double[] CreateInitialParameterGuess()
{
pParameters = new double[3];
LinearFit LF = new LinearFit(x, (from b in y select Math.Log(b)).ToArray());
pParameters[(int)ParametersIndex.P0Index] = Math.Exp(LF.Intercept);
pParameters[(int)ParametersIndex.rIndex] = LF.Slope;
pParameters[(int)ParametersIndex.Carrying] = 5;
return(pParameters);
}
/// <summary>
/// Makes an initial guess based on linear regreesion
/// </summary>
/// <returns></returns>
protected override double[] CreateInitialParameterGuess()
{
double[] logdata = y.ToArray();
logdata = Array.ConvertAll(logdata, z => Math.Log(z));
LinearFit LF = new LinearFit(x, logdata);
double[] ParamGuess = new double[2];
ParamGuess[(int)ParametersIndex.rIndex] = LF.Slope;
ParamGuess[(int)ParametersIndex.P0Index] = Math.Exp(LF.Intercept);
return(ParamGuess);
}
private void SetNoFit(string comment = "Not Fit")
{
this.ForEach(x => x.UsedInFit = false);
ValidDataSet = false;
ExpFit = null;
LinFit = null;
pGrowthRate.GrowthRate = Double.NaN;
pGrowthRate.NumPoints = 0;
pGrowthRate.R2 = Double.NaN;
pGrowthRate.RMSE = Double.NaN;
pGrowthRate.DisplayFittingUsed = "Not Fit";
pGrowthRate.Notes = comment;
OnPropertyChange(new PropertyChangedEventArgs("FittedValues"));
OnPropertyChange(new PropertyChangedEventArgs("FittedLogValues"));
}
protected double[] CreateInitialParameterGuess()
{
double[] logdata = y.ToArray();
var res = Enumerable.Zip(x, logdata, (z, yy) => new { x = z, yval = yy - CParamGuess });
var res2 = (from xx in res where xx.yval > 0 select xx).ToList();
double[] xToTry = (from yy in res2 select yy.x).ToArray();
double[] yToTry = (from yy in res2 select yy.yval).ToArray();
if (xToTry.Length >= 2)
{
logdata = Array.ConvertAll(yToTry, z => Math.Log(z));
LinearFit LF = new LinearFit(xToTry, logdata);
double[] ParamGuess = new double[3];
ParamGuess[(int)ParametersIndex.rIndex] = LF.Slope;;
ParamGuess[(int)ParametersIndex.P0Index] = Math.Exp(LF.Intercept);
ParamGuess[(int)ParametersIndex.OffSetIndex] = CParamGuess * 1.01;
return(ParamGuess);
}
else
{
throw new Exception("Exponential Offset model can't be fit with data that isn't above the parameter guess");
}
}
/// <summary>
/// Function fits data, should be called everytime the fit changes
/// </summary>
public void FitData()
{
ValidDataSet = true;
double[] XtoFit, YtoFit;
XtoFit = FittedXValues;
YtoFit = FittedYValues;
//MixtureErrorModel = null;
ExpFit = null;
LinFit = null;
if (XtoFit.Length >= 2)
{
//Linear Fit
LinFit = new LinearFit(XtoFit, FittedLogYValues);
pGrowthRate.DisplayFittingUsed = "Linear";
pGrowthRate.GrowthRate = LinFit.Slope;
pGrowthRate.R2 = LinFit.R2;
pGrowthRate.RMSE = LinFit.RMSE;
pGrowthRate.FitterUsed = LinFit;
//Exponential Fit and others
if (XtoFit.Length > 2 && USE_EXPONENTIAL_FITTING)
{
ExpFit = new ExponentialFit(XtoFit, YtoFit);
if (ExpFit.SuccessfulFit)
{
pGrowthRate.DisplayFittingUsed = "Exponential";
pGrowthRate.GrowthRate = ExpFit.GrowthRate;
pGrowthRate.R2 = ExpFit.R2;
pGrowthRate.RMSE = ExpFit.RMSE;
pGrowthRate.FitterUsed = ExpFit;
//(ExpFit.Y.Max() - ExpFit.Y.Min()) > .1
if (ExpFit.Y.Length > 5)
{
this.OffSetExp = new OffSetExponentialFit(ExpFit.X, ExpFit.Y, this[0].ODValue);
#if !MONO
this.LogisticModel = new LogisticModel(ExpFit.X, ExpFit.Y);
#endif
}
else
{
this.OffSetExp = null;
}
}
else
{
this.OffSetExp = null;
}
#if !MONO
QuadModel = new QuadraticLinearRegression(XtoFit, YtoFit);
#endif
}
//Now Set the Growth Rate
pGrowthRate.NumPoints = FittedXValues.Length;
OnPropertyChange(new PropertyChangedEventArgs("FittedValues"));
OnPropertyChange(new PropertyChangedEventArgs("FittedLogValues"));
}
else
{
SetNoFit();
}
}