Mercurial > dive4elements > river

annotate artifacts/src/main/java/org/dive4elements/river/artifacts/model/fixings/fitting/LinearLogLinearizedFitting.java @ 9646:0380717105ba

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Implemented alternative fitting strategy for Log-Linear function.
author Gernot Belger <g.belger@bjoernsen.de>
date Mon, 02 Dec 2019 17:56:15 +0100
parents
children
rev   line source
9646
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
Gernot Belger <g.belger@bjoernsen.de>
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1 /** Copyright (C) 2017 by Bundesanstalt für Gewässerkunde
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
Gernot Belger <g.belger@bjoernsen.de>
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2 * Software engineering by
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
Gernot Belger <g.belger@bjoernsen.de>
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3 * Björnsen Beratende Ingenieure GmbH
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Gernot Belger <g.belger@bjoernsen.de>
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4 * Dr. Schumacher Ingenieurbüro für Wasser und Umwelt
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5 *
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
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6 * This file is Free Software under the GNU AGPL (>=v3)
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Gernot Belger <g.belger@bjoernsen.de>
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7 * and comes with ABSOLUTELY NO WARRANTY! Check out the
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
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8 * documentation coming with Dive4Elements River for details.
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9 */
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10 package org.dive4elements.river.artifacts.model.fixings.fitting;
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11
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12 import org.apache.commons.math3.optim.MaxEval;
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13 import org.apache.commons.math3.optim.MaxIter;
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14 import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
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15 import org.apache.commons.math3.optim.univariate.BrentOptimizer;
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16 import org.apache.commons.math3.optim.univariate.SearchInterval;
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17 import org.apache.commons.math3.optim.univariate.UnivariateObjectiveFunction;
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18 import org.apache.commons.math3.optim.univariate.UnivariatePointValuePair;
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19
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20 /**
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21 * Alternative solver for W = a * ln(b + m*Q)
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
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22 * Basic approach is to optimize a, by locally solving b and m by linear regression within the error function.
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23 *
0380717105ba Implemented alternative fitting strategy for Log-Linear function.
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24 * @author Gernot Belger
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25 */
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26 public final class LinearLogLinearizedFitting {
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27
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28 public static final class Result {
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29
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30 private final double a;
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31
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32 private final double b;
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33
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34 private final double m;
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35
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36 private final double error;
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37
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38 private final int bestAindex;
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39
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40 private final double chiSquare;
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41
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42 public Result(final double a, final double b, final double m, final double error, final int bestAindex, final double chiSquare) {
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43 this.a = a;
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44 this.b = b;
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45 this.m = m;
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46 this.error = error;
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47 this.bestAindex = bestAindex;
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48 this.chiSquare = chiSquare;
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49 }
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50
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51 public double getA() {
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52 return this.a;
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53 }
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54
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55 public double getB() {
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56 return this.b;
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57 }
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58
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59 public double getM() {
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60 return this.m;
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61 }
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62
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63 public double getError() {
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64 return this.error;
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65 }
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66
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67 public int getBestAindex() {
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68 return this.bestAindex;
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69 }
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70
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71 public double getChiSquare() {
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72 return this.chiSquare;
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73 }
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74
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75 public Result withBestAIndex(final int i) {
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76 return new Result(this.a, this.b, this.m, this.error, i, this.chiSquare);
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77 }
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78 }
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79
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80 private final double[] obsDischarges;
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81 private final double[] obsWaterlevels;
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82 private final TargetFunction targetFunction;
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83 private final LinearRegressionSqrtErrorFunction errorFunction;
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84
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85 public LinearLogLinearizedFitting(final double[] obsDischarges, final double[] obsWaterlevels) {
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86 this.obsDischarges = obsDischarges;
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87 this.obsWaterlevels = obsWaterlevels;
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88
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89 this.targetFunction = new TargetFunction(this.obsDischarges);
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90 this.errorFunction = new LinearRegressionSqrtErrorFunction(this.obsDischarges, this.obsWaterlevels, this.targetFunction);
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91 }
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92
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93 public Result optimize() {
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94
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95 return estimateALinear();
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96 }
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97
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98 private Result estimateALinear() {
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99
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100 double bestA = Double.NaN;
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101 double leastError = Double.POSITIVE_INFINITY;
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102 int bestAindex = -1;
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103
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104 // FIXME: a sollte nicht so groß werden
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105 for (int i = 0; i < 20; i++) {
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106
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107 final double aLow = Math.pow(10, i - 1);
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108 final double aStart = Math.pow(10, i);
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109 final double aHigh = Math.pow(10, i + 1);
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110
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111 final double[] result = linearOptimize(aLow, aHigh, aStart, this.errorFunction);
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112
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113 final double a = result[0];
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114 final double error = result[1];
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115
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116 if (error < leastError) {
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117 leastError = error;
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118 bestA = a;
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119 bestAindex = i;
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120 }
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121 }
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122
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123 final double[] parameters = this.errorFunction.calc_parameters_trans(bestA);
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124 final double b = parameters[0];
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125 final double m = parameters[1];
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126
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127 // FIXME: noch post-optimieren?
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128
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129 /* calculate chi square */
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130 final ChiSquare chiSquareFunc = new ChiSquare(this.obsWaterlevels);
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131 final double[] waterlevels = this.targetFunction.calc_stages(bestAindex, b, m);
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132 final double chiSquare = chiSquareFunc.evaluate(waterlevels);
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133
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134 return new Result(bestA, b, m, leastError, bestAindex, chiSquare);
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135 }
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136
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137 private static double[] linearOptimize(final double aLow, final double aHigh, final double aStart, final LinearRegressionSqrtErrorFunction errorFunction) {
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138
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139 final BrentOptimizer optimizer = new BrentOptimizer(1e-10, 1e-12);
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140
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141 final SearchInterval searchInterval = new SearchInterval(aLow, aHigh, aStart);
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142 final UnivariateObjectiveFunction function = new UnivariateObjectiveFunction(errorFunction);
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143
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144 final MaxEval maxEval = new MaxEval(Integer.MAX_VALUE);
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145 final MaxIter maxIter = new MaxIter(Integer.MAX_VALUE);
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146
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147 final UnivariatePointValuePair result = optimizer.optimize(searchInterval, function, GoalType.MINIMIZE, maxEval, maxIter);
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148
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149 final double aEstimation = result.getPoint();
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150 final double error = result.getValue();
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151
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152 return new double[] { aEstimation, error };
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153 }
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154 }
http://dive4elements.wald.intevation.org