/// <summary>
/// Enterance method
/// </summary>
/// <param name="args">Comand line arguments</param>
private static void Main(string[] args)
{
Console.WriteLine(
new System.Text.RegularExpressions.Regex("^\\s*(?:\\+?(\\d{1,3}))?[-. (]*(\\d{3})[-. )]*(\\d{3})[-. ]*(\\d{4})(?: *x(\\d+))?\\s*$")
.Matches(Console.ReadLine()).Cast <System.Text.RegularExpressions.Capture>().Select(x => x.Value).Aggregate((i, j) => i + " \n" + j)
);
Console.ReadLine();
TrainerManager tm = new TrainerManager();
//ICalculatableNetwork nn2 =
// tm.GetTrainer(LearningSubject.AndGate, LearningObject.Network).Train<ICalculatableNetwork>();
//ICalculatableNeuron n1 =
// tm.GetTrainer(LearningSubject.AndGate, LearningObject.Neuron).Train<ICalculatableNeuron>();
//ICalculatableNeuron n2 =
// tm.GetTrainer(LearningSubject.OrGate, LearningObject.Neuron).Train<ICalculatableNeuron>();
//ICalculatableNetwork nn1 =
// tm.GetTrainer(LearningSubject.XorGate, LearningObject.Network).Train<ICalculatableNetwork>();
ITrainer imageTrainer =
tm.GetTrainer(LearningSubject.Image, LearningObject.Network);
var imageNetwork = imageTrainer.Train <ICalculatableNetwork>();
var d = imageTrainer.GetTestData();
double succesRate = 0;
foreach (var pair in d)
{
var di = (new DigitImage(pair.Key, (byte)pair.Value.ToList().IndexOf(1)));
var data = (imageNetwork.Calculate(pair.Key).ToList());
if (di.Label == data.IndexOf(data.Max()))
{
succesRate += 1.0 / d.Count;
}
}
Console.WriteLine(succesRate);
//var m = tm.GetTrainer(LearningSubject.XorGate, (ILearnable) nn);
//var m2 = tm.GetTrainer(LearningSubject.XorGate, LearningObject.Network).Train<ICalculatableNetwork>();
//var m3 = m2.Calculate(1,1).First();
//ITrainer andTrainer = new AndGateNeuronTrainer((ILearnableNeuron) n1);
//ITrainer orTrainer = new OrGateNeuronTrainer((ILearnableNeuron) n2);
//ITrainer xorNetworkTrainer = new XorGateNetworkTrainer((ILearnableNetwork) nn);
//andTrainer.Train();
//orTrainer.Train();
//// Not posible -> Use multilayer
//// xorNeuronTrainer.Train();
//xorNetworkTrainer.Train();
//Console.WriteLine("----------AND Gate----------");
//Console.WriteLine($"(1,1) => {n1.Calculate(1, 1)}");
//Console.WriteLine($"(1,0) => {n1.Calculate(1, 0)}");
//Console.WriteLine($"(0,1) => {n1.Calculate(0, 1)}");
//Console.WriteLine($"(0,0) => {n1.Calculate(0, 0)}");
//Console.WriteLine("----------OR Gate----------");
//Console.WriteLine($"(1,1) => {n2.Calculate(1, 1)}");
//Console.WriteLine($"(1,0) => {n2.Calculate(1, 0)}");
//Console.WriteLine($"(0,1) => {n2.Calculate(0, 1)}");
//Console.WriteLine($"(0,0) => {n2.Calculate(0, 0)}");
//Console.WriteLine("----------XOR Gate----------");
//Console.WriteLine($"(1,1) => {nn1.Calculate(1, 1).First()}");
//Console.WriteLine($"(1,0) => {nn1.Calculate(1, 0).First()}");
//Console.WriteLine($"(0,1) => {nn1.Calculate(0, 1).First()}");
//Console.WriteLine($"(0,0) => {nn1.Calculate(0, 0).First()}");
//Console.WriteLine("----------AND Gate----------");
//Console.WriteLine($"(1,1) => {nn2.Calculate(1, 1).First()}");
//Console.WriteLine($"(1,0) => {nn2.Calculate(1, 0).First()}");
//Console.WriteLine($"(0,1) => {nn2.Calculate(0, 1).First()}");
//Console.WriteLine($"(0,0) => {nn2.Calculate(0, 0).First()}");
//DigitImageLoader loader = new DigitImageLoader("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
//var data = loader.Load().OrderBy(x=>x.Label);
//foreach (DigitImage image in data)
//{
// Console.WriteLine(image);
// Console.ReadLine();
//}
Console.ReadLine();
}
