/*
* public void ShowGrid(int steps, ref DataGrid grid)
* {
* DataTable table = new DataTable();
* initGrid(ref table);
* for (int i = 0; i < steps; i++)
* {
* setOutputsToGrid(ref table, i + 1);
* feedForward();
* setInputsAsOutputs();
* storeOutputs();
* }
* grid.DataSource = table;
* }
*/
public void loadTrainingInstance(TrainingInstance instance)
{
for (int i = 0; i < instance.NoOfInputs; i++)
{
_input[i].value = instance.getInput(i);
}
for (int i = 0; i <= instance.NoOfOutputs - 1; i++)
{
_output[i].desiredValue = instance.getOutput(i);
}
}
public void addInstance(TrainingInstance newInstance)
{
instances.Add(newInstance);
NoOfInstances = instances.Count;
}
/*
* public void Save(ref string filename)
* {
* int i;
* classTrainingInstance inst;
* int FileNumber;
* FileNumber = FreeFile;
* FileOpen(FileNumber, filename, OpenMode.Output);
* PrintLine(FileNumber, "[Training Set]");
* PrintLine(FileNumber, instances.Count());
* for (int i = 0; i <= instances.Count() - 1; i++)
* {
* inst = getInstance(i);
* inst.Save(FileNumber);
* }
* FileClose(FileNumber);
* }
*
* public void Load(ref string filename)
* {
* int i;
* TrainingInstance inst;
* string dummy;
* int NoOfInstances;
* int FileNumber;
* Clear();
* FileNumber = FreeFile;
* FileOpen(FileNumber, filename, OpenMode.Input);
* Input(FileNumber, dummy);
* PrintLine(FileNumber, NoOfInstances);
* for (int i = 0; i <= NoOfInstances - 1; i++)
* {
* inst = new classTrainingInstance();
* inst.Load(FileNumber);
* }
* FileClose(FileNumber);
* }
*/
/*
* public void Train(Network bp)
* {
* TrainingInstance inst;
* for (int i = 1; i <= instances.Count; i++)
* {
* inst = (TrainingInstance)instances[i];
* bp.loadTrainingInstance(inst);
* bp.update();
* }
* }
*/
public void ImportTimeSeries(string filename, int Dimensions, int TimeDelay)
{
//int n;
double dv;
string[] dataArray = new string[7];
Regex rex = new Regex(",(?=(?:[^\"]*\"[^\"]*\")*(?![^\"]*\"))");
ArrayList _data = new ArrayList();
TrainingInstance inst;
double[] currentData = new double[Dimensions];
double[,] prevHistory = new double[Dimensions, TimeDelay + 1];
System.IO.StreamReader file_reader = null;
System.IO.StreamReader buffered_reader = null;
if ((filename != ""))
{
MinValue = new double[Dimensions];
MaxValue = new double[Dimensions];
for (int i = 0; i < Dimensions; i++)
{
MinValue[i] = 999999999;
MaxValue[i] = -999999999;
}
Clear();
if (File.Exists(filename))
{
try
{
file_reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
buffered_reader = new System.IO.StreamReader(file_reader.BaseStream, file_reader.CurrentEncoding);
string line = null;
buffered_reader.ReadLine();
System.Collections.ArrayList _temp = new System.Collections.ArrayList();
while ((line = buffered_reader.ReadLine()) != null)
{
dataArray = rex.Split(line);
for (int i = 0; i < dataArray.Length - 1; i++)
{
if ((double.Parse(dataArray[i + 1]) < MinValue[i]))
{
MinValue[i] = double.Parse(dataArray[i + 1]);
}
if ((double.Parse(dataArray[i + 1]) > MaxValue[i]))
{
MaxValue[i] = double.Parse(dataArray[i + 1]);
}
dv = MaxValue[i] - MinValue[i];
if ((dv > 0))
{
currentData[i] = System.Math.Abs(((double.Parse(dataArray[i + 1]) - MinValue[i]) * 0.6) / dv) + 0.2;
}
else
{
currentData[i] = 0;
}
inst = new TrainingInstance();
inst.init(Dimensions * TimeDelay, Dimensions);
int n = 0;
for (int k = 0; k < TimeDelay; k++)
{
for (int j = 0; j < Dimensions; j++)
{
inst.setInput(n, prevHistory[j, k]);
n = n + 1;
}
}
for (int j = 0; j < Dimensions; j++)
{
inst.setOutput(j, currentData[j]);
}
addInstance(inst);
for (int j = 0; j < Dimensions; j++)
{
for (int k = TimeDelay - 1; k > 0; k--)
{
prevHistory[j, k] = prevHistory[j, k - 1];
}
}
for (int j = 0; j < Dimensions; j++)
{
prevHistory[j, 0] = currentData[j];
}
}
}
}
finally
{
buffered_reader.Close();
file_reader.Close();
}
}
}
}
