mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-13 13:57:19 +01:00
152 lines
7.8 KiB
C++
152 lines
7.8 KiB
C++
#ifndef MLPP_COST_H
|
|
#define MLPP_COST_H
|
|
|
|
/*************************************************************************/
|
|
/* cost.h */
|
|
/*************************************************************************/
|
|
/* This file is part of: */
|
|
/* PMLPP Machine Learning Library */
|
|
/* https://github.com/Relintai/pmlpp */
|
|
/*************************************************************************/
|
|
/* Copyright (c) 2023-present Péter Magyar. */
|
|
/* Copyright (c) 2022-2023 Marc Melikyan */
|
|
/* */
|
|
/* Permission is hereby granted, free of charge, to any person obtaining */
|
|
/* a copy of this software and associated documentation files (the */
|
|
/* "Software"), to deal in the Software without restriction, including */
|
|
/* without limitation the rights to use, copy, modify, merge, publish, */
|
|
/* distribute, sublicense, and/or sell copies of the Software, and to */
|
|
/* permit persons to whom the Software is furnished to do so, subject to */
|
|
/* the following conditions: */
|
|
/* */
|
|
/* The above copyright notice and this permission notice shall be */
|
|
/* included in all copies or substantial portions of the Software. */
|
|
/* */
|
|
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
|
|
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
|
|
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
|
|
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
|
|
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
|
|
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
|
|
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
|
|
/*************************************************************************/
|
|
|
|
#include "core/math/math_defs.h"
|
|
|
|
#include "core/object/reference.h"
|
|
|
|
#include <vector>
|
|
|
|
#include "../lin_alg/mlpp_matrix.h"
|
|
#include "../lin_alg/mlpp_vector.h"
|
|
|
|
//void set_weights(const Ref<MLPPMatrix> &val);
|
|
//void set_bias(const Ref<MLPPVector> &val);
|
|
|
|
class MLPPCost : public Reference {
|
|
GDCLASS(MLPPCost, Reference);
|
|
|
|
public:
|
|
enum CostTypes {
|
|
COST_TYPE_MSE = 0,
|
|
COST_TYPE_RMSE,
|
|
COST_TYPE_MAE,
|
|
COST_TYPE_MBE,
|
|
COST_TYPE_LOGISTIC_LOSS,
|
|
COST_TYPE_CROSS_ENTROPY,
|
|
COST_TYPE_HINGE_LOSS,
|
|
COST_TYPE_WASSERSTEIN_LOSS,
|
|
};
|
|
|
|
public:
|
|
real_t msev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t msem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> mse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> mse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t rmsev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t rmsem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> rmse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> rmse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t maev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t maem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> mae_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> mae_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t mbev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t mbem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> mbe_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> mbe_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
// Classification Costs
|
|
real_t log_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t log_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> log_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> log_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t cross_entropyv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t cross_entropym(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> cross_entropy_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> cross_entropy_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t huber_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta);
|
|
real_t huber_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta);
|
|
|
|
Ref<MLPPVector> huber_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta);
|
|
Ref<MLPPMatrix> huber_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta);
|
|
|
|
real_t hinge_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t hinge_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> hinge_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> hinge_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t hinge_losswv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t C);
|
|
real_t hinge_losswm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, const Ref<MLPPMatrix> &weights, real_t C);
|
|
|
|
Ref<MLPPVector> hinge_loss_derivwv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t C);
|
|
Ref<MLPPMatrix> hinge_loss_derivwm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t C);
|
|
|
|
real_t wasserstein_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t wasserstein_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> wasserstein_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> wasserstein_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
real_t dual_form_svm(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y); // TO DO: DON'T forget to add non-linear kernelizations.
|
|
|
|
Ref<MLPPVector> dual_form_svm_deriv(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y);
|
|
|
|
typedef real_t (MLPPCost::*VectorCostFunctionPointer)(const Ref<MLPPVector> &, const Ref<MLPPVector> &);
|
|
typedef real_t (MLPPCost::*MatrixCostFunctionPointer)(const Ref<MLPPMatrix> &, const Ref<MLPPMatrix> &);
|
|
|
|
typedef Ref<MLPPVector> (MLPPCost::*VectorDerivCostFunctionPointer)(const Ref<MLPPVector> &, const Ref<MLPPVector> &);
|
|
typedef Ref<MLPPMatrix> (MLPPCost::*MatrixDerivCostFunctionPointer)(const Ref<MLPPMatrix> &, const Ref<MLPPMatrix> &);
|
|
|
|
VectorCostFunctionPointer get_cost_function_ptr_normal_vector(const CostTypes cost);
|
|
MatrixCostFunctionPointer get_cost_function_ptr_normal_matrix(const CostTypes cost);
|
|
|
|
VectorDerivCostFunctionPointer get_cost_function_ptr_deriv_vector(const CostTypes cost);
|
|
MatrixDerivCostFunctionPointer get_cost_function_ptr_deriv_matrix(const CostTypes cost);
|
|
|
|
real_t run_cost_norm_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
real_t run_cost_norm_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
Ref<MLPPVector> run_cost_deriv_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
|
Ref<MLPPMatrix> run_cost_deriv_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
};
|
|
|
|
VARIANT_ENUM_CAST(MLPPCost::CostTypes);
|
|
|
|
#endif /* Cost_hpp */
|