/// <summary>
/// Run this instance.
/// </summary>
public static void Run()
{
//Build Network
_TestNetworkStructure = new Network();
BuildStructure();
_TestNetworkStructure.SaveToFile("test.dat");
_TestNetworkStructure.RandomiseWeights(1.1d);
//PrepData
Double[][] dataSet = StandardDeviationVariance.ProduceDataset("TestData/Mackey-Glass-Pure.csv").DataSet;
//Prepare training activity
_SlidingWindowTraining = new SlidingWindow();
_SlidingWindowTraining.SetTargetNetwork(_TestNetworkStructure);
_SlidingWindowTraining.SetMomentum(0.5f);
_SlidingWindowTraining.SetLearningRate(0.004f);
_SlidingWindowTraining.SetDatasetReservedLength(120);
_SlidingWindowTraining.SetDistanceToForcastHorrison(3);
_SlidingWindowTraining.SetWindowWidth(12);
_SlidingWindowTraining.SetMaximumEpochs(1000);
_SlidingWindowTraining.SetInputNodes(_InputLayerNodes);
_SlidingWindowTraining.SetOutputNodes(_OuputLayerNodes);
_SlidingWindowTraining.SetWorkingDataset(dataSet);
_SlidingWindowTraining.SetRecurrentConextLayers(new List<Base>());
Console.WriteLine("Starting Training");
_SlidingWindowTraining.Start();
Thread.Sleep(1000);
while (_SlidingWindowTraining.IsRunning()) Thread.Sleep(20);
Console.WriteLine("Complete Training");
Console.WriteLine("Starting Testing");
Activity.Testing.SlidingWindow slidingWindowTesting = new Activity.Testing.SlidingWindow();
slidingWindowTesting.SetDatasetReservedLength(0);
slidingWindowTesting.SetInputNodes(_SlidingWindowTraining.GetTargetNetwork().GetDetectedBottomLayers()[0].GetNodes().ToList());
slidingWindowTesting.SetOutputNodes(_SlidingWindowTraining.GetTargetNetwork().GetDetectedTopLayers()[0].GetNodes().ToList());
slidingWindowTesting.SetRecurrentConextLayers(new List<Base>());
slidingWindowTesting.SetWorkingDataset(dataSet);
slidingWindowTesting.SetWindowWidth(6);
slidingWindowTesting.SetDistanceToForcastHorrison(3);
slidingWindowTesting.SetTargetNetwork(_SlidingWindowTraining.GetTargetNetwork());
Activity.Testing.SlidingWindow.SlidingWindowTestResults result = (Activity.Testing.SlidingWindow.SlidingWindowTestResults) slidingWindowTesting.TestNetwork();
Console.WriteLine(result.Rmse);
Functions.PrintArrayToFile(result.ActualOutputs, "ActualOutputs.csv");
Functions.PrintArrayToFile(result.ExpectedOutputs, "ExpectedOutputs.csv");
Console.WriteLine("Complete Testing");
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();
}
/// <summary>
/// Run this instance.
/// </summary>
public static void Run()
{
Double[][] dataSet = StandardDeviationVariance.ProduceDataset("TestData/Mackey-Glass-Pure.csv").DataSet;
List<Guid> outstandingWork = new List<Guid>();
CommsClient lobeConnection = new CommsClient();
lobeConnection.ConnectToManager("localhost", 17432);
for (Int32 x = 0; x < 20; x++)
{
Network testNetworkStructure;
SlidingWindow slidingWindowTraining;
Base inputLayer = new Base();
;
Base outputLayer = new Base();
;
List<Structure.Node.Base> inputLayerNodes = new List<Structure.Node.Base>();
List<Structure.Node.Base> ouputLayerNodes = new List<Structure.Node.Base>();
//Build Network
testNetworkStructure = new Network();
BuildStructure(inputLayer, outputLayer, inputLayerNodes, ouputLayerNodes, testNetworkStructure);
testNetworkStructure.SaveToFile("test.dat");
testNetworkStructure.RandomiseWeights(1.1d);
//PrepData
//Prepare training activity
slidingWindowTraining = new SlidingWindow();
slidingWindowTraining.SetTargetNetwork(testNetworkStructure);
slidingWindowTraining.SetMomentum(0.5f);
slidingWindowTraining.SetLearningRate(0.004f);
slidingWindowTraining.SetDatasetReservedLength(0);
slidingWindowTraining.SetDistanceToForcastHorrison(3);
slidingWindowTraining.SetWindowWidth(12);
slidingWindowTraining.SetMaximumEpochs(100);
slidingWindowTraining.SetInputNodes(inputLayerNodes);
slidingWindowTraining.SetOutputNodes(ouputLayerNodes);
slidingWindowTraining.SetWorkingDataset(dataSet);
slidingWindowTraining.SetRecurrentConextLayers(new List<Base>());
outstandingWork.Add(lobeConnection.SendJob(slidingWindowTraining));
}
while (outstandingWork.Count > 0)
{
Thread.Sleep(1000);
List<Guid> tempList = new List<Guid>(outstandingWork);
foreach (Guid guid in tempList)
{
SlidingWindow work = (SlidingWindow) lobeConnection.GetCompletedWork(guid);
if (work == null) continue;
outstandingWork.Remove(guid);
Console.WriteLine("Starting Testing");
Activity.Testing.SlidingWindow slidingWindowTesting = new Activity.Testing.SlidingWindow();
slidingWindowTesting.SetDatasetReservedLength(0);
slidingWindowTesting.SetInputNodes(work.GetTargetNetwork().GetDetectedBottomLayers()[0].GetNodes().ToList());
slidingWindowTesting.SetOutputNodes(work.GetTargetNetwork().GetDetectedTopLayers()[0].GetNodes().ToList());
slidingWindowTesting.SetRecurrentConextLayers(new List<Base>());
slidingWindowTesting.SetWorkingDataset(dataSet);
slidingWindowTesting.SetWindowWidth(12);
slidingWindowTesting.SetDistanceToForcastHorrison(3);
slidingWindowTesting.SetTargetNetwork(work.GetTargetNetwork());
Activity.Testing.SlidingWindow.SlidingWindowTestResults result = (Activity.Testing.SlidingWindow.SlidingWindowTestResults) slidingWindowTesting.TestNetwork();
Console.WriteLine(result.Rmse);
Functions.PrintArrayToFile(result.ActualOutputs, "ActualOutputs.csv");
Functions.PrintArrayToFile(result.ExpectedOutputs, "ExpectedOutputs.csv");
Console.WriteLine("Complete Testing");
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.WriteLine("all Jobs Done");
Console.ReadKey();
}
/// <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();
}