mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-11-13 13:57:19 +01:00
845 lines
26 KiB
C++
845 lines
26 KiB
C++
//
|
|
// Reg.cpp
|
|
//
|
|
// Created by Marc Melikyan on 1/16/21.
|
|
//
|
|
|
|
#include "cost.h"
|
|
#include "../lin_alg/lin_alg.h"
|
|
#include "../regularization/reg.h"
|
|
#include <cmath>
|
|
#include <iostream>
|
|
|
|
real_t MLPPCost::msev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]) * (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return sum / 2 * y_hat_size;
|
|
}
|
|
real_t MLPPCost::msem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]) * (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return sum / 2.0 * static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::mse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.subtractionnv(y_hat, y);
|
|
}
|
|
|
|
Ref<MLPPMatrix> MLPPCost::mse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.subtractionnm(y_hat, y);
|
|
}
|
|
|
|
real_t MLPPCost::rmsev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]) * (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return Math::sqrt(sum / static_cast<real_t>(y_hat_size));
|
|
}
|
|
real_t MLPPCost::rmsem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]) * (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return Math::sqrt(sum / static_cast<real_t>(y_hat->size().y));
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::rmse_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
return alg.scalar_multiplynv(1 / (2.0 * Math::sqrt(msev(y_hat, y))), mse_derivv(y_hat, y));
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::rmse_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
return alg.scalar_multiplynm(1 / (2.0 / Math::sqrt(msem(y_hat, y))), mse_derivm(y_hat, y));
|
|
}
|
|
|
|
real_t MLPPCost::maev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; i++) {
|
|
sum += ABS((y_hat_ptr[i] - y_ptr[i]));
|
|
}
|
|
return sum / static_cast<real_t>(y_hat_size);
|
|
}
|
|
real_t MLPPCost::maem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += ABS((y_hat_ptr[i] - y_ptr[i]));
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::mae_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
|
|
Ref<MLPPVector> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat_size);
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
int y_hat_ptr_i = y_hat_ptr[i];
|
|
|
|
if (y_hat_ptr_i < 0) {
|
|
deriv_ptr[i] = -1;
|
|
} else if (y_hat_ptr_i == 0) {
|
|
deriv_ptr[i] = 0;
|
|
} else {
|
|
deriv_ptr[i] = 1;
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::mae_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
|
|
Ref<MLPPMatrix> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat->size());
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
int y_hat_ptr_i = y_hat_ptr[i];
|
|
|
|
if (y_hat_ptr_i < 0) {
|
|
deriv_ptr[i] = -1;
|
|
} else if (y_hat_ptr_i == 0) {
|
|
deriv_ptr[i] = 0;
|
|
} else {
|
|
deriv_ptr[i] = 1;
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
|
|
real_t MLPPCost::mbev(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_size);
|
|
}
|
|
real_t MLPPCost::mbem(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += (y_hat_ptr[i] - y_ptr[i]);
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::mbe_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.onevecnv(y_hat->size());
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::mbe_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.onematnm(y_hat->size().x, y_hat->size().y);
|
|
}
|
|
|
|
// Classification Costs
|
|
real_t MLPPCost::log_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
real_t eps = 1e-8;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += -(y_ptr[i] * Math::log(y_hat_ptr[i] + eps) + (1 - y_ptr[i]) * Math::log(1 - y_hat_ptr[i] + eps));
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_size);
|
|
}
|
|
|
|
real_t MLPPCost::log_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
real_t eps = 1e-8;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += -(y_ptr[i] * Math::log(y_hat_ptr[i] + eps) + (1 - y_ptr[i]) * Math::log(1 - y_hat_ptr[i] + eps));
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::log_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.additionnv(
|
|
alg.scalar_multiplynv(-1, alg.division_element_wisenv(y, y_hat)),
|
|
alg.division_element_wisenv(alg.scalar_multiplynv(-1, alg.scalar_addnv(-1, y)), alg.scalar_multiplynv(-1, alg.scalar_addnv(-1, y_hat))));
|
|
}
|
|
|
|
Ref<MLPPMatrix> MLPPCost::log_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.additionnm(
|
|
alg.scalar_multiplynm(-1, alg.division_element_wisenvnm(y, y_hat)),
|
|
alg.division_element_wisenvnm(alg.scalar_multiplynm(-1, alg.scalar_addnm(-1, y)), alg.scalar_multiplynm(-1, alg.scalar_addnm(-1, y_hat))));
|
|
}
|
|
|
|
real_t MLPPCost::cross_entropyv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += y_ptr[i] * Math::log(y_hat_ptr[i]);
|
|
}
|
|
|
|
return -1 * sum;
|
|
}
|
|
real_t MLPPCost::cross_entropym(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += y_ptr[i] * Math::log(y_hat_ptr[i]);
|
|
}
|
|
|
|
return -1 * sum;
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::cross_entropy_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.scalar_multiplynv(-1, alg.division_element_wisenv(y, y_hat));
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::cross_entropy_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
return alg.scalar_multiplynm(-1, alg.division_element_wisenvnm(y, y_hat));
|
|
}
|
|
|
|
real_t MLPPCost::huber_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
MLPPLinAlg alg;
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
if (ABS(y_ptr[i] - y_hat_ptr[i]) <= delta) {
|
|
sum += (y_ptr[i] - y_hat_ptr[i]) * (y_ptr[i] - y_hat_ptr[i]);
|
|
} else {
|
|
sum += 2 * delta * ABS(y_ptr[i] - y_hat_ptr[i]) - delta * delta;
|
|
}
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
real_t MLPPCost::huber_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
MLPPLinAlg alg;
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
if (ABS(y_ptr[i] - y_hat_ptr[i]) <= delta) {
|
|
sum += (y_ptr[i] - y_hat_ptr[i]) * (y_ptr[i] - y_hat_ptr[i]);
|
|
} else {
|
|
sum += 2 * delta * ABS(y_ptr[i] - y_hat_ptr[i]) - delta * delta;
|
|
}
|
|
}
|
|
|
|
return sum;
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::huber_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t delta) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
MLPPLinAlg alg;
|
|
|
|
Ref<MLPPVector> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat->size());
|
|
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
if (ABS(y_ptr[i] - y_hat_ptr[i]) <= delta) {
|
|
deriv_ptr[i] = (-(y_ptr[i] - y_hat_ptr[i]));
|
|
} else {
|
|
if (y_hat_ptr[i] > 0 || y_hat_ptr[i] < 0) {
|
|
deriv_ptr[i] = (2 * delta * (y_hat_ptr[i] / ABS(y_hat_ptr[i])));
|
|
} else {
|
|
deriv_ptr[i] = (0);
|
|
}
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::huber_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t delta) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
MLPPLinAlg alg;
|
|
|
|
Ref<MLPPMatrix> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat->size());
|
|
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
if (ABS(y_ptr[i] - y_hat_ptr[i]) <= delta) {
|
|
deriv_ptr[i] = (-(y_ptr[i] - y_hat_ptr[i]));
|
|
} else {
|
|
if (y_hat_ptr[i] > 0 || y_hat_ptr[i] < 0) {
|
|
deriv_ptr[i] = (2 * delta * (y_hat_ptr[i] / ABS(y_hat_ptr[i])));
|
|
} else {
|
|
deriv_ptr[i] = (0);
|
|
}
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
|
|
real_t MLPPCost::hinge_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += MAX(0, 1 - y_ptr[i] * y_hat_ptr[i]);
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_size);
|
|
}
|
|
real_t MLPPCost::hinge_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += MAX(0, 1 - y_ptr[i] * y_hat_ptr[i]);
|
|
}
|
|
|
|
return sum / static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::hinge_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
Ref<MLPPVector> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat->size());
|
|
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
if (1 - y_ptr[i] * y_hat_ptr[i] > 0) {
|
|
deriv_ptr[i] = -y_ptr[i];
|
|
} else {
|
|
deriv_ptr[i] = 0;
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::hinge_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
Ref<MLPPMatrix> deriv;
|
|
deriv.instance();
|
|
deriv->resize(y_hat->size());
|
|
|
|
real_t *deriv_ptr = deriv->ptrw();
|
|
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
if (1 - y_ptr[i] * y_hat_ptr[i] > 0) {
|
|
deriv_ptr[i] = -y_ptr[i];
|
|
} else {
|
|
deriv_ptr[i] = 0;
|
|
}
|
|
}
|
|
|
|
return deriv;
|
|
}
|
|
|
|
real_t MLPPCost::hinge_losswv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t C) {
|
|
MLPPLinAlg alg;
|
|
MLPPReg regularization;
|
|
|
|
return C * hinge_lossv(y_hat, y) + regularization.reg_termv(weights, 1, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
|
}
|
|
real_t MLPPCost::hinge_losswm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, const Ref<MLPPMatrix> &weights, real_t C) {
|
|
MLPPLinAlg alg;
|
|
MLPPReg regularization;
|
|
|
|
return C * hinge_lossm(y_hat, y) + regularization.reg_termv(weights, 1, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::hinge_loss_derivwv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y, real_t C) {
|
|
MLPPLinAlg alg;
|
|
MLPPReg regularization;
|
|
|
|
return alg.scalar_multiplynv(C, hinge_loss_derivv(y_hat, y));
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::hinge_loss_derivwm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y, real_t C) {
|
|
MLPPLinAlg alg;
|
|
MLPPReg regularization;
|
|
|
|
return alg.scalar_multiplynm(C, hinge_loss_derivm(y_hat, y));
|
|
}
|
|
|
|
real_t MLPPCost::wasserstein_lossv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
int y_hat_size = y_hat->size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_size != y->size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_size; ++i) {
|
|
sum += y_hat_ptr[i] * y_ptr[i];
|
|
}
|
|
|
|
return -sum / static_cast<real_t>(y_hat_size);
|
|
}
|
|
real_t MLPPCost::wasserstein_lossm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
int y_hat_data_size = y_hat->data_size();
|
|
|
|
ERR_FAIL_COND_V(y_hat_data_size != y->data_size(), 0);
|
|
|
|
const real_t *y_hat_ptr = y_hat->ptr();
|
|
const real_t *y_ptr = y->ptr();
|
|
|
|
real_t sum = 0;
|
|
for (int i = 0; i < y_hat_data_size; ++i) {
|
|
sum += y_hat_ptr[i] * y_ptr[i];
|
|
}
|
|
|
|
return -sum / static_cast<real_t>(y_hat_data_size);
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::wasserstein_loss_derivv(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
return alg.scalar_multiplynv(-1, y); // Simple.
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::wasserstein_loss_derivm(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
return alg.scalar_multiplynm(-1, y); // Simple.
|
|
}
|
|
|
|
real_t MLPPCost::dual_form_svm(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
Ref<MLPPMatrix> Y = alg.diagnm(y); // Y is a diagnoal matrix. Y[i][j] = y[i] if i = i, else Y[i][j] = 0. Yt = Y.
|
|
Ref<MLPPMatrix> K = alg.matmultnm(X, alg.transposenm(X)); // TO DO: DON'T forget to add non-linear kernelizations.
|
|
Ref<MLPPMatrix> Q = alg.matmultnm(alg.matmultnm(alg.transposenm(Y), K), Y);
|
|
|
|
Ref<MLPPMatrix> alpha_m;
|
|
alpha_m.instance();
|
|
alpha_m->resize(Size2i(alpha->size(), 1));
|
|
alpha_m->row_set_mlpp_vector(0, alpha);
|
|
|
|
Ref<MLPPMatrix> alpha_m_res = alg.matmultnm(alg.matmultnm(alpha_m, Q), alg.transposenm(alpha_m));
|
|
|
|
real_t alphaQ = alpha_m_res->element_get(0, 0);
|
|
Ref<MLPPVector> one = alg.onevecnv(alpha->size());
|
|
|
|
return -alg.dotnv(one, alpha) + 0.5 * alphaQ;
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::dual_form_svm_deriv(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y) {
|
|
MLPPLinAlg alg;
|
|
|
|
Ref<MLPPMatrix> Y = alg.diagnm(y); // Y is a diagnoal matrix. Y[i][j] = y[i] if i = i, else Y[i][j] = 0. Yt = Y.
|
|
Ref<MLPPMatrix> K = alg.matmultnm(X, alg.transposenm(X)); // TO DO: DON'T forget to add non-linear kernelizations.
|
|
Ref<MLPPMatrix> Q = alg.matmultnm(alg.matmultnm(alg.transposenm(Y), K), Y);
|
|
Ref<MLPPVector> alphaQDeriv = alg.mat_vec_multnv(Q, alpha);
|
|
Ref<MLPPVector> one = alg.onevecnv(alpha->size());
|
|
|
|
return alg.subtractionnm(alphaQDeriv, one);
|
|
}
|
|
|
|
MLPPCost::VectorCostFunctionPointer MLPPCost::get_cost_function_ptr_normal_vector(const MLPPCost::CostTypes cost) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return &MLPPCost::msev;
|
|
case COST_TYPE_RMSE:
|
|
return &MLPPCost::rmsev;
|
|
case COST_TYPE_MAE:
|
|
return &MLPPCost::maev;
|
|
case COST_TYPE_MBE:
|
|
return &MLPPCost::mbev;
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return &MLPPCost::log_lossv;
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return &MLPPCost::cross_entropyv;
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return &MLPPCost::hinge_lossv;
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return &MLPPCost::wasserstein_lossv;
|
|
default:
|
|
return NULL;
|
|
}
|
|
}
|
|
MLPPCost::MatrixCostFunctionPointer MLPPCost::get_cost_function_ptr_normal_matrix(const MLPPCost::CostTypes cost) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return &MLPPCost::msem;
|
|
case COST_TYPE_RMSE:
|
|
return &MLPPCost::rmsem;
|
|
case COST_TYPE_MAE:
|
|
return &MLPPCost::maem;
|
|
case COST_TYPE_MBE:
|
|
return &MLPPCost::mbem;
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return &MLPPCost::log_lossm;
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return &MLPPCost::cross_entropym;
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return &MLPPCost::hinge_lossm;
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return &MLPPCost::wasserstein_lossm;
|
|
default:
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
MLPPCost::VectorDerivCostFunctionPointer MLPPCost::get_cost_function_ptr_deriv_vector(const MLPPCost::CostTypes cost) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return &MLPPCost::mse_derivv;
|
|
case COST_TYPE_RMSE:
|
|
return &MLPPCost::rmse_derivv;
|
|
case COST_TYPE_MAE:
|
|
return &MLPPCost::mae_derivv;
|
|
case COST_TYPE_MBE:
|
|
return &MLPPCost::mbe_derivv;
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return &MLPPCost::log_loss_derivv;
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return &MLPPCost::cross_entropy_derivv;
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return &MLPPCost::hinge_loss_derivv;
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return &MLPPCost::wasserstein_loss_derivv;
|
|
default:
|
|
return NULL;
|
|
}
|
|
}
|
|
MLPPCost::MatrixDerivCostFunctionPointer MLPPCost::get_cost_function_ptr_deriv_matrix(const MLPPCost::CostTypes cost) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return &MLPPCost::mse_derivm;
|
|
case COST_TYPE_RMSE:
|
|
return &MLPPCost::rmse_derivm;
|
|
case COST_TYPE_MAE:
|
|
return &MLPPCost::mae_derivm;
|
|
case COST_TYPE_MBE:
|
|
return &MLPPCost::mbe_derivm;
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return &MLPPCost::log_loss_derivm;
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return &MLPPCost::cross_entropy_derivm;
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return &MLPPCost::hinge_loss_derivm;
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return &MLPPCost::wasserstein_loss_derivm;
|
|
default:
|
|
return NULL;
|
|
}
|
|
}
|
|
|
|
real_t MLPPCost::run_cost_norm_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return msev(y_hat, y);
|
|
case COST_TYPE_RMSE:
|
|
return rmsev(y_hat, y);
|
|
case COST_TYPE_MAE:
|
|
return maev(y_hat, y);
|
|
case COST_TYPE_MBE:
|
|
return mbev(y_hat, y);
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return log_lossv(y_hat, y);
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return cross_entropyv(y_hat, y);
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return hinge_lossv(y_hat, y);
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return wasserstein_lossv(y_hat, y);
|
|
default:
|
|
return 0;
|
|
}
|
|
}
|
|
real_t MLPPCost::run_cost_norm_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return msem(y_hat, y);
|
|
case COST_TYPE_RMSE:
|
|
return rmsem(y_hat, y);
|
|
case COST_TYPE_MAE:
|
|
return maem(y_hat, y);
|
|
case COST_TYPE_MBE:
|
|
return mbem(y_hat, y);
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return log_lossm(y_hat, y);
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return cross_entropym(y_hat, y);
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return hinge_lossm(y_hat, y);
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return wasserstein_lossm(y_hat, y);
|
|
default:
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
Ref<MLPPVector> MLPPCost::run_cost_deriv_vector(const CostTypes cost, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return mse_derivv(y_hat, y);
|
|
case COST_TYPE_RMSE:
|
|
return rmse_derivv(y_hat, y);
|
|
case COST_TYPE_MAE:
|
|
return mae_derivv(y_hat, y);
|
|
case COST_TYPE_MBE:
|
|
return mbe_derivv(y_hat, y);
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return log_loss_derivv(y_hat, y);
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return cross_entropy_derivv(y_hat, y);
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return hinge_loss_derivv(y_hat, y);
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return wasserstein_loss_derivv(y_hat, y);
|
|
default:
|
|
return Ref<MLPPVector>();
|
|
}
|
|
}
|
|
Ref<MLPPMatrix> MLPPCost::run_cost_deriv_matrix(const CostTypes cost, const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y) {
|
|
switch (cost) {
|
|
case COST_TYPE_MSE:
|
|
return mse_derivm(y_hat, y);
|
|
case COST_TYPE_RMSE:
|
|
return rmse_derivm(y_hat, y);
|
|
case COST_TYPE_MAE:
|
|
return mae_derivm(y_hat, y);
|
|
case COST_TYPE_MBE:
|
|
return mbe_derivm(y_hat, y);
|
|
case COST_TYPE_LOGISTIC_LOSS:
|
|
return log_loss_derivm(y_hat, y);
|
|
case COST_TYPE_CROSS_ENTROPY:
|
|
return cross_entropy_derivm(y_hat, y);
|
|
case COST_TYPE_HINGE_LOSS:
|
|
return hinge_loss_derivm(y_hat, y);
|
|
case COST_TYPE_WASSERSTEIN_LOSS:
|
|
return wasserstein_loss_derivm(y_hat, y);
|
|
default:
|
|
return Ref<MLPPMatrix>();
|
|
}
|
|
}
|
|
|
|
void MLPPCost::_bind_methods() {
|
|
ClassDB::bind_method(D_METHOD("msev", "y_hat", "y"), &MLPPCost::msev);
|
|
ClassDB::bind_method(D_METHOD("msem", "y_hat", "y"), &MLPPCost::msem);
|
|
|
|
ClassDB::bind_method(D_METHOD("mse_derivv", "y_hat", "y"), &MLPPCost::mse_derivv);
|
|
ClassDB::bind_method(D_METHOD("mse_derivm", "y_hat", "y"), &MLPPCost::mse_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("rmsev", "y_hat", "y"), &MLPPCost::rmsev);
|
|
ClassDB::bind_method(D_METHOD("rmsem", "y_hat", "y"), &MLPPCost::rmsem);
|
|
|
|
ClassDB::bind_method(D_METHOD("rmse_derivv", "y_hat", "y"), &MLPPCost::rmse_derivv);
|
|
ClassDB::bind_method(D_METHOD("rmse_derivm", "y_hat", "y"), &MLPPCost::rmse_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("maev", "y_hat", "y"), &MLPPCost::maev);
|
|
ClassDB::bind_method(D_METHOD("maem", "y_hat", "y"), &MLPPCost::maem);
|
|
|
|
ClassDB::bind_method(D_METHOD("mae_derivv", "y_hat", "y"), &MLPPCost::mae_derivv);
|
|
ClassDB::bind_method(D_METHOD("mae_derivm", "y_hat", "y"), &MLPPCost::mae_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("mbev", "y_hat", "y"), &MLPPCost::mbev);
|
|
ClassDB::bind_method(D_METHOD("mbem", "y_hat", "y"), &MLPPCost::mbem);
|
|
|
|
ClassDB::bind_method(D_METHOD("mbe_derivv", "y_hat", "y"), &MLPPCost::mbe_derivv);
|
|
ClassDB::bind_method(D_METHOD("mbe_derivm", "y_hat", "y"), &MLPPCost::mbe_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("log_lossv", "y_hat", "y"), &MLPPCost::log_lossv);
|
|
ClassDB::bind_method(D_METHOD("log_lossm", "y_hat", "y"), &MLPPCost::log_lossm);
|
|
|
|
ClassDB::bind_method(D_METHOD("log_loss_derivv", "y_hat", "y"), &MLPPCost::log_loss_derivv);
|
|
ClassDB::bind_method(D_METHOD("log_loss_derivm", "y_hat", "y"), &MLPPCost::log_loss_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("cross_entropyv", "y_hat", "y"), &MLPPCost::cross_entropyv);
|
|
ClassDB::bind_method(D_METHOD("cross_entropym", "y_hat", "y"), &MLPPCost::cross_entropym);
|
|
|
|
ClassDB::bind_method(D_METHOD("cross_entropy_derivv", "y_hat", "y"), &MLPPCost::cross_entropy_derivv);
|
|
ClassDB::bind_method(D_METHOD("cross_entropy_derivm", "y_hat", "y"), &MLPPCost::cross_entropy_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("huber_lossv", "y_hat", "y"), &MLPPCost::huber_lossv);
|
|
ClassDB::bind_method(D_METHOD("huber_lossm", "y_hat", "y"), &MLPPCost::huber_lossm);
|
|
|
|
ClassDB::bind_method(D_METHOD("huber_loss_derivv", "y_hat", "y"), &MLPPCost::huber_loss_derivv);
|
|
ClassDB::bind_method(D_METHOD("huber_loss_derivm", "y_hat", "y"), &MLPPCost::huber_loss_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("hinge_lossv", "y_hat", "y"), &MLPPCost::hinge_lossv);
|
|
ClassDB::bind_method(D_METHOD("hinge_lossm", "y_hat", "y"), &MLPPCost::hinge_lossm);
|
|
|
|
ClassDB::bind_method(D_METHOD("hinge_loss_derivv", "y_hat", "y"), &MLPPCost::hinge_loss_derivv);
|
|
ClassDB::bind_method(D_METHOD("hinge_loss_derivm", "y_hat", "y"), &MLPPCost::hinge_loss_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("hinge_losswv", "y_hat", "y"), &MLPPCost::hinge_losswv);
|
|
ClassDB::bind_method(D_METHOD("hinge_losswm", "y_hat", "y"), &MLPPCost::hinge_losswm);
|
|
|
|
ClassDB::bind_method(D_METHOD("hinge_loss_derivwv", "y_hat", "y", "C"), &MLPPCost::hinge_loss_derivwv);
|
|
ClassDB::bind_method(D_METHOD("hinge_loss_derivwm", "y_hat", "y", "C"), &MLPPCost::hinge_loss_derivwm);
|
|
|
|
ClassDB::bind_method(D_METHOD("wasserstein_lossv", "y_hat", "y"), &MLPPCost::wasserstein_lossv);
|
|
ClassDB::bind_method(D_METHOD("wasserstein_lossm", "y_hat", "y"), &MLPPCost::wasserstein_lossm);
|
|
|
|
ClassDB::bind_method(D_METHOD("wasserstein_loss_derivv", "y_hat", "y"), &MLPPCost::wasserstein_loss_derivv);
|
|
ClassDB::bind_method(D_METHOD("wasserstein_loss_derivm", "y_hat", "y"), &MLPPCost::wasserstein_loss_derivm);
|
|
|
|
ClassDB::bind_method(D_METHOD("dual_form_svm", "alpha", "X", "y"), &MLPPCost::dual_form_svm);
|
|
ClassDB::bind_method(D_METHOD("dual_form_svm_deriv", "alpha", "X", "y"), &MLPPCost::dual_form_svm_deriv);
|
|
|
|
ClassDB::bind_method(D_METHOD("run_cost_norm_vector", "cost", "y_hat", "y"), &MLPPCost::run_cost_norm_vector);
|
|
ClassDB::bind_method(D_METHOD("run_cost_norm_matrix", "cost", "y_hat", "y"), &MLPPCost::run_cost_norm_matrix);
|
|
|
|
ClassDB::bind_method(D_METHOD("run_cost_deriv_vector", "cost", "y_hat", "y"), &MLPPCost::run_cost_deriv_vector);
|
|
ClassDB::bind_method(D_METHOD("run_cost_deriv_matrix", "cost", "y_hat", "y"), &MLPPCost::run_cost_deriv_matrix);
|
|
|
|
BIND_ENUM_CONSTANT(COST_TYPE_MSE);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_RMSE);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_MAE);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_MBE);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_LOGISTIC_LOSS);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_CROSS_ENTROPY);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_HINGE_LOSS);
|
|
BIND_ENUM_CONSTANT(COST_TYPE_WASSERSTEIN_LOSS);
|
|
}
|