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 LSQLVMGradientProcedureMatrix extends BaseLSQLVMGradientProcedure
{
/**
* The scaled Hessian curvature matrix (size n*n)
*/
public final double[][] alpha;
/**
* @param y
* Data to fit
* @param b
* Baseline pre-computed y-values
* @param func
* Gradient function
*/
public LSQLVMGradientProcedureMatrix(final double[] y, final double[] b, final Gradient1Function func)
{
super(y, b, func);
alpha = new double[n][n];
}
/*
* (non-Javadoc)
*
* @see gdsc.smlm.function.Gradient1Procedure#execute(double, double[])
*/
public void execute(double value, double[] dy_da)
{
final double dy = y[++yi] - value;
// Compute:
// - the scaled Hessian matrix (the square matrix of second-order partial derivatives of a function;
// that is, it describes the local curvature of a function of many variables.)
// - the scaled gradient vector of the function's partial first derivatives with respect to the parameters
for (int j = 0; j < n; j++)
{
final double wgt = dy_da[j];
for (int k = 0; k <= j; k++)
alpha[j][k] += wgt * dy_da[k];
beta[j] += wgt * dy;
}
this.value += dy * dy;
}
protected void initialiseGradient()
{
for (int i = 0; i < n; i++)
{
beta[i] = 0;
for (int j = 0; j <= i; j++)
alpha[i][j] = 0;
}
}
protected void finishGradient()
{
// Generate symmetric matrix
for (int i = 0; i < n - 1; i++)
for (int j = i + 1; j < n; j++)
alpha[i][j] = alpha[j][i];
}
protected boolean checkGradients()
{
for (int i = 0; i < n; i++)
{
if (Double.isNaN(beta[i]))
return true;
for (int j = 0; j <= i; j++)
if (Double.isNaN(alpha[i][j]))
return true;
}
return false;
}
@Override
public double[][] getAlphaMatrix()
{
return alpha;
}
@Override
public void getAlphaMatrix(double[][] alpha)
{
for (int i = 0; i < n; i++)
System.arraycopy(this.alpha, 0, alpha, 0, n);
}
@Override
public void getAlphaLinear(double[] alpha)
{
toLinear(this.alpha, alpha);
}
}