/// <summary>
/// Builds the structure of the neural network ready for training and testing
/// </summary>
public static void BuildStructure(Base inputLayer, Base outputLayer, List<Structure.Node.Base> inputLayerNodes, List<Structure.Node.Base> ouputLayerNodes, Network testNetworkStructure)
{
for (Int32 i = 0; i < 1; i++) inputLayerNodes.Add(new Structure.Node.Base(inputLayer, new Elliott()));
inputLayer.SetNodes(inputLayerNodes);
EchoReservoir echoLayer = new EchoReservoir(130, 0.4f, 0, 5, new Elliott());
for (Int32 i = 0; i < 1; i++) ouputLayerNodes.Add(new Output(outputLayer, new Elliott()));
outputLayer.SetNodes(ouputLayerNodes);
inputLayer.ConnectFowardLayer(echoLayer);
echoLayer.ConnectFowardLayer(outputLayer);
testNetworkStructure.AddLayer(inputLayer);
testNetworkStructure.AddLayer(echoLayer);
testNetworkStructure.AddLayer(outputLayer);
foreach (Base layer in testNetworkStructure.GetCurrentLayers()) layer.PopulateNodeConnections();
}
/// <summary>
/// Run this instance.
/// </summary>
public static void Run()
{
//Build Network
_TestNetworkStructure = new Network();
BuildStructure();
_TestNetworkStructure.RandomiseWeights(0.5f);
//PrepData
Double[][] dataSet = BuildDataSet(3000);
//Prepare training activity
_SlidingWindowTraining = new AdaptedSlidingWindowTraining();
_SlidingWindowTraining.SetTargetNetwork(_TestNetworkStructure);
_SlidingWindowTraining.SetMomentum(0.8f);
_SlidingWindowTraining.SetLearningRate(0.05f);
_SlidingWindowTraining.SetDatasetReservedLength(0);
_SlidingWindowTraining.SetDistanceToForcastHorrison(0);
_SlidingWindowTraining.SetWindowWidth(300);
_SlidingWindowTraining.SetMaximumEpochs(1000);
_SlidingWindowTraining.SetInputNodes(_InputLayerNodes);
_SlidingWindowTraining.SetOutputNodes(_OuputLayerNodes);
_SlidingWindowTraining.SetWorkingDataset(dataSet);
_SlidingWindowTraining.SetOutputToTarget(false);
_SlidingWindowTraining.SetRecurrentConextLayers(new List<Base>()
{
_RecurrentLayer
});
Console.WriteLine("Starting Training");
_SlidingWindowTraining.Start();
Thread.Sleep(1000);
while (_SlidingWindowTraining.IsRunning()) Thread.Sleep(20);
Console.WriteLine("Complete Training");
Console.WriteLine("Starting Testing");
foreach (Structure.Node.Base node in _TestNetworkStructure.GetCurrentLayers().SelectMany(layer => layer.GetNodes())) node.SetValue(0);
Double[] input = new Double[1000];
Double[] output = new Double[1000];
Single frequency = 0.5f;
for (Int32 x = 0; x < 1000; x++)
{
if (x%100 == 0) frequency = 0.5f;
// input[x] = frequency;
_InputLayerNodes[0].SetValue(frequency);
_TestNetworkStructure.FowardPass();
_RecurrentLayer.UpdateExtra();
output[x] = _OuputLayerNodes[0].GetValue();
}
Functions.PrintArrayToFile(input, "intput.csv");
Functions.PrintArrayToFile(output, "output.csv");
Console.WriteLine("Complete Testing");
Console.ReadKey();
}