) {
-			real_t wi = weights_ptr[i];
-			reg += alpha * ABS(wi); // Lasso Reg
-			reg += ((1 - alpha) / 2) * wi * wi; // Ridge Reg
-		}
-		return reg * lambda;
-	}
-
-	return 0;
-}
-
-Ref<MLPPVector> MLPPRegOld::reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType p_reg) {
-	MLPPLinAlg alg;
-
-	if (p_reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
-		return reg_deriv_termv(weights, lambda, alpha, p_reg);
-	}
-
-	return alg.subtractionnv(weights, reg_deriv_termv(weights, lambda, alpha, p_reg));
-
-	// for(int i = 0; i < weights.size(); i++){
-	//     weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i);
-	// }
-	// return weights;
-}
-Ref<MLPPMatrix> MLPPRegOld::reg_weightsm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType reg) {
-	MLPPLinAlg alg;
-
-	if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) {
-		return reg_deriv_termm(weights, lambda, alpha, reg);
-	}
-
-	return alg.subtractionm(weights, reg_deriv_termm(weights, lambda, alpha, reg));
-
-	// for(int i = 0; i < weights.size(); i++){
-	//     for(int j = 0; j < weights[i].size(); j++){
-	//         weights[i][j] -= regDerivTerm(weights, lambda, alpha, reg, i, j);
-	//     }
-	// }
-	// return weights;
-}
-
-Ref<MLPPVector> MLPPRegOld::reg_deriv_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType reg) {
-	Ref<MLPPVector> reg_driv;
-	reg_driv.instance();
-
-	int size = weights->size();
-
-	reg_driv->resize(size);
-
-	real_t *reg_driv_ptr = reg_driv->ptrw();
-
-	for (int i = 0; i < size; ++i) {
-		reg_driv_ptr[i] = reg_deriv_termvr(weights, lambda, alpha, reg, i);
-	}
-
-	return reg_driv;
-}
-Ref<MLPPMatrix> MLPPRegOld::reg_deriv_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType reg) {
-	Ref<MLPPMatrix> reg_driv;
-	reg_driv.instance();
-
-	Size2i size = weights->size();
-
-	reg_driv->resize(size);
-
-	real_t *reg_driv_ptr = reg_driv->ptrw();
-
-	for (int i = 0; i < size.y; ++i) {
-		for (int j = 0; j < size.x; ++j) {
-			reg_driv_ptr[reg_driv->calculate_index(i, j)] = reg_deriv_termmr(weights, lambda, alpha, reg, i, j);
-		}
-	}
-
-	return reg_driv;
-}
-
-MLPPRegOld::MLPPRegOld() {
-}
-MLPPRegOld::~MLPPRegOld() {
-}
-
-void MLPPRegOld::_bind_methods() {
-	ClassDB::bind_method(D_METHOD("reg_termv", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_termv);
-	ClassDB::bind_method(D_METHOD("reg_termm", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_termm);
-
-	ClassDB::bind_method(D_METHOD("reg_weightsv", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_weightsv);
-	ClassDB::bind_method(D_METHOD("reg_weightsm", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_weightsm);
-
-	ClassDB::bind_method(D_METHOD("reg_deriv_termv", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_deriv_termv);
-	ClassDB::bind_method(D_METHOD("reg_deriv_termm", "weights", "lambda", "alpha", "reg"), &MLPPRegOld::reg_deriv_termm);
-
-	BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_NONE);
-	BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_RIDGE);
-	BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_LASSO);
-	BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_ELASTIC_NET);
-	BIND_ENUM_CONSTANT(REGULARIZATION_TYPE_WEIGHT_CLIPPING);
-}
-
-real_t MLPPRegOld::reg_deriv_termvr(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType reg, int j) {
-	MLPPActivation act;
-
-	real_t wj = weights->get_element(j);
-
-	if (reg == REGULARIZATION_TYPE_RIDGE) {
-		return lambda * wj;
-	} else if (reg == REGULARIZATION_TYPE_LASSO) {
-		return lambda * act.sign(wj);
-	} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
-		return alpha * lambda * act.sign(wj) + (1 - alpha) * lambda * wj;
-	} else if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) { // Preparation for Wasserstein GANs.
-		// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
-		// alpha > lambda.
-		if (wj > alpha) {
-			return alpha;
-		} else if (wj < lambda) {
-			return lambda;
-		} else {
-			return wj;
-		}
-	} else {
-		return 0;
-	}
-}
-real_t MLPPRegOld::reg_deriv_termmr(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, MLPPRegOld::RegularizationType reg, int i, int j) {
-	MLPPActivation act;
-
-	real_t wj = weights->get_element(i, j);
-
-	if (reg == REGULARIZATION_TYPE_RIDGE) {
-		return lambda * wj;
-	} else if (reg == REGULARIZATION_TYPE_LASSO) {
-		return lambda * act.sign(wj);
-	} else if (reg == REGULARIZATION_TYPE_ELASTIC_NET) {
-		return alpha * lambda * act.sign(wj) + (1 - alpha) * lambda * wj;
-	} else if (reg == REGULARIZATION_TYPE_WEIGHT_CLIPPING) { // Preparation for Wasserstein GANs.
-		// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
-		// alpha > lambda.
-		if (wj > alpha) {
-			return alpha;
-		} else if (wj < lambda) {
-			return lambda;
-		} else {
-			return wj;
-		}
-	} else {
-		return 0;
-	}
-}
-
-real_t MLPPRegOld::regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string p_reg) {
-	if (p_reg == "Ridge") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			reg += weights[i] * weights[i];
-		}
-		return reg * lambda / 2;
-	} else if (p_reg == "Lasso") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			reg += abs(weights[i]);
-		}
-		return reg * lambda;
-	} else if (p_reg == "ElasticNet") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			reg += alpha * abs(weights[i]); // Lasso Reg
-			reg += ((1 - alpha) / 2) * weights[i] * weights[i]; // Ridge Reg
-		}
-		return reg * lambda;
-	}
-	return 0;
-}
-
-real_t MLPPRegOld::regTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string p_reg) {
-	if (p_reg == "Ridge") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			for (uint32_t j = 0; j < weights[i].size(); j++) {
-				reg += weights[i][j] * weights[i][j];
-			}
-		}
-		return reg * lambda / 2;
-	} else if (p_reg == "Lasso") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			for (uint32_t j = 0; j < weights[i].size(); j++) {
-				reg += abs(weights[i][j]);
-			}
-		}
-		return reg * lambda;
-	} else if (p_reg == "ElasticNet") {
-		real_t reg = 0;
-		for (uint32_t i = 0; i < weights.size(); i++) {
-			for (uint32_t j = 0; j < weights[i].size(); j++) {
-				reg += alpha * abs(weights[i][j]); // Lasso Reg
-				reg += ((1 - alpha) / 2) * weights[i][j] * weights[i][j]; // Ridge Reg
-			}
-		}
-		return reg * lambda;
-	}
-	return 0;
-}
-
 std::vector<real_t> MLPPRegOld::regWeights(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
 	MLPPLinAlg alg;
 	if (reg == "WeightClipping") {
diff --git a/mlpp/regularization/reg_old.h b/mlpp/regularization/reg_old.h
index 8aef595..a3e2ba9 100644
--- a/mlpp/regularization/reg_old.h
+++ b/mlpp/regularization/reg_old.h
@@ -11,45 +11,10 @@
 
 #include "core/math/math_defs.h"
 
-#include "core/object/reference.h"
-
-#include "../lin_alg/mlpp_matrix.h"
-#include "../lin_alg/mlpp_vector.h"
-
 #include <string>
 #include <vector>
 
-class MLPPRegOld : public Reference {
-	GDCLASS(MLPPRegOld, Reference);
-
-public:
-	enum RegularizationType {
-		REGULARIZATION_TYPE_NONE = 0,
-		REGULARIZATION_TYPE_RIDGE,
-		REGULARIZATION_TYPE_LASSO,
-		REGULARIZATION_TYPE_ELASTIC_NET,
-		REGULARIZATION_TYPE_WEIGHT_CLIPPING,
-	};
-
-	real_t reg_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-	real_t reg_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-
-	Ref<MLPPVector> reg_weightsv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-	Ref<MLPPMatrix> reg_weightsm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-
-	Ref<MLPPVector> reg_deriv_termv(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-	Ref<MLPPMatrix> reg_deriv_termm(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg);
-
-	MLPPRegOld();
-	~MLPPRegOld();
-
-protected:
-	static void _bind_methods();
-
-private:
-	real_t reg_deriv_termvr(const Ref<MLPPVector> &weights, real_t lambda, real_t alpha, RegularizationType reg, int j);
-	real_t reg_deriv_termmr(const Ref<MLPPMatrix> &weights, real_t lambda, real_t alpha, RegularizationType reg, int i, int j);
-
+class MLPPRegOld {
 public:
 	// ======== OLD =========
 
@@ -67,6 +32,4 @@ private:
 	real_t regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg, int i, int j);
 };
 
-VARIANT_ENUM_CAST(MLPPRegOld::RegularizationType);
-
 #endif /* Reg_hpp */