private void BackgroundWorkerDoWork(object sender, DoWorkEventArgs e)
{
var psdFileReader = new PreprocessedStockDataFileReader(acorFileLabel.Text);
var inputData = psdFileReader.GetInputData();
var outputData = psdFileReader.GetDesiredOutputData();
var datesData = psdFileReader.GetDates();
int examples = _lengthOfPrediction + (_windowWidth - 1);
int predictionsCnt = examples - (_windowWidth - 1);
double[][] predictions = new double[predictionsCnt][];
string[] dates = new string[predictionsCnt];
int predictionsCalculated = 0;
int offset = _startDayIndex + _windowWidth;
BackgroundWorker worker = sender as BackgroundWorker;
Parallel.For(0, predictionsCnt, i =>
{
var inSlice = inputData.SubArray(i + _startDayIndex, _windowWidth);
var outSlice = outputData.SubArray(i + _startDayIndex, _windowWidth);
var trainer = _trainerFactory.GetTrainer();
trainer.Initialize(inSlice, outSlice);
trainer.TrainNeuralNetwork();
double[] predictionExample = inputData[i + offset];
double accual = outputData[i + offset];
double prediction = trainer.AskNeuralNetwork(predictionExample);
predictions[i] = new double[] { accual, prediction, trainer.BestFittness() };
dates[i] = datesData[i + offset];
Interlocked.Increment(ref predictionsCalculated);
worker.ReportProgress((predictionsCalculated*100)/predictionsCnt);
});
Program.SavePredictions(_outputFilename, predictions, dates);
}
private static void GeneratePredictionsSequential(string filename, int windowWidth = 7)
{
_psdFileReader = new PreprocessedStockDataFileReader(filename);
var inputData = _psdFileReader.GetInputData();
var outputData = _psdFileReader.GetDesiredOutputData();
var datesData = _psdFileReader.GetDates();
int examples = _howManyDaysToPredict + (windowWidth - 1);
int predictionsCnt = examples - (windowWidth - 1);
double[][] predictions = new double[predictionsCnt][];
string[] dates = new string[predictionsCnt];
int offset = _startDayIndex + windowWidth;
//for (int i = windowWidth - 1; i < inputData.Length - 1; i++)
// for(int i = windowWidth - 1 + _startDayIndex; i < examples +_startDayIndex; i++)
for(int i=0; i < predictionsCnt; i++)
{
var inSlice = inputData.SubArray(i + _startDayIndex, windowWidth);
var outSlice = outputData.SubArray(i + _startDayIndex, windowWidth);
var trainer = _trainerFactory.GetTrainer();
trainer.Initialize(inSlice, outSlice);
trainer.TrainNeuralNetwork();
double[] predictionExample = inputData[i + offset];
double accual = outputData[i + offset];
double prediction = trainer.AskNeuralNetwork(predictionExample);
predictions[i] = new double[] { accual, prediction, trainer.BestFittness() };
dates[i] = datesData[i + offset];
}
SavePredictions(_predictionOutputFilename, predictions, dates);
}
