MOD: added export information to a few learning methods

src/LearningMethods/LevenbergMarquardt.cpp

@@ -239,10 +239,17 @@ namespace lib4neuro {
             COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
                                      << gradient_norm << ", Direction norm: " << update_norm << "\r");
 
-
+            if (ofs && ofs->is_open()) {
+                *ofs << "Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
+                     << gradient_norm << ", Direction norm: " << update_norm << std::endl;
+            }
         } while (iter_counter++ < this->p_impl->maximum_niters && (update_norm > this->p_impl->tolerance));
         COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
                                  << gradient_norm << ", Direction norm: " << update_norm << std::endl);
+        if (ofs && ofs->is_open()) {
+            *ofs << "Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
+                 << gradient_norm << ", Direction norm: " << update_norm << std::endl;
+        }
 
         /* Store the optimized parameters */
         this->optimal_parameters = *params_current;

src/LearningMethods/ParticleSwarm.cpp

@@ -295,6 +295,9 @@ namespace lib4neuro {
         //TODO add output to the 'ofs'
 
         COUT_INFO("Finding optima via Globalized Particle Swarm method..." << std::endl);
+        if (ofs && ofs->is_open()) {
+            *ofs << "Finding optima via Globalized Particle Swarm method..." << std::endl;
+        }
 
         if (this->epsilon < 0 || this->gamma < 0 || this->delta < 0) {
             THROW_INVALID_ARGUMENT_ERROR(
@@ -422,6 +425,13 @@ namespace lib4neuro {
                                                   << ". Total error: " << current_err
                                                   << ". Objective function value: " << optimal_value
                                                   << ".\r");
+            if (ofs && ofs->is_open()) {
+                *ofs << "Iteration: " << (outer_it)
+                     << ". Total error: " << current_err
+                     << ". Objective function value: " << optimal_value
+                     << std::endl;
+            }
+
 
             if (this->err_thresh) {
 
@@ -447,6 +457,12 @@ namespace lib4neuro {
                                               << ". Total error: " << current_err
                                               << ". Objective function value: " << optimal_value
                                               << "." << std::endl);
+        if (ofs && ofs->is_open()) {
+            *ofs << "Iteration: " << (outer_it)
+                 << ". Total error: " << current_err
+                 << ". Objective function value: " << optimal_value
+                 << std::endl;
+        }
 
         this->determine_optimal_coordinate_and_value(this->optimal_parameters,
                                                      optimal_value);
@@ -455,10 +471,18 @@ namespace lib4neuro {
             /* Convergence reached */
             COUT_INFO(std::endl << "Found optimum in " << outer_it << " iterations. Objective function value: "
                                 << optimal_value << std::endl);
+            if (ofs && ofs->is_open()) {
+                *ofs << "Found optimum in " << outer_it << " iterations. Objective function value: "
+                     << optimal_value << std::endl;
+            }
         } else {
             /* Maximal number of iterations reached */
             COUT_INFO(std::endl << "Max number of iterations reached (" << outer_it << ")!  Objective function value: "
                                 << optimal_value << std::endl);
+            if (ofs && ofs->is_open()) {
+                *ofs << "Max number of iterations reached (" << outer_it << ")!  Objective function value: "
+                     << optimal_value << std::endl;
+            }
         }
 
         ef.set_parameters(this->optimal_parameters);