tolerance <- 3

src/experimental_clfs/XA1DE.cpp

@@ -13,8 +13,8 @@ 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_ = weights;
+        weights_ = torch::full({ num_instances }, 1.0);
         normalize_weights(num_instances);
-        aode_.fit(X, y, features, className, states, weights_, true);
+        aode_.fit(X, y, features, className, states, weights_, true, smoothing);
     }
 }

src/experimental_clfs/XBAODE.cpp

@@ -26,7 +26,7 @@ namespace platform {
         y_train_ = TensorUtils::to_vector<int>(y_train);
         X_test_ = TensorUtils::to_matrix(X_test);
         y_test_ = TensorUtils::to_vector<int>(y_test);
-        maxTolerance = 5;
+        maxTolerance = 3;
         //
         // Logging setup
         //

src/experimental_clfs/XBAODE.h

@@ -15,7 +15,10 @@
 #include "ExpClf.h"
 
 namespace platform {
-    class XBAODE : public ExpClf {
+    class XBAODE {
+
+        // Hay que hacer un vector de modelos entrenados y hacer un predict ensemble con todos ellos
+        // Probar XA1DE con smooth original y laplace y comprobar diferencias si se pasan pesos a 1 o a 1/m
     public:
         XBAODE();
         std::string getVersion() override { return version; };

src/experimental_clfs/Xaode.hpp

@@ -18,6 +18,7 @@
 #include <limits>
 #include <sstream>
 #include <torch/torch.h>
+#include <bayesnet/network/Smoothing.h>
 
 
 namespace platform {
@@ -49,7 +50,7 @@ namespace platform {
         //
         // Internally, in COUNTS mode, data_ accumulates raw counts, then
         // computeProbabilities(...) normalizes them into conditionals.
-        void 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 torch::Tensor& weights, const bool all_parents)
+        void 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 torch::Tensor& weights, const bool all_parents, const bayesnet::Smoothing_t smoothing)
         {
             int num_instances = X[0].size();
             nFeatures_ = X.size();
@@ -110,8 +111,16 @@ namespace platform {
                 instance[nFeatures_] = y[n_instance];
                 addSample(instance, weights[n_instance].item<double>());
             }
-            // alpha_ Laplace smoothing adapted to the number of instances
-            alpha_ = 1.0 / static_cast<double>(num_instances);
+            switch (smoothing) {
+                case bayesnet::Smoothing_t::ORIGINAL:
+                    alpha_ = 1.0 / num_instances;
+                    break;
+                case bayesnet::Smoothing_t::LAPLACE:
+                    alpha_ = 1.0;
+                    break;
+                default:
+                    alpha_ = 0.0; // No smoothing 
+            }
             initializer_ = std::numeric_limits<double>::max() / (nFeatures_ * nFeatures_);
             computeProbabilities();
         }