Solved Ld poor results

.vscode/launch.json

@@ -25,12 +25,12 @@
             "program": "${workspaceFolder}/build/src/Platform/main",
             "args": [
                 "-m",
-                "TANLd",
+                "SPODELd",
                 "-p",
                 "/Users/rmontanana/Code/discretizbench/datasets",
                 "--stratified",
                 "-d",
-                "vehicle"
+                "iris"
             ],
             "cwd": "/Users/rmontanana/Code/discretizbench",
         },

src/BayesNet/AODELd.cc

@@ -12,7 +12,7 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
         Ensemble::fit(dataset, features, className, states);

src/BayesNet/KDBLd.cc

@@ -11,7 +11,7 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
         KDB::fit(dataset, features, className, states);

src/BayesNet/Proposal.cc

@@ -67,10 +67,12 @@ namespace bayesnet {
             model.fit(pDataset, pFeatures, pClassName, states);
         }
     }
-    void Proposal::fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y)
+    map<string, vector<int>> Proposal::fit_local_discretization(torch::Tensor& y)
     {
+        // Discretize the continuous input data and build pDataset (Classifier::dataset)
         int m = Xf.size(1);
         int n = Xf.size(0);
+        map<string, vector<int>> states;
         pDataset = torch::zeros({ n + 1, m }, kInt32);
         auto yv = vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
         // discretize input data by feature(row)
@@ -89,6 +91,8 @@ namespace bayesnet {
         auto yStates = vector<int>(n_classes);
         iota(yStates.begin(), yStates.end(), 0);
         states[pClassName] = yStates;
+        pDataset.index_put_({ n, "..." }, y);
+        return states;
     }
     torch::Tensor Proposal::prepareX(torch::Tensor& X)
     {

src/BayesNet/Proposal.h

@@ -15,7 +15,7 @@ namespace bayesnet {
     protected:
         torch::Tensor prepareX(torch::Tensor& X);
         void localDiscretizationProposal(map<string, vector<int>>& states, Network& model);
-        void fit_local_discretization(map<string, vector<int>>& states, torch::Tensor& y);
+        map<string, vector<int>> fit_local_discretization(torch::Tensor& y);
         torch::Tensor Xf; // X continuous nxm tensor
         torch::Tensor y; // y discrete nx1 tensor
         map<string, mdlp::CPPFImdlp*> discretizers;

src/BayesNet/SPODELd.cc

@@ -11,7 +11,7 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
         SPODE::fit(dataset, features, className, states);
@@ -27,7 +27,7 @@ namespace bayesnet {
         features = features_;
         className = className_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
         SPODE::fit(dataset, features, className, states);

src/BayesNet/TANLd.cc

@@ -11,7 +11,7 @@ namespace bayesnet {
         Xf = X_;
         y = y_;
         // Fills vectors Xv & yv with the data from tensors X_ (discretized) & y
-        fit_local_discretization(states, y);
+        states = fit_local_discretization(y);
         // We have discretized the input data
         // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
         TAN::fit(dataset, features, className, states);