static void Main()
{
// Load data from CSV.
List <StockDataPoint> dataPoints = StockDataUtility.ReadStockFile(@"msft.us.csv");
// Create a normalized training set of all of the CSV data.
StockDataSet trainingSet = new StockDataSet(dataPoints, true);
// Remove a percentage of data points from training set to create a testing set.
int testingSize = (int)(trainingSet.getSize() * 0.20f);
List <StockDataPoint> testingPoints = new List <StockDataPoint>();
for (int i = 0; i < testingSize; i++)
{
testingPoints.Add(trainingSet.RandomRemoveFromSet());
}
StockDataSet testingSet = new StockDataSet(testingPoints, false); // No need to normalize the data since it has already been normalized.
// Create the neural network.
var sizes = new List <int> {
5, 30, 30, 1
};
var net = new Network(sizes, Activation.Sigmoid);
// Driver code here.
//net.LoadNetwork(@"network.dat");
net.SGD(ref trainingSet, ref testingSet, 50, 10, 0.01f, true);
//net.StockTest(ref testingSet);
net.SaveNetwork(@"network.dat");
Console.ReadKey();
}
private void Normalize()
{
float lowOpen = float.MaxValue;
float highOpen = float.MinValue;
float lowHigh = float.MaxValue;
float highHigh = float.MinValue;
float lowLow = float.MaxValue;
float highLow = float.MinValue;
float lowClose = float.MaxValue;
float highClose = float.MinValue;
float lowVolume = float.MaxValue;
float highVolume = float.MinValue;
foreach (StockDataPoint p in dataSet)
{
if (p.Open < lowOpen)
{
lowOpen = p.Open;
}
if (p.Open > highOpen)
{
highOpen = p.Open;
}
if (p.High < lowHigh)
{
lowHigh = p.High;
}
if (p.High > highHigh)
{
highHigh = p.High;
}
if (p.Low < lowLow)
{
lowLow = p.Low;
}
if (p.Low > highLow)
{
highLow = p.Low;
}
if (p.Close < lowClose)
{
lowClose = p.Close;
}
if (p.Close > highClose)
{
highClose = p.Close;
}
if (p.Volume < lowVolume)
{
lowVolume = p.Volume;
}
if (p.Volume > highVolume)
{
highVolume = p.Volume;
}
}
foreach (StockDataPoint p in dataSet)
{
p.Open = StockDataUtility.NormalizeValue(lowOpen, highOpen, p.Open);
p.High = StockDataUtility.NormalizeValue(lowHigh, highHigh, p.High);
p.Low = StockDataUtility.NormalizeValue(lowLow, highLow, p.Low);
p.Close = StockDataUtility.NormalizeValue(lowClose, highClose, p.Close);
p.Volume = StockDataUtility.NormalizeValue(lowVolume, highVolume, p.Volume);
}
}
