//
//You can use the following additional attributes as you write your tests:
//
//Use ClassInitialize to run code before running the first test in the class
//[ClassInitialize()]
//public static void MyClassInitialize(TestContext testContext)
//{
//}
//
//Use ClassCleanup to run code after all tests in a class have run
//[ClassCleanup()]
//public static void MyClassCleanup()
//{
//}
//
//Use TestInitialize to run code before running each test
//[TestInitialize()]
//public void MyTestInitialize()
//{
//}
//
//Use TestCleanup to run code after each test has run
//[TestCleanup()]
//public void MyTestCleanup()
//{
//}
//
#endregion
/// <summary>
/// A test for GenerateInputAndOutput
///</summary>
//[TestMethod()]
public void GenerateInputAndOutputTest()
{
uint iLag = 1;
uint iWindowSize = 5;
int pointsNum = 360;
IInputProvider target = new WindowingInputProvider(iLag, iWindowSize);
Random rnd = new Random();
double[] iTimeSeries = new double[pointsNum];
for (int i = 0; i < pointsNum; i++)
{
iTimeSeries[i] = 1 * Math.Sin(2 * i * Math.PI / 180.0) + (Convert.ToDouble(rnd.Next(-2, 2)) / 5) * ((i % 3 == 0) ? 1 : 0);
}
double[][] oInput = null;
double[][] oOutput = null;
target.GenerateInputAndOutput(iTimeSeries, out oInput, out oOutput);
IForecastingModelBuilder modelBuilder = new ANNmodelBuilder();
IForecastingModel model = modelBuilder.TrainNewModel(oInput, oOutput);
double[] forecast = new double[oInput.LongLength], actual = new double[oInput.LongLength];
for (int i = 0; i < oInput.LongLength; ++i)
{
forecast[i] = model.CalculateOutput(oInput[i])[0]; actual[i] = oOutput[i][0];
}
BSDataObject forecastDO = new BSDataObject(forecast, "Forecast"), actualDO = new BSDataObject(actual, "Actual");
SimpleVisualizationHandler handler = new SimpleVisualizationHandler();
handler.AddToGraph(actualDO);
handler.AddToGraph(forecastDO);
handler._TestShow();
}
/// <summary>
/// A test for GenerateInputAndOutput
///</summary>
//[TestMethod()]
public void GenerateInputAndOutputTest()
{
uint iLag = 1;
uint iWindowSize = 5;
int pointsNum = 360;
IInputProvider target = new WindowingInputProvider(iLag, iWindowSize);
Random rnd = new Random();
double[] iTimeSeries = new double[pointsNum];
for (int i = 0; i < pointsNum; i++)
{
iTimeSeries[i] = 1 * Math.Sin(2 * i * Math.PI / 180.0) + (Convert.ToDouble(rnd.Next(-2, 2)) / 5) * ((i % 3 == 0) ? 1 : 0);
}
double[][] oInput = null;
double[][] oOutput = null;
target.GenerateInputAndOutput(iTimeSeries, out oInput, out oOutput);
IForecastingModelBuilder modelBuilder = new ANNmodelBuilder();
IForecastingModel model = modelBuilder.TrainNewModel(oInput, oOutput);
double[] forecast = new double[oInput.LongLength], actual = new double[oInput.LongLength];
for (int i = 0; i < oInput.LongLength; ++i)
{
forecast[i] = model.CalculateOutput(oInput[i])[0]; actual[i] = oOutput[i][0];
}
BSDataObject forecastDO = new BSDataObject(forecast, "Forecast"), actualDO = new BSDataObject(actual, "Actual");
SimpleVisualizationHandler handler = new SimpleVisualizationHandler();
handler.AddToGraph(actualDO);
handler.AddToGraph(forecastDO);
handler._TestShow();
}
public override void Start()
{
CheckIfInputsAreProvided();
DataObjects.BSDataObject tmp;
Inputs.TryPop(out tmp);
double[][] modelTrainInput, modelTrainOutput;
inputProvider = new WindowingInputProvider(kDefaultLag, _windowSize);
inputProvider.GenerateInputAndOutput(tmp.DataArray, out modelTrainInput, out modelTrainOutput);
IForecastingModel model = _modelBuilder.TrainNewModel(modelTrainInput, modelTrainOutput);
model.InputProvider = inputProvider;
Output = (DataObjects.BSDataObject)model;
}
