From b0222df3057f49d14d8e732b5c06a8002679adb3 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Wed, 23 Jan 2019 16:56:18 +0100
Subject: [PATCH] FIX: Fixed output formatting in MSE class
---
src/ErrorFunction/ErrorFunctions.cpp | 123 ++++++++++++++++++---------
1 file changed, 84 insertions(+), 39 deletions(-)
diff --git a/src/ErrorFunction/ErrorFunctions.cpp b/src/ErrorFunction/ErrorFunctions.cpp
index 59323bd8..e5bc62cf 100644
--- a/src/ErrorFunction/ErrorFunctions.cpp
+++ b/src/ErrorFunction/ErrorFunctions.cpp
@@ -114,11 +114,22 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size());
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'"
- " 'real output' 'predicted output' 'absolute error' 'relative error'):"
+ COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
+ 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);
- *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
error = 0.0;
@@ -170,27 +181,32 @@ namespace lib4neuro {
}
#ifdef L4N_DEBUG
- COUT_DEBUG("[" << i << "] "
- << ss_input.str() << " "
- << ss_real_output.str() << " "
- << ss_predicted_output.str() << " "
- << std::sqrt(error) << " "
- << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm))
- << std::endl);
-
- *results_file_path << "["
- << i << "] "
- << ss_input.str() << " "
- << ss_real_output.str() << " "
- << ss_predicted_output.str() << " "
- << std::sqrt(error) << " "
- << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm)) <<
- std::endl;
+ std::stringstream ss_ind;
+ ss_ind << "[" << i << "]";
+
+ COUT_DEBUG(R_ALIGN << ss_ind.str() << " "
+ << R_ALIGN << ss_input.str() << " "
+ << R_ALIGN << ss_real_output.str() << " "
+ << R_ALIGN << ss_predicted_output.str() << " "
+ << R_ALIGN << std::sqrt(error) << " "
+ << R_ALIGN << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm))
+ << std::endl);
+
+ *results_file_path << R_ALIGN << ss_ind.str() << " "
+ << R_ALIGN << ss_input.str() << " "
+ << R_ALIGN << ss_real_output.str() << " "
+ << R_ALIGN << ss_predicted_output.str() << " "
+ << R_ALIGN << std::sqrt(error) << " "
+ << R_ALIGN << 2 * std::sqrt(error) / (std::sqrt(error) + std::sqrt(output_norm))
+ << std::endl;
#endif
}
double result = std::sqrt(error) / n_elements;
+
+ COUT_DEBUG("MSE = " << result << std::endl);
*results_file_path << "MSE = " << result << std::endl;
+
return result;
}
@@ -207,16 +223,22 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size());
std::vector<double> output(dim_out);
- COUT_DEBUG(
- "Evaluation of the error function MSE on the given data-set (format 'input' 'real output' 'predicted output'):"
- << std::endl);
+ COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
+ COUT_DEBUG(R_ALIGN << "[Input]" << " "
+ << R_ALIGN << "[Real output]" << " "
+ << R_ALIGN << "[Predicted output]" << " "
+ << std::endl);
std::ofstream ofs(results_file_path);
if (!ofs.is_open()) {
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
@@ -243,14 +265,21 @@ namespace lib4neuro {
} else {
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) << " "
- << data->at(i).second.at(j) << " "
- << denormalized_output << std::endl;
+ std::stringstream ss_ind;
+ ss_ind << "[" << i << "]";
+
+ 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);
error += val * val;
@@ -259,9 +288,15 @@ namespace lib4neuro {
ofs << std::endl;
}
+
+ double result = std::sqrt(error) / n_elements;
+
+ ofs << "MSE = " << result << std::endl;
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) {
@@ -275,9 +310,11 @@ namespace lib4neuro {
std::vector<std::vector<double>> outputs(data->size());
std::vector<double> output(dim_out);
- COUT_DEBUG(
- "Evaluation of the error function MSE on the given data-set (format 'input' 'real output' 'predicted output'):"
- << std::endl);
+ COUT_DEBUG("Evaluation of the error function MSE on the given data-set" << std::endl);
+ COUT_DEBUG(R_ALIGN << "[Input]" << " "
+ << R_ALIGN << "[Real output]" << " "
+ << R_ALIGN << "[Predicted output]" << " "
+ << std::endl);
/* Compute predicted outputs */
for (auto i = 0; i < data->size(); i++) { // Iterate through every element in the test set
@@ -308,17 +345,25 @@ namespace lib4neuro {
} else {
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);
+
+ std::stringstream ss_ind;
+ ss_ind << "[" << i << "]";
+
+ 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);
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) {
--
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