Replacce pragma once with ifndef

src/main/Experiment.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef EXPERIMENT_H
+#define EXPERIMENT_H
 #include <torch/torch.h>
 #include <nlohmann/json.hpp>
 #include <string>
@@ -42,4 +42,5 @@ namespace platform {
         int nfolds{ 0 };
         int max_name{ 7 }; // max length of dataset name for formatting (default 7)
     };
-}
+}
+#endif

src/main/HyperParameters.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef HYPERPARAMETERS_H
+#define HYPERPARAMETERS_H
 #include <string>
 #include <map>
 #include <vector>
@@ -20,3 +20,4 @@ namespace platform {
         std::map<std::string, json> hyperparameters;
     };
 } /* namespace platform */
+#endif

src/main/Models.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef MODELS_H
+#define MODELS_H
 #include <map>
 #include <bayesnet/BaseClassifier.h>
 #include <bayesnet/ensembles/AODE.h>
@@ -42,3 +42,4 @@ namespace platform {
         Registrar(const std::string& className, function<bayesnet::BaseClassifier* (void)> classFactoryFunction);
     };
 }
+#endif

src/main/PartialResult.h

@@ -1,5 +1,5 @@
-#pragma once
-
+#ifndef PARTIAL_RESULT_H
+#define PARTIAL_RESULT_H
 #include <string>
 #include <nlohmann/json.hpp>
 
@@ -73,3 +73,4 @@ namespace platform {
         json data;
     };
 }
+#endif

src/main/Scores.h

@@ -4,6 +4,8 @@
 #include <string>
 #include <nlohmann/json.hpp>
 #include <torch/torch.h>
+#include <xlsxwriter.h>
+
 namespace platform {
     using json = nlohmann::ordered_json;
     class Scores {

src/main/modelRegister.h

@@ -1,4 +1,5 @@
-#pragma once
+#ifndef MODELREGISTER_H
+#define MODELREGISTER_H
 
 static platform::Registrar registrarT("TAN",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
@@ -33,4 +34,6 @@ static platform::Registrar registrarSvc("SVC",
 static platform::Registrar registrarRaF("RandomForest",
     [](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
 static platform::Registrar registrarXGB("XGBoost",
-    [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
+    [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
+
+#endif