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 scaled 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 abstract class BaseLSQLVMGradientProcedure extends LVMGradientProcedure { /** * @param y * Data to fit * @param func * Gradient function */ public BaseLSQLVMGradientProcedure(final double[] y, final Gradient1Function func) { super(y, func); } /** * @param y * Data to fit * @param b * Baseline pre-computed y-values * @param func * Gradient function */ public BaseLSQLVMGradientProcedure(final double[] y, final double[] b, final Gradient1Function func) { super(y, b, func); } /* * (non-Javadoc) * * @see gdsc.smlm.function.ValueProcedure#execute(double) */ public void execute(double value) { // Produce a sum-of-squares final double dy = y[++yi] - value; this.value += dy * dy; } @Override protected void initialiseValue() { // Do nothing } @Override protected void finishValue() { // Do nothing } }