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Commit b0222df3 authored by Martin Beseda's avatar Martin Beseda
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FIX: Fixed output formatting in MSE class

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src/ErrorFunction/ErrorFunctions.cpp
+ 84
− 39
View file @ b0222df3
...@@ -114,11 +114,22 @@ namespace lib4neuro { ...@@ -114,11 +114,22 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size()); std::vector<std::vector<double>> outputs(data->size());
std::vector<double> output(dim_out); std::vector<double> output(dim_out);
COUT_DEBUG("Evaluation of the error function MSE on the given data-set (format 'data-set element index' 'input'" COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
" 'real output' 'predicted output' 'absolute error' 'relative error'):" COUT_DEBUG(R_ALIGN << "[Element index]" << " "
<< R_ALIGN << "[Input]" << " "
<< R_ALIGN << "[Real output]" << " "
<< R_ALIGN << "[Predicted output]" << " "
<< R_ALIGN << "[Absolute error]" << " "
<< R_ALIGN << "[Relative error]"
<< std::endl); << std::endl);
*results_file_path << "[Data-set element index] [Input] [Real output] [Predicted output] [Abs. error] [Rel. error]" << std::endl; *results_file_path << R_ALIGN << "[Element index]" << " "
<< R_ALIGN << "[Input]" << " "
<< R_ALIGN << "[Real output]" << " "
<< R_ALIGN << "[Predicted output]" << " "
<< R_ALIGN << "[Abs. error]" << " "
<< R_ALIGN << "[Rel. error]"
<< std::endl;
for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set
error = 0.0; error = 0.0;
...@@ -170,27 +181,32 @@ namespace lib4neuro { ...@@ -170,27 +181,32 @@ namespace lib4neuro {
} }
#ifdef L4N_DEBUG #ifdef L4N_DEBUG
COUT_DEBUG("[" << i << "] " std::stringstream ss_ind;
<< ss_input.str() << " " ss_ind << "[" << i << "]";
<< ss_real_output.str() << " "
<< ss_predicted_output.str() << " " COUT_DEBUG(R_ALIGN << ss_ind.str() << " "
<< std::sqrt(error) << " " << R_ALIGN << ss_input.str() << " "
<< 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm)) << R_ALIGN << ss_real_output.str() << " "
<< std::endl); << R_ALIGN << ss_predicted_output.str() << " "
<< R_ALIGN << std::sqrt(error) << " "
*results_file_path << "[" << R_ALIGN << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm))
<< i << "] " << std::endl);
<< ss_input.str() << " "
<< ss_real_output.str() << " " *results_file_path << R_ALIGN << ss_ind.str() << " "
<< ss_predicted_output.str() << " " << R_ALIGN << ss_input.str() << " "
<< std::sqrt(error) << " " << R_ALIGN << ss_real_output.str() << " "
<< 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm)) << << R_ALIGN << ss_predicted_output.str() << " "
std::endl; << R_ALIGN << std::sqrt(error) << " "
<< R_ALIGN << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm))
<< std::endl;
#endif #endif
} }
double result = std::sqrt(error) / n_elements; double result = std::sqrt(error) / n_elements;
COUT_DEBUG("MSE = " << result << std::endl);
*results_file_path << "MSE = " << result << std::endl; *results_file_path << "MSE = " << result << std::endl;
return result; return result;
} }
...@@ -207,16 +223,22 @@ namespace lib4neuro { ...@@ -207,16 +223,22 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size()); std::vector<std::vector<double>> outputs(data->size());
std::vector<double> output(dim_out); std::vector<double> output(dim_out);
COUT_DEBUG( COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
"Evaluation of the error function MSE on the given data-set (format 'input' 'real output' 'predicted output'):" COUT_DEBUG(R_ALIGN << "[Input]" << " "
<< std::endl); << R_ALIGN << "[Real output]" << " "
<< R_ALIGN << "[Predicted output]" << " "
<< std::endl);
std::ofstream ofs(results_file_path); std::ofstream ofs(results_file_path);
if (!ofs.is_open()) { if (!ofs.is_open()) {
THROW_RUNTIME_ERROR("File path: " + results_file_path + " was not successfully opened!"); THROW_RUNTIME_ERROR("File path: " + results_file_path + " was not successfully opened!");
} }
ofs << "[Index ][Input] [Real output] [Predicted output]" << std::endl; ofs << R_ALIGN << "[Index]" << " "
<< R_ALIGN << "[Input]" << " "
<< R_ALIGN << "[Real output]" << " "
<< R_ALIGN << "[Predicted output]"
<< std::endl;
for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set
...@@ -243,14 +265,21 @@ namespace lib4neuro { ...@@ -243,14 +265,21 @@ namespace lib4neuro {
} else { } else {
denormalized_output = outputs.at(i).at(j); denormalized_output = outputs.at(i).at(j);
} }
COUT_DEBUG("[" << i << "] "
<< data->at(i).first.at(j) << " "
<< data->at(i).second.at(j) << " "
<< denormalized_output << std::endl);
ofs << data->at(i).first.at(j) << " " std::stringstream ss_ind;
<< data->at(i).second.at(j) << " " ss_ind << "[" << i << "]";
<< denormalized_output << std::endl;
COUT_DEBUG(R_ALIGN << ss_ind.str() << " "
<< R_ALIGN << data->at(i).first.at(j) << " "
<< R_ALIGN << data->at(i).second.at(j) << " "
<< R_ALIGN << denormalized_output
<< std::endl);
ofs << R_ALIGN << ss_ind.str() << " "
<< R_ALIGN << data->at(i).first.at(j) << " "
<< R_ALIGN << data->at(i).second.at(j) << " "
<< R_ALIGN << denormalized_output
<< std::endl;
val = denormalized_output - data->at(i).second.at(j); val = denormalized_output - data->at(i).second.at(j);
error += val * val; error += val * val;
...@@ -259,9 +288,15 @@ namespace lib4neuro { ...@@ -259,9 +288,15 @@ namespace lib4neuro {
ofs << std::endl; ofs << std::endl;
} }
double result = std::sqrt(error) / n_elements;
ofs << "MSE = " << result << std::endl;
ofs.close(); ofs.close();
return error / n_elements; COUT_DEBUG("MSE = " << result << std::endl);
return result;
} }
double MSE::eval_on_data_set(DataSet* data_set, std::vector<double>* weights) { double MSE::eval_on_data_set(DataSet* data_set, std::vector<double>* weights) {
...@@ -275,9 +310,11 @@ namespace lib4neuro { ...@@ -275,9 +310,11 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size()); std::vector<std::vector<double>> outputs(data->size());
std::vector<double> output(dim_out); std::vector<double> output(dim_out);
COUT_DEBUG( COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
"Evaluation of the error function MSE on the given data-set (format 'input' 'real output' 'predicted output'):" COUT_DEBUG(R_ALIGN << "[Input]" << " "
<< std::endl); << R_ALIGN << "[Real output]" << " "
<< R_ALIGN << "[Predicted output]" << " "
<< std::endl);
/* Compute predicted outputs */ /* Compute predicted outputs */
for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set
...@@ -308,17 +345,25 @@ namespace lib4neuro { ...@@ -308,17 +345,25 @@ namespace lib4neuro {
} else { } else {
denormalized_output = outputs.at(i).at(j); denormalized_output = outputs.at(i).at(j);
} }
COUT_DEBUG("[" << i << "] "
<< data->at(i).first.at(j) << " " std::stringstream ss_ind;
<< data->at(i).second.at(j) << " " ss_ind << "[" << i << "]";
<< denormalized_output << std::endl);
COUT_DEBUG(R_ALIGN << ss_ind.str() << " "
<< R_ALIGN << data->at(i).first.at(j) << " "
<< R_ALIGN << data->at(i).second.at(j) << " "
<< R_ALIGN << denormalized_output
<< std::endl);
val = denormalized_output - data->at(i).second.at(j); val = denormalized_output - data->at(i).second.at(j);
error += val * val; error += val * val;
} }
} }
return error / n_elements; double result = std::sqrt(result)/n_elements;
COUT_DEBUG("MSE = " << result << std::endl);
return result;
} }
double MSE::eval(std::vector<double>* weights) { double MSE::eval(std::vector<double>* weights) {
......
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