diff --git a/SCsub b/SCsub
index 0034c1c..b6950d9 100644
--- a/SCsub
+++ b/SCsub
@@ -76,6 +76,7 @@ sources = [
     "mlpp/bernoulli_nb/bernoulli_nb_old.cpp",
     "mlpp/ann/ann_old.cpp",
     "mlpp/numerical_analysis/numerical_analysis_old.cpp",
+    "mlpp/regularization/reg_old.cpp",
 
     "test/mlpp_tests.cpp",
 ]
diff --git a/mlpp/regularization/reg_old.cpp b/mlpp/regularization/reg_old.cpp
new file mode 100644
index 0000000..514a432
--- /dev/null
+++ b/mlpp/regularization/reg_old.cpp
@@ -0,0 +1,364 @@
+//
+//  Reg.cpp
+//
+//  Created by Marc Melikyan on 1/16/21.
+//
+
+#include "reg_old.h"
+
+#include "core/math/math_defs.h"
+
+#include "../activation/activation.h"
+#include "../lin_alg/lin_alg.h"
+
+#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") {
+		return regDerivTerm(weights, lambda, alpha, reg);
+	}
+	return alg.subtraction(weights, regDerivTerm(weights, lambda, alpha, reg));
+	// for(int i = 0; i < weights.size(); i++){
+	//     weights[i] -= regDerivTerm(weights, lambda, alpha, reg, i);
+	// }
+	// return weights;
+}
+
+std::vector<std::vector<real_t>> MLPPRegOld::regWeights(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
+	MLPPLinAlg alg;
+	if (reg == "WeightClipping") {
+		return regDerivTerm(weights, lambda, alpha, reg);
+	}
+	return alg.subtraction(weights, regDerivTerm(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;
+}
+
+std::vector<real_t> MLPPRegOld::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg) {
+	std::vector<real_t> regDeriv;
+	regDeriv.resize(weights.size());
+
+	for (uint32_t i = 0; i < regDeriv.size(); i++) {
+		regDeriv[i] = regDerivTerm(weights, lambda, alpha, reg, i);
+	}
+	return regDeriv;
+}
+
+std::vector<std::vector<real_t>> MLPPRegOld::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg) {
+	std::vector<std::vector<real_t>> regDeriv;
+	regDeriv.resize(weights.size());
+	for (uint32_t i = 0; i < regDeriv.size(); i++) {
+		regDeriv[i].resize(weights[0].size());
+	}
+
+	for (uint32_t i = 0; i < regDeriv.size(); i++) {
+		for (uint32_t j = 0; j < regDeriv[i].size(); j++) {
+			regDeriv[i][j] = regDerivTerm(weights, lambda, alpha, reg, i, j);
+		}
+	}
+	return regDeriv;
+}
+
+real_t MLPPRegOld::regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg, int j) {
+	MLPPActivation act;
+	if (reg == "Ridge") {
+		return lambda * weights[j];
+	} else if (reg == "Lasso") {
+		return lambda * act.sign(weights[j]);
+	} else if (reg == "ElasticNet") {
+		return alpha * lambda * act.sign(weights[j]) + (1 - alpha) * lambda * weights[j];
+	} else if (reg == "WeightClipping") { // Preparation for Wasserstein GANs.
+		// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
+		// alpha > lambda.
+		if (weights[j] > alpha) {
+			return alpha;
+		} else if (weights[j] < lambda) {
+			return lambda;
+		} else {
+			return weights[j];
+		}
+	} else {
+		return 0;
+	}
+}
+
+real_t MLPPRegOld::regDerivTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg, int i, int j) {
+	MLPPActivation act;
+	if (reg == "Ridge") {
+		return lambda * weights[i][j];
+	} else if (reg == "Lasso") {
+		return lambda * act.sign(weights[i][j]);
+	} else if (reg == "ElasticNet") {
+		return alpha * lambda * act.sign(weights[i][j]) + (1 - alpha) * lambda * weights[i][j];
+	} else if (reg == "WeightClipping") { // Preparation for Wasserstein GANs.
+		// We assume lambda is the lower clipping threshold, while alpha is the higher clipping threshold.
+		// alpha > lambda.
+		if (weights[i][j] > alpha) {
+			return alpha;
+		} else if (weights[i][j] < lambda) {
+			return lambda;
+		} else {
+			return weights[i][j];
+		}
+	} else {
+		return 0;
+	}
+}
diff --git a/mlpp/regularization/reg_old.h b/mlpp/regularization/reg_old.h
new file mode 100644
index 0000000..8aef595
--- /dev/null
+++ b/mlpp/regularization/reg_old.h
@@ -0,0 +1,72 @@
+
+
+#ifndef MLPP_REG_OLD_H
+#define MLPP_REG_OLD_H
+
+//
+//  Reg.hpp
+//
+//  Created by Marc Melikyan on 1/16/21.
+//
+
+#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);
+
+public:
+	// ======== OLD =========
+
+	real_t regTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg);
+	real_t regTerm(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg);
+
+	std::vector<real_t> regWeights(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg);
+	std::vector<std::vector<real_t>> regWeights(std::vector<std::vector<real_t>> weights, real_t lambda, real_t alpha, std::string reg);
+
+	std::vector<real_t> regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg);
+	std::vector<std::vector<real_t>> regDerivTerm(std::vector<std::vector<real_t>>, real_t lambda, real_t alpha, std::string reg);
+
+private:
+	real_t regDerivTerm(std::vector<real_t> weights, real_t lambda, real_t alpha, std::string reg, int j);
+	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 */