[9706] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[9706] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Linq;
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| 24 |
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| 25 | namespace HeuristicLab.Analysis.Statistics {
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| 26 | public class ExpFitting : IFitting {
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[11914] | 27 | private void ExpFunc(double[] c, double[] x, ref double func, object obj) {
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[9706] | 28 | func = Math.Exp(-c[0] * Math.Pow(x[0], 2));
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| 29 | }
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| 30 |
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| 31 | private double[] GetDefaultXValues(int n) {
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| 32 | var stdX = Enumerable.Range(1, n).Select(x => (double)x).ToArray();
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| 33 | return stdX;
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| 34 | }
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| 35 |
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[11914] | 36 | public void Calculate(double[] dataPoints, out double p0) {
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[9706] | 37 | var stdX = GetDefaultXValues(dataPoints.Count());
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[11914] | 38 | Calculate(dataPoints, stdX, out p0);
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[9706] | 39 | }
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| 40 |
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[11914] | 41 | public void Calculate(double[] y, double[] x, out double p0) {
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[9706] | 42 | if (y.Count() != x.Count()) {
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| 43 | throw new ArgumentException("The lenght of x and y needs do be equal. ");
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| 44 | }
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| 45 |
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| 46 | double[] c = new double[] { 0.3 };
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| 47 | double epsf = 0;
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| 48 | double epsx = 0.000001;
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| 49 | int maxits = 0;
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| 50 | int info;
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| 51 | alglib.lsfitstate state;
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| 52 | alglib.lsfitreport rep;
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| 53 | double diffstep = 0.0001;
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| 54 | double[,] xx = new double[x.Count(), 1];
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| 55 |
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| 56 | for (int i = 0; i < x.Count(); i++) {
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| 57 | xx[i, 0] = x[i];
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| 58 | }
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| 59 |
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| 60 | alglib.lsfitcreatef(xx, y, c, diffstep, out state);
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| 61 | alglib.lsfitsetcond(state, epsf, epsx, maxits);
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[11914] | 62 | alglib.lsfitfit(state, ExpFunc, null, null);
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[9706] | 63 | alglib.lsfitresults(state, out info, out c, out rep);
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| 64 |
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| 65 | p0 = c[0];
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| 66 | }
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| 67 |
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[11914] | 68 | public DataRow CalculateFittedLine(double[] dataPoints) {
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| 69 | DataRow newRow = new DataRow();
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| 70 | double c0;
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| 71 | Calculate(dataPoints, out c0);
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[9706] | 72 | var stdX = GetDefaultXValues(dataPoints.Count());
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| 73 |
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| 74 | for (int i = 0; i < stdX.Count(); i++) {
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| 75 | newRow.Values.Add(Math.Exp(-c0 * Math.Pow(stdX[i], 2)));
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| 76 | }
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| 77 |
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| 78 | return newRow;
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| 79 | }
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| 80 |
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[11914] | 81 | public DataRow CalculateFittedLine(double[] y, double[] x) {
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| 82 | DataRow newRow = new DataRow();
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| 83 | double c0;
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| 84 | Calculate(y, x, out c0);
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[9706] | 85 |
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| 86 | for (int i = 0; i < x.Count(); i++) {
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| 87 | newRow.Values.Add(Math.Exp(-c0 * Math.Pow(x[i], 2)));
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| 88 | }
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| 89 |
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| 90 | return newRow;
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| 91 | }
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| 92 |
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| 93 | public override string ToString() {
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| 94 | return "Exponential Fitting";
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| 95 | }
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| 96 | }
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| 97 | }
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