Fix Regularization error

.gitignore

@@ -36,3 +36,4 @@ build_Release
 build_Debug
 build
 libtorch
+Testing

include/svm_classifier/data_converter.hpp

@@ -138,6 +138,7 @@ namespace svm_classifier {
         std::vector<double> linear_y_space_;
 
         // Single sample storage (for prediction)
+        // Thread-local storage for single sample conversions
         std::vector<svm_node> single_svm_nodes_;
         std::vector<feature_node> single_linear_nodes_;
 

src/data_converter.cpp

@@ -92,7 +92,7 @@ namespace svm_classifier {
         linear_problem->n = n_features_;
         linear_problem->x = linear_x_space_.data();
         linear_problem->y = linear_y_space_.data();
-        linear_problem->bias = -1; // No bias term by default
+        linear_problem->bias = 1.0; // Add bias term with value 1.0
 
         return linear_problem;
     }

src/multiclass_strategy.cpp

@@ -321,7 +321,8 @@ namespace svm_classifier {
             linear_params.p = 0.1;
             linear_params.nu = 0.5;
             linear_params.init_sol = nullptr;
-            linear_params.regularize_bias = 0;
+            linear_params.regularize_bias = 1;
+            linear_params.w_recalc = false;
 
             // Check parameters
             const char* error_msg = check_parameter(problem.get(), &linear_params);
@@ -628,7 +629,8 @@ namespace svm_classifier {
             linear_params.p = 0.1;
             linear_params.nu = 0.5;
             linear_params.init_sol = nullptr;
-            linear_params.regularize_bias = 0;
+            linear_params.regularize_bias = 1;
+            linear_params.w_recalc = false;
 
             // Check parameters
             const char* error_msg = check_parameter(problem.get(), &linear_params);

test_param_change.cpp

@@ -0,0 +1,36 @@
+#include <svm_classifier/svm_classifier.hpp>
+#include <torch/torch.h>
+#include <iostream>
+
+using namespace svm_classifier;
+
+int main() {
+    try {
+        std::cout << "Creating linear SVM..." << std::endl;
+        SVMClassifier svm(KernelType::LINEAR, 1.0);
+        
+        std::cout << "Generating data..." << std::endl;
+        torch::manual_seed(42);
+        auto X = torch::randn({50, 3});
+        auto y = torch::randint(0, 2, {50});
+        
+        std::cout << "Training SVM..." << std::endl;
+        auto metrics = svm.fit(X, y);
+        std::cout << "Training completed successfully!" << std::endl;
+        std::cout << "Is fitted: " << svm.is_fitted() << std::endl;
+        
+        std::cout << "\nChanging to RBF kernel..." << std::endl;
+        nlohmann::json new_params = {{"kernel", "rbf"}};
+        svm.set_parameters(new_params);
+        std::cout << "Parameters changed successfully!" << std::endl;
+        std::cout << "Is fitted after param change: " << svm.is_fitted() << std::endl;
+        
+        std::cout << "\nAll operations completed successfully!" << std::endl;
+        
+    } catch (const std::exception& e) {
+        std::cerr << "Error: " << e.what() << std::endl;
+        return 1;
+    }
+    
+    return 0;
+}