diff --git a/mlpp/activation/activation_old.cpp b/mlpp/activation/activation_old.cpp
index 2f0f17f..9f0c2d8 100644
--- a/mlpp/activation/activation_old.cpp
+++ b/mlpp/activation/activation_old.cpp
@@ -6,2774 +6,11 @@
 
 #include "activation_old.h"
 #include "../lin_alg/lin_alg.h"
+
 #include <algorithm>
 #include <cmath>
 #include <iostream>
 
-MLPPActivationOld::RealActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_real(const ActivationFunction func, const bool deriv) {
-	if (deriv) {
-		return get_activation_function_ptr_normal_real(func);
-	} else {
-		return get_activation_function_ptr_deriv_real(func);
-	}
-}
-MLPPActivationOld::VectorActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_vector(const ActivationFunction func, const bool deriv) {
-	if (deriv) {
-		return get_activation_function_ptr_normal_vector(func);
-	} else {
-		return get_activation_function_ptr_deriv_vector(func);
-	}
-}
-MLPPActivationOld::MatrixActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_matrix(const ActivationFunction func, const bool deriv) {
-	if (deriv) {
-		return get_activation_function_ptr_normal_matrix(func);
-	} else {
-		return get_activation_function_ptr_deriv_matrix(func);
-	}
-}
-
-MLPPActivationOld::RealActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_normal_real(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_normr;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_normr;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_normr;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_normr;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_normr;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_normr;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_normr;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_normr;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_normr;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_normr;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_normr;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_normr;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_normr;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_normr;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_normr;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_normr;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_normr;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_normr;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_normr;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_normr;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_normr;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_normr;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_normr;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_normr;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_normr;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_normr;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_normr;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_normr;
-		default:
-			return NULL;
-	}
-}
-MLPPActivationOld::VectorActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_normal_vector(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_normv;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_normv;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_normv;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_normv;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_normv;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_normv;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_normv;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_normv;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_normv;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_normv;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_normv;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_normv;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_normv;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_normv;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_normv;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_normv;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_normv;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_normv;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_normv;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_normv;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_normv;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_normv;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_normv;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_normv;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_normv;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_normv;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_normv;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_normv;
-		default:
-			return NULL;
-	}
-}
-MLPPActivationOld::MatrixActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_normal_matrix(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_normm;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_normm;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_normm;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_normm;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_normm;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_normm;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_normm;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_normm;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_normm;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_normm;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_normm;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_normm;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_normm;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_normm;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_normm;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_normm;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_normm;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_normm;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_normm;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_normm;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_normm;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_normm;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_normm;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_normm;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_normm;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_normm;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_normm;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_normm;
-		default:
-			return NULL;
-	}
-}
-
-MLPPActivationOld::RealActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_deriv_real(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_normr;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_normr;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_normr;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_normr;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_normr;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_normr;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_normr;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_normr;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_normr;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_normr;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_normr;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_normr;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_normr;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_normr;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_normr;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_normr;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_normr;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_normr;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_normr;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_normr;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_normr;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_normr;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_normr;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_normr;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_normr;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_normr;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_normr;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_normr;
-		default:
-			return NULL;
-	}
-}
-MLPPActivationOld::VectorActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_deriv_vector(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_derivv;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_derivv;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_derivv;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_derivv;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_derivv;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_derivv;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_derivv;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_derivv;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_derivv;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_derivv;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_derivv;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_derivv;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_derivv;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_derivv;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_derivv;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_derivv;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_derivv;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_derivv;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_derivv;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_derivv;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_derivv;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_derivv;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_derivv;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_derivv;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_derivv;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_derivv;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_derivv;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_derivv;
-		default:
-			return NULL;
-	}
-}
-MLPPActivationOld::MatrixActivationFunctionPointer MLPPActivationOld::get_activation_function_ptr_deriv_matrix(const ActivationFunction func) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return &MLPPActivationOld::linear_derivm;
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return &MLPPActivationOld::sigmoid_derivm;
-		case ACTIVATION_FUNCTION_SWISH:
-			return &MLPPActivationOld::swish_derivm;
-		case ACTIVATION_FUNCTION_MISH:
-			return &MLPPActivationOld::mish_derivm;
-		case ACTIVATION_FUNCTION_SIN_C:
-			return &MLPPActivationOld::sinc_derivm;
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return &MLPPActivationOld::softmax_derivm;
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return &MLPPActivationOld::softplus_derivm;
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return &MLPPActivationOld::softsign_derivm;
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return &MLPPActivationOld::adj_softmax_derivm;
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return &MLPPActivationOld::cloglog_derivm;
-		case ACTIVATION_FUNCTION_LOGIT:
-			return &MLPPActivationOld::logit_derivm;
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return &MLPPActivationOld::gaussian_cdf_derivm;
-		case ACTIVATION_FUNCTION_RELU:
-			return &MLPPActivationOld::relu_derivm;
-		case ACTIVATION_FUNCTION_GELU:
-			return &MLPPActivationOld::gelu_derivm;
-		case ACTIVATION_FUNCTION_SIGN:
-			return &MLPPActivationOld::sign_derivm;
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return &MLPPActivationOld::unit_step_derivm;
-		case ACTIVATION_FUNCTION_SINH:
-			return &MLPPActivationOld::sinh_derivm;
-		case ACTIVATION_FUNCTION_COSH:
-			return &MLPPActivationOld::cosh_derivm;
-		case ACTIVATION_FUNCTION_TANH:
-			return &MLPPActivationOld::tanh_derivm;
-		case ACTIVATION_FUNCTION_CSCH:
-			return &MLPPActivationOld::csch_derivm;
-		case ACTIVATION_FUNCTION_SECH:
-			return &MLPPActivationOld::sech_derivm;
-		case ACTIVATION_FUNCTION_COTH:
-			return &MLPPActivationOld::coth_derivm;
-		case ACTIVATION_FUNCTION_ARSINH:
-			return &MLPPActivationOld::arsinh_derivm;
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return &MLPPActivationOld::arcosh_derivm;
-		case ACTIVATION_FUNCTION_ARTANH:
-			return &MLPPActivationOld::artanh_derivm;
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return &MLPPActivationOld::arcsch_derivm;
-		case ACTIVATION_FUNCTION_ARSECH:
-			return &MLPPActivationOld::arsech_derivm;
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return &MLPPActivationOld::arcoth_derivm;
-		default:
-			return NULL;
-	}
-}
-
-real_t MLPPActivationOld::run_activation_real(const ActivationFunction func, const real_t z, const bool deriv) {
-	if (deriv) {
-		return run_activation_norm_real(func, z);
-	} else {
-		return run_activation_deriv_real(func, z);
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::run_activation_vector(const ActivationFunction func, const Ref<MLPPVector> &z, const bool deriv) {
-	if (deriv) {
-		return run_activation_norm_vector(func, z);
-	} else {
-		return run_activation_deriv_vector(func, z);
-	}
-}
-Ref<MLPPMatrix> MLPPActivationOld::run_activation_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z, const bool deriv) {
-	if (deriv) {
-		return run_activation_norm_matrix(func, z);
-	} else {
-		return run_activation_deriv_matrix(func, z);
-	}
-}
-
-real_t MLPPActivationOld::run_activation_norm_real(const ActivationFunction func, const real_t z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_normr(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_normr(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_normr(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_normr(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_normr(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_normr(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_normr(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_normr(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_normr(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_normr(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_normr(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_normr(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_normr(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_normr(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_normr(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_normr(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_normr(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_normr(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_normr(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_normr(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_normr(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_normr(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_normr(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_normr(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_normr(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_normr(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_normr(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_normr(z);
-		default:
-			ERR_FAIL_V(0);
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::run_activation_norm_vector(const ActivationFunction func, const Ref<MLPPVector> &z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_normv(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_normv(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_normv(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_normv(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_normv(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_normv(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_normv(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_normv(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_normv(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_normv(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_normv(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_normv(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_normv(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_normv(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_normv(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_normv(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_normv(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_normv(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_normv(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_normv(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_normv(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_normv(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_normv(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_normv(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_normv(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_normv(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_normv(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_normv(z);
-		default:
-			ERR_FAIL_V(Ref<MLPPVector>());
-	}
-}
-Ref<MLPPMatrix> MLPPActivationOld::run_activation_norm_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_normm(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_normm(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_normm(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_normm(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_normm(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_normm(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_normm(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_normm(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_normm(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_normm(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_normm(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_normm(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_normm(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_normm(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_normm(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_normm(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_normm(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_normm(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_normm(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_normm(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_normm(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_normm(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_normm(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_normm(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_normm(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_normm(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_normm(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_normm(z);
-		default:
-			ERR_FAIL_V(Ref<MLPPMatrix>());
-	}
-}
-
-real_t MLPPActivationOld::run_activation_deriv_real(const ActivationFunction func, const real_t z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_normr(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_normr(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_normr(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_normr(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_normr(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_normr(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_normr(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_normr(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_normr(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_normr(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_normr(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_normr(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_normr(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_normr(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_normr(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_normr(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_normr(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_normr(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_normr(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_normr(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_normr(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_normr(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_normr(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_normr(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_normr(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_normr(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_normr(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_normr(z);
-		default:
-			ERR_FAIL_V(0);
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::run_activation_deriv_vector(const ActivationFunction func, const Ref<MLPPVector> &z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_derivv(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_derivv(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_derivv(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_derivv(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_derivv(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_derivv(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_derivv(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_derivv(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_derivv(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_derivv(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_derivv(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_derivv(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_derivv(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_derivv(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_derivv(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_derivv(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_derivv(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_derivv(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_derivv(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_derivv(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_derivv(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_derivv(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_derivv(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_derivv(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_derivv(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_derivv(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_derivv(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_derivv(z);
-		default:
-			ERR_FAIL_V(Ref<MLPPVector>());
-	}
-}
-Ref<MLPPMatrix> MLPPActivationOld::run_activation_deriv_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z) {
-	switch (func) {
-		case ACTIVATION_FUNCTION_LINEAR:
-			return linear_derivm(z);
-		case ACTIVATION_FUNCTION_SIGMOID:
-			return sigmoid_derivm(z);
-		case ACTIVATION_FUNCTION_SWISH:
-			return swish_derivm(z);
-		case ACTIVATION_FUNCTION_MISH:
-			return mish_derivm(z);
-		case ACTIVATION_FUNCTION_SIN_C:
-			return sinc_derivm(z);
-		case ACTIVATION_FUNCTION_SOFTMAX:
-			return softmax_derivm(z);
-		case ACTIVATION_FUNCTION_SOFTPLUS:
-			return softplus_derivm(z);
-		case ACTIVATION_FUNCTION_SOFTSIGN:
-			return softsign_derivm(z);
-		case ACTIVATION_FUNCTION_ADJ_SOFTMAX:
-			return adj_softmax_derivm(z);
-		case ACTIVATION_FUNCTION_C_LOG_LOG:
-			return cloglog_derivm(z);
-		case ACTIVATION_FUNCTION_LOGIT:
-			return logit_derivm(z);
-		case ACTIVATION_FUNCTION_GAUSSIAN_CDF:
-			return gaussian_cdf_derivm(z);
-		case ACTIVATION_FUNCTION_RELU:
-			return relu_derivm(z);
-		case ACTIVATION_FUNCTION_GELU:
-			return gelu_derivm(z);
-		case ACTIVATION_FUNCTION_SIGN:
-			return sign_derivm(z);
-		case ACTIVATION_FUNCTION_UNIT_STEP:
-			return unit_step_derivm(z);
-		case ACTIVATION_FUNCTION_SINH:
-			return sinh_derivm(z);
-		case ACTIVATION_FUNCTION_COSH:
-			return cosh_derivm(z);
-		case ACTIVATION_FUNCTION_TANH:
-			return tanh_derivm(z);
-		case ACTIVATION_FUNCTION_CSCH:
-			return csch_derivm(z);
-		case ACTIVATION_FUNCTION_SECH:
-			return sech_derivm(z);
-		case ACTIVATION_FUNCTION_COTH:
-			return coth_derivm(z);
-		case ACTIVATION_FUNCTION_ARSINH:
-			return arsinh_derivm(z);
-		case ACTIVATION_FUNCTION_ARCOSH:
-			return arcosh_derivm(z);
-		case ACTIVATION_FUNCTION_ARTANH:
-			return artanh_derivm(z);
-		case ACTIVATION_FUNCTION_ARCSCH:
-			return arcsch_derivm(z);
-		case ACTIVATION_FUNCTION_ARSECH:
-			return arsech_derivm(z);
-		case ACTIVATION_FUNCTION_ARCOTH:
-			return arcoth_derivm(z);
-		default:
-			ERR_FAIL_V(Ref<MLPPMatrix>());
-	}
-}
-
-Ref<MLPPVector> MLPPActivationOld::activationr(const Ref<MLPPVector> &z, real_t (*function)(real_t)) {
-	Ref<MLPPVector> a;
-	a.instance();
-
-	int size = z->size();
-
-	a->resize(size);
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < size; ++i) {
-		a_ptr[i] = function(z_ptr[i]);
-	}
-
-	return a;
-}
-
-//ACTIVATION FUNCTIONS
-
-//LINEAR
-real_t MLPPActivationOld::linear_normr(real_t z) {
-	return z;
-}
-Ref<MLPPVector> MLPPActivationOld::linear_normv(const Ref<MLPPVector> &z) {
-	return z->duplicate();
-}
-Ref<MLPPMatrix> MLPPActivationOld::linear_normm(const Ref<MLPPMatrix> &z) {
-	return z->duplicate();
-}
-
-real_t MLPPActivationOld::linear_derivr(real_t z) {
-	return 1;
-}
-Ref<MLPPVector> MLPPActivationOld::linear_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-	return alg.onevecv(z->size());
-}
-Ref<MLPPMatrix> MLPPActivationOld::linear_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-	return alg.onematm(z->size().x, z->size().y);
-}
-
-//SIGMOID
-real_t MLPPActivationOld::sigmoid_normr(real_t z) {
-	return 1 / (1 + exp(-z));
-}
-Ref<MLPPVector> MLPPActivationOld::sigmoid_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.additionnv(alg.onevecv(z->size()), alg.expv(alg.scalar_multiplynv(-1, z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sigmoid_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.additionm(alg.onematm(z->size().x, z->size().y), alg.expm(alg.scalar_multiplym(-1, z))));
-}
-
-real_t MLPPActivationOld::sigmoid_derivr(real_t z) {
-	real_t sig_norm = sigmoid_normr(z);
-
-	return sig_norm * (1 - sig_norm);
-}
-
-Ref<MLPPVector> MLPPActivationOld::sigmoid_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	Ref<MLPPVector> sig_norm = sigmoid_normv(z);
-
-	return alg.subtractionnv(sig_norm, alg.hadamard_productnv(sig_norm, sig_norm));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sigmoid_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	Ref<MLPPMatrix> sig_norm = sigmoid_normm(z);
-
-	return alg.subtractionm(sig_norm, alg.hadamard_productm(sig_norm, sig_norm));
-}
-
-//SOFTMAX
-
-real_t MLPPActivationOld::softmax_normr(real_t z) {
-	return z;
-}
-Ref<MLPPVector> MLPPActivationOld::softmax_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	int z_size = z->size();
-
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z_size);
-
-	Ref<MLPPVector> exp_z = alg.expv(z);
-	real_t sum = 0;
-
-	const real_t *exp_z_ptr = exp_z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		sum += exp_z_ptr[i];
-	}
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = exp_z_ptr[i] / sum;
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::softmax_normm(const Ref<MLPPMatrix> &z) {
-	Size2i z_size = z->size();
-
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z_size);
-
-	Ref<MLPPVector> row_tmp;
-	row_tmp.instance();
-	row_tmp->resize(z_size.x);
-
-	for (int i = 0; i < z_size.y; ++i) {
-		z->get_row_into_mlpp_vector(i, row_tmp);
-
-		Ref<MLPPVector> sfn = softmax_normv(row_tmp);
-
-		a->set_row_mlpp_vector(i, sfn);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::softmax_derivr(real_t z) {
-	return z;
-}
-Ref<MLPPVector> MLPPActivationOld::softmax_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	int z_size = z->size();
-
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z_size);
-
-	Ref<MLPPVector> exp_z = alg.expv(z);
-	real_t sum = 0;
-
-	const real_t *exp_z_ptr = exp_z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		sum += exp_z_ptr[i];
-	}
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = exp_z_ptr[i] / sum;
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::softmax_derivm(const Ref<MLPPMatrix> &z) {
-	Size2i z_size = z->size();
-
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z_size);
-
-	Ref<MLPPVector> row_tmp;
-	row_tmp.instance();
-	row_tmp->resize(z_size.x);
-
-	for (int i = 0; i < z_size.y; ++i) {
-		z->get_row_into_mlpp_vector(i, row_tmp);
-
-		Ref<MLPPVector> sfn = softmax_derivm(z);
-
-		a->set_row_mlpp_vector(i, sfn);
-	}
-
-	return a;
-}
-
-//ADJ_SOFTMAX
-
-real_t MLPPActivationOld::adj_softmax_normr(real_t z) {
-	return 0;
-}
-
-Ref<MLPPVector> MLPPActivationOld::adj_softmax_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	int size = z->size();
-	const real_t *z_ptr = z->ptr();
-	real_t c = -Math_INF;
-
-	for (int i = 0; i < size; ++i) {
-		int zpi = z_ptr[i];
-
-		if (c < zpi) {
-			c = zpi;
-		}
-	}
-
-	c = -c;
-
-	Ref<MLPPVector> n = alg.scalar_addnv(c, z);
-
-	return softmax_normv(n);
-}
-Ref<MLPPMatrix> MLPPActivationOld::adj_softmax_normm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> n = z->duplicate();
-
-	Size2i size = z->size();
-
-	Ref<MLPPVector> row_rmp;
-	row_rmp.instance();
-	row_rmp->resize(size.x);
-
-	for (int i = 0; i < size.y; ++i) {
-		z->get_row_into_mlpp_vector(i, row_rmp);
-
-		Ref<MLPPVector> nv = adj_softmax_normv(row_rmp);
-
-		n->set_row_mlpp_vector(i, nv);
-	}
-
-	return n;
-}
-
-real_t MLPPActivationOld::adj_softmax_derivr(real_t z) {
-	return 0;
-}
-
-Ref<MLPPVector> MLPPActivationOld::adj_softmax_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	int size = z->size();
-	const real_t *z_ptr = z->ptr();
-	real_t c = -Math_INF;
-
-	for (int i = 0; i < size; ++i) {
-		int zpi = z_ptr[i];
-
-		if (c < zpi) {
-			c = zpi;
-		}
-	}
-
-	c = -c;
-
-	Ref<MLPPVector> n = alg.scalar_addnv(c, z);
-
-	return adj_softmax_normv(n);
-}
-Ref<MLPPMatrix> MLPPActivationOld::adj_softmax_derivm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> n = z->duplicate();
-
-	Size2i size = z->size();
-
-	Ref<MLPPVector> row_rmp;
-	row_rmp.instance();
-	row_rmp->resize(size.x);
-
-	for (int i = 0; i < size.y; ++i) {
-		z->get_row_into_mlpp_vector(i, row_rmp);
-
-		Ref<MLPPVector> nv = adj_softmax_derivv(row_rmp);
-
-		n->set_row_mlpp_vector(i, nv);
-	}
-
-	return n;
-}
-
-//SOFTMAX DERIV
-
-Ref<MLPPMatrix> MLPPActivationOld::softmax_deriv_normv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a = softmax_normv(z);
-
-	int z_size = z->size();
-	int a_size = a->size();
-
-	Ref<MLPPMatrix> deriv;
-	deriv.instance();
-	deriv->resize(Size2i(a_size, a_size));
-
-	const real_t *a_ptr = a->ptr();
-
-	for (int i = 0; i < z_size; ++i) {
-		for (int j = 0; j < z_size; ++j) {
-			if (i == j) {
-				deriv->set_element(i, j, a_ptr[i] * (1 - a_ptr[i]));
-			} else {
-				deriv->set_element(i, j, -a_ptr[i] * a_ptr[j]);
-			}
-		}
-	}
-
-	return deriv;
-}
-Vector<Ref<MLPPMatrix>> MLPPActivationOld::softmax_deriv_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	int z_size_y = z->size().y;
-
-	Ref<MLPPMatrix> a = softmax_normm(z);
-	int a_size_y = a->size().y;
-	int a_size_x = a->size().x;
-
-	Vector<Ref<MLPPMatrix>> deriv;
-	deriv.resize(a_size_y);
-
-	Ref<MLPPVector> a_i_tmp;
-	a_i_tmp.instance();
-	a_i_tmp->resize(a_size_x);
-
-	Ref<MLPPVector> a_j_tmp;
-	a_j_tmp.instance();
-	a_j_tmp->resize(a_size_x);
-
-	for (int i = 0; i < deriv.size(); ++i) {
-		Ref<MLPPMatrix> d;
-		d.instance();
-		d->resize(Size2i(a_size_x, z_size_y));
-
-		for (int j = 0; j < z_size_y; ++j) {
-			a->get_row_into_mlpp_vector(i, a_i_tmp);
-
-			if (i == j) {
-				Ref<MLPPVector> d_j = alg.subtractionnv(a_i_tmp, alg.hadamard_productnv(a_i_tmp, a_i_tmp));
-				d->set_row_mlpp_vector(j, d_j);
-			} else {
-				a->get_row_into_mlpp_vector(j, a_j_tmp);
-				Ref<MLPPVector> d_j = alg.scalar_multiplynv(-1, alg.hadamard_productnv(a_i_tmp, a_j_tmp));
-				d->set_row_mlpp_vector(j, d_j);
-			}
-		}
-
-		deriv.write[i] = d;
-	}
-
-	return deriv;
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::softmax_deriv_derivv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a = softmax_normv(z);
-
-	int z_size = z->size();
-	int a_size = a->size();
-
-	Ref<MLPPMatrix> deriv;
-	deriv.instance();
-	deriv->resize(Size2i(a_size, a_size));
-
-	const real_t *a_ptr = a->ptr();
-
-	for (int i = 0; i < z_size; ++i) {
-		for (int j = 0; j < z_size; ++j) {
-			if (i == j) {
-				deriv->set_element(i, j, a_ptr[i] * (1 - a_ptr[i]));
-			} else {
-				deriv->set_element(i, j, -a_ptr[i] * a_ptr[j]);
-			}
-		}
-	}
-
-	return deriv;
-}
-Vector<Ref<MLPPMatrix>> MLPPActivationOld::softmax_deriv_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	int z_size_y = z->size().y;
-
-	Ref<MLPPMatrix> a = softmax_normm(z);
-	int a_size_y = a->size().y;
-	int a_size_x = a->size().x;
-
-	Vector<Ref<MLPPMatrix>> deriv;
-	deriv.resize(a_size_y);
-
-	Ref<MLPPVector> a_i_tmp;
-	a_i_tmp.instance();
-	a_i_tmp->resize(a_size_x);
-
-	Ref<MLPPVector> a_j_tmp;
-	a_j_tmp.instance();
-	a_j_tmp->resize(a_size_x);
-
-	for (int i = 0; i < deriv.size(); ++i) {
-		Ref<MLPPMatrix> d;
-		d.instance();
-		d->resize(Size2i(a_size_x, z_size_y));
-
-		for (int j = 0; j < z_size_y; ++j) {
-			a->get_row_into_mlpp_vector(i, a_i_tmp);
-
-			if (i == j) {
-				Ref<MLPPVector> d_j = alg.subtractionnv(a_i_tmp, alg.hadamard_productnv(a_i_tmp, a_i_tmp));
-				d->set_row_mlpp_vector(j, d_j);
-			} else {
-				a->get_row_into_mlpp_vector(j, a_j_tmp);
-				Ref<MLPPVector> d_j = alg.scalar_multiplynv(-1, alg.hadamard_productnv(a_i_tmp, a_j_tmp));
-				d->set_row_mlpp_vector(j, d_j);
-			}
-		}
-
-		deriv.write[i] = d;
-	}
-
-	return deriv;
-}
-
-//SOFTPLUS
-
-real_t MLPPActivationOld::softplus_normr(real_t z) {
-	return std::log(1 + exp(z));
-}
-Ref<MLPPVector> MLPPActivationOld::softplus_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(alg.additionnv(alg.onevecv(z->size()), alg.expv(z)));
-}
-Ref<MLPPMatrix> MLPPActivationOld::softplus_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(alg.additionnv(alg.onematm(z->size().x, z->size().y), alg.expv(z)));
-}
-
-real_t MLPPActivationOld::softplus_derivr(real_t z) {
-	return sigmoid_normr(z);
-}
-Ref<MLPPVector> MLPPActivationOld::softplus_derivv(const Ref<MLPPVector> &z) {
-	return sigmoid_normv(z);
-}
-Ref<MLPPMatrix> MLPPActivationOld::softplus_derivm(const Ref<MLPPMatrix> &z) {
-	return sigmoid_normm(z);
-}
-
-//SOFTSIGN
-
-real_t MLPPActivationOld::softsign_normr(real_t z) {
-	return z / (1 + abs(z));
-}
-Ref<MLPPVector> MLPPActivationOld::softsign_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(z, alg.additionnv(alg.onevecv(z->size()), alg.absv(z)));
-}
-Ref<MLPPMatrix> MLPPActivationOld::softsign_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(z, alg.additionnv(alg.onematm(z->size().x, z->size().y), alg.absm(z)));
-}
-
-real_t MLPPActivationOld::softsign_derivr(real_t z) {
-	return 1 / ((1 + abs(z)) * (1 + abs(z)));
-}
-Ref<MLPPVector> MLPPActivationOld::softsign_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.exponentiatev(alg.additionnv(alg.onevecv(z->size()), alg.absv(z)), 2));
-}
-Ref<MLPPMatrix> MLPPActivationOld::softsign_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.exponentiatev(alg.additionm(alg.onematm(z->size().x, z->size().y), alg.absm(z)), 2));
-}
-
-//GAUSSIANCDF
-
-real_t MLPPActivationOld::gaussian_cdf_normr(real_t z) {
-	return 0.5 * (1 + erf(z / sqrt(2)));
-}
-Ref<MLPPVector> MLPPActivationOld::gaussian_cdf_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(0.5, alg.additionnv(alg.onevecv(z->size()), alg.erfv(alg.scalar_multiplynv(1 / sqrt(2), z))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::gaussian_cdf_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(0.5, alg.additionm(alg.onematm(z->size().x, z->size().y), alg.erfm(alg.scalar_multiplym(1 / sqrt(2), z))));
-}
-
-real_t MLPPActivationOld::gaussian_cdf_derivr(real_t z) {
-	return (1 / sqrt(2 * M_PI)) * exp(-z * z / 2);
-}
-Ref<MLPPVector> MLPPActivationOld::gaussian_cdf_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(1 / Math::sqrt(2 * M_PI), alg.expv(alg.scalar_multiplynv(-1 / 2.0, alg.hadamard_productnv(z, z))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::gaussian_cdf_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(1 / Math::sqrt(2 * M_PI), alg.expm(alg.scalar_multiplym(-1 / 2.0, alg.hadamard_productm(z, z))));
-}
-
-//CLOGLOG
-
-real_t MLPPActivationOld::cloglog_normr(real_t z) {
-	return 1 - exp(-exp(z));
-}
-Ref<MLPPVector> MLPPActivationOld::cloglog_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(-1, alg.scalar_addnv(-1, alg.expv(alg.scalar_multiplynv(-1, alg.expv(z)))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::cloglog_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(-1, alg.scalar_addm(-1, alg.expm(alg.scalar_multiplym(-1, alg.expm(z)))));
-}
-
-real_t MLPPActivationOld::cloglog_derivr(real_t z) {
-	return exp(z - exp(z));
-}
-Ref<MLPPVector> MLPPActivationOld::cloglog_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.expv(alg.scalar_multiplynv(-1, alg.expv(z)));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::cloglog_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.expm(alg.scalar_multiplym(-1, alg.expm(z)));
-}
-
-//LOGIT
-
-real_t MLPPActivationOld::logit_normr(real_t z) {
-	return std::log(z / (1 - z));
-}
-Ref<MLPPVector> MLPPActivationOld::logit_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(alg.element_wise_division(z, alg.subtractionnv(alg.onevecv(z->size()), z)));
-}
-Ref<MLPPMatrix> MLPPActivationOld::logit_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logm(alg.element_wise_divisionm(z, alg.subtractionm(alg.onematm(z->size().x, z->size().y), z)));
-}
-
-real_t MLPPActivationOld::logit_derivr(real_t z) {
-	return 1 / z - 1 / (z - 1);
-}
-Ref<MLPPVector> MLPPActivationOld::logit_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.subtractionnv(
-			alg.element_wise_division(alg.onevecv(z->size()), z),
-			alg.element_wise_division(alg.onevecv(z->size()), alg.subtractionnv(z, alg.onevecv(z->size()))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::logit_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.subtractionm(
-			alg.element_wise_divisionm(
-					alg.onematm(z->size().x, z->size().y), z),
-			alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y),
-					alg.subtractionm(z, alg.onematm(z->size().x, z->size().y))));
-}
-
-//UNITSTEP
-
-real_t MLPPActivationOld::unit_step_normr(real_t z) {
-	return z < 0 ? 0 : 1;
-}
-Ref<MLPPVector> MLPPActivationOld::unit_step_normv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = unit_step_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::unit_step_normm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = unit_step_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::unit_step_derivr(real_t z) {
-	return 0;
-}
-Ref<MLPPVector> MLPPActivationOld::unit_step_derivv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-	a->fill(0);
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::unit_step_derivm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-	a->fill(0);
-
-	return a;
-}
-
-//SWISH
-
-real_t MLPPActivationOld::swish_normr(real_t z) {
-	return z * sigmoid_normr(z);
-}
-Ref<MLPPVector> MLPPActivationOld::swish_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(z, sigmoid_normv(z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::swish_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(z, sigmoid_normm(z));
-}
-
-real_t MLPPActivationOld::swish_derivr(real_t z) {
-	return swish_normr(z) + sigmoid_normr(z) * (1 - swish_normr(z));
-}
-Ref<MLPPVector> MLPPActivationOld::swish_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.additionnv(swish_normv(z), alg.subtractionnv(sigmoid_normv(z), alg.hadamard_productnv(sigmoid_normv(z), swish_normv(z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::swish_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.additionnv(swish_normm(z), alg.subtractionnv(sigmoid_normm(z), alg.hadamard_productm(sigmoid_normm(z), swish_normm(z))));
-}
-
-//MISH
-
-real_t MLPPActivationOld::mish_normr(real_t z) {
-	return z * tanh(softplus(z));
-}
-Ref<MLPPVector> MLPPActivationOld::mish_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(z, tanh_normv(softplus_normv(z)));
-}
-Ref<MLPPMatrix> MLPPActivationOld::mish_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productm(z, tanh_normm(softplus_normm(z)));
-}
-
-real_t MLPPActivationOld::mish_derivr(real_t z) {
-	return sech(softplus_normr(z)) * sech(softplus_normr(z)) * z * sigmoid_normr(z) + mish_normr(z) / z;
-}
-Ref<MLPPVector> MLPPActivationOld::mish_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.additionnv(
-			alg.hadamard_productnv(
-					alg.hadamard_productnv(
-							alg.hadamard_productnv(
-									sech_normv(softplus_normv(z)), sech_normv(softplus_normv(z))),
-							z),
-					sigmoid_normv(z)),
-			alg.element_wise_division(mish_normv(z), z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::mish_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.additionnv(
-			alg.hadamard_productm(
-					alg.hadamard_productm(
-							alg.hadamard_productm(
-									sech_normm(softplus_normm(z)), sech_normm(softplus_normm(z))),
-							z),
-					sigmoid_normm(z)),
-			alg.element_wise_divisionm(mish_normm(z), z));
-}
-
-//SINC
-
-real_t MLPPActivationOld::sinc_normr(real_t z) {
-	return std::sin(z) / z;
-}
-Ref<MLPPVector> MLPPActivationOld::sinc_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.sinv(z), z);
-}
-Ref<MLPPMatrix> MLPPActivationOld::sinc_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.sinm(z), z);
-}
-
-real_t MLPPActivationOld::sinc_derivr(real_t z) {
-	return (z * std::cos(z) - std::sin(z)) / (z * z);
-}
-Ref<MLPPVector> MLPPActivationOld::sinc_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.subtractionnv(alg.hadamard_productnv(z, alg.cosv(z)), alg.sinv(z)), alg.hadamard_productnv(z, z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sinc_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.subtractionm(alg.hadamard_productm(z, alg.cosm(z)), alg.sinm(z)), alg.hadamard_productm(z, z));
-}
-
-//RELU
-
-real_t MLPPActivationOld::relu_normr(real_t z) {
-	return fmax(0, z);
-}
-Ref<MLPPVector> MLPPActivationOld::relu_normv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = relu_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::relu_normm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = relu_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::relu_derivr(real_t z) {
-	if (z <= 0) {
-		return 0;
-	} else {
-		return 1;
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::relu_derivv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = relu_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::relu_derivm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = relu_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-//LEAKYRELU
-
-real_t MLPPActivationOld::leaky_relu_normr(real_t z, real_t c) {
-	return fmax(c * z, z);
-}
-Ref<MLPPVector> MLPPActivationOld::leaky_relu_normv(const Ref<MLPPVector> &z, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = leaky_relu_normr(z_ptr[i], c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::leaky_relu_normm(const Ref<MLPPMatrix> &z, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = leaky_relu_normr(z_ptr[i], c);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::leaky_relu_derivr(real_t z, real_t c) {
-	if (z <= 0) {
-		return c;
-	} else {
-		return 1;
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::leaky_relu_derivv(const Ref<MLPPVector> &z, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = leaky_relu_derivr(z_ptr[i], c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::leaky_relu_derivm(const Ref<MLPPMatrix> &z, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = leaky_relu_derivr(z_ptr[i], c);
-	}
-
-	return a;
-}
-
-//ELU
-
-real_t MLPPActivationOld::elu_normr(real_t z, real_t c) {
-	if (z >= 0) {
-		return z;
-	} else {
-		return c * (exp(z) - 1);
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::elu_normv(const Ref<MLPPVector> &z, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = elu_normr(z_ptr[i], c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::elu_normm(const Ref<MLPPMatrix> &z, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = elu_normr(z_ptr[i], c);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::elu_derivr(real_t z, real_t c) {
-	if (z <= 0) {
-		return c * exp(z);
-	} else {
-		return 1;
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::elu_derivv(const Ref<MLPPVector> &z, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = elu_derivr(z_ptr[i], c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::elu_derivm(const Ref<MLPPMatrix> &z, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = elu_derivr(z_ptr[i], c);
-	}
-
-	return a;
-}
-
-//SELU
-
-real_t MLPPActivationOld::selu_normr(real_t z, real_t lambda, real_t c) {
-	return lambda * ELU(z, c);
-}
-Ref<MLPPVector> MLPPActivationOld::selu_normv(const Ref<MLPPVector> &z, real_t lambda, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = selu_normr(z_ptr[i], lambda, c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::selu_normm(const Ref<MLPPMatrix> &z, real_t lambda, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = selu_normr(z_ptr[i], lambda, c);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::selu_derivr(real_t z, real_t lambda, real_t c) {
-	return elu_derivr(z, c);
-}
-Ref<MLPPVector> MLPPActivationOld::selu_derivv(const Ref<MLPPVector> &z, real_t lambda, real_t c) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = selu_derivr(z_ptr[i], lambda, c);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::selu_derivm(const Ref<MLPPMatrix> &z, real_t lambda, real_t c) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = selu_derivr(z_ptr[i], lambda, c);
-	}
-
-	return a;
-}
-
-//GELU
-
-real_t MLPPActivationOld::gelu_normr(real_t z) {
-	return 0.5 * z * (1 + tanh(sqrt(2 / M_PI) * (z + 0.044715 * Math::pow(z, 3))));
-}
-Ref<MLPPVector> MLPPActivationOld::gelu_normv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = gelu_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::gelu_normm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = gelu_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::gelu_derivr(real_t z) {
-	return 0.5 * tanh(0.0356774 * std::pow(z, 3) + 0.797885 * z) + (0.0535161 * std::pow(z, 3) + 0.398942 * z) * std::pow(sech(0.0356774 * std::pow(z, 3) + 0.797885 * z), 2) + 0.5;
-}
-Ref<MLPPVector> MLPPActivationOld::gelu_derivv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = gelu_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::gelu_derivm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = gelu_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-//SIGN
-
-real_t MLPPActivationOld::sign_normr(real_t z) {
-	if (z < 0) {
-		return -1;
-	} else if (z == 0) {
-		return 0;
-	} else {
-		return 1;
-	}
-}
-Ref<MLPPVector> MLPPActivationOld::sign_normv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = sign_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::sign_normm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = sign_normr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-real_t MLPPActivationOld::sign_derivr(real_t z) {
-	return 0;
-}
-Ref<MLPPVector> MLPPActivationOld::sign_derivv(const Ref<MLPPVector> &z) {
-	Ref<MLPPVector> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_size = z->size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_size; ++i) {
-		a_ptr[i] = sign_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-Ref<MLPPMatrix> MLPPActivationOld::sign_derivm(const Ref<MLPPMatrix> &z) {
-	Ref<MLPPMatrix> a;
-	a.instance();
-	a->resize(z->size());
-
-	int z_data_size = z->data_size();
-
-	const real_t *z_ptr = z->ptr();
-	real_t *a_ptr = a->ptrw();
-
-	for (int i = 0; i < z_data_size; ++i) {
-		a_ptr[i] = sign_derivr(z_ptr[i]);
-	}
-
-	return a;
-}
-
-//SINH
-
-real_t MLPPActivationOld::sinh_normr(real_t z) {
-	return 0.5 * (Math::exp(z) - Math::exp(-z));
-}
-Ref<MLPPVector> MLPPActivationOld::sinh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-	return alg.scalar_multiplynv(0.5, alg.subtractionnv(alg.expv(z), alg.expv(alg.scalar_multiplynv(-1, z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sinh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-	return alg.scalar_multiplym(0.5, alg.subtractionm(alg.expm(z), alg.expm(alg.scalar_multiplym(-1, z))));
-}
-
-real_t MLPPActivationOld::sinh_derivr(real_t z) {
-	return cosh_normr(z);
-}
-Ref<MLPPVector> MLPPActivationOld::sinh_derivv(const Ref<MLPPVector> &z) {
-	return cosh_normv(z);
-}
-Ref<MLPPMatrix> MLPPActivationOld::sinh_derivm(const Ref<MLPPMatrix> &z) {
-	return cosh_normm(z);
-}
-
-//COSH
-
-real_t MLPPActivationOld::cosh_normr(real_t z) {
-	return 0.5 * (Math::exp(z) + Math::exp(-z));
-}
-Ref<MLPPVector> MLPPActivationOld::cosh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-	return alg.scalar_multiplynv(0.5, alg.additionnv(alg.expv(z), alg.expv(alg.scalar_multiplynv(-1, z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::cosh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-	return alg.scalar_multiplym(0.5, alg.additionnv(alg.expm(z), alg.expm(alg.scalar_multiplym(-1, z))));
-}
-
-real_t MLPPActivationOld::cosh_derivr(real_t z) {
-	return sinh_normr(z);
-}
-Ref<MLPPVector> MLPPActivationOld::cosh_derivv(const Ref<MLPPVector> &z) {
-	return sinh_normv(z);
-}
-Ref<MLPPMatrix> MLPPActivationOld::cosh_derivm(const Ref<MLPPMatrix> &z) {
-	return sinh_normm(z);
-}
-
-//TANH
-
-real_t MLPPActivationOld::tanh_normr(real_t z) {
-	return (Math::exp(z) - Math::exp(-z)) / (Math::exp(z) + Math::exp(-z));
-}
-Ref<MLPPVector> MLPPActivationOld::tanh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.subtractionnv(alg.expv(z), alg.expv(alg.scalar_multiplynv(-1, z))), alg.additionnv(alg.expv(z), alg.expv(alg.scalar_multiplynv(-1, z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::tanh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.subtractionm(alg.expm(z), alg.expm(alg.scalar_multiplym(-1, z))), alg.additionm(alg.expm(z), alg.expm(alg.scalar_multiplym(-1, z))));
-}
-
-real_t MLPPActivationOld::tanh_derivr(real_t z) {
-	return 1 - tanh(z) * tanh(z);
-}
-Ref<MLPPVector> MLPPActivationOld::tanh_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(-1, alg.scalar_addnv(-1, alg.hadamard_productnv(tanh_normv(z), tanh_normv(z))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::tanh_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(-1, alg.scalar_addm(-1, alg.hadamard_productm(tanh_normm(z), tanh_normm(z))));
-}
-
-//CSCH
-
-real_t MLPPActivationOld::csch_normr(real_t z) {
-	return 1 / sinh(z);
-}
-Ref<MLPPVector> MLPPActivationOld::csch_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), sinh_normv(z));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::csch_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), sinh_normm(z));
-}
-
-real_t MLPPActivationOld::csch_derivr(real_t z) {
-	return -csch(z) * coth(z);
-}
-Ref<MLPPVector> MLPPActivationOld::csch_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(alg.scalar_multiplynv(-1, csch_normv(z)), coth_normv(z));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::csch_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productm(alg.scalar_multiplym(-1, csch_normm(z)), coth_normm(z));
-}
-
-//SECH
-
-real_t MLPPActivationOld::sech_normr(real_t z) {
-	return 1 / cosh(z);
-}
-
-Ref<MLPPVector> MLPPActivationOld::sech_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), cosh_normv(z));
-
-	// return activation(z, deriv, static_cast<void (*)(real_t, bool)>(&sech));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sech_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), cosh_normm(z));
-
-	// return activation(z, deriv, static_cast<void (*)(real_t, bool)>(&sech));
-}
-
-real_t MLPPActivationOld::sech_derivr(real_t z) {
-	return -sech(z) * tanh(z);
-}
-
-Ref<MLPPVector> MLPPActivationOld::sech_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(alg.scalar_multiplynv(-1, sech_normv(z)), tanh_normv(z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::sech_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productm(alg.scalar_multiplym(-1, sech_normm(z)), tanh_normm(z));
-}
-
-//COTH
-
-real_t MLPPActivationOld::coth_normr(real_t z) {
-	return 1 / tanh(z);
-}
-Ref<MLPPVector> MLPPActivationOld::coth_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), tanh_normv(z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::coth_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), tanh_normm(z));
-}
-
-real_t MLPPActivationOld::coth_derivr(real_t z) {
-	return -csch_normr(z) * csch_normr(z);
-}
-Ref<MLPPVector> MLPPActivationOld::coth_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productnv(alg.scalar_multiplynv(-1, csch_normv(z)), csch_normv(z));
-}
-Ref<MLPPMatrix> MLPPActivationOld::coth_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.hadamard_productm(alg.scalar_multiplym(-1, csch_normm(z)), csch_normm(z));
-}
-
-//ARSINH
-
-real_t MLPPActivationOld::arsinh_normr(real_t z) {
-	return std::log(z + sqrt(z * z + 1));
-}
-
-Ref<MLPPVector> MLPPActivationOld::arsinh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(alg.additionnv(z, alg.sqrtv(alg.additionnv(alg.hadamard_productnv(z, z), alg.onevecv(z->size())))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arsinh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logm(alg.additionm(z, alg.sqrtm(alg.additionm(alg.hadamard_productm(z, z), alg.onematm(z->size().x, z->size().y)))));
-}
-
-real_t MLPPActivationOld::arsinh_derivr(real_t z) {
-	return 1 / sqrt(z * z + 1);
-}
-
-Ref<MLPPVector> MLPPActivationOld::arsinh_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.sqrtv(alg.additionnv(alg.hadamard_productnv(z, z), alg.onevecv(z->size()))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arsinh_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.sqrtm(alg.additionm(alg.hadamard_productm(z, z), alg.onematm(z->size().x, z->size().y))));
-}
-
-//ARCOSH
-
-real_t MLPPActivationOld::arcosh_normr(real_t z) {
-	return std::log(z + sqrt(z * z - 1));
-}
-Ref<MLPPVector> MLPPActivationOld::arcosh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(alg.additionnv(z, alg.sqrtv(alg.subtractionnv(alg.hadamard_productnv(z, z), alg.onevecv(z->size())))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arcosh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logm(alg.additionm(z, alg.sqrtm(alg.subtractionm(alg.hadamard_productm(z, z), alg.onematm(z->size().x, z->size().y)))));
-}
-
-real_t MLPPActivationOld::arcosh_derivr(real_t z) {
-	return 1 / sqrt(z * z - 1);
-}
-Ref<MLPPVector> MLPPActivationOld::arcosh_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.sqrtv(alg.subtractionnv(alg.hadamard_productnv(z, z), alg.onevecv(z->size()))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arcosh_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.sqrtm(alg.subtractionm(alg.hadamard_productm(z, z), alg.onematm(z->size().x, z->size().y))));
-}
-
-//ARTANH
-
-real_t MLPPActivationOld::artanh_normr(real_t z) {
-	return 0.5 * std::log((1 + z) / (1 - z));
-}
-Ref<MLPPVector> MLPPActivationOld::artanh_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(0.5, alg.logv(alg.element_wise_division(alg.additionnv(alg.onevecv(z->size()), z), alg.subtractionnv(alg.onevecv(z->size()), z))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::artanh_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(0.5, alg.logm(alg.element_wise_divisionm(alg.additionm(alg.onematm(z->size().x, z->size().y), z), alg.subtractionm(alg.onematm(z->size().x, z->size().y), z))));
-}
-
-real_t MLPPActivationOld::artanh_derivr(real_t z) {
-	return 1 / (1 - z * z);
-}
-Ref<MLPPVector> MLPPActivationOld::artanh_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.subtractionnv(alg.onevecv(z->size()), alg.hadamard_productnv(z, z)));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::artanh_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.subtractionnv(alg.onematm(z->size().x, z->size().y), alg.hadamard_productm(z, z)));
-}
-
-//ARCSCH
-
-real_t MLPPActivationOld::arcsch_normr(real_t z) {
-	return std::log(sqrt(1 + (1 / (z * z))) + (1 / z));
-}
-Ref<MLPPVector> MLPPActivationOld::arcsch_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(
-			alg.additionnv(
-					alg.sqrtv(
-							alg.additionnv(
-									alg.onevecv(z->size()),
-									alg.element_wise_division(alg.onevecv(z->size()), alg.hadamard_productnv(z, z)))),
-					alg.element_wise_division(alg.onevecv(z->size()), z)));
-}
-Ref<MLPPMatrix> MLPPActivationOld::arcsch_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logm(
-			alg.additionm(
-					alg.sqrtm(
-							alg.additionm(alg.onematm(z->size().x, z->size().y),
-									alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.hadamard_productm(z, z)))),
-					alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), z)));
-}
-
-real_t MLPPActivationOld::arcsch_derivr(real_t z) {
-	return -1 / ((z * z) * sqrt(1 + (1 / (z * z))));
-}
-Ref<MLPPVector> MLPPActivationOld::arcsch_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(
-			alg.fullv(z->size(), -1),
-			alg.hadamard_productm(
-					alg.hadamard_productnv(z, z),
-					alg.sqrtv(alg.additionnv(alg.onevecv(z->size()), alg.element_wise_division(alg.onevecv(z->size()), alg.hadamard_productnv(z, z))))));
-}
-Ref<MLPPMatrix> MLPPActivationOld::arcsch_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(
-			alg.fullm(z->size().x, z->size().y, -1),
-			alg.hadamard_productm(alg.hadamard_productm(z, z),
-					alg.sqrtm(alg.additionm(alg.onematm(z->size().x, z->size().y),
-							alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.hadamard_productm(z, z))))));
-}
-
-//ARSECH
-
-real_t MLPPActivationOld::arsech_normr(real_t z) {
-	return std::log((1 / z) + ((1 / z) + 1) * ((1 / z) - 1));
-}
-
-Ref<MLPPVector> MLPPActivationOld::arsech_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logv(
-			alg.additionnv(
-					alg.element_wise_division(
-							alg.onevecv(z->size()), z),
-					alg.hadamard_productnv(
-							alg.additionnv(alg.element_wise_division(alg.onevecv(z->size()), z), alg.onevecv(z->size())),
-							alg.subtractionnv(alg.element_wise_division(alg.onevecv(z->size()), z), alg.onevecv(z->size())))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arsech_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.logm(
-			alg.additionm(
-					alg.element_wise_divisionm(
-							alg.onematm(z->size().x, z->size().y), z),
-					alg.hadamard_productm(
-							alg.additionm(
-									alg.element_wise_divisionm(
-											alg.onematm(z->size().x, z->size().y), z),
-									alg.onematm(z->size().x, z->size().y)),
-							alg.subtractionm(
-									alg.element_wise_divisionm(
-											alg.onematm(z->size().x, z->size().y), z),
-									alg.onematm(z->size().x, z->size().y)))));
-}
-
-real_t MLPPActivationOld::arsech_derivr(real_t z) {
-	return -1 / (z * sqrt(1 - z * z));
-}
-
-Ref<MLPPVector> MLPPActivationOld::arsech_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(
-			alg.fullv(z->size(), -1),
-			alg.hadamard_productnv(
-					z,
-					alg.sqrtv(
-							alg.subtractionnv(alg.onevecv(z->size()), alg.hadamard_productnv(z, z)))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arsech_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(
-			alg.fullm(z->size().x, z->size().y, -1),
-			alg.hadamard_productm(
-					z,
-					alg.sqrtm(alg.subtractionm(alg.onematm(z->size().x, z->size().y), alg.hadamard_productm(z, z)))));
-}
-
-//ARCOTH
-
-real_t MLPPActivationOld::arcoth_normr(real_t z) {
-	return 0.5 * std::log((1 + z) / (z - 1));
-}
-Ref<MLPPVector> MLPPActivationOld::arcoth_normv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplynv(
-			0.5,
-			alg.logv(alg.element_wise_division(alg.additionnv(alg.onevecv(z->size()), z), alg.subtractionnv(z, alg.onevecv(z->size())))));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arcoth_normm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.scalar_multiplym(
-			0.5,
-			alg.logm(alg.element_wise_divisionm(alg.additionm(alg.onematm(z->size().x, z->size().y), z), alg.subtractionm(z, alg.onematm(z->size().x, z->size().y)))));
-}
-
-real_t MLPPActivationOld::arcoth_derivr(real_t z) {
-	return 1 / (1 - z * z);
-}
-Ref<MLPPVector> MLPPActivationOld::arcoth_derivv(const Ref<MLPPVector> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_division(alg.onevecv(z->size()), alg.subtractionnv(alg.onevecv(z->size()), alg.hadamard_productnv(z, z)));
-}
-
-Ref<MLPPMatrix> MLPPActivationOld::arcoth_derivm(const Ref<MLPPMatrix> &z) {
-	MLPPLinAlg alg;
-
-	return alg.element_wise_divisionm(alg.onematm(z->size().x, z->size().y), alg.subtractionm(alg.onematm(z->size().x, z->size().y), alg.hadamard_productm(z, z)));
-}
-
-void MLPPActivationOld::_bind_methods() {
-	ClassDB::bind_method(D_METHOD("run_activation_real", "func", "z", "deriv"), &MLPPActivationOld::run_activation_real, false);
-	ClassDB::bind_method(D_METHOD("run_activation_vector", "func", "z", "deriv"), &MLPPActivationOld::run_activation_vector, false);
-	ClassDB::bind_method(D_METHOD("run_activation_matrix", "func", "z", "deriv"), &MLPPActivationOld::run_activation_matrix, false);
-
-	ClassDB::bind_method(D_METHOD("run_activation_norm_real", "func", "z"), &MLPPActivationOld::run_activation_norm_real);
-	ClassDB::bind_method(D_METHOD("run_activation_norm_vector", "func", "z"), &MLPPActivationOld::run_activation_norm_vector);
-	ClassDB::bind_method(D_METHOD("run_activation_norm_matrix", "func", "z"), &MLPPActivationOld::run_activation_norm_matrix);
-
-	real_t run_activation_norm_real(const ActivationFunction func, const real_t z);
-	Ref<MLPPVector> run_activation_norm_vector(const ActivationFunction func, const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> run_activation_norm_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z);
-
-	ClassDB::bind_method(D_METHOD("run_activation_deriv_real", "func", "z"), &MLPPActivationOld::run_activation_deriv_real);
-	ClassDB::bind_method(D_METHOD("run_activation_deriv_vector", "func", "z"), &MLPPActivationOld::run_activation_deriv_vector);
-	ClassDB::bind_method(D_METHOD("run_activation_deriv_matrix", "func", "z"), &MLPPActivationOld::run_activation_deriv_matrix);
-
-	//LINEAR
-
-	ClassDB::bind_method(D_METHOD("linear_normr", "z"), &MLPPActivationOld::linear_normr);
-	ClassDB::bind_method(D_METHOD("linear_normv", "z"), &MLPPActivationOld::linear_normv);
-	ClassDB::bind_method(D_METHOD("linear_normm", "z"), &MLPPActivationOld::linear_normm);
-
-	ClassDB::bind_method(D_METHOD("linear_derivr", "z"), &MLPPActivationOld::linear_derivr);
-	ClassDB::bind_method(D_METHOD("linear_derivv", "z"), &MLPPActivationOld::linear_derivv);
-	ClassDB::bind_method(D_METHOD("linear_derivm", "z"), &MLPPActivationOld::linear_derivm);
-
-	//SIGMOID
-
-	ClassDB::bind_method(D_METHOD("sigmoid_normr", "z"), &MLPPActivationOld::sigmoid_normr);
-	ClassDB::bind_method(D_METHOD("sigmoid_normv", "z"), &MLPPActivationOld::sigmoid_normv);
-	ClassDB::bind_method(D_METHOD("sigmoid_normm", "z"), &MLPPActivationOld::sigmoid_normm);
-
-	ClassDB::bind_method(D_METHOD("sigmoid_derivr", "z"), &MLPPActivationOld::sigmoid_derivr);
-	ClassDB::bind_method(D_METHOD("sigmoid_derivv", "z"), &MLPPActivationOld::sigmoid_derivv);
-	ClassDB::bind_method(D_METHOD("sigmoid_derivm", "z"), &MLPPActivationOld::sigmoid_derivm);
-
-	//SOFTMAX
-
-	ClassDB::bind_method(D_METHOD("softmax_normr", "z"), &MLPPActivationOld::softmax_normr);
-	ClassDB::bind_method(D_METHOD("softmax_normv", "z"), &MLPPActivationOld::softmax_normv);
-	ClassDB::bind_method(D_METHOD("softmax_normm", "z"), &MLPPActivationOld::softmax_normm);
-
-	ClassDB::bind_method(D_METHOD("softmax_derivr", "z"), &MLPPActivationOld::softmax_derivr);
-	ClassDB::bind_method(D_METHOD("softmax_derivv", "z"), &MLPPActivationOld::softmax_derivv);
-	ClassDB::bind_method(D_METHOD("softmax_derivm", "z"), &MLPPActivationOld::softmax_derivm);
-
-	//ADJ_SOFTMAX
-
-	real_t adj_softmax_normr(real_t z);
-	Ref<MLPPVector> adj_softmax_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> adj_softmax_normm(const Ref<MLPPMatrix> &z);
-
-	real_t adj_softmax_derivr(real_t z);
-	Ref<MLPPVector> adj_softmax_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> adj_softmax_derivm(const Ref<MLPPMatrix> &z);
-
-	//SOFTPLUS
-
-	ClassDB::bind_method(D_METHOD("softplus_normr", "z"), &MLPPActivationOld::softplus_normr);
-	ClassDB::bind_method(D_METHOD("softplus_normv", "z"), &MLPPActivationOld::softplus_normv);
-	ClassDB::bind_method(D_METHOD("softplus_normm", "z"), &MLPPActivationOld::softplus_normm);
-
-	ClassDB::bind_method(D_METHOD("softplus_derivr", "z"), &MLPPActivationOld::softplus_derivr);
-	ClassDB::bind_method(D_METHOD("softplus_derivv", "z"), &MLPPActivationOld::softplus_derivv);
-	ClassDB::bind_method(D_METHOD("softplus_derivm", "z"), &MLPPActivationOld::softplus_derivm);
-
-	//SOFTSIGN
-
-	ClassDB::bind_method(D_METHOD("softsign_normr", "z"), &MLPPActivationOld::softsign_normr);
-	ClassDB::bind_method(D_METHOD("softsign_normv", "z"), &MLPPActivationOld::softsign_normv);
-	ClassDB::bind_method(D_METHOD("softsign_normm", "z"), &MLPPActivationOld::softsign_normm);
-
-	ClassDB::bind_method(D_METHOD("softsign_derivr", "z"), &MLPPActivationOld::softsign_derivr);
-	ClassDB::bind_method(D_METHOD("softsign_derivv", "z"), &MLPPActivationOld::softsign_derivv);
-	ClassDB::bind_method(D_METHOD("softsign_derivm", "z"), &MLPPActivationOld::softsign_derivm);
-
-	//GAUSSIANCDF
-
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_normr", "z"), &MLPPActivationOld::gaussian_cdf_normr);
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_normv", "z"), &MLPPActivationOld::gaussian_cdf_normv);
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_normm", "z"), &MLPPActivationOld::gaussian_cdf_normm);
-
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_derivr", "z"), &MLPPActivationOld::gaussian_cdf_derivr);
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_derivv", "z"), &MLPPActivationOld::gaussian_cdf_derivv);
-	ClassDB::bind_method(D_METHOD("gaussian_cdf_derivm", "z"), &MLPPActivationOld::gaussian_cdf_derivm);
-
-	//CLOGLOG
-
-	ClassDB::bind_method(D_METHOD("cloglog_normr", "z"), &MLPPActivationOld::cloglog_normr);
-	ClassDB::bind_method(D_METHOD("cloglog_normv", "z"), &MLPPActivationOld::cloglog_normv);
-	ClassDB::bind_method(D_METHOD("cloglog_normm", "z"), &MLPPActivationOld::cloglog_normm);
-
-	ClassDB::bind_method(D_METHOD("cloglog_derivr", "z"), &MLPPActivationOld::cloglog_derivr);
-	ClassDB::bind_method(D_METHOD("cloglog_derivv", "z"), &MLPPActivationOld::cloglog_derivv);
-	ClassDB::bind_method(D_METHOD("cloglog_derivm", "z"), &MLPPActivationOld::cloglog_derivm);
-
-	//LOGIT
-
-	ClassDB::bind_method(D_METHOD("logit_normr", "z"), &MLPPActivationOld::logit_normr);
-	ClassDB::bind_method(D_METHOD("logit_normv", "z"), &MLPPActivationOld::logit_normv);
-	ClassDB::bind_method(D_METHOD("logit_normm", "z"), &MLPPActivationOld::logit_normm);
-
-	ClassDB::bind_method(D_METHOD("logit_derivr", "z"), &MLPPActivationOld::logit_derivr);
-	ClassDB::bind_method(D_METHOD("logit_derivv", "z"), &MLPPActivationOld::logit_derivv);
-	ClassDB::bind_method(D_METHOD("logit_derivm", "z"), &MLPPActivationOld::logit_derivm);
-
-	//UNITSTEP
-
-	ClassDB::bind_method(D_METHOD("unit_step_normr", "z"), &MLPPActivationOld::unit_step_normr);
-	ClassDB::bind_method(D_METHOD("unit_step_normv", "z"), &MLPPActivationOld::unit_step_normv);
-	ClassDB::bind_method(D_METHOD("unit_step_normm", "z"), &MLPPActivationOld::unit_step_normm);
-
-	ClassDB::bind_method(D_METHOD("unit_step_derivr", "z"), &MLPPActivationOld::unit_step_derivr);
-	ClassDB::bind_method(D_METHOD("unit_step_derivv", "z"), &MLPPActivationOld::unit_step_derivv);
-	ClassDB::bind_method(D_METHOD("unit_step_derivm", "z"), &MLPPActivationOld::unit_step_derivm);
-
-	//SWISH
-
-	ClassDB::bind_method(D_METHOD("swish_normr", "z"), &MLPPActivationOld::swish_normr);
-	ClassDB::bind_method(D_METHOD("swish_normv", "z"), &MLPPActivationOld::swish_normv);
-	ClassDB::bind_method(D_METHOD("swish_normm", "z"), &MLPPActivationOld::swish_normm);
-
-	ClassDB::bind_method(D_METHOD("swish_derivr", "z"), &MLPPActivationOld::swish_derivr);
-	ClassDB::bind_method(D_METHOD("swish_derivv", "z"), &MLPPActivationOld::swish_derivv);
-	ClassDB::bind_method(D_METHOD("swish_derivm", "z"), &MLPPActivationOld::swish_derivm);
-
-	//MISH
-
-	ClassDB::bind_method(D_METHOD("mish_normr", "z"), &MLPPActivationOld::mish_normr);
-	ClassDB::bind_method(D_METHOD("mish_normv", "z"), &MLPPActivationOld::mish_normv);
-	ClassDB::bind_method(D_METHOD("mish_normm", "z"), &MLPPActivationOld::mish_normm);
-
-	ClassDB::bind_method(D_METHOD("mish_derivr", "z"), &MLPPActivationOld::mish_derivr);
-	ClassDB::bind_method(D_METHOD("mish_derivv", "z"), &MLPPActivationOld::mish_derivv);
-	ClassDB::bind_method(D_METHOD("mish_derivm", "z"), &MLPPActivationOld::mish_derivm);
-
-	//SINC
-
-	ClassDB::bind_method(D_METHOD("sinc_normr", "z"), &MLPPActivationOld::sinc_normr);
-	ClassDB::bind_method(D_METHOD("sinc_normv", "z"), &MLPPActivationOld::sinc_normv);
-	ClassDB::bind_method(D_METHOD("sinc_normm", "z"), &MLPPActivationOld::sinc_normm);
-
-	ClassDB::bind_method(D_METHOD("sinc_derivr", "z"), &MLPPActivationOld::sinc_derivr);
-	ClassDB::bind_method(D_METHOD("sinc_derivv", "z"), &MLPPActivationOld::sinc_derivv);
-	ClassDB::bind_method(D_METHOD("sinc_derivm", "z"), &MLPPActivationOld::sinc_derivm);
-
-	//RELU
-
-	ClassDB::bind_method(D_METHOD("relu_normr", "z"), &MLPPActivationOld::relu_normr);
-	ClassDB::bind_method(D_METHOD("relu_normv", "z"), &MLPPActivationOld::relu_normv);
-	ClassDB::bind_method(D_METHOD("relu_normm", "z"), &MLPPActivationOld::relu_normm);
-
-	ClassDB::bind_method(D_METHOD("relu_derivr", "z"), &MLPPActivationOld::relu_derivr);
-	ClassDB::bind_method(D_METHOD("relu_derivv", "z"), &MLPPActivationOld::relu_derivv);
-	ClassDB::bind_method(D_METHOD("relu_derivm", "z"), &MLPPActivationOld::relu_derivm);
-
-	//LEAKYRELU
-
-	ClassDB::bind_method(D_METHOD("leaky_relu_normr", "z"), &MLPPActivationOld::leaky_relu_normr);
-	ClassDB::bind_method(D_METHOD("leaky_relu_normv", "z"), &MLPPActivationOld::leaky_relu_normv);
-	ClassDB::bind_method(D_METHOD("leaky_relu_normm", "z"), &MLPPActivationOld::leaky_relu_normm);
-
-	ClassDB::bind_method(D_METHOD("leaky_relu_derivr", "z"), &MLPPActivationOld::leaky_relu_derivr);
-	ClassDB::bind_method(D_METHOD("leaky_relu_derivv", "z"), &MLPPActivationOld::leaky_relu_derivv);
-	ClassDB::bind_method(D_METHOD("leaky_relu_derivm", "z"), &MLPPActivationOld::leaky_relu_derivm);
-
-	//ELU
-
-	ClassDB::bind_method(D_METHOD("elu_normr", "z"), &MLPPActivationOld::elu_normr);
-	ClassDB::bind_method(D_METHOD("elu_normv", "z"), &MLPPActivationOld::elu_normv);
-	ClassDB::bind_method(D_METHOD("elu_normm", "z"), &MLPPActivationOld::elu_normm);
-
-	ClassDB::bind_method(D_METHOD("elu_derivr", "z"), &MLPPActivationOld::elu_derivr);
-	ClassDB::bind_method(D_METHOD("elu_derivv", "z"), &MLPPActivationOld::elu_derivv);
-	ClassDB::bind_method(D_METHOD("elu_derivm", "z"), &MLPPActivationOld::elu_derivm);
-
-	//SELU
-
-	ClassDB::bind_method(D_METHOD("selu_normr", "z"), &MLPPActivationOld::selu_normr);
-	ClassDB::bind_method(D_METHOD("selu_normv", "z"), &MLPPActivationOld::selu_normv);
-	ClassDB::bind_method(D_METHOD("selu_normm", "z"), &MLPPActivationOld::selu_normm);
-
-	ClassDB::bind_method(D_METHOD("selu_derivr", "z"), &MLPPActivationOld::selu_derivr);
-	ClassDB::bind_method(D_METHOD("selu_derivv", "z"), &MLPPActivationOld::selu_derivv);
-	ClassDB::bind_method(D_METHOD("selu_derivm", "z"), &MLPPActivationOld::selu_derivm);
-
-	//GELU
-
-	ClassDB::bind_method(D_METHOD("gelu_normr", "z"), &MLPPActivationOld::gelu_normr);
-	ClassDB::bind_method(D_METHOD("gelu_normv", "z"), &MLPPActivationOld::gelu_normv);
-	ClassDB::bind_method(D_METHOD("gelu_normm", "z"), &MLPPActivationOld::gelu_normm);
-
-	ClassDB::bind_method(D_METHOD("gelu_derivr", "z"), &MLPPActivationOld::gelu_derivr);
-	ClassDB::bind_method(D_METHOD("gelu_derivv", "z"), &MLPPActivationOld::gelu_derivv);
-	ClassDB::bind_method(D_METHOD("gelu_derivm", "z"), &MLPPActivationOld::gelu_derivm);
-
-	//SIGN
-
-	ClassDB::bind_method(D_METHOD("sign_normr", "z"), &MLPPActivationOld::sign_normr);
-	ClassDB::bind_method(D_METHOD("sign_normv", "z"), &MLPPActivationOld::sign_normv);
-	ClassDB::bind_method(D_METHOD("sign_normm", "z"), &MLPPActivationOld::sign_normm);
-
-	ClassDB::bind_method(D_METHOD("sign_derivr", "z"), &MLPPActivationOld::sign_derivr);
-	ClassDB::bind_method(D_METHOD("sign_derivv", "z"), &MLPPActivationOld::sign_derivv);
-	ClassDB::bind_method(D_METHOD("sign_derivm", "z"), &MLPPActivationOld::sign_derivm);
-
-	//SINH
-
-	ClassDB::bind_method(D_METHOD("sinh_normr", "z"), &MLPPActivationOld::sinh_normr);
-	ClassDB::bind_method(D_METHOD("sinh_normv", "z"), &MLPPActivationOld::sinh_normv);
-	ClassDB::bind_method(D_METHOD("sinh_normm", "z"), &MLPPActivationOld::sinh_normm);
-
-	ClassDB::bind_method(D_METHOD("sinh_derivr", "z"), &MLPPActivationOld::sinh_derivr);
-	ClassDB::bind_method(D_METHOD("sinh_derivv", "z"), &MLPPActivationOld::sinh_derivv);
-	ClassDB::bind_method(D_METHOD("sinh_derivm", "z"), &MLPPActivationOld::sinh_derivm);
-
-	//COSH
-
-	ClassDB::bind_method(D_METHOD("cosh_normr", "z"), &MLPPActivationOld::cosh_normr);
-	ClassDB::bind_method(D_METHOD("cosh_normv", "z"), &MLPPActivationOld::cosh_normv);
-	ClassDB::bind_method(D_METHOD("cosh_normm", "z"), &MLPPActivationOld::cosh_normm);
-
-	ClassDB::bind_method(D_METHOD("cosh_derivr", "z"), &MLPPActivationOld::cosh_derivr);
-	ClassDB::bind_method(D_METHOD("cosh_derivv", "z"), &MLPPActivationOld::cosh_derivv);
-	ClassDB::bind_method(D_METHOD("cosh_derivm", "z"), &MLPPActivationOld::cosh_derivm);
-
-	//TANH
-
-	ClassDB::bind_method(D_METHOD("tanh_normr", "z"), &MLPPActivationOld::tanh_normr);
-	ClassDB::bind_method(D_METHOD("tanh_normv", "z"), &MLPPActivationOld::tanh_normv);
-	ClassDB::bind_method(D_METHOD("tanh_normm", "z"), &MLPPActivationOld::tanh_normm);
-
-	ClassDB::bind_method(D_METHOD("tanh_derivr", "z"), &MLPPActivationOld::tanh_derivr);
-	ClassDB::bind_method(D_METHOD("tanh_derivv", "z"), &MLPPActivationOld::tanh_derivv);
-	ClassDB::bind_method(D_METHOD("tanh_derivm", "z"), &MLPPActivationOld::tanh_derivm);
-
-	//CSCH
-
-	ClassDB::bind_method(D_METHOD("csch_normr", "z"), &MLPPActivationOld::csch_normr);
-	ClassDB::bind_method(D_METHOD("csch_normv", "z"), &MLPPActivationOld::csch_normv);
-	ClassDB::bind_method(D_METHOD("csch_normm", "z"), &MLPPActivationOld::csch_normm);
-
-	ClassDB::bind_method(D_METHOD("csch_derivr", "z"), &MLPPActivationOld::csch_derivr);
-	ClassDB::bind_method(D_METHOD("csch_derivv", "z"), &MLPPActivationOld::csch_derivv);
-	ClassDB::bind_method(D_METHOD("csch_derivm", "z"), &MLPPActivationOld::csch_derivm);
-
-	//SECH
-
-	ClassDB::bind_method(D_METHOD("sech_normr", "z"), &MLPPActivationOld::sech_normr);
-	ClassDB::bind_method(D_METHOD("sech_normv", "z"), &MLPPActivationOld::sech_normv);
-	ClassDB::bind_method(D_METHOD("sech_normm", "z"), &MLPPActivationOld::sech_normm);
-
-	ClassDB::bind_method(D_METHOD("sech_derivr", "z"), &MLPPActivationOld::sech_derivr);
-	ClassDB::bind_method(D_METHOD("sech_derivv", "z"), &MLPPActivationOld::sech_derivv);
-	ClassDB::bind_method(D_METHOD("sech_derivm", "z"), &MLPPActivationOld::sech_derivm);
-
-	//COTH
-
-	ClassDB::bind_method(D_METHOD("coth_normr", "z"), &MLPPActivationOld::coth_normr);
-	ClassDB::bind_method(D_METHOD("coth_normv", "z"), &MLPPActivationOld::coth_normv);
-	ClassDB::bind_method(D_METHOD("coth_normm", "z"), &MLPPActivationOld::coth_normm);
-
-	ClassDB::bind_method(D_METHOD("coth_derivr", "z"), &MLPPActivationOld::coth_derivr);
-	ClassDB::bind_method(D_METHOD("coth_derivv", "z"), &MLPPActivationOld::coth_derivv);
-	ClassDB::bind_method(D_METHOD("coth_derivm", "z"), &MLPPActivationOld::coth_derivm);
-
-	//ARSINH
-
-	ClassDB::bind_method(D_METHOD("arsinh_normr", "z"), &MLPPActivationOld::arsinh_normr);
-	ClassDB::bind_method(D_METHOD("arsinh_normv", "z"), &MLPPActivationOld::arsinh_normv);
-	ClassDB::bind_method(D_METHOD("arsinh_normm", "z"), &MLPPActivationOld::arsinh_normm);
-
-	ClassDB::bind_method(D_METHOD("arsinh_derivr", "z"), &MLPPActivationOld::arsinh_derivr);
-	ClassDB::bind_method(D_METHOD("arsinh_derivv", "z"), &MLPPActivationOld::arsinh_derivv);
-	ClassDB::bind_method(D_METHOD("arsinh_derivm", "z"), &MLPPActivationOld::arsinh_derivm);
-
-	//ARCOSH
-
-	ClassDB::bind_method(D_METHOD("arcosh_normr", "z"), &MLPPActivationOld::arcosh_normr);
-	ClassDB::bind_method(D_METHOD("arcosh_normv", "z"), &MLPPActivationOld::arcosh_normv);
-	ClassDB::bind_method(D_METHOD("arcosh_normm", "z"), &MLPPActivationOld::arcosh_normm);
-
-	ClassDB::bind_method(D_METHOD("arcosh_derivr", "z"), &MLPPActivationOld::arcosh_derivr);
-	ClassDB::bind_method(D_METHOD("arcosh_derivv", "z"), &MLPPActivationOld::arcosh_derivv);
-	ClassDB::bind_method(D_METHOD("arcosh_derivm", "z"), &MLPPActivationOld::arcosh_derivm);
-
-	//ARTANH
-
-	ClassDB::bind_method(D_METHOD("artanh_normr", "z"), &MLPPActivationOld::artanh_normr);
-	ClassDB::bind_method(D_METHOD("artanh_normv", "z"), &MLPPActivationOld::artanh_normv);
-	ClassDB::bind_method(D_METHOD("artanh_normm", "z"), &MLPPActivationOld::artanh_normm);
-
-	ClassDB::bind_method(D_METHOD("artanh_derivr", "z"), &MLPPActivationOld::artanh_derivr);
-	ClassDB::bind_method(D_METHOD("artanh_derivv", "z"), &MLPPActivationOld::artanh_derivv);
-	ClassDB::bind_method(D_METHOD("artanh_derivm", "z"), &MLPPActivationOld::artanh_derivm);
-
-	//ARCSCH
-
-	ClassDB::bind_method(D_METHOD("arcsch_normr", "z"), &MLPPActivationOld::arcsch_normr);
-	ClassDB::bind_method(D_METHOD("arcsch_normv", "z"), &MLPPActivationOld::arcsch_normv);
-	ClassDB::bind_method(D_METHOD("arcsch_normm", "z"), &MLPPActivationOld::arcsch_normm);
-
-	ClassDB::bind_method(D_METHOD("arcsch_derivr", "z"), &MLPPActivationOld::arcsch_derivr);
-	ClassDB::bind_method(D_METHOD("arcsch_derivv", "z"), &MLPPActivationOld::arcsch_derivv);
-	ClassDB::bind_method(D_METHOD("arcsch_derivm", "z"), &MLPPActivationOld::arcsch_derivm);
-
-	//ARSECH
-
-	ClassDB::bind_method(D_METHOD("arsech_normr", "z"), &MLPPActivationOld::arsech_normr);
-	ClassDB::bind_method(D_METHOD("arsech_normv", "z"), &MLPPActivationOld::arsech_normv);
-	ClassDB::bind_method(D_METHOD("arsech_normm", "z"), &MLPPActivationOld::arsech_normm);
-
-	ClassDB::bind_method(D_METHOD("arsech_derivr", "z"), &MLPPActivationOld::arsech_derivr);
-	ClassDB::bind_method(D_METHOD("arsech_derivv", "z"), &MLPPActivationOld::arsech_derivv);
-	ClassDB::bind_method(D_METHOD("arsech_derivm", "z"), &MLPPActivationOld::arsech_derivm);
-
-	//ARCOTH
-
-	ClassDB::bind_method(D_METHOD("arcoth_normr", "z"), &MLPPActivationOld::arcoth_normr);
-	ClassDB::bind_method(D_METHOD("arcoth_normv", "z"), &MLPPActivationOld::arcoth_normv);
-	ClassDB::bind_method(D_METHOD("arcoth_normm", "z"), &MLPPActivationOld::arcoth_normm);
-
-	ClassDB::bind_method(D_METHOD("arcoth_derivr", "z"), &MLPPActivationOld::arcoth_derivr);
-	ClassDB::bind_method(D_METHOD("arcoth_derivv", "z"), &MLPPActivationOld::arcoth_derivv);
-	ClassDB::bind_method(D_METHOD("arcoth_derivm", "z"), &MLPPActivationOld::arcoth_derivm);
-
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_LINEAR);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SIGMOID);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SWISH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_MISH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SIN_C);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SOFTMAX);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SOFTPLUS);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SOFTSIGN);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ADJ_SOFTMAX);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_C_LOG_LOG);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_LOGIT);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_GAUSSIAN_CDF);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_RELU);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_GELU);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SIGN);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_UNIT_STEP);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SINH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_COSH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_TANH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_CSCH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_SECH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_COTH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARSINH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARCOSH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARTANH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARCSCH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARSECH);
-	BIND_ENUM_CONSTANT(ACTIVATION_FUNCTION_ARCOTH);
-}
-
-//======================== OLD =============================
-
 real_t MLPPActivationOld::linear(real_t z, bool deriv) {
 	if (deriv) {
 		return 1;
diff --git a/mlpp/activation/activation_old.h b/mlpp/activation/activation_old.h
index a580948..fe15657 100644
--- a/mlpp/activation/activation_old.h
+++ b/mlpp/activation/activation_old.h
@@ -10,405 +10,10 @@
 
 #include "core/math/math_defs.h"
 
-#include "core/object/func_ref.h"
-#include "core/object/reference.h"
-
-#include "../lin_alg/mlpp_matrix.h"
-#include "../lin_alg/mlpp_vector.h"
-
 #include <vector>
 
-//TODO Activation functions should either have a variant which does not allocate, or they should just be reworked altogether
-//TODO Methods here should probably use error macros, in a way where they get disabled in non-tools(?) (maybe release?) builds
-
-class MLPPActivationOld : public Reference {
-	GDCLASS(MLPPActivationOld, Reference);
-
+class MLPPActivationOld {
 public:
-	enum ActivationFunction {
-		ACTIVATION_FUNCTION_LINEAR = 0,
-		ACTIVATION_FUNCTION_SIGMOID,
-		ACTIVATION_FUNCTION_SWISH,
-		ACTIVATION_FUNCTION_MISH,
-		ACTIVATION_FUNCTION_SIN_C,
-		ACTIVATION_FUNCTION_SOFTMAX,
-		ACTIVATION_FUNCTION_SOFTPLUS,
-		ACTIVATION_FUNCTION_SOFTSIGN,
-		ACTIVATION_FUNCTION_ADJ_SOFTMAX,
-		ACTIVATION_FUNCTION_C_LOG_LOG,
-		ACTIVATION_FUNCTION_LOGIT,
-		ACTIVATION_FUNCTION_GAUSSIAN_CDF,
-		ACTIVATION_FUNCTION_RELU,
-		ACTIVATION_FUNCTION_GELU,
-		ACTIVATION_FUNCTION_SIGN,
-		ACTIVATION_FUNCTION_UNIT_STEP,
-		ACTIVATION_FUNCTION_SINH,
-		ACTIVATION_FUNCTION_COSH,
-		ACTIVATION_FUNCTION_TANH,
-		ACTIVATION_FUNCTION_CSCH,
-		ACTIVATION_FUNCTION_SECH,
-		ACTIVATION_FUNCTION_COTH,
-		ACTIVATION_FUNCTION_ARSINH,
-		ACTIVATION_FUNCTION_ARCOSH,
-		ACTIVATION_FUNCTION_ARTANH,
-		ACTIVATION_FUNCTION_ARCSCH,
-		ACTIVATION_FUNCTION_ARSECH,
-		ACTIVATION_FUNCTION_ARCOTH,
-	};
-
-public:
-	typedef real_t (MLPPActivationOld::*RealActivationFunctionPointer)(real_t);
-	typedef Ref<MLPPVector> (MLPPActivationOld::*VectorActivationFunctionPointer)(const Ref<MLPPVector> &);
-	typedef Ref<MLPPMatrix> (MLPPActivationOld::*MatrixActivationFunctionPointer)(const Ref<MLPPMatrix> &);
-
-	RealActivationFunctionPointer get_activation_function_ptr_real(const ActivationFunction func, const bool deriv = false);
-	VectorActivationFunctionPointer get_activation_function_ptr_vector(const ActivationFunction func, const bool deriv = false);
-	MatrixActivationFunctionPointer get_activation_function_ptr_matrix(const ActivationFunction func, const bool deriv = false);
-
-	RealActivationFunctionPointer get_activation_function_ptr_normal_real(const ActivationFunction func);
-	VectorActivationFunctionPointer get_activation_function_ptr_normal_vector(const ActivationFunction func);
-	MatrixActivationFunctionPointer get_activation_function_ptr_normal_matrix(const ActivationFunction func);
-
-	RealActivationFunctionPointer get_activation_function_ptr_deriv_real(const ActivationFunction func);
-	VectorActivationFunctionPointer get_activation_function_ptr_deriv_vector(const ActivationFunction func);
-	MatrixActivationFunctionPointer get_activation_function_ptr_deriv_matrix(const ActivationFunction func);
-
-	real_t run_activation_real(const ActivationFunction func, const real_t z, const bool deriv = false);
-	Ref<MLPPVector> run_activation_vector(const ActivationFunction func, const Ref<MLPPVector> &z, const bool deriv = false);
-	Ref<MLPPMatrix> run_activation_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z, const bool deriv = false);
-
-	real_t run_activation_norm_real(const ActivationFunction func, const real_t z);
-	Ref<MLPPVector> run_activation_norm_vector(const ActivationFunction func, const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> run_activation_norm_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z);
-
-	real_t run_activation_deriv_real(const ActivationFunction func, const real_t z);
-	Ref<MLPPVector> run_activation_deriv_vector(const ActivationFunction func, const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> run_activation_deriv_matrix(const ActivationFunction func, const Ref<MLPPMatrix> &z);
-
-	Ref<MLPPVector> activationr(const Ref<MLPPVector> &z, real_t (*function)(real_t));
-
-	//ACTIVATION FUNCTIONS
-
-	//LINEAR
-
-	real_t linear_normr(real_t z);
-	Ref<MLPPVector> linear_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> linear_normm(const Ref<MLPPMatrix> &z);
-
-	real_t linear_derivr(real_t z);
-	Ref<MLPPVector> linear_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> linear_derivm(const Ref<MLPPMatrix> &z);
-
-	//SIGMOID
-
-	real_t sigmoid_normr(real_t z);
-	Ref<MLPPVector> sigmoid_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sigmoid_normm(const Ref<MLPPMatrix> &z);
-
-	real_t sigmoid_derivr(real_t z);
-	Ref<MLPPVector> sigmoid_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sigmoid_derivm(const Ref<MLPPMatrix> &z);
-
-	//SOFTMAX
-
-	real_t softmax_normr(real_t z);
-	Ref<MLPPVector> softmax_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softmax_normm(const Ref<MLPPMatrix> &z);
-
-	real_t softmax_derivr(real_t z);
-	Ref<MLPPVector> softmax_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softmax_derivm(const Ref<MLPPMatrix> &z);
-
-	//ADJ_SOFTMAX
-
-	real_t adj_softmax_normr(real_t z);
-	Ref<MLPPVector> adj_softmax_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> adj_softmax_normm(const Ref<MLPPMatrix> &z);
-
-	real_t adj_softmax_derivr(real_t z);
-	Ref<MLPPVector> adj_softmax_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> adj_softmax_derivm(const Ref<MLPPMatrix> &z);
-
-	//SOFTMAX DERIV
-
-	Ref<MLPPMatrix> softmax_deriv_normv(const Ref<MLPPVector> &z);
-	Vector<Ref<MLPPMatrix>> softmax_deriv_normm(const Ref<MLPPMatrix> &z);
-
-	Ref<MLPPMatrix> softmax_deriv_derivv(const Ref<MLPPVector> &z);
-	Vector<Ref<MLPPMatrix>> softmax_deriv_derivm(const Ref<MLPPMatrix> &z);
-
-	//SOFTPLUS
-
-	real_t softplus_normr(real_t z);
-	Ref<MLPPVector> softplus_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softplus_normm(const Ref<MLPPMatrix> &z);
-
-	real_t softplus_derivr(real_t z);
-	Ref<MLPPVector> softplus_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softplus_derivm(const Ref<MLPPMatrix> &z);
-
-	//SOFTSIGN
-
-	real_t softsign_normr(real_t z);
-	Ref<MLPPVector> softsign_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softsign_normm(const Ref<MLPPMatrix> &z);
-
-	real_t softsign_derivr(real_t z);
-	Ref<MLPPVector> softsign_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> softsign_derivm(const Ref<MLPPMatrix> &z);
-
-	//GAUSSIANCDF
-
-	real_t gaussian_cdf_normr(real_t z);
-	Ref<MLPPVector> gaussian_cdf_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> gaussian_cdf_normm(const Ref<MLPPMatrix> &z);
-
-	real_t gaussian_cdf_derivr(real_t z);
-	Ref<MLPPVector> gaussian_cdf_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> gaussian_cdf_derivm(const Ref<MLPPMatrix> &z);
-
-	//CLOGLOG
-
-	real_t cloglog_normr(real_t z);
-	Ref<MLPPVector> cloglog_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> cloglog_normm(const Ref<MLPPMatrix> &z);
-
-	real_t cloglog_derivr(real_t z);
-	Ref<MLPPVector> cloglog_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> cloglog_derivm(const Ref<MLPPMatrix> &z);
-
-	//LOGIT
-
-	real_t logit_normr(real_t z);
-	Ref<MLPPVector> logit_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> logit_normm(const Ref<MLPPMatrix> &z);
-
-	real_t logit_derivr(real_t z);
-	Ref<MLPPVector> logit_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> logit_derivm(const Ref<MLPPMatrix> &z);
-
-	//UNITSTEP
-
-	real_t unit_step_normr(real_t z);
-	Ref<MLPPVector> unit_step_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> unit_step_normm(const Ref<MLPPMatrix> &z);
-
-	real_t unit_step_derivr(real_t z);
-	Ref<MLPPVector> unit_step_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> unit_step_derivm(const Ref<MLPPMatrix> &z);
-
-	//SWISH
-
-	real_t swish_normr(real_t z);
-	Ref<MLPPVector> swish_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> swish_normm(const Ref<MLPPMatrix> &z);
-
-	real_t swish_derivr(real_t z);
-	Ref<MLPPVector> swish_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> swish_derivm(const Ref<MLPPMatrix> &z);
-
-	//MISH
-
-	real_t mish_normr(real_t z);
-	Ref<MLPPVector> mish_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> mish_normm(const Ref<MLPPMatrix> &z);
-
-	real_t mish_derivr(real_t z);
-	Ref<MLPPVector> mish_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> mish_derivm(const Ref<MLPPMatrix> &z);
-
-	//SINC
-
-	real_t sinc_normr(real_t z);
-	Ref<MLPPVector> sinc_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sinc_normm(const Ref<MLPPMatrix> &z);
-
-	real_t sinc_derivr(real_t z);
-	Ref<MLPPVector> sinc_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sinc_derivm(const Ref<MLPPMatrix> &z);
-
-	//RELU
-
-	real_t relu_normr(real_t z);
-	Ref<MLPPVector> relu_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> relu_normm(const Ref<MLPPMatrix> &z);
-
-	real_t relu_derivr(real_t z);
-	Ref<MLPPVector> relu_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> relu_derivm(const Ref<MLPPMatrix> &z);
-
-	//LEAKYRELU
-
-	real_t leaky_relu_normr(real_t z, real_t c);
-	Ref<MLPPVector> leaky_relu_normv(const Ref<MLPPVector> &z, real_t c);
-	Ref<MLPPMatrix> leaky_relu_normm(const Ref<MLPPMatrix> &z, real_t c);
-
-	real_t leaky_relu_derivr(real_t z, real_t c);
-	Ref<MLPPVector> leaky_relu_derivv(const Ref<MLPPVector> &z, real_t c);
-	Ref<MLPPMatrix> leaky_relu_derivm(const Ref<MLPPMatrix> &z, real_t c);
-
-	//ELU
-
-	real_t elu_normr(real_t z, real_t c);
-	Ref<MLPPVector> elu_normv(const Ref<MLPPVector> &z, real_t c);
-	Ref<MLPPMatrix> elu_normm(const Ref<MLPPMatrix> &z, real_t c);
-
-	real_t elu_derivr(real_t z, real_t c);
-	Ref<MLPPVector> elu_derivv(const Ref<MLPPVector> &z, real_t c);
-	Ref<MLPPMatrix> elu_derivm(const Ref<MLPPMatrix> &z, real_t c);
-
-	//SELU
-
-	real_t selu_normr(real_t z, real_t lambda, real_t c);
-	Ref<MLPPVector> selu_normv(const Ref<MLPPVector> &z, real_t lambda, real_t c);
-	Ref<MLPPMatrix> selu_normm(const Ref<MLPPMatrix> &z, real_t lambda, real_t c);
-
-	real_t selu_derivr(real_t z, real_t lambda, real_t c);
-	Ref<MLPPVector> selu_derivv(const Ref<MLPPVector> &z, real_t lambda, real_t c);
-	Ref<MLPPMatrix> selu_derivm(const Ref<MLPPMatrix> &z, real_t lambda, real_t c);
-
-	//GELU
-
-	real_t gelu_normr(real_t z);
-	Ref<MLPPVector> gelu_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> gelu_normm(const Ref<MLPPMatrix> &z);
-
-	real_t gelu_derivr(real_t z);
-	Ref<MLPPVector> gelu_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> gelu_derivm(const Ref<MLPPMatrix> &z);
-
-	//SIGN
-
-	real_t sign_normr(real_t z);
-	Ref<MLPPVector> sign_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sign_normm(const Ref<MLPPMatrix> &z);
-
-	real_t sign_derivr(real_t z);
-	Ref<MLPPVector> sign_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sign_derivm(const Ref<MLPPMatrix> &z);
-
-	//SINH
-
-	real_t sinh_normr(real_t z);
-	Ref<MLPPVector> sinh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sinh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t sinh_derivr(real_t z);
-	Ref<MLPPVector> sinh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sinh_derivm(const Ref<MLPPMatrix> &z);
-
-	//COSH
-
-	real_t cosh_normr(real_t z);
-	Ref<MLPPVector> cosh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> cosh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t cosh_derivr(real_t z);
-	Ref<MLPPVector> cosh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> cosh_derivm(const Ref<MLPPMatrix> &z);
-
-	//TANH
-
-	real_t tanh_normr(real_t z);
-	Ref<MLPPVector> tanh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> tanh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t tanh_derivr(real_t z);
-	Ref<MLPPVector> tanh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> tanh_derivm(const Ref<MLPPMatrix> &z);
-
-	//CSCH
-
-	real_t csch_normr(real_t z);
-	Ref<MLPPVector> csch_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> csch_normm(const Ref<MLPPMatrix> &z);
-
-	real_t csch_derivr(real_t z);
-	Ref<MLPPVector> csch_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> csch_derivm(const Ref<MLPPMatrix> &z);
-
-	//SECH
-
-	real_t sech_normr(real_t z);
-	Ref<MLPPVector> sech_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sech_normm(const Ref<MLPPMatrix> &z);
-
-	real_t sech_derivr(real_t z);
-	Ref<MLPPVector> sech_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> sech_derivm(const Ref<MLPPMatrix> &z);
-
-	//COTH
-
-	real_t coth_normr(real_t z);
-	Ref<MLPPVector> coth_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> coth_normm(const Ref<MLPPMatrix> &z);
-
-	real_t coth_derivr(real_t z);
-	Ref<MLPPVector> coth_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> coth_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARSINH
-
-	real_t arsinh_normr(real_t z);
-	Ref<MLPPVector> arsinh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arsinh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t arsinh_derivr(real_t z);
-	Ref<MLPPVector> arsinh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arsinh_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARCOSH
-
-	real_t arcosh_normr(real_t z);
-	Ref<MLPPVector> arcosh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcosh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t arcosh_derivr(real_t z);
-	Ref<MLPPVector> arcosh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcosh_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARTANH
-
-	real_t artanh_normr(real_t z);
-	Ref<MLPPVector> artanh_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> artanh_normm(const Ref<MLPPMatrix> &z);
-
-	real_t artanh_derivr(real_t z);
-	Ref<MLPPVector> artanh_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> artanh_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARCSCH
-
-	real_t arcsch_normr(real_t z);
-	Ref<MLPPVector> arcsch_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcsch_normm(const Ref<MLPPMatrix> &z);
-
-	real_t arcsch_derivr(real_t z);
-	Ref<MLPPVector> arcsch_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcsch_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARSECH
-
-	real_t arsech_normr(real_t z);
-	Ref<MLPPVector> arsech_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arsech_normm(const Ref<MLPPMatrix> &z);
-
-	real_t arsech_derivr(real_t z);
-	Ref<MLPPVector> arsech_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arsech_derivm(const Ref<MLPPMatrix> &z);
-
-	//ARCOTH
-
-	real_t arcoth_normr(real_t z);
-	Ref<MLPPVector> arcoth_normv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcoth_normm(const Ref<MLPPMatrix> &z);
-
-	real_t arcoth_derivr(real_t z);
-	Ref<MLPPVector> arcoth_derivv(const Ref<MLPPVector> &z);
-	Ref<MLPPMatrix> arcoth_derivm(const Ref<MLPPMatrix> &z);
-
-	// =========    OLD    ===========
-
 	real_t linear(real_t z, bool deriv = false);
 	std::vector<real_t> linear(std::vector<real_t> z, bool deriv = false);
 	std::vector<std::vector<real_t>> linear(std::vector<std::vector<real_t>> z, bool deriv = false);
@@ -535,11 +140,6 @@ public:
 	std::vector<std::vector<real_t>> arcoth(std::vector<std::vector<real_t>> z, bool deriv = false);
 
 	std::vector<real_t> activation(std::vector<real_t> z, bool deriv, real_t (*function)(real_t, bool));
-
-protected:
-	static void _bind_methods();
 };
 
-VARIANT_ENUM_CAST(MLPPActivationOld::ActivationFunction);
-
 #endif /* Activation_hpp */