From b9201cfeea28caf2f68b90c4adfd895426916722 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ricardo=20Montan=CC=83ana?= <rmontanana@gmail.com>
Date: Wed, 5 Jul 2023 20:45:21 +0200
Subject: [PATCH] Check result with KDB(k=3) Same experiment with KDB(k=3),
 dataset glass, from bayesclass & bayesnet Had same result 0.841121

---
 .vscode/launch.json | 2 +-
 sample/ArffFiles.cc | 6 +++---
 sample/main.cc      | 8 +++++++-
 3 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index fde5435..63a5fcf 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -8,7 +8,7 @@
             "program": "${workspaceFolder}/build/sample/main",
             "args": [
                 "-f",
-                "iris"
+                "glass"
             ],
             "cwd": "${workspaceFolder}",
             "preLaunchTask": "CMake: build"
diff --git a/sample/ArffFiles.cc b/sample/ArffFiles.cc
index b576699..b3336fe 100644
--- a/sample/ArffFiles.cc
+++ b/sample/ArffFiles.cc
@@ -63,7 +63,7 @@ void ArffFiles::load(const string& fileName, bool classLast)
             type = "";
             while (ss >> type_w)
                 type += type_w + " ";
-            attributes.emplace_back(attribute, trim(type));
+            attributes.emplace_back(trim(attribute), trim(type));
             continue;
         }
         if (line[0] == '@') {
@@ -111,8 +111,8 @@ void ArffFiles::generateDataset(bool classLast)
 string ArffFiles::trim(const string& source)
 {
     string s(source);
-    s.erase(0, s.find_first_not_of(" \n\r\t"));
-    s.erase(s.find_last_not_of(" \n\r\t") + 1);
+    s.erase(0, s.find_first_not_of(" '\n\r\t"));
+    s.erase(s.find_last_not_of(" '\n\r\t") + 1);
     return s;
 }
 
diff --git a/sample/main.cc b/sample/main.cc
index b5cec25..56ee711 100644
--- a/sample/main.cc
+++ b/sample/main.cc
@@ -213,7 +213,13 @@ int main(int argc, char** argv)
     map<string, int> maxes;
     tie(Xd, maxes) = discretize(X, y, features);
     maxes[className] = *max_element(y.begin(), y.end()) + 1;
-    // Build Network    
+    cout << "Features: ";
+    for (auto feature : features) {
+        cout << "[" << feature << "] ";
+    }
+    cout << endl;
+    cout << "Class name: " << className << endl;
+    // Build Network
     auto network = bayesnet::Network();
     build_network(network, network_name, maxes);
     network.fit(Xd, y, features, className);