static void run(string[] args)
{
/*
* var t0 = 103.6374;
* using (var writer = new StreamWriter(@"C:\Users\mhorowitzgelb\Desktop\basicAsymetricGaussian.csv"))
* {
* for (double x = -3; x <= 3.1; x += 0.01)
* {
* var _x = x + t0;
* var y = function.GetY(_x, new[] {56564, t0, 0.00001, 1});
* writer.WriteLine(_x+","+y);
* }
* }*/
var unalteredData = loadCsv(@"C:\Users\mhorowitzgelb\Desktop\multiplePeaks.csv", 6);
unalteredData = new[]
{ unalteredData[0], unalteredData[1] };
var function = new SummedAlteredGaussian(3, 1, 10, unalteredData);
//var function = new MultiTransitionAlteredGaussian(5,10,unalteredData);
//{14000,5,5,5,5,5,43.9,0.01,0.15,1000,1300,2100,1900,900,500,44.9,0.01,0.15,100,5,4000,5,5,5,46,0.01,0.15}
var lma = new LMA(function, new [] { 14000, 43.9, 0.01, 0.15, 1000, 44.9, 0.01, 0.15, 100, 46, 0.01, 0.15 }, function.AlteredData, null, new DotNetMatrix.GeneralMatrix(12, 12), 1, 1000);
lma.Fit();
writeCSV(lma, function, unalteredData, @"C:\Users\mhorowitzgelb\Desktop\multiPeaksFit.csv", 1);
}
public void RunSin()
{
//produce test data - a is a vecor with exact solution
double[] x = { 0.0, 0.1, 0.2, 0.3, 0.5, 0.7 };
double[] a = { 2.2, 0.4 };
LMAFunction f = new SinFunction();
double[][] dataPoints = f.GenerateData(a, x);
LMA algorithm = new LMA(f, new double[] { 0.1, 10 },
dataPoints, null, new GeneralMatrix(2, 2), 1d - 20, 100);
algorithm.Fit();
for (int i = 0; i < a.Length; i++)
{
Assert.IsTrue(System.Math.Abs(algorithm.Parameters[i] - a[i]) < 0.0001);
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
Trace.WriteLine("# of iterations =" + algorithm.Iterations.ToString());
}
public void RunLine()
{
double[] x = { 0, 2, 6, 8, 9 };
double[] a = { 2, 0.51 };
LMAFunction f = new LineFunction();
double[][] dataPoints = f.GenerateData(a, x);
LMA algorithm = new LMA(f, new double[] { 100, -100 },
dataPoints, null, new GeneralMatrix(2, 2), 1d - 20, 100);
algorithm.Fit();
for (int i = 0; i < a.Length; i++)
{
Assert.IsTrue(System.Math.Abs(algorithm.Parameters[i] - a[i]) < 0.0001);
Trace.WriteLine("Parameter" + i.ToString() + " " + algorithm.Parameters[i].ToString());
}
Trace.WriteLine("# of iterations =" + algorithm.Iterations.ToString());
}
public static void writeCSV(LMA lma, SummedAlteredGaussian function, double[][] originalData, string path, int transitions)
{
using (var writer = new StreamWriter(path))
{
var xList = originalData[0];
for (int i = 0; i < xList.Length; i++)
{
var x = xList[i];
writer.Write("" + x);
for (int j = 0; j < transitions; j++)
{
var y = originalData[1 + j][i];
writer.Write("," + y);
}
for (int j = 0; j < transitions; j++)
{
var fitValue = function.GetY(x, j, lma.Parameters);
writer.Write("," + fitValue);
}
writer.WriteLine();
}
}
}
// LORENTZIAN FITTING //
/****************************************************************************
* GetLorentzParams
* Get lorentzian fitting parameters
****************************************************************************/
public void GetLorentzParams(FFTData fftData, LorentzSettings lorentzSettings, ref LorentzParams lorentzParams)
{
double[][] dataPoints = new double[][] { fftData.freq, fftData.fft };
LMA algorithm;
if (lorentzSettings.isYShiftLorentz)
{
LMAFunction lorentzShift = new LorenzianFunctionShift();
double[] startPoint = new double[4];
try
{
startPoint[0] = lorentzSettings.startPointLP.A;
}
catch (Exception)
{
startPoint[0] = 10;
}
try
{
startPoint[1] = lorentzSettings.startPointLP.f0;
}
catch (Exception)
{
startPoint[1] = 25;
}
try
{
startPoint[2] = lorentzSettings.startPointLP.gamma;
}
catch (Exception)
{
startPoint[2] = 1;
}
try
{
startPoint[3] = lorentzSettings.startPointLP.up;
}
catch (Exception)
{
startPoint[3] = 1;
}
algorithm = new LMA(lorentzShift, startPoint,
dataPoints, null, new GeneralMatrix(4, 4), 1d - 20, lorentzSettings.nIter);
algorithm.Fit();
lorentzParams.A = algorithm.Parameters[0];
lorentzParams.gamma = algorithm.Parameters[2];
lorentzParams.f0 = algorithm.Parameters[1];
lorentzParams.up = algorithm.Parameters[3];
}
else
{
LMAFunction lorentz = new LorenzianFunction();
double[] startPoint = new double[3];
try
{
startPoint[0] = lorentzSettings.startPointLP.A;
}
catch (Exception)
{
startPoint[0] = 10;
}
try
{
startPoint[1] = lorentzSettings.startPointLP.f0;
}
catch (Exception)
{
startPoint[1] = 25;
}
try
{
startPoint[2] = lorentzSettings.startPointLP.gamma;
}
catch (Exception)
{
startPoint[2] = 1;
}
algorithm = new LMA(lorentz, startPoint,
dataPoints, null, new GeneralMatrix(3, 3), 1d - 20, lorentzSettings.nIter);
algorithm.Fit();
lorentzParams.A = algorithm.Parameters[0];
lorentzParams.gamma = algorithm.Parameters[2];
lorentzParams.f0 = algorithm.Parameters[1];
lorentzParams.up = 0;
}
}