From a70ac3e883f221b8e04a2051d7721e6e201e737c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana=20G=C3=B3mez?=
 <rmontanana@gmail.com>
Date: Sun, 9 Mar 2025 11:21:31 +0100
Subject: [PATCH] Add namespace to Smoothing.h

---
 bayesnet/ensembles/BoostAODE.cc | 22 +++++++++++-----------
 bayesnet/network/Smoothing.h    | 14 ++++++++------
 2 files changed, 19 insertions(+), 17 deletions(-)

diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc
index 2de0986..a1bc4b6 100644
--- a/bayesnet/ensembles/BoostAODE.cc
+++ b/bayesnet/ensembles/BoostAODE.cc
@@ -37,9 +37,9 @@ namespace bayesnet {
         //
         // Logging setup
         //
-        loguru::set_thread_name("BoostAODE");
-        loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
-        loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
+        // loguru::set_thread_name("BoostAODE");
+        // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
+        // loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
 
         // Algorithm based on the adaboost algorithm for classification
         // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
@@ -57,7 +57,7 @@ namespace bayesnet {
             for (int i = 0; i < n_models; ++i) {
                 significanceModels.push_back(alpha_t);
             }
-            VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
+            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
             if (finished) {
                 return;
             }
@@ -87,7 +87,7 @@ namespace bayesnet {
             );
             int k = bisection ? pow(2, tolerance) : 1;
             int counter = 0; // The model counter of the current pack
-            VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
+            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
             while (counter++ < k && featureSelection.size() > 0) {
                 auto feature = featureSelection[0];
                 featureSelection.erase(featureSelection.begin());
@@ -124,7 +124,7 @@ namespace bayesnet {
                 models.push_back(std::move(model));
                 significanceModels.push_back(alpha_t);
                 n_models++;
-                VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
+                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
             }
             if (block_update) {
                 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
@@ -138,10 +138,10 @@ namespace bayesnet {
                     improvement = accuracy - priorAccuracy;
                 }
                 if (improvement < convergence_threshold) {
-                    VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance++;
                 } else {
-                    VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
+                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
                     tolerance = 0; // Reset the counter if the model performs better
                     numItemsPack = 0;
                 }
@@ -153,13 +153,13 @@ namespace bayesnet {
                     priorAccuracy = accuracy;
                 }
             }
-            VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", 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) {
             if (numItemsPack < n_models) {
                 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-                VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
+                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
                 for (int i = 0; i < numItemsPack; ++i) {
                     significanceModels.pop_back();
                     models.pop_back();
@@ -167,7 +167,7 @@ namespace bayesnet {
                 }
             } else {
                 notes.push_back("Convergence threshold reached & 0 models eliminated");
-                VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
+                // VLG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
             }
         }
         if (featuresUsed.size() != features.size()) {
diff --git a/bayesnet/network/Smoothing.h b/bayesnet/network/Smoothing.h
index 021f298..4efc0d2 100644
--- a/bayesnet/network/Smoothing.h
+++ b/bayesnet/network/Smoothing.h
@@ -6,10 +6,12 @@
 
 #ifndef SMOOTHING_H
 #define SMOOTHING_H
-enum class Smoothing_t {
-    NONE = -1,
-    ORIGINAL = 0,
-    LAPLACE,
-    CESTNIK
-};
+namespace bayesnet {
+    enum class Smoothing_t {
+        NONE = -1,
+        ORIGINAL = 0,
+        LAPLACE,
+        CESTNIK
+    };
+}
 #endif // SMOOTHING_H
\ No newline at end of file