rmontanana/BayesNet
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Fix some lint warnings

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Ricardo Montañana Gómez
2023-07-29 20:37:51 +02:00
parent 5efa3beaee
commit 8b2ed26ab7
7 changed files with 34 additions and 51 deletions
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13
src/Platform/Experiment.cc
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@@ -48,7 +48,7 @@ namespace platform {
result["seeds"] = randomSeeds;
result["duration"] = duration;
result["results"] = json::array();
for (auto& r : results) {
for (const auto& r : results) {
json j;
j["dataset"] = r.getDataset();
j["hyperparameters"] = r.getHyperparameters();
@@ -78,7 +78,7 @@ namespace platform {
}
return result;
}
void Experiment::save(string path)
void Experiment::save(const string& path)
{
json data = build_json();
ofstream file(path + "/" + get_file_name());
@@ -97,14 +97,12 @@ namespace platform {
cout << "*** Starting experiment: " << title << " ***" << endl;
for (auto fileName : filesToProcess) {
cout << "- " << setw(20) << left << fileName << " " << right << flush;
auto result = cross_validation(path, fileName);
result.setDataset(fileName);
addResult(result);
cross_validation(path, fileName);
cout << endl;
}
}
Result Experiment::cross_validation(const string& path, const string& fileName)
void Experiment::cross_validation(const string& path, const string& fileName)
{
auto datasets = platform::Datasets(path, true, platform::ARFF);
// Get dataset
@@ -172,6 +170,7 @@ namespace platform {
result.setScoreTestStd(torch::std(accuracy_test).item<double>()).setScoreTrainStd(torch::std(accuracy_train).item<double>());
result.setTrainTime(torch::mean(train_time).item<double>()).setTestTime(torch::mean(test_time).item<double>());
result.setNodes(torch::mean(nodes).item<double>()).setLeaves(torch::mean(edges).item<double>()).setDepth(torch::mean(num_states).item<double>());
return result;
result.setDataset(fileName);
addResult(result);
}
}