private void SimplePerceptronTrain()
{
InitializeWeights();
decimal tempSum = 0;
for (int i = 0; i < iterationLimit; i++)
{
foreach (Dots member in ExtractData.GetDataTrain())
{
#region Compute Output
for (int j = 0; j < numClusters; j++)
{
decimal a = Activation(member.X, ClusterCenters[j, 0], ClusterSpreads[j]);
decimal w = Weights[j];
tempSum += a * w;
}
#endregion
#region UpdateWeights
decimal delta = 0;
for (int j = 0; j < numClusters; j++)
{
delta = trainingCoefficient * (member.Y - tempSum) * Activation(member.X, ClusterCenters[j, 0], ClusterSpreads[j]);
Weights[j] += delta;
}
#endregion
tempSum = 0;
}
}
}
private void FindSpreads()
{
ClusterSpreads = new decimal[numClusters];
for (int i = 0; i < numClusters; i++)
{
ClusterSpreads[i] = Utils.SimpleDistance(ClusterCenters[i, 0], ExtractData.GetDataTrain().Max(x => x.X)) / 2;
}
}
public RBF(int ParamNumClusters, int numOfIterations)
{
iterationLimit = numOfIterations;
numClusters = ParamNumClusters;
AssignClusterCenters(numClusters);
InitialCluster();
FindSpreads();
SimplePerceptronTrain();
SimplePerceptronValidate();
List <Dots> asdasd = ExtractData.GetDataTrain();
}
private void ReCalculateCenters()
{
for (int i = 0; i < numClusters; i++)
{
decimal count = Convert.ToDecimal(ExtractData.GetDataTrain().Where(x => x.BelongingCluster[numClusters - 1] == i + 1).Count());
decimal summ = ExtractData.GetDataTrain().Where(x => x.BelongingCluster[numClusters - 1] == i + 1).Sum(y => y.X);
if (count != 0)
{
ClusterCenters[i, 0] = summ / count;
}
}
}
private void InitialCluster()
{
foreach (Dots item in ExtractData.GetDataTrain())
{
decimal Dist = 500000m;
for (int i = 0; i < numClusters; i++)
{
item.DistanceToCentersByX[i] = Utils.SimpleDistance(ClusterCenters[i, 0], item.X);
}
for (int i = 0; i < numClusters; i++)
{
if (item.DistanceToCentersByX[i] < Dist)
{
Dist = item.DistanceToCentersByX[i];
item.BelongingCluster[numClusters - 1] = i + 1;
}
}
}
ReCalculateCenters();
foreach (Dots item in ExtractData.GetDataValidation())
{
decimal Dist = 500000m;
for (int i = 0; i < numClusters; i++)
{
item.DistanceToCentersByX[i] = Utils.SimpleDistance(ClusterCenters[i, 0], item.X);
}
for (int i = 0; i < numClusters; i++)
{
if (item.DistanceToCentersByX[i] < Dist)
{
Dist = item.DistanceToCentersByX[i];
item.BelongingCluster[numClusters - 1] = i + 1;
}
}
}
}
public void SetupRBF()
{
ExtractData.SetDataTrain();
ExtractData.SetDataValidation();
Random rrr = new Random();
//Clustering ClusterData = new Clustering();
//ClusterData.Cluster();
//StringBuilder str = new StringBuilder();
//str.AppendLine("X\tY\t");
//foreach (Dots x in ExtractData.GetDataValidation())
//{
// str.AppendLine(String.Format("{0}\t{1}\t", x.X, x.Y));
//}
//textBox1.Text = str.ToString();
for (int l = 0; l < 100; l++)
{
StringBuilder str = new StringBuilder();
StringBuilder str2 = new StringBuilder();
for (int k = 100; k < 10001; k *= 10)
{
for (int i = 1; i < 11; i++) // Cluster Number Iteration
{
string Key = MD5Sifrele(DateTime.Now.ToString() + rrr.Next().ToString());
int cluster_count = i;
RBF rbf = new RBF(cluster_count, k);
str.AppendLine(Key);
str.AppendLine(String.Format("RBF with {0} Epoch, {1} Training Coefficient, {2} Clusters", k, 0.1, i));
str.AppendLine("X\tY\tResult\tCluster\t");
foreach (Dots x in ExtractData.GetDataValidation().OrderBy(x => x.BelongingCluster[cluster_count - 1]))
{
str.AppendLine(String.Format("{0}\t{1}\t{2}\t{3}\t", x.X, x.Y, x.outY, x.BelongingCluster[cluster_count - 1]));
}
str.AppendLine(""); str.AppendLine(""); str.AppendLine("");
str2.AppendLine(Key);
str2.AppendLine(rbf.ToString());
}
}
System.IO.StreamWriter file = new System.IO.StreamWriter("d:\\Results\\OutputRBF_Stats_TestRun_#" + (l + 1) + ".txt");
file.Write(str2.ToString());
file.Close();
System.IO.StreamWriter file2 = new System.IO.StreamWriter("d:\\Results\\OutputRBF_Predictions_TestRun_#" + (l + 1) + ".txt");
file2.Write(str.ToString());
file2.Close();
}
//Mean Error - Mean Square Error Stats Exporter
//StringBuilder str = new StringBuilder();
//str.AppendLine("ID\tEpoch\tCluster\tMean Error\tMean Square Error\t");
//int id = 0;
//for (int l = 0; l < 100; l++)
//{
// for (int k = 100; k < 10001; k *= 10)
// {
// for (int i = 1; i < 11; i++) // Cluster Number Iteration
// {
// id++;
// //string Key = MD5Sifrele(DateTime.Now.ToString() + rrr.Next().ToString());
// int cluster_count = i;
// RBF rbf = new RBF(cluster_count, k);
// str.AppendLine(String.Format("{0}\t{1}\t{2}\t{3}\t{4}",id, k, cluster_count, rbf.MeanError.ToString("#.##"), rbf.SumOfSquareErrorMean.ToString("#.##")));
// }
// }
//}
//System.IO.StreamWriter file2 = new System.IO.StreamWriter("d:\\Results\\OutputRBF_Predictions_General_Plot" + ".txt");
//file2.Write(str.ToString());
//file2.Close();
//textBox1.Text = str.ToString() ;
//textBox2.Text = str2.ToString();
}
private void SimplePerceptronValidate()
{
decimal LocalError = 0;
CorrectCount = 0;
FalseCount = 0;
foreach (Dots member in ExtractData.GetDataValidation())
{
#region Compute Output
decimal output = 0;
for (int j = 0; j < numClusters; j++)
{
output += Activation(member.X, ClusterCenters[j, 0], ClusterSpreads[j]) * Weights[j];
}
member.outY = output;
#endregion
#region Information Collection
LocalError = member.Y - output;
if (Math.Abs(Convert.ToDouble(output)) <= Math.Abs(Convert.ToDouble(member.Y)) * 1.2 && Math.Abs(Convert.ToDouble(output)) >= Math.Abs(Convert.ToDouble(member.Y)) * 0.8)
{
CorrectCount++;
}
else
{
FalseCount++;
}
SumOfError += LocalError;
SumOfSquareError += LocalError * LocalError;
SumOfOutput += output;
SumOfSquareOutput += output * output;
SumOfValue += member.Y;
SumOfSquareValue += member.Y * member.Y;
#endregion
}
SumOfSquareErrorMean = SumOfSquareError / ExtractData.GetDataValidation().Count;
MeanError = SumOfError / ExtractData.GetDataValidation().Count;
ErrorStdDev = Convert.ToDecimal(Math.Sqrt(Convert.ToDouble((SumOfSquareError - (MeanError * MeanError * ExtractData.GetDataValidation().Count)) / ExtractData.GetDataValidation().Count)));
SumOfSquareValueMean = SumOfSquareValue / ExtractData.GetDataValidation().Count;
MeanValue = SumOfValue / ExtractData.GetDataValidation().Count;
ValueStdDev = Convert.ToDecimal(Math.Sqrt(Convert.ToDouble((SumOfSquareValue - (MeanValue * MeanValue * ExtractData.GetDataValidation().Count)) / ExtractData.GetDataValidation().Count)));
SumOfSquareOutputMean = SumOfSquareOutput / ExtractData.GetDataValidation().Count;
MeanOutput = SumOfOutput / ExtractData.GetDataValidation().Count;
OutputStdDev = Convert.ToDecimal(Math.Sqrt(Convert.ToDouble((SumOfSquareOutput - (MeanOutput * MeanOutput * ExtractData.GetDataValidation().Count)) / ExtractData.GetDataValidation().Count)));
}