Mercurial > dive4elements > river
view flys-artifacts/src/main/java/de/intevation/flys/artifacts/model/sq/Fitting.java @ 4241:49cb65d5932d
Improved the historical discharge calculation.
The calculation now creates new HistoricalWQKms (new subclass of WQKms). Those WQKms are used
to create new facets from (new) type 'HistoricalDischargeCurveFacet'. The chart generator is
improved to support those facets.
author | Ingo Weinzierl <ingo.weinzierl@intevation.de> |
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date | Wed, 24 Oct 2012 14:34:35 +0200 |
parents | b8b1280606c2 |
children | aaf810d4ec82 |
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package de.intevation.flys.artifacts.model.sq; import de.intevation.flys.artifacts.math.fitting.Function; import java.util.ArrayList; import java.util.List; import org.apache.commons.math.MathException; import org.apache.commons.math.optimization.fitting.CurveFitter; import org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer; import org.apache.log4j.Logger; public class Fitting implements Outlier.Callback { private static Logger log = Logger.getLogger(Fitting.class); public interface Callback { void afterIteration( double [] parameters, SQ [] measurements, SQ [] outliers, double standardDeviation, double chiSqr); } // interfacte protected Function function; protected double [] coeffs; protected de.intevation.flys.artifacts.math.Function instance; protected double stdDevFactor; protected double chiSqr; protected Callback callback; public Fitting() { } public Fitting(Function function, double stdDevFactor) { this(); this.function = function; this.stdDevFactor = stdDevFactor; } public Function getFunction() { return function; } public void setFunction(Function function) { this.function = function; } public double getStdDevFactor() { return stdDevFactor; } public void setStdDevFactor(double stdDevFactor) { this.stdDevFactor = stdDevFactor; } @Override public void initialize(List<SQ> sqs) throws MathException { LevenbergMarquardtOptimizer lmo = new LevenbergMarquardtOptimizer(); CurveFitter cf = new CurveFitter(lmo); for (SQ sq: sqs) { cf.addObservedPoint(sq.getQ(), sq.getS()); } coeffs = cf.fit( function, function.getInitialGuess()); instance = function.instantiate(coeffs); chiSqr = lmo.getChiSquare(); } @Override public double eval(SQ sq) { double s = instance.value(sq.q); return sq.s - s; } @Override public void iterationFinished( double standardDeviation, SQ outlier, List<SQ> remainings ) { if (log.isDebugEnabled()) { log.debug("iterationFinished ----"); log.debug(" num remainings: " + remainings.size()); log.debug(" has outlier: " + outlier != null); log.debug(" standardDeviation: " + standardDeviation); log.debug(" Chi^2: " + chiSqr); log.debug("---- iterationFinished"); } callback.afterIteration( coeffs, remainings.toArray(new SQ[remainings.size()]), outlier != null ? new SQ [] { outlier} : new SQ [] {}, standardDeviation, chiSqr); } protected static final List<SQ> onlyValid(List<SQ> sqs) { List<SQ> good = new ArrayList<SQ>(sqs.size()); for (SQ sq: sqs) { if (sq.isValid()) { good.add(sq); } } return good; } public boolean fit(List<SQ> sqs, Callback callback) { sqs = onlyValid(sqs); if (sqs.size() < 2) { log.warn("Too less points for fitting."); return false; } this.callback = callback; try { Outlier.detectOutliers(this, sqs, stdDevFactor); } catch (MathException me) { log.warn(me); return false; } return true; } } // vim:set ts=4 sw=4 si et sta sts=4 fenc=utf8 :