Java tutorial
package org.geogebra.common.kernel.optimization; /* GeoGebra - Dynamic Mathematics for Everyone http://www.geogebra.org This file is part of GeoGebra. 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. */ import org.geogebra.common.kernel.Kernel; import org.geogebra.common.kernel.StringTemplate; import org.geogebra.common.kernel.arithmetic.ExpressionNode; import org.geogebra.common.kernel.arithmetic.ExpressionValue; import org.geogebra.common.kernel.arithmetic.Function; import org.geogebra.common.kernel.arithmetic.FunctionVariable; import org.geogebra.common.kernel.arithmetic.MyDouble; import org.geogebra.common.kernel.arithmetic.NumberValue; import org.geogebra.common.kernel.geos.GeoElement; /** * <h3>FitRealFunction</h3> * * <pre> * Class with FitRealFunction which will be used when Fit[<list>,<function>] does * nonlinear curve-fitting on a copy of <function> where gliders a,b,c,... * are used as parameters. * * Implements: * * org.apache.commons.math.optimization.fitting.ParametricRealFunction * which can be given to org.apache....fitting.CurveFitter which * does the rest of the job. * * Interface: * * FitRealFunction(Function) Makes a copy of Function with gliders replaced by mydouble parameters * value(double,double[]) Evaluates for x and pars[] * gradient(double,double[]) Evaluates a gradient for x and pars[] numerically * * For AlgoFitNL: * * getNumberOfParameters() Get number of gliders/parameters found and changed * getStartParameters() Get array of startvalues for parameters. * getGeoFunction(double[]) Get FitFunction as GeoFunction with parameters replaced * * For later extensions and external use: * * evaluate(double,double[]) As value(...), perhaps implementing other interfaces later? * evaluate(double) As an ordinary function * evaluate() Last value * getFunction(double[]) Get as Function with parameters replaced * * ToDo: The gradient could be more sophisticated, but the Apache lib is quite robust :-) * Some tuning of numerical precision both here and in the setup of LM-optimizer * * Should probably make an abstract, and make this a subclass, * will do if the need arises. * </pre> * * @author Hans-Petter Ulven * @version 15.03.2011 */ public class FitRealFunction implements org.apache.commons.math.optimization.fitting.ParametricRealFunction { // / --- Properties --- /// private Kernel kernel = null; private int numberOfParameters = 0; private Object[] gliders = null; // Pointers to gliders, need for new // startvalues private Function newf = null; private double lastvalue = 0.0d; private MyDouble[] mydoubles = null; public boolean parametersOK = true; // / --- Interface --- /// /** Probably not needed? */ public FitRealFunction() { }// Constructor /** * Main constructor * * @param f * Function to be copied and manipulated */ public FitRealFunction(Function f) { super(); setFunction(f); }// Constructor /** * Implementing org.apache...fitting.ParametricRealFunction * * @param x * double variable * @param pars * double[] parameters * @return functionvalue */ public final double value(double x, double[] pars) { for (int i = 0; i < numberOfParameters; i++) { mydoubles[i].set(pars[i]); // mydoubles[i].setLabel("p_{"+i+"}"); } // for all parameter lastvalue = newf.evaluate(x); return lastvalue; }// evaluate(x,pars[]) /** * Implementing org.apache...fitting.ParametricRealFunction * * @param x * double variable * @param pars * double[] parameters */ public final double[] gradient(double x, double[] pars) { double oldf, newf; double deltap = 1.0E-5;// 1E-10 and 1E-15 is far too small, keep E-5 // until search algo is made double[] gradient = new double[numberOfParameters]; for (int i = 0; i < numberOfParameters; i++) { oldf = value(x, pars); pars[i] += deltap; newf = value(x, pars); gradient[i] = (newf - oldf) / deltap; pars[i] -= deltap; } // for all parameters return gradient; }// gradient(x,pars) public void setFunction(Function f) { kernel = f.getKernel(); FunctionVariable fvar = f.getFunctionVariable(); java.util.HashSet<GeoElement> hash = f.getVariables(); // Get a,b,c,... // to array if (hash == null) { //throw (new Exception("No gliders/parameters in fit-function...")); this.parametersOK = false; } else { gliders = hash.toArray(); } // if no gliders numberOfParameters = gliders.length; mydoubles = new MyDouble[numberOfParameters]; // Make my own parameters double temp; for (int i = 0; i < numberOfParameters; i++) { temp = ((NumberValue) gliders[i]).getDouble(); mydoubles[i] = new MyDouble(kernel); mydoubles[i].set(temp); // Set mydoubles to start values from a,b,c } // for all parameters ExpressionNode node = f.getExpression(); ExpressionNode enf = (ExpressionNode) node.deepCopy(kernel); // Make new // tree // for // new // function // ExpressionNode enf=new ExpressionNode(kernel,evf); for (int i = 0; i < numberOfParameters; i++) { enf = enf.replace((ExpressionValue) gliders[i], mydoubles[i].evaluate(StringTemplate.defaultTemplate)) .wrap(); // System.out.println("Replaced: "+((NumberValue)pars[i]).toString()+"with: "+mydoubles[i].toString()); } // for all parameters // System.out.println("enf(etter replace): "+enf.toString()); enf.resolveVariables(); // should we dispose this??? if(this.newf!=null) this.newf = new Function(enf, fvar); // System.out.println("new function: "+newf.toString()); }// setFunction(Function) public final int getNumberOfParameters() { return numberOfParameters; } // Needed by AlgoFitNL public final double[] getStartValues() { double[] startvalues = new double[numberOfParameters]; for (int i = 0; i < numberOfParameters; i++) { startvalues[i] = ((NumberValue) gliders[i]).getDouble(); // Only // first // time: // mydoubles[i].getDouble(); } // for all parameters return startvalues; }// getStartValues() /** For other uses later? */ public final double evaluate(double x, double[] pars) { return value(x, pars); } public final double evaluate() { return lastvalue; } public final double evaluate(double x) { return newf.evaluate(x); }// evaluate(x); public final Function getFunction() { return newf; } public MyDouble[] getCoeffs() { return mydoubles; } // --- SNIP --- /// }// Class FitRealFunction