public void WriteData(double[] input, BayesianLearning teacher)
{
double[] writeOutput = teacher.network.Compute(input);
using (StreamWriter writetext = new StreamWriter("log.txt", append: true))
{
for (int i = 0; i < writeOutput.Length; i++)
{
writetext.WriteLine(input[i] + " " + writeOutput[i]);
}
}
}
private void Learn()
{
int samples = data.GetLength(0);
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for (int i = 0; i < samples; i++)
{
input[i] = new double[1];
output[i] = new double[1];
input[i][0] = (data[i, 0]);
output[i][0] = (data[i, 1]);
}
ActivationNetwork network = new ActivationNetwork(
new BipolarSigmoidFunction(sigmoidAlphaValue),
1, neuronsInFirstLayer, 1);
BayesianLearning teacher = new BayesianLearning(network);
teacher.LearningRate = learningRate;
int iteration = 1;
while (!needToStop)
{
double error = teacher.RunEpoch(input, output) / samples;
Console.WriteLine("The avg error is: " + error);
iteration++;
//Mikor álljunk meg? Ha az iterációk száma eléri a megadottat, vagy a hiba kisebb mint valami
if ((iterations != 0) && (iteration > iterations) || error < 1)
{
for (int i = 0, n = input.Length; i < n; i++)
{
WriteData(input[i], teacher);
}
break;
}
}
LoadTestData();
for (int i = 0; i < 30; i++)
{
double[] test = teacher.network.Compute(testingData[i]);
}
}
