TrainingLogger::TrainingLogger(StringRef LogFileName)
: LogFileName(LogFileName) {
- for (size_t I = 0; I < NumberOfFeatures; ++I) {
+ for (size_t I = 0; I < NumberOfFeatures; ++I)
Features.push_back(InlineFeatures());
- }
}
/// Log one inlining event.
void TrainingLogger::logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner) {
- for (size_t I = 0; I < NumberOfFeatures; ++I) {
+ for (size_t I = 0; I < NumberOfFeatures; ++I)
Features[I].push_back(ModelRunner.getFeature(I));
- }
+
Decisions.push_back(Event.AdvisedDecision);
Effects.push_back(Event.Effect);
Rewards.push_back(Event.Reward);