uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient.FastLogMleLvmGradientProcedureX6 Maven / Gradle / Ivy
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/*-
* #%L
* Genome Damage and Stability Centre SMLM Package
*
* Software for single molecule localisation microscopy (SMLM)
* %%
* Copyright (C) 2011 - 2023 Alex Herbert
* %%
* 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.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public
* License along with this program. If not, see
* .
* #L%
*/
package uk.ac.sussex.gdsc.smlm.fitting.nonlinear.gradient;
import uk.ac.sussex.gdsc.core.utils.ValidationUtils;
import uk.ac.sussex.gdsc.smlm.function.FastLog;
import uk.ac.sussex.gdsc.smlm.function.Gradient1Function;
/**
* 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).
*
* This procedure computes a modified Chi-squared expression to perform Maximum Likelihood
* Estimation assuming Poisson model. See Laurence & Chromy (2010) Efficient maximum likelihood
* estimator. Nature Methods 7, 338-339. The input data must be Poisson distributed for this to be
* relevant.
*/
public class FastLogMleLvmGradientProcedureX6 extends FastLogMleLvmGradientProcedureX {
/**
* Instantiates a new procedure.
*
* @param y Data to fit (must be strictly positive)
* @param func Gradient function
* @param fastLog the fast log
*/
public FastLogMleLvmGradientProcedureX6(final double[] y, final Gradient1Function func,
FastLog fastLog) {
super(y, func, fastLog);
ValidationUtils.checkArgument(numberOfGradients == 6, "Function must compute 6 gradients");
}
@Override
public void execute(double fi, double[] dfiDa) {
++yi;
if (fi > 0) {
final double xi = y[yi];
// We assume y[i] is strictly positive
value += (fi - xi - xi * fastLog.fastLog(fi / xi));
final double xi_fi2 = xi / fi / fi;
final double e = 1 - (xi / fi);
beta[0] -= e * dfiDa[0];
beta[1] -= e * dfiDa[1];
beta[2] -= e * dfiDa[2];
beta[3] -= e * dfiDa[3];
beta[4] -= e * dfiDa[4];
beta[5] -= e * dfiDa[5];
alpha[0] += dfiDa[0] * xi_fi2 * dfiDa[0];
double wgt;
wgt = dfiDa[1] * xi_fi2;
alpha[1] += wgt * dfiDa[0];
alpha[2] += wgt * dfiDa[1];
wgt = dfiDa[2] * xi_fi2;
alpha[3] += wgt * dfiDa[0];
alpha[4] += wgt * dfiDa[1];
alpha[5] += wgt * dfiDa[2];
wgt = dfiDa[3] * xi_fi2;
alpha[6] += wgt * dfiDa[0];
alpha[7] += wgt * dfiDa[1];
alpha[8] += wgt * dfiDa[2];
alpha[9] += wgt * dfiDa[3];
wgt = dfiDa[4] * xi_fi2;
alpha[10] += wgt * dfiDa[0];
alpha[11] += wgt * dfiDa[1];
alpha[12] += wgt * dfiDa[2];
alpha[13] += wgt * dfiDa[3];
alpha[14] += wgt * dfiDa[4];
wgt = dfiDa[5] * xi_fi2;
alpha[15] += wgt * dfiDa[0];
alpha[16] += wgt * dfiDa[1];
alpha[17] += wgt * dfiDa[2];
alpha[18] += wgt * dfiDa[3];
alpha[19] += wgt * dfiDa[4];
alpha[20] += wgt * dfiDa[5];
}
}
@Override
protected void initialiseGradient() {
GradientProcedureHelper.initialiseWorkingMatrix6(alpha);
beta[0] = 0;
beta[1] = 0;
beta[2] = 0;
beta[3] = 0;
beta[4] = 0;
beta[5] = 0;
}
@Override
public void getAlphaMatrix(double[][] alpha) {
GradientProcedureHelper.getMatrix6(this.alpha, alpha);
}
@Override
public void getAlphaLinear(double[] alpha) {
GradientProcedureHelper.getMatrix6(this.alpha, alpha);
}
}