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Fixed warnings in MLPPCost.

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Relintai 2023-02-12 23:00:44 +01:00
parent 5a148683ad
commit 5a375225e9
1 changed files with 40 additions and 43 deletions
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83
mlpp/cost/cost.cpp
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@ -351,7 +351,6 @@ Ref<MLPPVector> MLPPCost::huber_loss_derivv(const Ref<MLPPVector> &y_hat, const
const real_t *y_ptr = y->ptr();
MLPPLinAlg alg;
real_t sum = 0;
Ref<MLPPVector> deriv;
deriv.instance();
@ -382,7 +381,6 @@ Ref<MLPPMatrix> MLPPCost::huber_loss_derivm(const Ref<MLPPMatrix> &y_hat, const
const real_t *y_ptr = y->ptr();
MLPPLinAlg alg;
real_t sum = 0;
Ref<MLPPMatrix> deriv;
deriv.instance();
@ -769,7 +767,7 @@ Ref<MLPPMatrix> MLPPCost::run_cost_deriv_matrix(const CostTypes cost, const Ref<
real_t MLPPCost::MSE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += (y_hat[i] - y[i]) * (y_hat[i] - y[i]);
}
return sum / 2 * y_hat.size();
@ -777,8 +775,8 @@ real_t MLPPCost::MSE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::MSE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += (y_hat[i][j] - y[i][j]) * (y_hat[i][j] - y[i][j]);
}
}
@ -797,7 +795,7 @@ std::vector<std::vector<real_t>> MLPPCost::MSEDeriv(std::vector<std::vector<real
real_t MLPPCost::RMSE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += (y_hat[i] - y[i]) * (y_hat[i] - y[i]);
}
return sqrt(sum / y_hat.size());
@ -805,8 +803,8 @@ real_t MLPPCost::RMSE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::RMSE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += (y_hat[i][j] - y[i][j]) * (y_hat[i][j] - y[i][j]);
}
}
@ -825,7 +823,7 @@ std::vector<std::vector<real_t>> MLPPCost::RMSEDeriv(std::vector<std::vector<rea
real_t MLPPCost::MAE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += abs((y_hat[i] - y[i]));
}
return sum / y_hat.size();
@ -833,8 +831,8 @@ real_t MLPPCost::MAE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::MAE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += abs((y_hat[i][j] - y[i][j]));
}
}
@ -844,7 +842,7 @@ real_t MLPPCost::MAE(std::vector<std::vector<real_t>> y_hat, std::vector<std::ve
std::vector<real_t> MLPPCost::MAEDeriv(std::vector<real_t> y_hat, std::vector<real_t> y) {
std::vector<real_t> deriv;
deriv.resize(y_hat.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
if (y_hat[i] < 0) {
deriv[i] = -1;
} else if (y_hat[i] == 0) {
@ -859,11 +857,11 @@ std::vector<real_t> MLPPCost::MAEDeriv(std::vector<real_t> y_hat, std::vector<re
std::vector<std::vector<real_t>> MLPPCost::MAEDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
std::vector<std::vector<real_t>> deriv;
deriv.resize(y_hat.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
deriv.resize(y_hat[i].size());
}
for (int i = 0; i < deriv.size(); i++) {
for (int j = 0; j < deriv[i].size(); j++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
for (uint32_t j = 0; j < deriv[i].size(); j++) {
if (y_hat[i][j] < 0) {
deriv[i][j] = -1;
} else if (y_hat[i][j] == 0) {
@ -878,7 +876,7 @@ std::vector<std::vector<real_t>> MLPPCost::MAEDeriv(std::vector<std::vector<real
real_t MLPPCost::MBE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += (y_hat[i] - y[i]);
}
return sum / y_hat.size();
@ -886,8 +884,8 @@ real_t MLPPCost::MBE(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::MBE(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += (y_hat[i][j] - y[i][j]);
}
}
@ -907,7 +905,7 @@ std::vector<std::vector<real_t>> MLPPCost::MBEDeriv(std::vector<std::vector<real
real_t MLPPCost::LogLoss(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
real_t eps = 1e-8;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += -(y[i] * std::log(y_hat[i] + eps) + (1 - y[i]) * std::log(1 - y_hat[i] + eps));
}
@ -917,8 +915,8 @@ real_t MLPPCost::LogLoss(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::LogLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
real_t eps = 1e-8;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += -(y[i][j] * std::log(y_hat[i][j] + eps) + (1 - y[i][j]) * std::log(1 - y_hat[i][j] + eps));
}
}
@ -938,7 +936,7 @@ std::vector<std::vector<real_t>> MLPPCost::LogLossDeriv(std::vector<std::vector<
real_t MLPPCost::CrossEntropy(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += y[i] * std::log(y_hat[i]);
}
@ -947,8 +945,8 @@ real_t MLPPCost::CrossEntropy(std::vector<real_t> y_hat, std::vector<real_t> y)
real_t MLPPCost::CrossEntropy(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += y[i][j] * std::log(y_hat[i][j]);
}
}
@ -969,7 +967,7 @@ std::vector<std::vector<real_t>> MLPPCost::CrossEntropyDeriv(std::vector<std::ve
real_t MLPPCost::HuberLoss(std::vector<real_t> y_hat, std::vector<real_t> y, real_t delta) {
MLPPLinAlg alg;
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
if (abs(y[i] - y_hat[i]) <= delta) {
sum += (y[i] - y_hat[i]) * (y[i] - y_hat[i]);
} else {
@ -982,8 +980,8 @@ real_t MLPPCost::HuberLoss(std::vector<real_t> y_hat, std::vector<real_t> y, rea
real_t MLPPCost::HuberLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, real_t delta) {
MLPPLinAlg alg;
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
if (abs(y[i][j] - y_hat[i][j]) <= delta) {
sum += (y[i][j] - y_hat[i][j]) * (y[i][j] - y_hat[i][j]);
} else {
@ -996,11 +994,10 @@ real_t MLPPCost::HuberLoss(std::vector<std::vector<real_t>> y_hat, std::vector<s
std::vector<real_t> MLPPCost::HuberLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y, real_t delta) {
MLPPLinAlg alg;
real_t sum = 0;
std::vector<real_t> deriv;
deriv.resize(y_hat.size());
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
if (abs(y[i] - y_hat[i]) <= delta) {
deriv.push_back(-(y[i] - y_hat[i]));
} else {
@ -1016,15 +1013,15 @@ std::vector<real_t> MLPPCost::HuberLossDeriv(std::vector<real_t> y_hat, std::vec
std::vector<std::vector<real_t>> MLPPCost::HuberLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y, real_t delta) {
MLPPLinAlg alg;
real_t sum = 0;
std::vector<std::vector<real_t>> deriv;
deriv.resize(y_hat.size());
for (int i = 0; i < deriv.size(); i++) {
for (uint32_t i = 0; i < deriv.size(); i++) {
deriv[i].resize(y_hat[i].size());
}
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
if (abs(y[i][j] - y_hat[i][j]) <= delta) {
deriv[i].push_back(-(y[i][j] - y_hat[i][j]));
} else {
@ -1041,7 +1038,7 @@ std::vector<std::vector<real_t>> MLPPCost::HuberLossDeriv(std::vector<std::vecto
real_t MLPPCost::HingeLoss(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += fmax(0, 1 - y[i] * y_hat[i]);
}
@ -1050,8 +1047,8 @@ real_t MLPPCost::HingeLoss(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t MLPPCost::HingeLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += fmax(0, 1 - y[i][j] * y_hat[i][j]);
}
}
@ -1062,7 +1059,7 @@ real_t MLPPCost::HingeLoss(std::vector<std::vector<real_t>> y_hat, std::vector<s
std::vector<real_t> MLPPCost::HingeLossDeriv(std::vector<real_t> y_hat, std::vector<real_t> y) {
std::vector<real_t> deriv;
deriv.resize(y_hat.size());
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
if (1 - y[i] * y_hat[i] > 0) {
deriv[i] = -y[i];
} else {
@ -1074,8 +1071,8 @@ std::vector<real_t> MLPPCost::HingeLossDeriv(std::vector<real_t> y_hat, std::vec
std::vector<std::vector<real_t>> MLPPCost::HingeLossDeriv(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
std::vector<std::vector<real_t>> deriv;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
if (1 - y[i][j] * y_hat[i][j] > 0) {
deriv[i][j] = -y[i][j];
} else {
@ -1088,7 +1085,7 @@ std::vector<std::vector<real_t>> MLPPCost::HingeLossDeriv(std::vector<std::vecto
real_t MLPPCost::WassersteinLoss(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
sum += y_hat[i] * y[i];
}
return -sum / y_hat.size();
@ -1096,8 +1093,8 @@ real_t MLPPCost::WassersteinLoss(std::vector<real_t> y_hat, std::vector<real_t>
real_t MLPPCost::WassersteinLoss(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t sum = 0;
for (int i = 0; i < y_hat.size(); i++) {
for (int j = 0; j < y_hat[i].size(); j++) {
for (uint32_t i = 0; i < y_hat.size(); i++) {
for (uint32_t j = 0; j < y_hat[i].size(); j++) {
sum += y_hat[i][j] * y[i][j];
}
}
@ -1150,7 +1147,7 @@ real_t MLPPCost::dualFormSVM(std::vector<real_t> alpha, std::vector<std::vector<
std::vector<real_t> MLPPCost::dualFormSVMDeriv(std::vector<real_t> alpha, std::vector<std::vector<real_t>> X, std::vector<real_t> y) {
MLPPLinAlg alg;
std::vector<std::vector<real_t>> Y = alg.zeromat(y.size(), y.size());
for (int i = 0; i < y.size(); i++) {
for (uint32_t i = 0; i < y.size(); i++) {
Y[i][i] = y[i]; // Y is a diagnoal matrix. Y[i][j] = y[i] if i = i, else Y[i][j] = 0. Yt = Y.
}
std::vector<std::vector<real_t>> K = alg.matmult(X, alg.transpose(X)); // TO DO: DON'T forget to add non-linear kernelizations.