diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc index e4df61d..745042c 100644 --- a/bayesnet/ensembles/BoostAODE.cc +++ b/bayesnet/ensembles/BoostAODE.cc @@ -215,7 +215,7 @@ namespace bayesnet { while (!finished) { // Step 1: Build ranking with mutual information auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted - VLOG_SCOPE_F(1, "featureSelection.size: %d featuresUsed.size: %d", featureSelection.size(), featuresUsed.size()); + VLOG_SCOPE_F(1, "featureSelection.size: %zu featuresUsed.size: %zu", featureSelection.size(), featuresUsed.size()); if (order_algorithm == Orders.RAND) { std::shuffle(featureSelection.begin(), featureSelection.end(), g); } @@ -226,7 +226,7 @@ namespace bayesnet { ); int k = pow(2, tolerance); int counter = 0; // The model counter of the current pack - VLOG_SCOPE_F(1, "k=%d featureSelection.size: %d", k, featureSelection.size()); + VLOG_SCOPE_F(1, "k=%d featureSelection.size: %zu", k, featureSelection.size()); while (counter++ < k && featureSelection.size() > 0) { VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d", counter, numItemsPack); auto feature = featureSelection[0]; @@ -248,7 +248,7 @@ namespace bayesnet { models.push_back(std::move(model)); significanceModels.push_back(alpha_t); n_models++; - VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %d", numItemsPack, n_models, featuresUsed.size()); + VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size()); } if (convergence && !finished) { auto y_val_predict = predict(X_test); @@ -271,7 +271,7 @@ namespace bayesnet { priorAccuracy = std::max(accuracy, priorAccuracy); // priorAccuracy = accuracy; } - VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %d features.size: %d", tolerance, featuresUsed.size(), features.size()); + VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size()); finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size(); } if (tolerance > maxTolerance) {