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Added MLPPCostOld.
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@ -84,6 +84,7 @@ sources = [
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"mlpp/lin_alg/lin_alg_old.cpp",
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"mlpp/hypothesis_testing/hypothesis_testing_old.cpp",
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"mlpp/data/data_old.cpp",
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"mlpp/cost/cost_old.cpp",
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"test/mlpp_tests.cpp",
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]
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1239
mlpp/cost/cost_old.cpp
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1239
mlpp/cost/cost_old.cpp
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mlpp/cost/cost_old.h
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mlpp/cost/cost_old.h
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#ifndef MLPP_COST_OLD_H
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#define MLPP_COST_OLD_H
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//
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// Cost.hpp
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//
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// Created by Marc Melikyan on 1/16/21.
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//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include <vector>
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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//void set_weights(const Ref<MLPPMatrix> &val);
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//void set_bias(const Ref<MLPPVector> &val);
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class MLPPCostOld : public Reference {
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GDCLASS(MLPPCostOld, Reference);
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public:
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enum CostTypes {
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COST_TYPE_MSE = 0,
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COST_TYPE_RMSE,
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COST_TYPE_MAE,
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COST_TYPE_MBE,
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COST_TYPE_LOGISTIC_LOSS,
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COST_TYPE_CROSS_ENTROPY,
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COST_TYPE_HINGE_LOSS,
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COST_TYPE_WASSERSTEIN_LOSS,
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};
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public:
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real_t msev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t msem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> mse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> mse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t rmsev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t rmsem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> rmse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> rmse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t maev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t maem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> mae_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> mae_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t mbev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t mbem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> mbe_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> mbe_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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// Classification Costs
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real_t log_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t log_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> log_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> log_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t cross_entropyv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t cross_entropym(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> cross_entropy_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> cross_entropy_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t huber_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta);
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real_t huber_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta);
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Ref<MLPPVector> huber_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta);
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Ref<MLPPMatrix> huber_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta);
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real_t hinge_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t hinge_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> hinge_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> hinge_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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real_t hinge_losswv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t C);
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real_t hinge_losswm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, const Ref<MLPPMatrix> &weights, real_t C);
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Ref<MLPPVector> hinge_loss_derivwv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t C);
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Ref<MLPPMatrix> hinge_loss_derivwm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t C);
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real_t wasserstein_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t wasserstein_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> wasserstein_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> wasserstein_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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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.
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Ref<MLPPVector> dual_form_svm_deriv(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y);
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typedef real_t (MLPPCostOld::*VectorCostFunctionPointer)(const Ref<MLPPVector> &, const Ref<MLPPVector> &);
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typedef real_t (MLPPCostOld::*MatrixCostFunctionPointer)(const Ref<MLPPMatrix> &, const Ref<MLPPMatrix> &);
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typedef Ref<MLPPVector> (MLPPCostOld::*VectorDerivCostFunctionPointer)(const Ref<MLPPVector> &, const Ref<MLPPVector> &);
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typedef Ref<MLPPMatrix> (MLPPCostOld::*MatrixDerivCostFunctionPointer)(const Ref<MLPPMatrix> &, const Ref<MLPPMatrix> &);
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VectorCostFunctionPointer get_cost_function_ptr_normal_vector(const CostTypes cost);
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MatrixCostFunctionPointer get_cost_function_ptr_normal_matrix(const CostTypes cost);
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VectorDerivCostFunctionPointer get_cost_function_ptr_deriv_vector(const CostTypes cost);
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MatrixDerivCostFunctionPointer get_cost_function_ptr_deriv_matrix(const CostTypes cost);
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real_t run_cost_norm_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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real_t run_cost_norm_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> run_cost_deriv_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
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Ref<MLPPMatrix> run_cost_deriv_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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// Regression Costs
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real_t MSE(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t MSE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> MSEDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> MSEDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t RMSE(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t RMSE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> RMSEDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> RMSEDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t MAE(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t MAE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> MAEDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> MAEDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t MBE(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t MBE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> MBEDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> MBEDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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// Classification Costs
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real_t LogLoss(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t LogLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> LogLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> LogLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t CrossEntropy(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t CrossEntropy(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> CrossEntropyDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> CrossEntropyDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t HuberLoss(std::vector<real_t> y_hat, std::vector<real_t> y, real_t delta);
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real_t HuberLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, real_t delta);
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std::vector<real_t> HuberLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y, real_t delta);
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std::vector<std::vector<real_t>> HuberLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, real_t delta);
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real_t HingeLoss(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t HingeLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> HingeLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> HingeLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t HingeLoss(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
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real_t HingeLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, std::vector<std::vector<real_t>> weights, real_t C);
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std::vector<real_t> HingeLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y, real_t C);
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std::vector<std::vector<real_t>> HingeLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, real_t C);
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real_t WassersteinLoss(std::vector<real_t> y_hat, std::vector<real_t> y);
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real_t WassersteinLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<real_t> WassersteinLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<std::vector<real_t>> WassersteinLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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real_t dualFormSVM(std::vector<real_t> alpha, std::vector<std::vector<real_t>> X, std::vector<real_t> y); // TO DO: DON'T forget to add non-linear kernelizations.
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std::vector<real_t> dualFormSVMDeriv(std::vector<real_t> alpha, std::vector<std::vector<real_t>> X, std::vector<real_t> y);
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protected:
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static void _bind_methods();
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};
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VARIANT_ENUM_CAST(MLPPCostOld::CostTypes);
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#endif /* Cost_hpp */
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