static void Main(string[] args)
{
int i = 0;
while (i < 10)
{
nn = new NeuralNetwork(9, 3, activationFunction);
// preparing neural network
//dla 9 elementowej sieci
nn.AppendLayer(9);
nn.AppendLayer(7);
nn.AppendLayer(5);
// SOU - EVE
double[] trainingInput = new double[] { 2, 2, 1, 3, 4, 3, 4, 3, 3 };
double[] trainingOutput = new double[] { .48, .27, .25 };
Training(trainingInput, trainingOutput);
// HUL - WBA
trainingInput = new double[] { 1, 2, 1, 1, 1, 9, 3, 3, 4 };
trainingOutput = new double[] { .31, .29, .42 };
Training(trainingInput, trainingOutput);
//BUR -MCI
trainingInput = new double[] { 1, 0, 4, 3, 2, 5, 6, 3, 1 };
trainingOutput = new double[] { .1, 0.15, 0.75 };
Training(trainingInput, trainingOutput);
////LEI - MID
//trainingInput = new double[] { 3, 2, 0, 3, 2, 5, 1, 4, 5 };
//trainingOutput = new double[] { .52, 0.26, 0.22 };
//Training(trainingInput, trainingOutput);
////BUR-MCI
//double[] inputData = new double[] { 1, 0, 4, 3, 2, 5, 6, 3, 1 };
//LEI - MID
double[] inputData = new double[] { 3, 2, 0, 3, 2, 5, 1, 4, 5 };
Predict(inputData, i + 1);
i++;
}
Console.ReadKey();
}
/// <summary>
/// Standardowa propagacja sieci neuronowej
/// </summary>
static void Task1(double[] trainingData)
{
Console.WriteLine("Propagation");
NeuralNetwork nn = new NeuralNetwork(trainingData.Length, 1, activationFunction);
nn.AppendLayer(5);
var result = nn.Propagate(trainingData);
Console.WriteLine("Result:");
MatrixHelper.MatrixDisplay(result);
}
/// <summary>
/// Propagacja sieci neuronowej z wykorzystaniem wstecznej propagacji
/// </summary>
static void Task2(double[] trainingData)
{
try
{
Console.WriteLine("Back propagation");
NeuralNetwork nn = new NeuralNetwork(trainingData.Length, 3, activationFunction);
nn.AppendLayer(5);
for (int i = 0; i < 10000; i++)
{
nn.PropagateBack(trainingData, new double[] { 0.1, 0.2, 0.3 }, activationFunction);
}
var result = nn.Propagate(trainingData);
Console.WriteLine("Result:");
MatrixHelper.MatrixDisplay(result);
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
