/// <summary>
/// Run this instance.
/// </summary>
public static void Run()
{
//Build Network
_TestNetworkStructure = new Network();
BuildStructure();
_TestNetworkStructure.RandomiseWeights(0.01d);
//PrepData
Double[][] dataSet = StandardDeviationVariance.ProduceDataset("TestData/Mackey-Glass-Pure.csv").DataSet;
//Prepare training activity
_SlidingWindowTraining = new SlidingWindow();
_SlidingWindowTraining.SetTargetNetwork(_TestNetworkStructure);
// the target network for the training to take place on
_SlidingWindowTraining.SetMomentum(0.7f);
// The ammount of the previous weight change applied to current weight change - google if u need to know more
_SlidingWindowTraining.SetLearningRate(0.004f);
// The rate at which the neural entwork learns (the more agressive this is the harded itll be for the network)
_SlidingWindowTraining.SetDatasetReservedLength(0);
// How many elements off the end of the dataset should not be used for training
_SlidingWindowTraining.SetDistanceToForcastHorrison(3);
// How far beyond the window should be be trying to predict
_SlidingWindowTraining.SetWindowWidth(12);
// The window of elements that should be presented before the backward pass is performed
_SlidingWindowTraining.SetMaximumEpochs(300); // The maximum number of epochs the network can train for
_SlidingWindowTraining.SetInputNodes(_InputLayerNodes); // Setting the nodes that are used for input
_SlidingWindowTraining.SetOutputNodes(_OuputLayerNodes); // Setting the nodes that are generating output
_SlidingWindowTraining.SetWorkingDataset(dataSet); // Setting the working dataset for the training phase
_SlidingWindowTraining.SetDynamicLearningRateDelegate(DynamicLearningRate);
// Sets the contect layers that are used as part of the training (have to updates)
List<Base> contextLayers = new List<Base> {_ContextLayer};
_SlidingWindowTraining.SetRecurrentConextLayers(contextLayers);
////////////////////////////////////////////////
////////////////////////////////////////////////
Console.WriteLine("Starting Training");
_SlidingWindowTraining.Start();
Thread.Sleep(1000);
while (_SlidingWindowTraining.IsRunning()) Thread.Sleep(20);
////////////////////////////////////////////////
////////////////////////////////////////////////
Console.WriteLine("Starting Testing");
Lib.Activity.Testing.SlidingWindow slidingWindowTesting = new Lib.Activity.Testing.SlidingWindow();
slidingWindowTesting.SetDatasetReservedLength(0);
slidingWindowTesting.SetInputNodes(_InputLayerNodes);
slidingWindowTesting.SetOutputNodes(_OuputLayerNodes);
slidingWindowTesting.SetRecurrentConextLayers(contextLayers);
slidingWindowTesting.SetWorkingDataset(dataSet);
slidingWindowTesting.SetWindowWidth(12);
slidingWindowTesting.SetDistanceToForcastHorrison(3);
slidingWindowTesting.SetTargetNetwork(_TestNetworkStructure);
Lib.Activity.Testing.SlidingWindow.SlidingWindowTestResults result = (Lib.Activity.Testing.SlidingWindow.SlidingWindowTestResults) slidingWindowTesting.TestNetwork();
Console.WriteLine(result.Rmse);
Functions.PrintArrayToFile(result.ActualOutputs, "ActualOutputs.csv");
Functions.PrintArrayToFile(result.ExpectedOutputs, "ExpectedOutputs.csv");
Console.WriteLine("Comparing Against Random Walk 3 Step");
Console.WriteLine(Math.Round(RandomWalkCompare.CalculateError(result.ExpectedOutputs, result.ActualOutputs, 3)[0]*100, 3));
Console.WriteLine("Comparing Against Random Walk 2 Step");
Console.WriteLine(Math.Round(RandomWalkCompare.CalculateError(result.ExpectedOutputs, result.ActualOutputs, 2)[0]*100, 3));
Console.WriteLine("Comparing Against Random Walk 1 Step");
Console.WriteLine(Math.Round(RandomWalkCompare.CalculateError(result.ExpectedOutputs, result.ActualOutputs, 1)[0]*100, 3));
Console.ReadKey();
}