Mercurial > dive4elements > river
annotate artifacts/src/main/java/org/dive4elements/river/artifacts/model/sq/Fitting.java @ 6761:9479cb7c8cd5
flys/issue748: Force linear curve fitting. This is a real hack! Set the system property "minfo.sq.fitting.nonlinear" to re-enable the old behavior.
author | Sascha L. Teichmann <teichmann@intevation.de> |
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date | Tue, 06 Aug 2013 17:00:49 +0200 |
parents | af13ceeba52a |
children | 48f6780c372d |
rev | line source |
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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.sq; |
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10 |
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11 import org.dive4elements.river.artifacts.math.fitting.Function; |
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12 import org.dive4elements.river.artifacts.math.fitting.Linear; |
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13 |
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14 import java.util.ArrayList; |
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15 import java.util.List; |
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16 |
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17 import org.apache.commons.math.MathException; |
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18 |
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19 import org.apache.commons.math.optimization.fitting.CurveFitter; |
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20 |
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21 import org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer; |
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22 import org.apache.commons.math.optimization.general.GaussNewtonOptimizer; |
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23 import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer; |
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24 |
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25 import org.apache.log4j.Logger; |
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26 |
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27 public class Fitting |
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28 implements Outlier.Callback |
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29 { |
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30 // XXX: Hack to force linear fitting! |
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31 private static final boolean USE_NON_LINEAR_FITTING = |
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32 Boolean.getBoolean("minfo.sq.fitting.nonlinear"); |
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33 |
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34 private static Logger log = Logger.getLogger(Fitting.class); |
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35 |
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36 public interface Callback { |
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37 |
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38 void afterIteration( |
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39 double [] parameters, |
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40 SQ [] measurements, |
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41 SQ [] outliers, |
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42 double standardDeviation, |
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43 double chiSqr); |
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44 } // interfacte |
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45 |
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46 protected Function function; |
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47 |
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48 protected double [] coeffs; |
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49 |
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50 protected org.dive4elements.river.artifacts.math.Function instance; |
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51 |
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52 protected double stdDevFactor; |
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53 protected double chiSqr; |
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54 |
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55 protected Callback callback; |
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56 |
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57 public Fitting() { |
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58 } |
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59 |
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60 public Fitting(Function function, double stdDevFactor) { |
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61 this(); |
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62 this.function = function; |
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63 this.stdDevFactor = stdDevFactor; |
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64 } |
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65 |
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66 public Function getFunction() { |
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67 return function; |
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68 } |
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69 |
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70 public void setFunction(Function function) { |
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71 this.function = function; |
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72 } |
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73 |
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74 public double getStdDevFactor() { |
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75 return stdDevFactor; |
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76 } |
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77 |
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78 public void setStdDevFactor(double stdDevFactor) { |
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79 this.stdDevFactor = stdDevFactor; |
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80 } |
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81 |
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82 @Override |
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83 public void initialize(List<SQ> sqs) throws MathException { |
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84 |
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85 AbstractLeastSquaresOptimizer optimizer = getOptimizer(); |
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86 |
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87 CurveFitter cf = new CurveFitter(optimizer); |
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88 double [] values = new double[2]; |
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89 for (SQ sq: sqs) { |
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90 values[0] = sq.getQ(); |
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91 values[1] = sq.getS(); |
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92 transformInputValues(values); |
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93 cf.addObservedPoint(values[0], values[1]); |
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94 } |
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95 |
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96 coeffs = cf.fit( |
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97 function, function.getInitialGuess()); |
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98 |
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99 transformCoeffsBack(coeffs); |
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100 |
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101 instance = function.instantiate(coeffs); |
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102 |
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103 chiSqr = optimizer.getChiSquare(); |
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104 } |
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105 |
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106 protected Function getFunction(Function function) { |
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107 return USE_NON_LINEAR_FITTING |
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108 ? function |
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109 : Linear.INSTANCE; |
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110 } |
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111 |
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112 protected void transformInputValues(double [] values) { |
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113 if (!USE_NON_LINEAR_FITTING) { |
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114 for (int i = 0; i < values.length; ++i) { |
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115 values[i] = Math.log(values[i]); |
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116 } |
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117 } |
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118 } |
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119 |
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120 protected AbstractLeastSquaresOptimizer getOptimizer() { |
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121 return USE_NON_LINEAR_FITTING |
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122 ? new LevenbergMarquardtOptimizer() |
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123 : new GaussNewtonOptimizer(false); |
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124 } |
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125 |
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126 protected void transformCoeffsBack(double [] coeffs) { |
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127 if (!USE_NON_LINEAR_FITTING && coeffs.length > 0) { |
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128 coeffs[0] = Math.exp(coeffs[0]); |
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129 } |
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130 } |
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131 |
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132 @Override |
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133 public double eval(SQ sq) { |
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134 double s = instance.value(sq.q); |
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135 return sq.s - s; |
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136 } |
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137 |
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138 @Override |
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139 public void iterationFinished( |
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140 double standardDeviation, |
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141 SQ outlier, |
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142 List<SQ> remainings |
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143 ) { |
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144 if (log.isDebugEnabled()) { |
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145 log.debug("iterationFinished ----"); |
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146 log.debug(" num remainings: " + remainings.size()); |
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147 log.debug(" has outlier: " + outlier != null); |
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148 log.debug(" standardDeviation: " + standardDeviation); |
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149 log.debug(" Chi^2: " + chiSqr); |
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150 log.debug("---- iterationFinished"); |
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151 } |
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152 callback.afterIteration( |
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153 coeffs, |
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154 remainings.toArray(new SQ[remainings.size()]), |
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155 outlier != null ? new SQ [] { outlier} : new SQ [] {}, |
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156 standardDeviation, |
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157 chiSqr); |
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158 } |
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159 |
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160 protected static final List<SQ> onlyValid(List<SQ> sqs) { |
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161 |
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162 List<SQ> good = new ArrayList<SQ>(sqs.size()); |
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163 |
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164 for (SQ sq: sqs) { |
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165 if (sq.isValid()) { |
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166 good.add(sq); |
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167 } |
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168 } |
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169 |
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170 return good; |
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171 } |
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172 |
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173 public boolean fit(List<SQ> sqs, String method, Callback callback) { |
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174 |
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175 sqs = onlyValid(sqs); |
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176 |
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177 if (sqs.size() < 2) { |
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178 log.warn("Too less points for fitting."); |
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179 return false; |
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180 } |
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181 |
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182 this.callback = callback; |
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183 |
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184 try { |
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185 Outlier.detectOutliers(this, sqs, stdDevFactor, method); |
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186 } |
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187 catch (MathException me) { |
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188 log.warn(me); |
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189 return false; |
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190 } |
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191 |
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192 return true; |
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193 } |
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194 } |
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195 // vim:set ts=4 sw=4 si et sta sts=4 fenc=utf8 : |