Mercurial > dive4elements > river
view artifacts/src/main/java/org/dive4elements/river/artifacts/model/sq/Fitting.java @ 6152:0587819960c3
Waterlevel differences & bed height differences: Add new model LinearInterpolated intented to unify the two very similiar calculations. The focus of the current implementation is correctness and not speed! The fact that the data sets more mostly sorted by station is not exploited. Doing so would improve performance significantly.
author | Sascha L. Teichmann <teichmann@intevation.de> |
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date | Sun, 02 Jun 2013 17:52:53 +0200 |
parents | af13ceeba52a |
children | 9479cb7c8cd5 |
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/* Copyright (C) 2011, 2012, 2013 by Bundesanstalt für Gewässerkunde * Software engineering by Intevation GmbH * * This file is Free Software under the GNU AGPL (>=v3) * and comes with ABSOLUTELY NO WARRANTY! Check out the * documentation coming with Dive4Elements River for details. */ package org.dive4elements.river.artifacts.model.sq; import org.dive4elements.river.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 org.dive4elements.river.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, String method, 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, method); } catch (MathException me) { log.warn(me); return false; } return true; } } // vim:set ts=4 sw=4 si et sta sts=4 fenc=utf8 :