public void PSO()
{
PSO_NN sos_NN = new PSO_NN((2 * 8) + (8 * 2) + 2, 30, 1000, new NeuralNetwork(2, 2, 8), TrainingData.Take(TrainingData.Count() - 1).ToArray(), TrainingData.Skip(1).ToArray());
sos_NN.Train();
sos_NN.UpdateNeuralNetworkWithBestWeights();
NeuralNetwork nn = sos_NN.GetNeuralNetwork();
nn.Evaluate(TestingData.Take(TestingData.Count() - 1).ToArray(), TestingData.Skip(1).ToArray());
List <string> display = new List <string>();
label6.Text = "PSO + FEEDFOWARD NEURAL NETWORK";
List <int> OpenActualLabelsX = new List <int>();
List <int> OpenPredictedLabelsX = new List <int>();
List <double> OpenActualData = new List <double>();
List <double> OpenPredictedData = new List <double>();
List <int> CloseActualLabelsX = new List <int>();
List <int> ClosePredictedLabelsX = new List <int>();
List <double> CloseActualData = new List <double>();
List <double> ClosePredictedData = new List <double>();
//
int c1 = 0;
foreach (var item in TrainingData.Select(x => x[0]).ToArray())
{
c1++;
OpenActualLabelsX.Add(c1);
OpenActualData.Add(item);
}
int d1 = c1;
foreach (var item in TestingData.Select(x => x[0]).ToArray())
{
d1++;
OpenActualLabelsX.Add(d1);
OpenActualData.Add(item);
}
d1 = c1;
//
int c2 = 0;
foreach (var item in TrainingData.Select(x => x[1]).ToArray())
{
c2++;
CloseActualLabelsX.Add(c2);
CloseActualData.Add(item);
}
int d2 = c2;
foreach (var item in TestingData.Select(x => x[1]).ToArray())
{
d2++;
CloseActualLabelsX.Add(d2);
CloseActualData.Add(item);
}
rmse = new double[] { 0, 0 };
mad = new double[] { 0, 0 };
mape = new double[] { 0, 0 };
mae = new double[] { 0, 0 };
mse = new double[] { 0, 0 };
List <double> open = new List <double>();
List <double> close = new List <double>();
for (int i = 0; i < TestingData.Count() - 1; i++)
{
d1++;
double[] prediction = nn.Compute(TestingData[i]);
//display.Add("Input: (" + TestingData[i][0].ToString("F3") + ", " + TestingData[i][1].ToString("F3") + ") | Expected Output: (" + TestingData[i][0].ToString("F3") + ", " + TestingData[i][1].ToString("F3") + ") | Predicted Output: (" + predicted[0][i].ToString("F3") + ", " + predicted[1][i].ToString("F3") + ") | RMSE: " + rmse[i].ToString("F3"));
OpenPredictedLabelsX.Add(d1);
OpenPredictedData.Add(prediction[0]);
ClosePredictedLabelsX.Add(d1);
ClosePredictedData.Add(prediction[1]);
rmse[0] += Math.Pow(TestingData[i + 1][0] - prediction[0], 2);
rmse[1] += Math.Pow(TestingData[i + 1][1] - prediction[1], 2);
mse[0] += Math.Pow(TestingData[i + 1][0] - prediction[0], 2);
mse[1] += Math.Pow(TestingData[i + 1][1] - prediction[1], 2);
mae[0] += Math.Abs((TestingData[i + 1][0] - prediction[0]));
mae[1] += Math.Abs((TestingData[i + 1][1] - prediction[1]));
mape[0] += (Math.Abs((TestingData[i + 1][0] - prediction[0])) / Math.Abs(TestingData[i + 1][0]));
mape[1] += (Math.Abs((TestingData[i + 1][1] - prediction[1])) / Math.Abs(TestingData[i + 1][1]));
open.Add(prediction[0]);
close.Add(prediction[1]);
}
double openMean = open.Average();
double closeMean = close.Average();
for (int i = 0; i < TestingData.Count() - 1; i++)
{
mad[0] += Math.Abs((open[0] - openMean));
mad[1] += Math.Abs((close[1] - closeMean));
}
mse[0] /= TestingData.Count() - 1;
mse[1] /= TestingData.Count() - 1;
mae[0] = (mae[0] / (TestingData.Count() - 1));
mae[1] = (mae[1] / (TestingData.Count() - 1));
rmse[0] /= TestingData.Count() - 1;
rmse[1] /= TestingData.Count() - 1;
rmse[0] = Math.Sqrt(rmse[0]);
rmse[1] = Math.Sqrt(rmse[1]);
mape[0] = (mape[0] / (TestingData.Count() - 1)) * 100;
mape[1] = (mape[1] / (TestingData.Count() - 1)) * 100;
mad[0] = (mad[0] / (TestingData.Count() - 1));
mad[1] = (mad[1] / (TestingData.Count() - 1));
openActualForecast.Points.DataBindXY(OpenActualLabelsX.ToArray(), OpenActualData.ToArray());
openActualForecast.ChartType = SeriesChartType.Line;
openPredictedForecast.Points.DataBindXY(OpenPredictedLabelsX.ToArray(), OpenPredictedData.ToArray());
openPredictedForecast.ChartType = SeriesChartType.Line;
openActualForecast.Color = Color.RoyalBlue;
openPredictedForecast.Color = Color.Red;
closeActualForecast.Points.DataBindXY(CloseActualLabelsX.ToArray(), CloseActualData.ToArray());
closeActualForecast.ChartType = SeriesChartType.Line;
closePredictedForecast.Points.DataBindXY(ClosePredictedLabelsX.ToArray(), ClosePredictedData.ToArray());
closePredictedForecast.ChartType = SeriesChartType.Line;
closeActualForecast.Color = Color.RoyalBlue;
closePredictedForecast.Color = Color.Red;
chart2.Series.Clear();
chart2.Series.Add(openActualForecast);
chart2.Series.Add(openPredictedForecast);
chart2.ChartAreas[0].AxisX.Title = "Day";
chart2.ChartAreas[0].AxisY.Title = "Normalized Stock Price";
textBox4.Text = mad[0].ToString().Replace(",", ".");
textBox3.Text = mape[0].ToString().Replace(",", ".");
textBox2.Text = rmse[0].ToString().Replace(",", ".");
textBox1.Text = mse[0].ToString().Replace(",", ".");
textBox5.Text = mae[1].ToString().Replace(",", ".");
}
public void GA()
{
GA_FFNN ga = new GA_FFNN(30, TrainingData.Take(TrainingData.Count() - 1).ToArray(), TrainingData.Skip(1).ToArray());
for (int i = 0; i < 1000; i++)
{
ga.EvolvePopulation();
}
var ind = ga.GetBestIndividual();
NeuralNetwork nn = new NeuralNetwork(2, 2, 8);
nn.UpdateWeights(ind);
nn.Evaluate(TestingData.Take(TestingData.Count() - 1).ToArray(), TestingData.Skip(1).ToArray());
List <string> display = new List <string>();
label6.Text = "GA + FEEDFOWARD NEURAL NETWORK";
List <int> OpenActualLabelsX = new List <int>();
List <int> OpenPredictedLabelsX = new List <int>();
List <double> OpenActualData = new List <double>();
List <double> OpenPredictedData = new List <double>();
List <int> CloseActualLabelsX = new List <int>();
List <int> ClosePredictedLabelsX = new List <int>();
List <double> CloseActualData = new List <double>();
List <double> ClosePredictedData = new List <double>();
//
int c1 = 0;
foreach (var item in TrainingData.Select(x => x[0]).ToArray())
{
c1++;
OpenActualLabelsX.Add(c1);
OpenActualData.Add(item);
}
int d1 = c1;
foreach (var item in TestingData.Select(x => x[0]).ToArray())
{
d1++;
OpenActualLabelsX.Add(d1);
OpenActualData.Add(item);
}
d1 = c1;
//
int c2 = 0;
foreach (var item in TrainingData.Select(x => x[1]).ToArray())
{
c2++;
CloseActualLabelsX.Add(c2);
CloseActualData.Add(item);
}
int d2 = c2;
foreach (var item in TestingData.Select(x => x[1]).ToArray())
{
d2++;
CloseActualLabelsX.Add(d2);
CloseActualData.Add(item);
}
rmse = new double[] { 0, 0 };
mad = new double[] { 0, 0 };
mape = new double[] { 0, 0 };
mae = new double[] { 0, 0 };
mse = new double[] { 0, 0 };
List <double> open = new List <double>();
List <double> close = new List <double>();
for (int i = 0; i < TestingData.Count() - 1; i++)
{
d1++;
double[] prediction = nn.Compute(TestingData[i]);
//display.Add("Input: (" + TestingData[i][0].ToString("F3") + ", " + TestingData[i][1].ToString("F3") + ") | Expected Output: (" + TestingData[i][0].ToString("F3") + ", " + TestingData[i][1].ToString("F3") + ") | Predicted Output: (" + predicted[0][i].ToString("F3") + ", " + predicted[1][i].ToString("F3") + ") | RMSE: " + rmse[i].ToString("F3"));
OpenPredictedLabelsX.Add(d1);
OpenPredictedData.Add(prediction[0]);
ClosePredictedLabelsX.Add(d1);
ClosePredictedData.Add(prediction[1]);
rmse[0] += Math.Pow(TestingData[i + 1][0] - prediction[0], 2);
rmse[1] += Math.Pow(TestingData[i + 1][1] - prediction[1], 2);
mse[0] += Math.Pow(TestingData[i + 1][0] - prediction[0], 2);
mse[1] += Math.Pow(TestingData[i + 1][1] - prediction[1], 2);
mae[0] += Math.Abs((TestingData[i + 1][0] - prediction[0]));
mae[1] += Math.Abs((TestingData[i + 1][1] - prediction[1]));
mape[0] += (Math.Abs((TestingData[i + 1][0] - prediction[0])) / Math.Abs(TestingData[i + 1][0]));
mape[1] += (Math.Abs((TestingData[i + 1][1] - prediction[1])) / Math.Abs(TestingData[i + 1][1]));
open.Add(prediction[0]);
close.Add(prediction[1]);
}
double openMean = open.Average();
double closeMean = close.Average();
for (int i = 0; i < TestingData.Count() - 1; i++)
{
mad[0] += Math.Abs((open[0] - openMean));
mad[1] += Math.Abs((close[1] - closeMean));
}
mse[0] /= TestingData.Count() - 1;
mse[1] /= TestingData.Count() - 1;
mae[0] = (mae[0] / (TestingData.Count() - 1));
mae[1] = (mae[1] / (TestingData.Count() - 1));
rmse[0] /= TestingData.Count() - 1;
rmse[1] /= TestingData.Count() - 1;
rmse[0] = Math.Sqrt(rmse[0]);
rmse[1] = Math.Sqrt(rmse[1]);
mape[0] = (mape[0] / (TestingData.Count() - 1)) * 100;
mape[1] = (mape[1] / (TestingData.Count() - 1)) * 100;
mad[0] = (mad[0] / (TestingData.Count() - 1));
mad[1] = (mad[1] / (TestingData.Count() - 1));
openActualForecast.Points.DataBindXY(OpenActualLabelsX.ToArray(), OpenActualData.ToArray());
openActualForecast.ChartType = SeriesChartType.Line;
openPredictedForecast.Points.DataBindXY(OpenPredictedLabelsX.ToArray(), OpenPredictedData.ToArray());
openPredictedForecast.ChartType = SeriesChartType.Line;
openActualForecast.Color = Color.RoyalBlue;
openPredictedForecast.Color = Color.Red;
closeActualForecast.Points.DataBindXY(CloseActualLabelsX.ToArray(), CloseActualData.ToArray());
closeActualForecast.ChartType = SeriesChartType.Line;
closePredictedForecast.Points.DataBindXY(ClosePredictedLabelsX.ToArray(), ClosePredictedData.ToArray());
closePredictedForecast.ChartType = SeriesChartType.Line;
closeActualForecast.Color = Color.RoyalBlue;
closePredictedForecast.Color = Color.Red;
chart2.Series.Clear();
chart2.Series.Add(openActualForecast);
chart2.Series.Add(openPredictedForecast);
chart2.ChartAreas[0].AxisX.Title = "Day";
chart2.ChartAreas[0].AxisY.Title = "Normalized Stock Price";
//label7.Text = (Selection)(operators[0]) + " Selection + " + (Crossover)(operators[1]) + " Crossover + " + (Mutation)(operators[2]) + " Mutations";
//double[][] predictedValues = new double[rmse.Length][];
//for (int i = 0; i < rmse.Length; i++)
//{
// double[] prediction = nn.Compute(TestingData[i]);
// predictedValues[i] = prediction;
// display.Add("Input: (" + TestingData[i][0].ToString("F3") + ", " + TestingData[i][1].ToString("F3") + ") | Expected Output: (" + TestingData[i + 1][0].ToString("F3") + ", " + TestingData[i + 1][1].ToString("F3") + ") | Predicted Output: (" + prediction[0].ToString("F3") + ", " + prediction[1].ToString("F3") + ") | RMSE: " + rmse[i].ToString("F3"));
//}
//textBox1.Lines = display.ToArray();
//double[] mape = MAPE(TestingData.Skip(1).ToArray(), predictedValues);
//double mad = MAD(TestingData.Skip(1).ToArray(), predictedValues);
textBox4.Text = mad[0].ToString().Replace(",", ".");
textBox3.Text = mape[0].ToString().Replace(",", ".");
textBox2.Text = rmse[0].ToString().Replace(",", ".");
textBox1.Text = mse[0].ToString().Replace(",", ".");
textBox5.Text = mae[0].ToString().Replace(",", ".");
}