From da3e9b554a90861ea3a0b54b711ed4f6787b5f33 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Tue, 18 Dec 2018 23:09:54 +0100
Subject: [PATCH] ENH: Added FileNotFoundException handling.
---
src/examples/simulator.cpp | 129 +++++++++++++++++++------------------
1 file changed, 67 insertions(+), 62 deletions(-)
diff --git a/src/examples/simulator.cpp b/src/examples/simulator.cpp
index a53d9c6e..785f0002 100644
--- a/src/examples/simulator.cpp
+++ b/src/examples/simulator.cpp
@@ -36,29 +36,29 @@ double get_rel_error(std::vector<double> &d1, std::vector<double> &d2){
int main(int argc, char** argv){
- /* Read data from the file */
- l4n::CSVReader reader("/home/martin/Desktop/ANN_DATA_1_SET.txt", "\t", true);
- reader.read();
+ try {
- /* Create data set for both the training and testing of the neural network */
- std::vector<unsigned int> inputs = {2,3,4,5,6,7,8,26,27,28};
- std::vector<unsigned int> outputs = {17,18,19,20,21,22,23,24,25};
- l4n::DataSet ds = reader.get_data_set(&inputs, &outputs);
+ /* Read data from the file */
+ l4n::CSVReader reader("/home/martin/5Desktop/ANN_DATA_1_SET.txt", "\t", true);
+ reader.read();
- /* Normalize data in the set for easier training of the network */
- ds.normalize();
+ /* Create data set for both the training and testing of the neural network */
+ std::vector<unsigned int> inputs = {2, 3, 4, 5, 6, 7, 8, 26, 27, 28};
+ std::vector<unsigned int> outputs = {17, 18, 19, 20, 21, 22, 23, 24, 25};
- /* Neural network construction */
- std::vector<unsigned int> neuron_numbers_in_layers = {10,10,10,9};
- l4n::FullyConnectedFFN nn(&neuron_numbers_in_layers, l4n::NEURON_TYPE::LOGISTIC);
+ l4n::DataSet ds = reader.get_data_set(&inputs, &outputs);
- /* Error function */
- l4n::MSE mse(&nn, &ds);
+ /* Neural network construction */
+ std::vector<unsigned int> neuron_numbers_in_layers = {10, 10, 10, 9};
+ l4n::FullyConnectedFFN nn(&neuron_numbers_in_layers, l4n::NEURON_TYPE::LOGISTIC);
- /* Domain */
- std::vector<double> domain_bounds(2 * (nn.get_n_weights() + nn.get_n_biases()));
+ /* Error function */
+ l4n::MSE mse(&nn, &ds);
- /* Training method */
+ /* Domain */
+ std::vector<double> domain_bounds(2 * (nn.get_n_weights() + nn.get_n_biases()));
+
+ /* Training method */
// for(size_t i = 0; i < domain_bounds.size() / 2; ++i){
// domain_bounds[2 * i] = -10;
// domain_bounds[2 * i + 1] = 10;
@@ -72,50 +72,55 @@ int main(int argc, char** argv){
// 0.7,
// 600,
// 1000);
- l4n::GradientDescent gs(1e-3, 100, 100000);
-
- nn.randomize_weights();
-
- /* Cross - validation */
- l4n::CrossValidator cv(&gs, &mse);
- cv.run_k_fold_test(10, 1);
-
- /* Save network to the file */
- nn.save_text("test_net.4n");
-
- /* Check of the saved network */
- std::cout << std::endl << "The original network info:" << std::endl;
- nn.print_stats();
- nn.print_weights();
-
- 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();
-
- /* Example of evaluation of a single input, normalized input, de-normalized output */
- std::vector<double> input_norm(ds.get_input_dim()),
- input(ds.get_input_dim()),
- output_norm(ds.get_output_dim()),
- expected_output_norm(ds.get_output_dim()),
- output(ds.get_output_dim());
-
- size_t data_idx = 0;
- ds.get_input( input_norm, data_idx );
- ds.get_output( expected_output_norm, data_idx );
-
- nn_loaded.eval_single(input_norm, output_norm);
-
- ds.de_normalize_single(output_norm, output);
- ds.de_normalize_single(input_norm, input);
-
- 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 << "error of the " << data_idx << "-th element: "
- << get_rel_error(output_norm, expected_output_norm) << std::endl;
+ l4n::GradientDescent gs(1e-3, 100, 100000);
+
+ nn.randomize_weights();
+
+ /* Cross - validation */
+ l4n::CrossValidator cv(&gs, &mse);
+ cv.run_k_fold_test(10, 1);
+
+ /* Save network to the file */
+ nn.save_text("test_net.4n");
+
+ /* Check of the saved network */
+ std::cout << std::endl << "The original network info:" << std::endl;
+ nn.print_stats();
+ nn.print_weights();
+
+ 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();
+
+ /* Example of evaluation of a single input, normalized input, de-normalized output */
+ std::vector<double> input_norm(ds.get_input_dim()),
+ input(ds.get_input_dim()),
+ output_norm(ds.get_output_dim()),
+ expected_output_norm(ds.get_output_dim()),
+ output(ds.get_output_dim());
+
+ size_t data_idx = 0;
+ ds.get_input(input_norm, data_idx);
+ ds.get_output(expected_output_norm, data_idx);
+
+ nn_loaded.eval_single(input_norm, output_norm);
+
+ ds.de_normalize_single(output_norm, output);
+ ds.de_normalize_single(input_norm, input);
+
+ 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 << "error of the " << data_idx << "-th element: "
+ << get_rel_error(output_norm, expected_output_norm) << std::endl;
+
+ } catch(const lib4neuro::FileNotFoundException& e) {
+ std::cout << e.what();
+ }
+
return 0;
}
--
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