Mercurial > dive4elements > river

annotate artifacts/src/main/java/org/dive4elements/river/artifacts/model/fixings/Fitting.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 bc50ecfc58c5
children
rev   line source
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Sascha L. Teichmann <teichmann@intevation.de>
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1 /* Copyright (C) 2011, 2012, 2013 by Bundesanstalt für Gewässerkunde
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2 * Software engineering by Intevation GmbH
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3 *
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4 * This file is Free Software under the GNU AGPL (>=v3)
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5 * and comes with ABSOLUTELY NO WARRANTY! Check out the
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6 * documentation coming with Dive4Elements River for details.
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7 */
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8
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9 package org.dive4elements.river.artifacts.model.fixings;
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10
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11 import java.util.ArrayList;
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12 import java.util.Date;
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13 import java.util.List;
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14
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15 import org.apache.log4j.Logger;
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16 import org.dive4elements.river.artifacts.math.GrubbsOutlier;
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17 import org.dive4elements.river.artifacts.math.fitting.Function;
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18 import org.dive4elements.river.artifacts.math.fitting.LogLinearAlternative;
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19
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20 public class Fitting {
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21 private static Logger log = Logger.getLogger(Fitting.class);
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22
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23 /** Use instance of this factory to find meta infos for outliers. */
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24 public interface QWDFactory {
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25 QWD create(double q, double w, double deltaW, boolean isOutlier);
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26 }
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27
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28 private final double chiSqr;
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29
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30 private final double[] parameters;
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31
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32 private final double standardDeviation;
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33
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34 private final double maxQ;
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35
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36 public Fitting(final double[] parameters, final double standardDeviation, final double chiSqr, final double maxQ) {
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37 this.parameters = parameters;
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38 this.standardDeviation = standardDeviation;
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39 this.chiSqr = chiSqr;
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40 this.maxQ = maxQ;
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41 }
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42
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43 public double getChiSquare() {
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44 return this.chiSqr;
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45 }
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46
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47 public double getMaxQ() {
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48 return this.maxQ;
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49 }
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50
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51 public double[] getParameters() {
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52 return this.parameters;
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53 }
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54
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55 public double getStandardDeviation() {
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56 return this.standardDeviation;
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57 }
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58
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59 public static Fitting fit(final FixResultColumns resultColumns, final double km, final Function function, final boolean checkOutliers,
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60 final List<FixingColumnWithData> eventColumns) {
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61
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62 final int numEvents = eventColumns.size();
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63 final List<FittingData> data = new ArrayList<>(numEvents);
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64
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65 final double[] wTemp = new double[1];
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66
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67 for (final FixingColumnWithData event : eventColumns) {
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68
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69 final double q = event.getQ(km);
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70 if (!Double.isNaN(q)) {
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71 final boolean isInterpolated = !event.getW(km, wTemp, 0);
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72 final double w = wTemp[0];
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73
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74 if (!Double.isNaN(w))
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75 data.add(new FittingData(event, w, q, isInterpolated));
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76 }
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77 }
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78
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79 if (data.size() < 2) {
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80 log.warn("Not enough data for fitting.");
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81 return null;
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82 }
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83
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84 final List<FittingData> outliers = new ArrayList<>(data.size());
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85
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86 org.dive4elements.river.artifacts.math.Function instance = null;
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87 Fitting fitting = null;
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88
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89 final IFittingOperation operation = createOperation(function);
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90 for (;;) {
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91
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92 fitting = operation.execute(data);
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93
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94 if (fitting == null) {
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95 /*
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96 * log.debug("Parameters is null");
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97 * for (int i = 0, N = xs.size(); i < N; ++i) {
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98 * log.debug("DATA: " + xs.getQuick(i) + " " + ys.getQuick(i));
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99 * }
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100 */
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101 return null;
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102 }
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103
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104 // This is the parameterized function for a given km.
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105 final double[] parameters = fitting.getParameters();
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106 instance = function.instantiate(parameters);
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107
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108 if (!checkOutliers)
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109 break;
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110
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111 /* find the outlier */
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112 final List<Double> inputs = new ArrayList<>(data.size());
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113 for (final FittingData fittingData : data) {
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114
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115 double y = instance.value(fittingData.q);
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116 if (Double.isNaN(y))
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117 y = Double.MAX_VALUE;
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118
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119 inputs.add(Double.valueOf(fittingData.w - y));
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120 }
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121
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122 final Integer outlier = GrubbsOutlier.findOutlier(inputs);
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123 if (outlier == null)
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124 break;
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125
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126 final int idx = outlier.intValue();
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127
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128 // outliers.add(qwdFactory.create(xs.getQuick(idx), ys.getQuick(idx), Double.NaN, true));
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129 final FittingData removed = data.remove(idx);
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130 outliers.add(removed);
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131 }
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132
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133 /* now build result data */
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134 final List<QWD> qwds = new ArrayList<>(data.size());
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135
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136 /* calculate dW of outliers against the resulting function and add them to results */
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137 for (final FittingData outlier : outliers) {
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138 final QWD qwd = createQWD(outlier, instance, true);
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139 qwds.add(qwd);
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140 resultColumns.addQWD(outlier.event, km, qwd);
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141 }
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142
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143 /*
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144 * calculate dW of used values against the resulting function and add them to results
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145 */
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146 for (final FittingData fittingData : data) {
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147
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148 final QWD qwd = createQWD(fittingData, instance, false);
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149 qwds.add(qwd);
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150 resultColumns.addQWD(fittingData.event, km, qwd);
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151 }
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152
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153 return fitting;
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154 }
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155
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156 private static IFittingOperation createOperation(final Function function) {
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157
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158 if (function instanceof LogLinearAlternative)
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159 return new LogLinearFittingOperation((LogLinearAlternative) function);
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160
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161 return new LevenbergMarquardtFittingOperation(function);
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162 }
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163
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164 private static QWD createQWD(final FittingData data, final org.dive4elements.river.artifacts.math.Function function, final boolean isOutlier) {
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165
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166 final FixingColumnWithData event = data.event;
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167 final Date date = event.getDate();
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168 final boolean isInterpolated = data.isInterpolated;
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169
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170 final double w = data.w;
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171 final double q = data.q;
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172 final double dw = (w - function.value(q)) * 100.0;
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173
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174 return new QWD(q, w, date, isInterpolated, dw, isOutlier);
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175 }
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176 }
http://dive4elements.wald.intevation.org