Continue refactoring

src/experimental_clfs/ExpClf.cpp

@@ -13,6 +13,22 @@ namespace platform {
         validHyperparameters = {};
     }
     //
+    // Parents
+    //
+    void ExpClf::add_active_parents(const std::vector<int>& active_parents)
+    {
+        for (const auto& parent : active_parents)
+            aode_.add_active_parent(parent);
+    }
+    void ExpClf::add_active_parent(int parent)
+    {
+        aode_.add_active_parent(parent);
+    }
+    void ExpClf::remove_last_parent()
+    {
+        aode_.remove_last_parent();
+    }
+    //
     // Predict
     //
     std::vector<int> ExpClf::predict_spode(std::vector<std::vector<int>>& test_data, int parent)

src/experimental_clfs/ExpClf.h

@@ -39,9 +39,9 @@ namespace platform {
         bayesnet::status_t getStatus() const override { return status; }
         std::vector<std::string> getNotes() const override { return notes; }
         std::vector<std::string> graph(const std::string& title = "") const override { return {}; }
-        void set_active_parents(const std::vector<int>& active_parents) { for (const auto& parent : active_parents) aode_.add_active_parent(parent); }
-        void add_active_parent(int parent) { aode_.add_active_parent(parent); }
-        void remove_last_parent() { aode_.remove_last_parent(); }
+        void add_active_parents(const std::vector<int>& active_parents);
+        void add_active_parent(int parent);
+        void remove_last_parent();
     protected:
         bool debug = false;
         Xaode aode_;

src/experimental_clfs/XA1DE.cpp

@@ -13,7 +13,7 @@ namespace platform {
         auto X = TensorUtils::to_matrix(dataset.slice(0, 0, dataset.size(0) - 1));
         auto y = TensorUtils::to_vector<int>(dataset.index({ -1, "..." }));
         int num_instances = X[0].size();
-        weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
+        weights_ = weights;
         normalize_weights(num_instances);
         aode_.fit(X, y, features, className, states, weights_, true);
     }

src/experimental_clfs/XBAODE.cpp

@@ -43,13 +43,13 @@ namespace platform {
         n_models = 0;
         if (selectFeatures) {
             featuresUsed = featureSelection(weights_);
-            set_active_parents(featuresUsed);
+            add_active_parents(featuresUsed);
             notes.push_back("Used features in initialization: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
             auto ypred = ExpClf::predict(X_train);
             std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
             // Update significance of the models
             for (const auto& parent : featuresUsed) {
-                aode_.significance_models[parent] = alpha_t;
+                aode_.significance_models_[parent] = alpha_t;
             }
             n_models = featuresUsed.size();
             VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t, n_models);
@@ -95,12 +95,12 @@ namespace platform {
                     //
                     // Add the model to the ensemble
                     n_models++;
-                    aode_.significance_models[feature] = 1.0;
+                    aode_.significance_models_[feature] = 1.0;
                     aode_.add_active_parent(feature);
                     // Compute the prediction
                     ypred = ExpClf::predict(X_train_);
                     // Remove the model from the ensemble
-                    aode_.significance_models[feature] = 0.0;
+                    aode_.significance_models_[feature] = 0.0;
                     aode_.remove_last_parent();
                     n_models--;
                 } else {
@@ -113,7 +113,7 @@ namespace platform {
                 numItemsPack++;
                 featuresUsed.push_back(feature);
                 aode_.add_active_parent(feature);
-                aode_.significance_models[feature] = alpha_t;
+                aode_.significance_models_[feature] = alpha_t;
                 n_models++;
                 VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d featuresUsed: %zu", finished, numItemsPack, n_models, featuresUsed.size());
             } // End of the pack
@@ -150,7 +150,7 @@ namespace platform {
                 VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
                 for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
                     aode_.remove_last_parent();
-                    aode_.significance_models[featuresUsed[i]] = 0.0;
+                    aode_.significance_models_[featuresUsed[i]] = 0.0;
                     n_models--;
                 }
                 VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features used.", n_models, featuresUsed.size());

src/experimental_clfs/Xaode.hpp

@@ -29,7 +29,7 @@ namespace platform {
             COUNTS,
             PROBS
         };
-        std::vector<double> significance_models;
+        std::vector<double> significance_models_;
         Xaode() : nFeatures_{ 0 }, statesClass_{ 0 }, matrixState_{ MatrixState::EMPTY } {}
         // -------------------------------------------------------
         // fit
@@ -43,7 +43,7 @@ namespace platform {
             int num_instances = X[0].size();
             int n_features_ = X.size();
 
-            significance_models.resize(n_features_, (all_parents ? 1.0 : 0.0));
+            significance_models_.resize(n_features_, (all_parents ? 1.0 : 0.0));
             std::vector<int> statesv;
             for (int i = 0; i < n_features_; i++) {
                 if (all_parents) active_parents.push_back(i);