Fix predict_proba in AdaBoost

src/experimental_clfs/AdaBoost.cpp

@@ -74,32 +74,53 @@ namespace bayesnet {
                 break;  // Stop boosting
             }
 
+            // Check for perfect classification BEFORE calculating alpha
+            if (weighted_error <= 1e-10) {
+                if (debug) std::cout << "  Perfect classification achieved (error=" << weighted_error << ")" << std::endl;
+
+                // For perfect classification, use a large but finite alpha
+                double alpha = 10.0 + std::log(static_cast<double>(n_classes - 1));
+
+                // Store the estimator and its weight
+                models.push_back(std::move(estimator));
+                alphas.push_back(alpha);
+
+                if (debug) {
+                    std::cout << "Iteration " << iter << ":" << std::endl;
+                    std::cout << "  Weighted error: " << weighted_error << std::endl;
+                    std::cout << "  Alpha (finite): " << alpha << std::endl;
+                    std::cout << "  Random guess error: " << random_guess_error << std::endl;
+                }
+
+                break;  // Stop training as we have a perfect classifier
+            }
+
             // Calculate alpha (estimator weight) using SAMME formula
             // alpha = log((1 - err) / err) + log(K - 1)
-            double alpha = std::log((1.0 - weighted_error) / weighted_error) +
+            // Clamp weighted_error to avoid division by zero and infinite alpha
+            double clamped_error = std::max(1e-15, std::min(1.0 - 1e-15, weighted_error));
+            double alpha = std::log((1.0 - clamped_error) / clamped_error) +
                 std::log(static_cast<double>(n_classes - 1));
 
+            // Clamp alpha to reasonable bounds to avoid numerical issues
+            alpha = std::max(-10.0, std::min(10.0, alpha));
+
             // Store the estimator and its weight
             models.push_back(std::move(estimator));
             alphas.push_back(alpha);
 
-            // Update sample weights
-            updateSampleWeights(models.back().get(), alpha);
-
-            // Normalize weights
-            normalizeWeights();
+            // Update sample weights (only if this is not the last iteration)
+            if (iter < n_estimators - 1) {
+                updateSampleWeights(models.back().get(), alpha);
+                normalizeWeights();
+            }
 
             if (debug) {
                 std::cout << "Iteration " << iter << ":" << std::endl;
                 std::cout << "  Weighted error: " << weighted_error << std::endl;
                 std::cout << "  Alpha: " << alpha << std::endl;
                 std::cout << "  Random guess error: " << random_guess_error << std::endl;
-            }
-
-            // Check for perfect classification
-            if (weighted_error < 1e-10) {
-                if (debug) std::cout << "  Perfect classification achieved, stopping" << std::endl;
-                break;
+                std::cout << "  Random guess error: " << random_guess_error << std::endl;
             }
         }