package gdsc.smlm.fitting.nonlinear.gradient;
import gdsc.smlm.function.Gradient1Function;
/*-----------------------------------------------------------------------------
* GDSC SMLM Software
*
* Copyright (C) 2017 Alex Herbert
* Genome Damage and Stability Centre
* University of Sussex, UK
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*---------------------------------------------------------------------------*/
/**
* Calculates the Hessian matrix (the square matrix of second-order partial derivatives of a function)
* and the scaled gradient vector of the function's partial first derivatives with respect to the parameters.
* This is used within the Levenberg-Marquardt method to fit a nonlinear model with coefficients (a) for a
* set of data points (x, y).
* <p>
* Note that the Hessian matrix is scaled by 1/2 and the gradient vector is scaled by -1/2 for convenience in solving
* the non-linear model. See Numerical Recipes in C++, 2nd Ed. Equation 15.5.8 for Nonlinear Models.
*/
public class LSQLVMGradientProcedure5 extends LSQLVMGradientProcedure
{
/**
* @param y
* Data to fit
* @param func
* Gradient function
*/
public LSQLVMGradientProcedure5(final double[] y, final Gradient1Function func)
{
super(y, func);
if (n != 5)
throw new IllegalArgumentException("Function must compute 5 gradients");
}
/**
* @param y
* Data to fit
* @param b
* Baseline pre-computed y-values
* @param func
* Gradient function
*/
public LSQLVMGradientProcedure5(final double[] y, final double[] b, final Gradient1Function func)
{
super(y, b, func);
if (n != 5)
throw new IllegalArgumentException("Function must compute 5 gradients");
}
/*
* (non-Javadoc)
*
* @see gdsc.smlm.function.Gradient1Procedure#execute(double, double[])
*/
public void execute(double value, double[] dy_da)
{
final double dy = y[++yi] - value;
alpha[0] += dy_da[0] * dy_da[0];
alpha[1] += dy_da[1] * dy_da[0];
alpha[2] += dy_da[1] * dy_da[1];
alpha[3] += dy_da[2] * dy_da[0];
alpha[4] += dy_da[2] * dy_da[1];
alpha[5] += dy_da[2] * dy_da[2];
alpha[6] += dy_da[3] * dy_da[0];
alpha[7] += dy_da[3] * dy_da[1];
alpha[8] += dy_da[3] * dy_da[2];
alpha[9] += dy_da[3] * dy_da[3];
alpha[10] += dy_da[4] * dy_da[0];
alpha[11] += dy_da[4] * dy_da[1];
alpha[12] += dy_da[4] * dy_da[2];
alpha[13] += dy_da[4] * dy_da[3];
alpha[14] += dy_da[4] * dy_da[4];
beta[0] += dy_da[0] * dy;
beta[1] += dy_da[1] * dy;
beta[2] += dy_da[2] * dy;
beta[3] += dy_da[3] * dy;
beta[4] += dy_da[4] * dy;
this.value += dy * dy;
}
@Override
protected void initialiseGradient()
{
GradientProcedureHelper.initialiseWorkingMatrix5(alpha);
beta[0] = 0;
beta[1] = 0;
beta[2] = 0;
beta[3] = 0;
beta[4] = 0;
}
@Override
public void getAlphaMatrix(double[][] alpha)
{
GradientProcedureHelper.getMatrix5(this.alpha, alpha);
}
@Override
public void getAlphaLinear(double[] alpha)
{
GradientProcedureHelper.getMatrix5(this.alpha, alpha);
}
}