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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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package org.apache.commons.math.estimation;
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import java.io.Serializable;
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import org.apache.commons.math.linear.InvalidMatrixException;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.linear.RealMatrixImpl;
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* This class implements a solver for estimation problems.
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* <p>This class solves estimation problems using a weighted least
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* squares criterion on the measurement residuals. It uses a
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* Gauss-Newton algorithm.</p>
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* @version $Revision: 627987 $ $Date: 2008-02-15 03:01:26 -0700 (Fri, 15 Feb 2008) $
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public class GaussNewtonEstimator extends AbstractEstimator implements Serializable {
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* <p>This constructor builds an estimator and stores its convergence
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* characteristics.</p>
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* <p>An estimator is considered to have converged whenever either
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* the criterion goes below a physical threshold under which
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* improvements are considered useless or when the algorithm is
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* unable to improve it (even if it is still high). The first
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* condition that is met stops the iterations.</p>
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* <p>The fact an estimator has converged does not mean that the
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* model accurately fits the measurements. It only means no better
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* solution can be found, it does not mean this one is good. Such an
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* analysis is left to the caller.</p>
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* <p>If neither conditions are fulfilled before a given number of
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* iterations, the algorithm is considered to have failed and an
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* {@link EstimationException} is thrown.</p>
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* @param maxCostEval maximal number of cost evaluations allowed
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* @param convergence criterion threshold below which we do not need
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* to improve the criterion anymore
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* @param steadyStateThreshold steady state detection threshold, the
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* problem has converged has reached a steady state if
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* <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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public GaussNewtonEstimator(int maxCostEval,
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double steadyStateThreshold) {
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setMaxCostEval(maxCostEval);
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this.steadyStateThreshold = steadyStateThreshold;
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this.convergence = convergence;
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* Solve an estimation problem using a least squares criterion.
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* <p>This method set the unbound parameters of the given problem
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* starting from their current values through several iterations. At
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* each step, the unbound parameters are changed in order to
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* minimize a weighted least square criterion based on the
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* measurements of the problem.</p>
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* <p>The iterations are stopped either when the criterion goes
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* below a physical threshold under which improvement are considered
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* useless or when the algorithm is unable to improve it (even if it
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* is still high). The first condition that is met stops the
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* iterations. If the convergence it nos reached before the maximum
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* number of iterations, an {@link EstimationException} is
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* @param problem estimation problem to solve
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* @exception EstimationException if the problem cannot be solved
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* @see EstimationProblem
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public void estimate(EstimationProblem problem)
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throws EstimationException {
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initializeEstimate(problem);
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double[] grad = new double[parameters.length];
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RealMatrixImpl bDecrement = new RealMatrixImpl(parameters.length, 1);
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double[][] bDecrementData = bDecrement.getDataRef();
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RealMatrixImpl wGradGradT = new RealMatrixImpl(parameters.length, parameters.length);
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double[][] wggData = wGradGradT.getDataRef();
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// iterate until convergence is reached
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double previous = Double.POSITIVE_INFINITY;
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// build the linear problem
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incrementJacobianEvaluationsCounter();
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RealMatrix b = new RealMatrixImpl(parameters.length, 1);
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RealMatrix a = new RealMatrixImpl(parameters.length, parameters.length);
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for (int i = 0; i < measurements.length; ++i) {
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if (! measurements [i].isIgnored()) {
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double weight = measurements[i].getWeight();
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double residual = measurements[i].getResidual();
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// compute the normal equation
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for (int j = 0; j < parameters.length; ++j) {
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grad[j] = measurements[i].getPartial(parameters[j]);
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bDecrementData[j][0] = weight * residual * grad[j];
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// build the contribution matrix for measurement i
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for (int k = 0; k < parameters.length; ++k) {
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double[] wggRow = wggData[k];
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for (int l = 0; l < parameters.length; ++l) {
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wggRow[l] = weight * gk * grad[l];
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// update the matrices
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a = a.add(wGradGradT);
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b = b.add(bDecrement);
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// solve the linearized least squares problem
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RealMatrix dX = a.solve(b);
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// update the estimated parameters
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for (int i = 0; i < parameters.length; ++i) {
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parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i, 0));
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} catch(InvalidMatrixException e) {
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throw new EstimationException("unable to solve: singular problem", new Object[0]);
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updateResidualsAndCost();
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} while ((getCostEvaluations() < 2) ||
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(Math.abs(previous - cost) > (cost * steadyStateThreshold) &&
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(Math.abs(cost) > convergence)));
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/** Threshold for cost steady state detection. */
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private double steadyStateThreshold;
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/** Threshold for cost convergence. */
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private double convergence;
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/** Serializable version identifier */
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private static final long serialVersionUID = 5485001826076289109L;
added
removed
src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java
