public FitReportALGLIB(SolverReport report, bool didConverge)
{
AverageError = report.AverageError;
DidConverge = didConverge;
IterationCount = report.IterationCount;
MaxError = report.MaxError;
PerPointNoise = report.PerPointNoise;
RmsError = report.RmsError;
RSquared = report.RSquared;
WeightedRmsError = report.WeightedRmsError;
}
public void SolveAsymmetricGaussianFactory(string path, BasisFunctionsEnum functionChoise)
{
var data = ReadXicDatabase(Path.Combine(TestPathSingleton.TestDirectory, path));
var newData = new List <Xic>();
foreach (var xic in data)
{
var basisFunction = BasisFunctionFactory.BasisFunctionSelector(functionChoise);
var coefficients = basisFunction.Coefficients;
var start = xic.x.Min() - .5;
var stop = xic.x.Max() + .5;
var A = xic.y.Max();
A += (A * .05);
var width = Math.Abs(stop - start);
coefficients[0] = start + ((stop - start) / 2);
coefficients[1] = A;
coefficients[2] = width * .5;
coefficients[3] = coefficients[2];
//Console.WriteLine("Xic {0}", xic.id);
//Console.WriteLine();
for (var i = 0; i < xic.x.Count; i++)
{
// Console.WriteLine("{0}\t{1}", xic.x[i], xic.y[i]);
}
SolverReport worked = null;
Console.WriteLine();
var showDetails = false;
try
{
worked = EvaluateFunction(xic.x, xic.y, basisFunction, ref coefficients, showDetails);
}
catch (Exception)
{
continue;
}
// First solve for the function
var solver = new LevenburgMarquadtSolver();
var numberOfSamples = 100;
// Calculate the width an spacing of each of the trapezoids.
var delta = width / Convert.ToDouble(numberOfSamples);
var x = start;
var newXic = new Xic();
newXic.x = new List <double>();
newXic.y = new List <double>();
newXic.charge = xic.charge;
newXic.scoreName = xic.scoreName;
newXic.score = xic.score;
newXic.id = xic.id;
newXic.prevXic = xic;
newXic.report = worked;
newXic.reviewer = xic.reviewer;
// We already evaluated the first point, now for each element within
for (var i = 0; i < numberOfSamples + 1; i++)
{
x += delta;
var y = basisFunction.Evaluate(coefficients, x);
newXic.x.Add(x);
newXic.y.Add(y);
// Console.WriteLine("{0}\t{1}", x, y);
}
//Console.WriteLine();
newData.Add(newXic);
}
var newPath = path.Replace(".xic", functionChoise + "_fit.xic");
using (TextWriter writer = File.CreateText(newPath))
{
foreach (var feature in newData)
{
writer.WriteLine("id\t{0}\tcharge\t{1}", feature.id, feature.charge);
writer.WriteLine("score\t{0}\tscoreName\t{1}", feature.score, feature.scoreName);
writer.WriteLine("reviewer\t{0}", feature.reviewer);
writer.WriteLine("R2\t{0}", feature.report.RSquared);
writer.WriteLine("Rms\t{0}", feature.report.RmsError);
writer.WriteLine("Converged\t{0}", feature.report.DidConverge);
writer.WriteLine("mz\ttime\tintensity");
var prev = feature.prevXic;
for (var i = 0; i < feature.x.Count; i++)
{
var prevTime = "";
var prevInt = "";
if (prev != null && i < prev.x.Count)
{
prevTime = prev.x[i].ToString();
prevInt = prev.y[i].ToString();
}
writer.WriteLine("{0}\t{1}\t{2}\t{3}\t{4}", feature.mz, feature.x[i], feature.y[i], prevTime, prevInt);
}
writer.WriteLine();
}
}
}
