private void PopulateSeriesData()
{
FourierTransform discreteTransform = new Dft();
FourierTransform fastTransform = new Fft();
FastWalshTransform walshTransform = new FastWalshTransform();
Discretizer discretizer = new Discretizer();
LineSeriesBuilder seriesBuilder = new LineSeriesBuilder();
IDictionary<double, double> discreteData = discretizer.Discretize(f, 0, Period, Period/N)
.Take(N) // Take only the given number of elements. Otherwise problem appears in FFT.
.ToDictionary(x => x.Key, x => x.Value);
double[] fwtData = walshTransform.DoTransform(discreteData.Values.ToArray());
ICollection<Complex> discreteTransformData = discreteTransform.DoTransform(discreteData.Values);
ICollection<Complex> fastTransformData = fastTransform.DoTransform(discreteData.Values);
seriesManager.Add(SeriesNames.DftMagnitudes, seriesBuilder.CreateSeries("DFT Magnitude", discreteData.Keys, discreteTransform.Magnitudes));
seriesManager.Add(SeriesNames.FftMagnitudes, seriesBuilder.CreateSeries("FFT Magnitude", discreteData.Keys, fastTransform.Magnitudes));
seriesManager.Add(SeriesNames.DftPhases, seriesBuilder.CreateSeries("DFT Phase", discreteData.Keys, discreteTransform.Phases));
seriesManager.Add(SeriesNames.FftPhases, seriesBuilder.CreateSeries("FFT Phase", discreteData.Keys, fastTransform.Phases));
seriesManager.Add(SeriesNames.ForwardFwt, seriesBuilder.CreateSeries("FWT", discreteData.Keys, fwtData));
double[] fwtDataInverse = walshTransform.DoTransformInverse(fwtData);
seriesManager.Add(SeriesNames.InverseFwt, seriesBuilder.CreateSeries("IFWT", discreteData.Keys, fwtDataInverse));
PopulateStats();
discreteTransform.DoTransformInverse(discreteTransformData);
fastTransform.DoTransformInverse(fastTransformData);
seriesManager.Add(SeriesNames.OriginalF, seriesBuilder.CreateSeries("Original F(x)", discretizer.Discretize(f, 0, Period, 0.01)));
seriesManager.Add(SeriesNames.InverseFft, seriesBuilder.CreateSeries("Inverse FFT", discreteData.Keys, fastTransform.Magnitudes));
seriesManager.Add(SeriesNames.InverseDft, seriesBuilder.CreateSeries("Inverse DFT", discreteData.Keys, discreteTransform.Magnitudes));
}
public override ICollection<Complex> DoTransform(Func<Double, Double> func, Int32 n)
{
Discretizer discretizer = new Discretizer();
ICollection<Double> data = discretizer.Discretize(func, n, 1.0);
ICollection<Complex> result = DoTransform(data);
return result;
}
public void DiscretizationTest()
{
Discretizer.DiscretizeTrainingSets();
Discretizer.ProcessAdditionalMethodCases();
Discretizer.ProcessAdditionalClassCases();
}
public BayesianNetwork(string networkFile)
{
SmileLicense.ActivateLicense();
LoadNetworkFromFile(networkFile);
_networkFile = networkFile;
if (!Discretizer.IsDiscretized)
{
Discretizer.DiscretizeTrainingSets();
}
}
public void Start( /*Func<double, double> x, Func<double, double> y*/)
{
const double start = 0;
const double end = 4*Math.PI;
const int dotsNumber = 256;
Func<double, double> x = b => Math.Sin(4*b);
Func<double, double> y = a => Math.Sin(a) + Math.Cos(4*a);
Discretizer discretizer = new Discretizer();
IDictionary<Double, Double> xData = discretizer.Discretize(x, start, end, dotsNumber);
IDictionary<Double, Double> yData = discretizer.Discretize(y, start, end, dotsNumber);
DiscreteConvolution dConvolution = new DiscreteConvolution();
ICollection<Double> discreteConvolutionResult = dConvolution.Do(xData.Values, yData.Values, xData.Count);
FastConvolution fConvolution = new FastConvolution();
ICollection<Double> fastConvolutionResult = fConvolution.Do(xData.Values, yData.Values, xData.Count);
DiscreteConvolution = discreteConvolutionResult
.Select((v, i) => new KeyValuePair<Double, Double>(xData.Keys.ElementAt(i), v))
.ToDictionary(kp => kp.Key, kp => kp.Value);
FastConvolution = fastConvolutionResult
.Select((v, i) => new KeyValuePair<Double, Double>(xData.Keys.ElementAt(i), v))
.ToDictionary(kp => kp.Key, kp => kp.Value);
DiscreteCorrelation dCorrelation = new DiscreteCorrelation();
ICollection<Double> discreteCorrelationResult = dCorrelation.Do(xData.Values, yData.Values, xData.Count);
FastCorrelation fCorrelation = new FastCorrelation();
ICollection<Double> fastCorrelationResult = fCorrelation.Do(xData.Values, yData.Values, xData.Count);
DiscreteCorrelation = discreteCorrelationResult
.Select((v, i) => new KeyValuePair<Double, Double>(xData.Keys.ElementAt(i), v))
.ToDictionary(kp => kp.Key, kp => kp.Value);
FastCorrelation = fastCorrelationResult
.Select((v, i) => new KeyValuePair<Double, Double>(xData.Keys.ElementAt(i), v))
.ToDictionary(kp => kp.Key, kp => kp.Value);
}
public void Learn()
{
IDictionary <string, DiscretizedData> map = GenerateBinMap();
SetOutcomeIds();
LaplaceEstimator.LaplaceEstimation(Discretizer.MethodDataset, _network, map, "Long_Method", 1);
var cases = Discretizer.ProcessAdditionalMethodCases();
if (cases.Count > 0)
{
//calculate the fading factor according the samplesize of the trainingset:
double originalCount = Discretizer.MethodDataset.Tables[0].Rows.Count;
double q = (originalCount - cases.Count) / originalCount;
LaplaceEstimator.Adapt(cases, Discretizer.MethodDataset, _network, map, "Long_Method", 1, q);
}
_network.SaveNetwork();
}
/// <summary> Does fast fouries transform. </summary>
/// <param name="func"></param>
/// <param name="n"></param>
/// <returns></returns>
public override ICollection<Complex> DoTransform(Func<Double, Double> func, Int32 n)
{
Discretizer discretizer = new Discretizer();
IList<Double> discrete = (IList<Double>)discretizer.Discretize(func, n, 1.0);
return DoTransform(discrete);
}
