ENH: Several improvements made in Simulator example.

src/examples/simulator.cpp

@@ -29,6 +29,13 @@ int main(int argc, char** argv){
         l4n::CSVReader reader("/home/martin/Desktop/data_Heaviside.txt", "\t", true);  // File, separator, skip 1st line
         reader.read();  // Read from the file
 
+        /* Open file for writing */
+        std::string filename = "simulator_output.txt";
+        std::ofstream output_file(filename);
+        if(!output_file.is_open()) {
+            throw std::runtime_error("File '" + filename + "' can't be opened!");
+        }
+
         /* Create data set for both the training and testing of the neural network */
         std::vector<unsigned int> inputs = { 3 };  // Possible multiple inputs, e.g. {0,3}
         std::vector<unsigned int> outputs = { 1 };  // Possible multiple outputs, e.g. {1,2}
@@ -83,7 +90,7 @@ int main(int argc, char** argv){
         // 1) Threshold for the successful ending of the optimization - deviation from minima
         // 2) Number of iterations to reset step size to tolerance/10.0
         // 3) Maximal number of iterations - optimization will stop after that, even if not converged
-        l4n::GradientDescent gs(1e-3, 100, 10);
+        l4n::GradientDescent gs(1e-3, 100, 200);
 
         // Weight and bias randomization in the network according to the uniform distribution
         // Calling methods nn.randomize_weights() and nn.randomize_biases()
@@ -97,21 +104,37 @@ int main(int argc, char** argv){
         /* Cross - validation */
         l4n::CrossValidator cv(&gs, &mse);
 
-        // Parameters: 1) Number of data-set parts used for CV, 2) Number of tests performed
-        cv.run_k_fold_test(10, 1);
+        // Parameters:
+        // 1) Number of data-set parts used for CV
+        // 2) Number of tests performed
+        // git 3) File-path to the files with data from cross-validation (one CV run - one file)
+        cv.run_k_fold_test(10, 3, &output_file);
 
         /* Save network to the text file */
         nn.save_text("test_net.4n");
 
-        /* Check of the saved network */
+        /* Check of the saved network - print to STDOUT */
         std::cout << std::endl << "The original network info:" << std::endl;
-        nn.print_stats();
-        nn.print_weights();
+        nn.write_stats();
+        nn.write_weights();
+        nn.write_biases();
 
         l4n::NeuralNetwork nn_loaded("test_net.4n");
         std::cout << std::endl << "The loaded network info:" << std::endl;
-        nn_loaded.print_stats();
-        nn_loaded.print_weights();
+        nn_loaded.write_stats();
+        nn.write_weights();
+        nn.write_biases();
+
+        /* Check of the saved network - write to the file */
+        output_file << std::endl << "The original network info:" << std::endl;
+        nn.write_stats(&output_file);
+        nn.write_weights(&output_file);
+        nn.write_biases(&output_file);
+
+        output_file << std::endl << "The loaded network info:" << std::endl;
+        nn_loaded.write_stats(&output_file);
+        nn.write_weights(&output_file);
+        nn.write_biases(&output_file);
 
         /* Example of evaluation of a single input, normalized input, de-normalized output */
         std::vector<double> input_norm(ds.get_input_dim()),
@@ -131,25 +154,29 @@ int main(int argc, char** argv){
         ds.de_normalize_single(input_norm, input);
         ds.de_normalize_single(expected_output_norm, expected_output);
 
-        std::cout << std::endl << "input: ";
-        for (auto el: input_norm) { std::cout << el << ", "; }
-        std::cout << std::endl;
-        std::cout << "output: ";
-        for (auto el: output) { std::cout << el << ", "; }
-        std::cout << std::endl;
-        std::cout << "expected output: ";
-        for (auto el: expected_output) { std::cout << el << ", "; }
-        std::cout << std::endl;
+        /* Evaluate network on an arbitrary data-set and save results into the file */
+        l4n::DataSet ds2;
+
+        std::vector<double> inp, out;
+        for(double i = 0; i < 5; i++) {
+            inp = {i};
+            out = {i+2};
+
+            ds2.add_data_pair(inp, out);
+        }
+
+        output_file << std::endl << "Evaluating network on the dataset: " << std::endl;
+        ds2.store_data_text(&output_file);
+
+        output_file << "Output and the error:" << std::endl;
+        mse.eval_on_data_set(&ds2, &output_file);
+
+        /* Close the output file for writing */
+        output_file.close();
 
         return 0;
 
-    } catch(const std::runtime_error& e) {
-        std::cerr << e.what() << std::endl;
-        exit(EXIT_FAILURE);
-    } catch(const std::out_of_range& e) {
-        std::cerr << e.what() << std::endl;
-        exit(EXIT_FAILURE);
-    } catch(const std::invalid_argument& e) {
+    } catch(const std::exception& e) {
         std::cerr << e.what() << std::endl;
         exit(EXIT_FAILURE);
     }