public double[] getPrice(int index)
{
CSVNormalisation normaliser = new CSVNormalisation();
mFeedforward.calculate(index);
double[] prices = new double[2];
prices[0] = normaliser.deNormalise(mMinVals[mMinVals.Length - 1], mMaxVals[mMinVals.Length - 1], mFeedforward.Output);
prices[1] = normaliser.deNormalise(mMinVals[mMinVals.Length - 1], mMaxVals[mMinVals.Length - 1],
mDataSets[2][index][mDataSets[2][index].Length - 1]);
return(prices);
}
public void loadANN(bool reset)
{
List <List <double[]> > savedWeights = null;
if (!reset)
{
if (File.Exists(weightFileName))
{
Stream TestFileStream = File.OpenRead(weightFileName);
BinaryFormatter deserializer = new BinaryFormatter();
savedWeights = (List <List <double[]> >)deserializer.Deserialize(TestFileStream);
TestFileStream.Close();
}
}
List <double[]> normDataset = new List <double[]>();
List <double> idealOutputs = new List <double>();
CSVNormalisation normaliser = new CSVNormalisation();
loadData();
normaliser.normaliseAll(mMinVals, mMaxVals, mRawData, out normDataset);
normDataset.Shuffle();
mDataSets = normDataset.ChunkBy(60);
mForm.updateComboBox(mDataSets[2]);
mNetwork = new Network();
mNetwork.addLayer(new Layer(true, mDataSets[0][0].Length - 1));
mNetwork.addLayer(new Layer(false, 8));
mNetwork.addLayer(new Layer(false, 1));
mNetwork.createStructure();
if (savedWeights != null)
{
for (int i = 0; i < mNetwork.VectorLayers.Count; i++)
{
mNetwork.VectorLayers[i].Weights = savedWeights[i];
}
}
mFeedforward = new FeedForward(mNetwork, 0.005, mDataSets[0][0].Length - 1, 1);
mFeedforward.DataSets = mDataSets;
}
