public void NewtonGaussFit(Comb.mzI[] experimentalData, Comb.mzI[] intensities, int charge, double deltaR, fitDataStrt initialParams)
{
//Clone the data to the class
expData = (Comb.mzI[])experimentalData.Clone();
iCharge = charge;
dDeltaR = deltaR;
intensPeaks = (Comb.mzI[])intensities.Clone();
//Assign parameters HAY QUE CAMBIAR ESTO PARA QUE SEA AJUSTABLE DESDE FUERA
alpha = 0.01;
PRS = 1e-8;
RSH = 1e-4;
//assign initial parameters to the parameters' matrix
mB[1] = initialParams.A; //Amount of A
mB[2] = initialParams.alpha; //leptokurtosis
mB[3] = initialParams.B; //Amount of B
mB[4] = initialParams.deltaMz; //experimental mass/charge deviation
mB[5] = initialParams.f; //efficiency
mB[6] = initialParams.sigma; //Gaussian width
mB[7] = initialParams.signoise; //signal/noise relation
nIterations = 0;
//assign initial parameters as new parameters
for (int i = 1; i <= mB.GetUpperBound(0); i++)
{
mA[i] = mB[i];
}
//Calculate the initial fit
fitData = Gaussians.calEnvelope(intensities, expData, initialParams, charge, deltaR);
//first chi^2 test.
sumSQ1 = sumSquares(expData, fitData);
//control form of the iterations
if (controlFit.IsDisposed)
{
controlFit = null;
controlFit = new OPctrFit();
}
controlFit.Show();
controlFit.Activate();
//controlFit.Owner = this;
controlFit.iterAdd(nIterations, initialParams, sumSQ1, false);
}
public int[] Classify(double[][] observations)
{
return(Gaussians.Nearest(observations));
}
public int Classify(double[] observation, out double[] responses)
{
return(Gaussians.Nearest(observation, out responses));
}
