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Fixed warnings in MLPPUtilities.

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Relintai 2023-02-13 17:13:57 +01:00
parent b4faca4a34
commit 4657a97d63
1 changed files with 39 additions and 21 deletions
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60
mlpp/utilities/utilities.cpp
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@ -281,7 +281,7 @@ void MLPPUtilities::bias_initializationv(Ref<MLPPVector> z) {
real_t MLPPUtilities::performance(std::vector<real_t> y_hat, std::vector<real_t> outputSet) { real_t MLPPUtilities::performance(std::vector<real_t> y_hat, std::vector<real_t> outputSet) {
real_t correct = 0; real_t correct = 0;
for (int i = 0; i < y_hat.size(); i++) { for (uint32_t i = 0; i < y_hat.size(); i++) {
if (std::round(y_hat[i]) == outputSet[i]) { if (std::round(y_hat[i]) == outputSet[i]) {
correct++; correct++;
} }
@ -291,9 +291,9 @@ real_t MLPPUtilities::performance(std::vector<real_t> y_hat, std::vector<real_t>
real_t MLPPUtilities::performance(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) { real_t MLPPUtilities::performance(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
real_t correct = 0; real_t correct = 0;
for (int i = 0; i < y_hat.size(); i++) { for (uint32_t i = 0; i < y_hat.size(); i++) {
int sub_correct = 0; uint32_t sub_correct = 0;
for (int j = 0; j < y_hat[i].size(); j++) { for (uint32_t j = 0; j < y_hat[i].size(); j++) {
if (std::round(y_hat[i][j]) == y[i][j]) { if (std::round(y_hat[i][j]) == y[i][j]) {
sub_correct++; sub_correct++;
} }
@ -368,7 +368,7 @@ void MLPPUtilities::saveParameters(std::string fileName, std::vector<real_t> wei
} }
saveFile << "Weight(s)" << layer_info << std::endl; saveFile << "Weight(s)" << layer_info << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
saveFile << weights[i] << std::endl; saveFile << weights[i] << std::endl;
} }
saveFile << "Bias" << layer_info << std::endl; saveFile << "Bias" << layer_info << std::endl;
@ -396,12 +396,12 @@ void MLPPUtilities::saveParameters(std::string fileName, std::vector<real_t> wei
} }
saveFile << "Weight(s)" << layer_info << std::endl; saveFile << "Weight(s)" << layer_info << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
saveFile << weights[i] << std::endl; saveFile << weights[i] << std::endl;
} }
saveFile << "Initial(s)" << layer_info << std::endl; saveFile << "Initial(s)" << layer_info << std::endl;
for (int i = 0; i < initial.size(); i++) { for (uint32_t i = 0; i < initial.size(); i++) {
saveFile << initial[i] << std::endl; saveFile << initial[i] << std::endl;
} }
@ -430,13 +430,13 @@ void MLPPUtilities::saveParameters(std::string fileName, std::vector<std::vector
} }
saveFile << "Weight(s)" << layer_info << std::endl; saveFile << "Weight(s)" << layer_info << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
for (int j = 0; j < weights[i].size(); j++) { for (uint32_t j = 0; j < weights[i].size(); j++) {
saveFile << weights[i][j] << std::endl; saveFile << weights[i][j] << std::endl;
} }
} }
saveFile << "Bias(es)" << layer_info << std::endl; saveFile << "Bias(es)" << layer_info << std::endl;
for (int i = 0; i < bias.size(); i++) { for (uint32_t i = 0; i < bias.size(); i++) {
saveFile << bias[i] << std::endl; saveFile << bias[i] << std::endl;
} }
@ -445,7 +445,7 @@ void MLPPUtilities::saveParameters(std::string fileName, std::vector<std::vector
void MLPPUtilities::UI(std::vector<real_t> weights, real_t bias) { void MLPPUtilities::UI(std::vector<real_t> weights, real_t bias) {
std::cout << "Values of the weight(s):" << std::endl; std::cout << "Values of the weight(s):" << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
std::cout << weights[i] << std::endl; std::cout << weights[i] << std::endl;
} }
std::cout << "Value of the bias:" << std::endl; std::cout << "Value of the bias:" << std::endl;
@ -454,24 +454,24 @@ void MLPPUtilities::UI(std::vector<real_t> weights, real_t bias) {
void MLPPUtilities::UI(std::vector<std::vector<real_t>> weights, std::vector<real_t> bias) { void MLPPUtilities::UI(std::vector<std::vector<real_t>> weights, std::vector<real_t> bias) {
std::cout << "Values of the weight(s):" << std::endl; std::cout << "Values of the weight(s):" << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
for (int j = 0; j < weights[i].size(); j++) { for (uint32_t j = 0; j < weights[i].size(); j++) {
std::cout << weights[i][j] << std::endl; std::cout << weights[i][j] << std::endl;
} }
} }
std::cout << "Value of the biases:" << std::endl; std::cout << "Value of the biases:" << std::endl;
for (int i = 0; i < bias.size(); i++) { for (uint32_t i = 0; i < bias.size(); i++) {
std::cout << bias[i] << std::endl; std::cout << bias[i] << std::endl;
} }
} }
void MLPPUtilities::UI(std::vector<real_t> weights, std::vector<real_t> initial, real_t bias) { void MLPPUtilities::UI(std::vector<real_t> weights, std::vector<real_t> initial, real_t bias) {
std::cout << "Values of the weight(s):" << std::endl; std::cout << "Values of the weight(s):" << std::endl;
for (int i = 0; i < weights.size(); i++) { for (uint32_t i = 0; i < weights.size(); i++) {
std::cout << weights[i] << std::endl; std::cout << weights[i] << std::endl;
} }
std::cout << "Values of the initial(s):" << std::endl; std::cout << "Values of the initial(s):" << std::endl;
for (int i = 0; i < initial.size(); i++) { for (uint32_t i = 0; i < initial.size(); i++) {
std::cout << initial[i] << std::endl; std::cout << initial[i] << std::endl;
} }
std::cout << "Value of the bias:" << std::endl; std::cout << "Value of the bias:" << std::endl;
@ -782,8 +782,11 @@ Array MLPPUtilities::create_mini_batchesmm_bind(const Ref<MLPPMatrix> &input_set
} }
std::tuple<real_t, real_t, real_t, real_t> MLPPUtilities::TF_PN(std::vector<real_t> y_hat, std::vector<real_t> y) { std::tuple<real_t, real_t, real_t, real_t> MLPPUtilities::TF_PN(std::vector<real_t> y_hat, std::vector<real_t> y) {
real_t TP, FP, TN, FN = 0; real_t TP = 0;
for (int i = 0; i < y_hat.size(); i++) { real_t FP = 0;
real_t TN = 0;
real_t FN = 0;
for (uint32_t i = 0; i < y_hat.size(); i++) {
if (y_hat[i] == y[i]) { if (y_hat[i] == y[i]) {
if (y_hat[i] == 1) { if (y_hat[i] == 1) {
TP++; TP++;
@ -802,17 +805,32 @@ std::tuple<real_t, real_t, real_t, real_t> MLPPUtilities::TF_PN(std::vector<real
} }
real_t MLPPUtilities::recall(std::vector<real_t> y_hat, std::vector<real_t> y) { real_t MLPPUtilities::recall(std::vector<real_t> y_hat, std::vector<real_t> y) {
auto [TP, FP, TN, FN] = TF_PN(y_hat, y); auto res = TF_PN(y_hat, y);
auto TP = std::get<0>(res);
//auto FP = std::get<1>(res);
//auto TN = std::get<2>(res);
auto FN = std::get<3>(res);
return TP / (TP + FN); return TP / (TP + FN);
} }
real_t MLPPUtilities::precision(std::vector<real_t> y_hat, std::vector<real_t> y) { real_t MLPPUtilities::precision(std::vector<real_t> y_hat, std::vector<real_t> y) {
auto [TP, FP, TN, FN] = TF_PN(y_hat, y); auto res = TF_PN(y_hat, y);
auto TP = std::get<0>(res);
auto FP = std::get<1>(res);
//auto TN = std::get<2>(res);
//auto FN = std::get<3>(res);
return TP / (TP + FP); return TP / (TP + FP);
} }
real_t MLPPUtilities::accuracy(std::vector<real_t> y_hat, std::vector<real_t> y) { real_t MLPPUtilities::accuracy(std::vector<real_t> y_hat, std::vector<real_t> y) {
auto [TP, FP, TN, FN] = TF_PN(y_hat, y); auto res = TF_PN(y_hat, y);
auto TP = std::get<0>(res);
auto FP = std::get<1>(res);
auto TN = std::get<2>(res);
auto FN = std::get<3>(res);
return (TP + TN) / (TP + FP + FN + TN); return (TP + TN) / (TP + FP + FN + TN);
} }
real_t MLPPUtilities::f1_score(std::vector<real_t> y_hat, std::vector<real_t> y) { real_t MLPPUtilities::f1_score(std::vector<real_t> y_hat, std::vector<real_t> y) {