private void button2_Click(object sender, EventArgs e)
{
double lerningSpeed = 0.1;
bool osiagnieto1 = true, osiagnieto2 = true, osiagnieto3 = true;
/////WCZYTYWANIE DANYCH//////
string path = @"1.txt";
List <double> inputList = new List <double>();;
List <double> outputList = new List <double>();
ReadFile(@"1.txt", inputList, outputList);
List <double> testNumbers = new List <double>();
List <double> testOutputs = new List <double>();
ReadFile(@"test.txt", testNumbers, testOutputs);
/////////////////////////////
int liczba_danych1 = inputList.Count;
double[] inputData = new double[liczba_danych1];
inputList.CopyTo(inputData);
int inputAmount = 1;
int outputAmount = 1;
double[] outputData = new double[liczba_danych1];
outputList.CopyTo(outputData);
IFunc inputFunction = new Linear();
IFunc sigmoidFunction = new Sigmoid();
IFunc outputFunction = new Linear();
double error = 0;
MLP s1 = new MLP(inputAmount, getNeuralAmount(), outputAmount, lerningSpeed, inputFunction, sigmoidFunction, outputFunction, Bias.Checked);
Wykres.Series["Dane 1"].Points.Clear();
Wykres.Series["Dane 2"].Points.Clear();
Wykres.Series["Dane 3"].Points.Clear();
for (int i = 0; i < 10000; i++)
{
s1.MultiLayerPerceptron(1, inputData, outputData);
error = s1.GetGlobalError();
// if (error > 0.001)
// break;
}
//DANE TESTWOE
double[] datain = new double[testNumbers.Count];
testNumbers.CopyTo(datain);
double[] dataout = new double[testNumbers.Count];
testOutputs.CopyTo(dataout);
for (int i = 0; i < testNumbers.Count; i++)
{
Wykres.Series["Dane 1"].Points.AddXY(datain[i], dataout[i]);
}
for (int i = 0; i < testNumbers.Count; i++)
{
Wykres.Series["Dane 2"].Points.AddXY(datain[i], s1.CountOne(datain[i]));
}
}
//ZAD 1
private void button1_Click(object sender, EventArgs e)
{
double lerningSpeed = 0.3;
//Wykresy
double lerningSpeed2 = 0.3;
double lerningSpeed3 = 0.3;
double error2 = 0;
double error3 = 0;
bool osiagnieto1 = true, osiagnieto2 = true, osiagnieto3 = true;
/////////////////////////////
double[,] inputData = new double[4, 4]
{
{ 1, 0, 0, 0 }, { 0, 1, 0, 0 }, { 0, 0, 1, 0 }, { 0, 0, 0, 1 }
};
double[][] testy = new double[4][];
for (int i = 0; i < 4; i++)
{
testy[i] = new double[4] {
0, 0, 0, 0
};
testy[i][i] = 1;
}
int inputAmount = 4;
int outputAmount = 4;
double[,] outputData = inputData;
IFunc inputFunction = new Linear();
IFunc sigmoidFunction = new Sigmoid();
IFunc outputFunction = new Sigmoid();
double error = 0;
MLP s1 = new MLP(inputAmount, getNeuralAmount(), outputAmount, lerningSpeed, inputFunction, sigmoidFunction, outputFunction, Bias.Checked);
//Wykresy
//MLP s1 = new MLP(inputAmount, 1, outputAmount, lerningSpeed, inputFunction, sigmoidFunction, outputFunction, Bias.Checked);
// MLP s2 = new MLP(inputAmount, getNeuralAmount(), outputAmount, lerningSpeed2, inputFunction, sigmoidFunction, outputFunction, Bias.Checked);
// MLP s3 = new MLP(inputAmount, getNeuralAmount(), outputAmount, lerningSpeed3, inputFunction, sigmoidFunction, outputFunction, Bias.Checked);
//////////////////////////
// Wykres.Series.Add("Lerning Rate 0.3");
// Wykres.Series.Add("Lerning Rate 0.6");
// Wykres.Series.Add("Lerning Rate 0.9");
/*
* Wykres.Series["Lerning Rate 0.3"].Points.Clear();
* Wykres.Series["Lerning Rate 0.6"].Points.Clear();
* Wykres.Series["Lerning Rate 0.9"].Points.Clear();
*/
Wykres.Series["Dane 1"].Points.Clear();
Wykres.Series["Dane 2"].Points.Clear();
Wykres.Series["Dane 3"].Points.Clear();
for (int i = 0; i < 10000; i++)
{
s1.MultiLayerPerceptron(4, inputData, outputData);
error = s1.GetGlobalError();
Wykres.Series["Dane 1"].Points.AddXY(i, error);
/*// s2.MultiLayerPerceptron(4, inputData, outputData);
* error2 = s2.GetGlobalError();
* Wykres.Series["Dane 2"].Points.AddXY(i, error2);
* s3.MultiLayerPerceptron(4, inputData, outputData);
* error3 = s3.GetGlobalError();
* Wykres.Series["Dane 3"].Points.AddXY(i, error3);
* /////////*/
// Do Labeli
if (error <= 0.01 && osiagnieto1)
{
label22.Text = i.ToString(); osiagnieto1 = false;
}
if (error2 <= 0.01 && osiagnieto2)
{
label23.Text = i.ToString(); osiagnieto2 = false;
}
if (error3 <= 0.01 && osiagnieto3)
{
label24.Text = i.ToString(); osiagnieto3 = false;
}
}
List <double> teemp = s1.getWeight();
saveFile(@"wagi.txt", teemp);
teemp.Clear();
for (int i = 0; i < 4; i++)
{
double[] temp = s1.Count(testy[i]);
foreach (double x in temp)
{
teemp.Add(x);
}
}
saveFile(@"wyniki.txt", teemp);
double[] input = new double[4];
double[] results = new double[16];
for (int j = 0; j < 4; j++)
{
for (int i = 0; i < 4; i++)
{
input[i] = inputData[i, j];
}
double[] output = new double[4];
output = s1.Count2(input);
for (int i = 0; i < getNeuralAmount(); i++)
{
results[j * getNeuralAmount() + i] = output[i];
}
label1.Text = String.Format("{0:N3}", results[0]);
label2.Text = String.Format("{0:N3}", results[1]);
label3.Text = String.Format("{0:N3}", results[2]);
label4.Text = String.Format("{0:N3}", results[3]);
label8.Text = String.Format("{0:N3}", results[4]);
label7.Text = String.Format("{0:N3}", results[5]);
label6.Text = String.Format("{0:N3}", results[6]);
label5.Text = String.Format("{0:N3}", results[7]);
label9.Text = String.Format("{0:N3}", results[8]);
label10.Text = String.Format("{0:N3}", results[9]);
label11.Text = String.Format("{0:N3}", results[10]);
label12.Text = String.Format("{0:N3}", results[11]);
}
}