Rename suffix of proposal classifier to Ld

src/BayesNet/CMakeLists.txt

@@ -1,5 +1,5 @@
 include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
 include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
 add_library(BayesNet bayesnetUtils.cc Network.cc Node.cc BayesMetrics.cc Classifier.cc 
-    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANNew.cc KDBNew.cc SPODENew.cc Mst.cc Proposal.cc)
+    KDB.cc TAN.cc SPODE.cc Ensemble.cc AODE.cc TANLd.cc KDBLd.cc SPODELd.cc Mst.cc Proposal.cc)
 target_link_libraries(BayesNet mdlp ArffFiles "${TORCH_LIBRARIES}")

src/BayesNet/KDBLd.cc

@@ -1,9 +1,9 @@
-#include "KDBNew.h"
+#include "KDBLd.h"
 
 namespace bayesnet {
     using namespace std;
-    KDBNew::KDBNew(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
+    KDBLd::KDBLd(int k) : KDB(k), Proposal(KDB::Xv, KDB::yv, features, className) {}
-    KDBNew& KDBNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
     {
         // This first part should go in a Classifier method called fit_local_discretization o fit_float...
         features = features_;
@@ -23,12 +23,12 @@ namespace bayesnet {
         model.fit(KDB::Xv, KDB::yv, features, className);
         return *this;
     }
-    Tensor KDBNew::predict(Tensor& X)
+    Tensor KDBLd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return KDB::predict(Xt);
     }
-    vector<string> KDBNew::graph(const string& name)
+    vector<string> KDBLd::graph(const string& name)
     {
         return KDB::graph(name);
     }

src/BayesNet/KDBLd.h

@@ -0,0 +1,19 @@
+#ifndef KDBLD_H
+#define KDBLD_H
+#include "KDB.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class KDBLd : public KDB, public Proposal {
+    private:
+    public:
+        explicit KDBLd(int k);
+        virtual ~KDBLd() = default;
+        KDBLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "KDB") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !KDBLD_H

src/BayesNet/KDBNew.h

@@ -1,19 +0,0 @@
-#ifndef KDBNEW_H
-#define KDBNEW_H
-#include "KDB.h"
-#include "Proposal.h"
-
-namespace bayesnet {
-    using namespace std;
-    class KDBNew : public KDB, public Proposal {
-    private:
-    public:
-        explicit KDBNew(int k);
-        virtual ~KDBNew() = default;
-        KDBNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "KDB") override;
-        Tensor predict(Tensor& X) override;
-        static inline string version() { return "0.0.1"; };
-    };
-}
-#endif // !KDBNew_H

src/BayesNet/SPODELd.cc

@@ -1,9 +1,9 @@
-#include "SPODENew.h"
+#include "SPODELd.h"
 
 namespace bayesnet {
     using namespace std;
-    SPODENew::SPODENew(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
+    SPODELd::SPODELd(int root) : SPODE(root), Proposal(SPODE::Xv, SPODE::yv, features, className) {}
-    SPODENew& SPODENew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
     {
         // This first part should go in a Classifier method called fit_local_discretization o fit_float...
         features = features_;
@@ -23,12 +23,12 @@ namespace bayesnet {
         model.fit(SPODE::Xv, SPODE::yv, features, className);
         return *this;
     }
-    Tensor SPODENew::predict(Tensor& X)
+    Tensor SPODELd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return SPODE::predict(Xt);
     }
-    vector<string> SPODENew::graph(const string& name)
+    vector<string> SPODELd::graph(const string& name)
     {
         return SPODE::graph(name);
     }

src/BayesNet/SPODELd.h

@@ -0,0 +1,19 @@
+#ifndef SPODELD_H
+#define SPODELD_H
+#include "SPODE.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class SPODELd : public SPODE, public Proposal {
+    private:
+    public:
+        explicit SPODELd(int root);
+        virtual ~SPODELd() = default;
+        SPODELd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "SPODE") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !SPODELD_H

src/BayesNet/SPODENew.h

@@ -1,19 +0,0 @@
-#ifndef SPODENEW_H
-#define SPODENEW_H
-#include "SPODE.h"
-#include "Proposal.h"
-
-namespace bayesnet {
-    using namespace std;
-    class SPODENew : public SPODE, public Proposal {
-    private:
-    public:
-        explicit SPODENew(int root);
-        virtual ~SPODENew() = default;
-        SPODENew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "SPODE") override;
-        Tensor predict(Tensor& X) override;
-        static inline string version() { return "0.0.1"; };
-    };
-}
-#endif // !SPODENew_H

src/BayesNet/TANLd.cc

@@ -1,9 +1,9 @@
-#include "TANNew.h"
+#include "TANLd.h"
 
 namespace bayesnet {
     using namespace std;
-    TANNew::TANNew() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
+    TANLd::TANLd() : TAN(), Proposal(TAN::Xv, TAN::yv, features, className) {}
-    TANNew& TANNew::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
+    TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, vector<string>& features_, string className_, map<string, vector<int>>& states_)
     {
         // This first part should go in a Classifier method called fit_local_discretization o fit_float...
         features = features_;
@@ -23,12 +23,12 @@ namespace bayesnet {
         model.fit(TAN::Xv, TAN::yv, features, className);
         return *this;
     }
-    Tensor TANNew::predict(Tensor& X)
+    Tensor TANLd::predict(Tensor& X)
     {
         auto Xt = prepareX(X);
         return TAN::predict(Xt);
     }
-    vector<string> TANNew::graph(const string& name)
+    vector<string> TANLd::graph(const string& name)
     {
         return TAN::graph(name);
     }

src/BayesNet/TANLd.h

@@ -0,0 +1,19 @@
+#ifndef TANLD_H
+#define TANLD_H
+#include "TAN.h"
+#include "Proposal.h"
+
+namespace bayesnet {
+    using namespace std;
+    class TANLd : public TAN, public Proposal {
+    private:
+    public:
+        TANLd();
+        virtual ~TANLd() = default;
+        TANLd& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
+        vector<string> graph(const string& name = "TAN") override;
+        Tensor predict(Tensor& X) override;
+        static inline string version() { return "0.0.1"; };
+    };
+}
+#endif // !TANLD_H

src/BayesNet/TANNew.h

@@ -1,19 +0,0 @@
-#ifndef TANNEW_H
-#define TANNEW_H
-#include "TAN.h"
-#include "Proposal.h"
-
-namespace bayesnet {
-    using namespace std;
-    class TANNew : public TAN, public Proposal {
-    private:
-    public:
-        TANNew();
-        virtual ~TANNew() = default;
-        TANNew& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
-        vector<string> graph(const string& name = "TAN") override;
-        Tensor predict(Tensor& X) override;
-        static inline string version() { return "0.0.1"; };
-    };
-}
-#endif // !TANNEW_H

src/Platform/Models.h

@@ -6,9 +6,9 @@
 #include "TAN.h"
 #include "KDB.h"
 #include "SPODE.h"
-#include "TANNew.h"
+#include "TANLd.h"
-#include "KDBNew.h"
+#include "KDBLd.h"
-#include "SPODENew.h"
+#include "SPODELd.h"
 namespace platform {
     class Models {
     private:

src/Platform/modelRegister.h

@@ -2,16 +2,16 @@
 #define MODEL_REGISTER_H
 static platform::Registrar registrarT("TAN",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TAN();});
-static platform::Registrar registrarTN("TANNew",
+static platform::Registrar registrarTN("TANLd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANNew();});
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::TANLd();});
 static platform::Registrar registrarS("SPODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODE(2);});
-static platform::Registrar registrarSN("SPODENew",
+static platform::Registrar registrarSN("SPODELd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODENew(2);});
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::SPODELd(2);});
 static platform::Registrar registrarK("KDB",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDB(2);});
-static platform::Registrar registrarKN("KDBNew",
+static platform::Registrar registrarKN("KDBLd",
-    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBNew(2);});
+    [](void) -> bayesnet::BaseClassifier* { return new bayesnet::KDBLd(2);});
 static platform::Registrar registrarA("AODE",
     [](void) -> bayesnet::BaseClassifier* { return new bayesnet::AODE();});
 #endif