Complete integration with memory failure

src/CMakeLists.txt

@@ -25,6 +25,7 @@ add_executable(
     main/Models.cpp main/Scores.cpp
     reports/ReportExcel.cpp reports/ReportBase.cpp reports/ExcelFile.cpp
     results/Result.cpp
+    experimental_clfs/XA1DE.cpp
 )
 target_link_libraries(b_best Boost::boost "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 
@@ -36,6 +37,7 @@ add_executable(b_grid commands/b_grid.cpp ${grid_sources}
     main/HyperParameters.cpp main/Models.cpp main/Experiment.cpp main/Scores.cpp main/ArgumentsExperiment.cpp
     reports/ReportConsole.cpp reports/ReportBase.cpp 
     results/Result.cpp
+    experimental_clfs/XA1DE.cpp
 )
 target_link_libraries(b_grid ${MPI_CXX_LIBRARIES} "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy)
 
@@ -45,6 +47,7 @@ add_executable(b_list commands/b_list.cpp
     main/Models.cpp main/Scores.cpp
     reports/ReportExcel.cpp reports/ExcelFile.cpp reports/ReportBase.cpp reports/DatasetsExcel.cpp reports/DatasetsConsole.cpp reports/ReportsPaged.cpp
     results/Result.cpp results/ResultsDatasetExcel.cpp results/ResultsDataset.cpp results/ResultsDatasetConsole.cpp
+    experimental_clfs/XA1DE.cpp
 )
 target_link_libraries(b_list "${PyClassifiers}" "${BayesNet}" fimdlp ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ${LIBTORCH_PYTHON} Boost::python Boost::numpy "${XLSXWRITER_LIB}")
 

src/experimental_clfs/XA1DE.cpp

@@ -66,6 +66,9 @@ namespace platform {
     }
     std::vector<std::vector<double>> XA1DE::predict_proba(std::vector<std::vector<int>>& test_data)
     {
+        if (use_threads) {
+            return predict_proba_threads(test_data);
+        }
         int test_size = test_data[0].size();
         std::vector<std::vector<double>> probabilities;
 
@@ -151,7 +154,8 @@ namespace platform {
         }
         return static_cast<float>(correct) / predictions.size();
     }
-    std::vector<std::vector<int>> to_matrix(const torch::Tensor& X)
+
+    std::vector<std::vector<int>> XA1DE::to_matrix(const torch::Tensor& X)
     {
         // Ensure tensor is contiguous in memory
         auto X_contig = X.contiguous();
@@ -174,24 +178,45 @@ namespace platform {
         }
         return data;
     }
-    std::vector<int> to_vector(const torch::Tensor& y)
+    template <typename T>
+    std::vector<T> XA1DE::to_vector(const torch::Tensor& y)
     {
         // Ensure the tensor is contiguous in memory
         auto y_contig = y.contiguous();
 
         // Access data pointer
-        auto data_ptr = y_contig.data_ptr<int>();
+        auto data_ptr = y_contig.data_ptr<T>();
 
         // Prepare output container
-        std::vector<int> data(y.size(0));
+        std::vector<T> data(y.size(0));
 
         // Copy data efficiently
         std::copy(data_ptr, data_ptr + y.size(0), data.begin());
 
         return data;
     }
+
+    //
+    // Fit
+    //
+    // fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
     XA1DE& XA1DE::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
     {
-        return fit(to_matrix(X), to_vector(y), features, className, states, smoothing);
+        auto X_ = to_matrix(X);
+        int a = 1;
+        std::vector<int> y_ = to_vector<int>(y);
+        return fit(X_, y_, features, className, states, smoothing);
+    }
+    XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing)
+    {
+        torch::Tensor y = dataset[dataset.size(0) - 1];
+        torch::Tensor X = dataset.slice(0, 0, dataset.size(0) - 1);
+        return fit(X, y, features, className, states, smoothing);
+    }
+    XA1DE& XA1DE::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing)
+    {
+        double b = 1;
+        weights_ = to_vector<double>(weights);
+        return fit(dataset, features, className, states, smoothing);
     }
 }

src/experimental_clfs/XA1DE.h

@@ -17,6 +17,7 @@
 #include "Xaode.hpp"
 
 namespace platform {
+
     class XA1DE : public bayesnet::BaseClassifier {
     public:
         XA1DE();
@@ -28,13 +29,11 @@ namespace platform {
         float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
         std::vector<int> predict(std::vector<std::vector<int>>& X) override;
         XA1DE& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-
+        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override { return *this; };
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const bayesnet::Smoothing_t smoothing) override { return *this; };
+        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override;
-        XA1DE& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override { return *this; };
         torch::Tensor predict(torch::Tensor& X) override { return torch::zeros(0); };
         torch::Tensor predict_proba(torch::Tensor& X) override { return torch::zeros(0); };
-
         int getNumberOfNodes() const override { return 0; };
         int getNumberOfEdges() const override { return 0; };
         int getNumberOfStates() const override { return 0; };
@@ -49,6 +48,7 @@ namespace platform {
         void setHyperparameters(const nlohmann::json& hyperparameters) override;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
         void setDebug(bool debug) { this->debug = debug; }
+        std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
     protected:
         void trainModel(const torch::Tensor& weights, const bayesnet::Smoothing_t smoothing) override {};
 
@@ -57,13 +57,15 @@ namespace platform {
         {
             double sum = std::accumulate(weights_.begin(), weights_.end(), 0.0);
             if (sum == 0) {
-                throw std::runtime_error("Weights sum zero.");
+                weights_ = std::vector<double>(weights_.size(), 1.0);
-            }
+            } else {
-            for (double& w : weights_) {
+                for (double& w : weights_) {
-                w = w * num_instances / sum;
+                    w = w * num_instances / sum;
+                }
             }
         }
-        std::vector<int> to_vector(const torch::Tensor& y);
+        template <typename T>
+        std::vector<T> to_vector(const torch::Tensor& y);
         std::vector<std::vector<int>> to_matrix(const torch::Tensor& X);
         Xaode aode_;
         std::vector<double> weights_;

src/main/modelRegister.h

@@ -35,5 +35,7 @@ static platform::Registrar registrarRaF("RandomForest",
     [](void) -> bayesnet::BaseClassifier* { return new pywrap::RandomForest();});
 static platform::Registrar registrarXGB("XGBoost",
     [](void) -> bayesnet::BaseClassifier* { return new pywrap::XGBoost();});
+static platform::Registrar registrarXA1DE("XA1DE",
+    [](void) -> bayesnet::BaseClassifier* { return new platform::XA1DE();});
 
 #endif