Mercurial > dive4elements > river
view artifacts/src/main/java/org/dive4elements/river/artifacts/model/sq/Fitting.java @ 6780:b8f94e865875
S/Q relation: Part I of 'faking' fitting linear data. S/Qs can now be subclassed and processed through views. TODO: Add knob to setup a linear data path.
author | Sascha L. Teichmann <teichmann@intevation.de> |
---|---|
date | Thu, 08 Aug 2013 12:17:03 +0200 |
parents | 48f6780c372d |
children | 51eb6491c537 |
line wrap: on
line source
/* 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.commons.math.stat.regression.SimpleRegression; import org.apache.log4j.Logger; public class Fitting implements Outlier.Callback { // XXX: Hack to force linear fitting! private static final boolean USE_NON_LINEAR_FITTING = Boolean.getBoolean("minfo.sq.fitting.nonlinear"); 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; protected SQ.View sqView; public Fitting() { } public Fitting(Function function, double stdDevFactor, SQ.View sqView) { this.function = function; this.stdDevFactor = stdDevFactor; this.sqView = sqView; } 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 { if (USE_NON_LINEAR_FITTING || function.getInitialGuess().length != 2) { nonLinearFitting(sqs); } else { linearFitting(sqs); } } protected void linearFitting(List<SQ> sqs) { coeffs = linearRegression(sqs); instance = function.instantiate(coeffs); } protected double [] linearRegression(List<SQ> sqs) { SimpleRegression reg = new SimpleRegression(); int invalidPoints = 0; for (SQ sq: sqs) { double s = sq.getS(); double q = sq.getQ(); if (s <= 0d || q <= 0d) { ++invalidPoints; continue; } reg.addData(Math.log(q), Math.log(s)); } if (sqs.size() - invalidPoints < 2) { log.debug("not enough points"); return new double [] { 0, 0 }; } double a = Math.exp(reg.getIntercept()); double b = reg.getSlope(); if (log.isDebugEnabled()) { log.debug("invalid points: " + invalidPoints + " (" + sqs.size() + ")"); log.debug("a: " + a + " (" + Math.log(a) + ")"); log.debug("b: " + b); } return new double [] { a, b }; } protected void nonLinearFitting(List<SQ> sqs) throws MathException { LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer(); CurveFitter cf = new CurveFitter(optimizer); for (SQ sq: sqs) { cf.addObservedPoint(sq.getS(), sq.getQ()); } coeffs = cf.fit( function, function.getInitialGuess()); instance = function.instantiate(coeffs); chiSqr = optimizer.getChiSquare(); } @Override public double eval(SQ sq) { double s = instance.value(sqView.getQ(sq)); return sqView.getS(sq) - 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); } public boolean fit(List<SQ> sqs, String method, Callback callback) { if (sqs.size() < 2) { log.warn("Too less points for fitting."); return false; } sqs = new ArrayList<SQ>(sqs); 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 :