private static bool Load()
{
Console.WriteLine("What is brain´s name?");
var name = Console.ReadLine();
var exist = Brain.ActivationNetworkExists(name);
if (exist)
{
_brain = new Brain(name);
_brain.Run().Wait();
Console.WriteLine("Ok, this brain alredy exists.");
}
else
{
Console.WriteLine("Ok, this brain must learn.");
//Datos de entrada dispuestos en 6 columnas, las cuatro primeras son inputs y las dos ultimas outputs
DataTable table = new ExcelReader("examples.xls").GetWorksheet("Classification - Circle");
// Convert the DataTable to input and output vectors
var inputs = table.ToJagged <double>("X", "Y", "X2", "Y2");
var outputs = table.ToJagged <double>("G1", "G2");
//Distribucion de las 'caja negra', capas y neuronas por capa
int[] neuronsCount = new int[3] {
5, 4, 2
};
//Tolerancia, hasta que el error no sea menor a 1e-5 no se acaba el aprendizaje
int tol = -5;
double alpha = .5;
var activationFunction = new Accord.Neuro.BipolarSigmoidFunction();
_brain = new Brain(name, inputs, outputs, neuronsCount, activationFunction, tol);
Console.WriteLine("Learning...");
_brain.Run().Wait();
Console.WriteLine("I just know kung-foo");
}
return(_brain != null);
}
