private void InitStock(List <int> layersVal, float trainSetPercentage)
{
int trainSetEndIndex = (int)(trainSetPercentage * eid.CsvLines.Count);
List <DenseVector> allInputs = eid.CsvLines.Select(v => v.CreateSubVector(0, eid.InputCount)).ToList();
List <DenseVector> allOutputs = eid.CsvLines.Select(v => v.CreateSubVector(eid.InputCount, eid.OutputCount)).ToList();
trainSet = new StockDataSet(allInputs.ExtractList(0, trainSetEndIndex),
allOutputs.ExtractList(0, trainSetEndIndex), 0);
if (trainSetEndIndex >= allInputs.Count - 1)
{
testSet = trainSet.Clone();
}
else
{
testSet = new StockDataSet(allInputs.ExtractList(trainSetEndIndex, eid.CsvLines.Count),
allOutputs.ExtractList(trainSetEndIndex, eid.CsvLines.Count), trainSetEndIndex);
}
}
private void InitCTS(List <int> layersVal, float trainSetPercentage)
{
int historyLength = 1; // always historyLength = 1
int trainSetEndIndex = (int)(trainSetPercentage * eid.CsvLines.Count);
List <DenseVector> chaoticValues = eid.CsvLines; // no need for further parsing
List <DenseVector> trainValues = chaoticValues.ExtractList(0, trainSetEndIndex);
trainSet = new ChaoticDataSet(trainValues, historyLength, 0);
if (trainSetEndIndex >= chaoticValues.Count - 1)
{
testSet = trainSet.Clone();
}
else
{
List <DenseVector> testValues = chaoticValues.ExtractList(trainSetEndIndex - 1, chaoticValues.Count);
testSet = new ChaoticDataSet(testValues, historyLength, trainSetEndIndex);
}
}
