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 LSQLVMGradientProcedureMatrix5 extends LSQLVMGradientProcedureMatrix
{
/**
* @param y
* Data to fit
* @param b
* Baseline pre-computed y-values
* @param func
* Gradient function
*/
public LSQLVMGradientProcedureMatrix5(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][0] += dy_da[0] * dy_da[0];
alpha[1][0] += dy_da[1] * dy_da[0];
alpha[1][1] += dy_da[1] * dy_da[1];
alpha[2][0] += dy_da[2] * dy_da[0];
alpha[2][1] += dy_da[2] * dy_da[1];
alpha[2][2] += dy_da[2] * dy_da[2];
alpha[3][0] += dy_da[3] * dy_da[0];
alpha[3][1] += dy_da[3] * dy_da[1];
alpha[3][2] += dy_da[3] * dy_da[2];
alpha[3][3] += dy_da[3] * dy_da[3];
alpha[4][0] += dy_da[4] * dy_da[0];
alpha[4][1] += dy_da[4] * dy_da[1];
alpha[4][2] += dy_da[4] * dy_da[2];
alpha[4][3] += dy_da[4] * dy_da[3];
alpha[4][4] += 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;
}
protected void initialiseGradient()
{
alpha[0][0] = 0;
alpha[1][0] = 0;
alpha[1][1] = 0;
alpha[2][0] = 0;
alpha[2][1] = 0;
alpha[2][2] = 0;
alpha[3][0] = 0;
alpha[3][1] = 0;
alpha[3][2] = 0;
alpha[3][3] = 0;
alpha[4][0] = 0;
alpha[4][1] = 0;
alpha[4][2] = 0;
alpha[4][3] = 0;
alpha[4][4] = 0;
beta[0] = 0;
beta[1] = 0;
beta[2] = 0;
beta[3] = 0;
beta[4] = 0;
}
protected void finishGradient()
{
alpha[0][1] = alpha[1][0];
alpha[0][2] = alpha[2][0];
alpha[0][3] = alpha[3][0];
alpha[0][4] = alpha[4][0];
alpha[1][2] = alpha[2][1];
alpha[1][3] = alpha[3][1];
alpha[1][4] = alpha[4][1];
alpha[2][3] = alpha[3][2];
alpha[2][4] = alpha[4][2];
alpha[3][4] = alpha[4][3];
}
}