int main(int argc, char **argv) {
/* NeuralNetwork nn(Eigen::Vector4d(3,3,3,1), 0.2, 0.1);
typedef Eigen::Matrix<double,1,1> Vector1d;
Vector1d rtrue;
rtrue << 1;
Vector1d rfalse;
rfalse << 0;
int NUM_ITERS = 100000;
std::cout << "Beginning learning phase...\niteration 0";
std::vector<std::pair<Eigen::VectorXd, Eigen::VectorXd>> training_data;
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(1,1,1), rtrue));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(1,0,0), rtrue));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(0,1,0), rtrue));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(0,0,1), rtrue));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(1,1,0), rfalse));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(0,1,1), rfalse));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(1,0,1), rfalse));
training_data.push_back(std::make_pair<Eigen::VectorXd, Eigen::VectorXd>(Eigen::Vector3d(0,0,0), rfalse));
nn.TrainBatch(training_data, 100000, 0.000001);
std::cout <<"Learning done."<< std::endl;
std::cout <<"Beginning testing phase...\n";
std::cout << nn.Evaluate(Eigen::Vector3d(1,1,1)) << " vs " << 1 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(1,1,0)) << " vs " << 0 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(1,0,0)) << " vs " << 1 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(1,0,1)) << " vs " << 0 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(0,1,1)) << " vs " << 0 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(0,1,0)) << " vs " << 1 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(0,0,1)) << " vs " << 1 << std::endl;
std::cout << nn.Evaluate(Eigen::Vector3d(0,0,0)) << " vs " << 0 << std::endl;
*/
Reader reader;
Eigen::VectorXd layers(4);
layers << 10, 10, 5,1;
NeuralNetwork nn(layers, 0.2, 0.1);
std::cout << "Reading training data..." << std::flush;
std::vector<std::pair<Eigen::VectorXd, Eigen::VectorXd>> training_data;
std::cout << reader.ReadTrainingData("training_sample.csv",10,1,training_data) << " lines read." << std::endl;
std::cout << "Beginning learning phase..." << std::endl;
nn.TrainBatch(training_data, 50000, 0.001);
std::cout << "End training phase..." << std::endl;
nn.PrintWeights();
std::cout << "Reading evaluation data..." << std::flush;
std::vector<Eigen::VectorXd> evaluation_input_data;
std::vector<Eigen::VectorXd> evaluation_output_data;
int evaluation_rowcount = 0;
std::cout << (evaluation_rowcount = reader.ReadEvaluationData("training_normalized.csv",10,1,evaluation_input_data, evaluation_output_data)) << " lines read." << std::endl;
std::cout << "Beginning testing phase..." << std::endl;
double cumulative_mse = 0;
int success_count = 0;
double success_square_error_threshold = 0.16;
for(int i = 0; i < evaluation_rowcount; ++i) {
Eigen::VectorXd output = nn.Evaluate(evaluation_input_data[i]);
Eigen::VectorXd error = evaluation_output_data[i] - output;
double square_error = error.cwiseProduct(error).sum() / error.size();
cumulative_mse += square_error;
if(square_error < success_square_error_threshold) {
++success_count;
}
//std::cout << nn.Evaluate(evaluation_input_data[i]) << " vs. " << evaluation_output_data[i] << std::endl;
}
nn.PrintWeights();
nn.PrintStates();
std::cout << "End testing phase. Mean square error " << cumulative_mse/evaluation_rowcount << "\n";
std::cout << success_count << " / " << evaluation_rowcount << " rows classified correctly (mse < " << success_square_error_threshold << ")\n";
//std::cout << nn.Evaluate(evaluation_input_data[0]) << " vs. " << evaluation_output_data[0] << std::endl;
//nn.PrintStates();
//std::cout << nn.Evaluate(evaluation_input_data[1]) << " vs. " << evaluation_output_data[1] << std::endl;
//nn.PrintStates();
//std::cout << nn.Evaluate(evaluation_input_data[4]) << " vs. " << evaluation_output_data[4] << std::endl;
//nn.PrintStates();
return 0;
}
