/// <summary>
/// Executes a run of processing the stock data
/// </summary>
/// <param name="session">The session configuration</param>
public static void Run(StockSession session)
{
// Create the stock processor
StockProcessor processor = new StockProcessor(session);
// Get the sizes of the features and labels
int dstIdx = 0;
int numDataPoints = 0;
int numFeatures, numLabels;
float[,] tmpDst = new float[1, 1024];
int[,] tmpLabels = new int[1, 1];
var tmpDataPoint = processor.DerivedData.First().Value[0];
tmpDataPoint.Load(session);
PopulateData(tmpDataPoint, tmpDst, tmpLabels, ref dstIdx, out numFeatures, out numLabels);
// Determine the total number of data points
List <string> symbols = new List <string>()
{
"GNTX"
};
foreach (var s in symbols)
{
List <StockDataSet <StockDataSink> > sources;
if (!processor.DerivedData.TryGetValue(s, out sources))
{
continue;
}
for (int i = 0; i < sources.Count; i++)
{
sources[i].Load(session);
numDataPoints += sources[i].Count;
}
}
// Allocate the feature and label arrays
float[,] features = new float[numDataPoints, numFeatures];
int[,] labels = new int[numDataPoints, numLabels];
// Load the data
dstIdx = 0;
foreach (var s in symbols)
{
List <StockDataSet <StockDataSink> > sources;
if (!processor.DerivedData.TryGetValue(s, out sources))
{
continue;
}
// Create a table of each data point in the specified range
for (int i = 0; i < sources.Count; i++)
{
sources[i].Load(session);
PopulateData(sources[i], features, labels, ref dstIdx, out numFeatures, out numLabels);
}
}
// Create the Machine Learning instance
MLInstance ml = new MLInstance();
ml.BuildFullyConnectedGraph(new int[] { numFeatures, numFeatures, numLabels });
ml.PrepareData(features, labels);
ml.Train();
}
