| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.commons.math.estimation; |
| |
| import java.io.Serializable; |
| |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.linear.InvalidMatrixException; |
| import org.apache.commons.math.linear.LUDecompositionImpl; |
| import org.apache.commons.math.linear.MatrixUtils; |
| import org.apache.commons.math.linear.RealMatrix; |
| import org.apache.commons.math.linear.RealVector; |
| import org.apache.commons.math.linear.ArrayRealVector; |
| import org.apache.commons.math.util.FastMath; |
| |
| /** |
| * This class implements a solver for estimation problems. |
| * |
| * <p>This class solves estimation problems using a weighted least |
| * squares criterion on the measurement residuals. It uses a |
| * Gauss-Newton algorithm.</p> |
| * |
| * @version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ |
| * @since 1.2 |
| * @deprecated as of 2.0, everything in package org.apache.commons.math.estimation has |
| * been deprecated and replaced by package org.apache.commons.math.optimization.general |
| * |
| */ |
| @Deprecated |
| public class GaussNewtonEstimator extends AbstractEstimator implements Serializable { |
| |
| /** Serializable version identifier */ |
| private static final long serialVersionUID = 5485001826076289109L; |
| |
| /** Default threshold for cost steady state detection. */ |
| private static final double DEFAULT_STEADY_STATE_THRESHOLD = 1.0e-6; |
| |
| /** Default threshold for cost convergence. */ |
| private static final double DEFAULT_CONVERGENCE = 1.0e-6; |
| |
| /** Threshold for cost steady state detection. */ |
| private double steadyStateThreshold; |
| |
| /** Threshold for cost convergence. */ |
| private double convergence; |
| |
| /** Simple constructor with default settings. |
| * <p> |
| * The estimator is built with default values for all settings. |
| * </p> |
| * @see #DEFAULT_STEADY_STATE_THRESHOLD |
| * @see #DEFAULT_CONVERGENCE |
| * @see AbstractEstimator#DEFAULT_MAX_COST_EVALUATIONS |
| */ |
| public GaussNewtonEstimator() { |
| this.steadyStateThreshold = DEFAULT_STEADY_STATE_THRESHOLD; |
| this.convergence = DEFAULT_CONVERGENCE; |
| } |
| |
| /** |
| * Simple constructor. |
| * |
| * <p>This constructor builds an estimator and stores its convergence |
| * characteristics.</p> |
| * |
| * <p>An estimator is considered to have converged whenever either |
| * the criterion goes below a physical threshold under which |
| * improvements are considered useless or when the algorithm is |
| * unable to improve it (even if it is still high). The first |
| * condition that is met stops the iterations.</p> |
| * |
| * <p>The fact an estimator has converged does not mean that the |
| * model accurately fits the measurements. It only means no better |
| * solution can be found, it does not mean this one is good. Such an |
| * analysis is left to the caller.</p> |
| * |
| * <p>If neither conditions are fulfilled before a given number of |
| * iterations, the algorithm is considered to have failed and an |
| * {@link EstimationException} is thrown.</p> |
| * |
| * @param maxCostEval maximal number of cost evaluations allowed |
| * @param convergence criterion threshold below which we do not need |
| * to improve the criterion anymore |
| * @param steadyStateThreshold steady state detection threshold, the |
| * problem has converged has reached a steady state if |
| * <code>FastMath.abs(J<sub>n</sub> - J<sub>n-1</sub>) < |
| * J<sub>n</sub> × convergence</code>, where <code>J<sub>n</sub></code> |
| * and <code>J<sub>n-1</sub></code> are the current and preceding criterion |
| * values (square sum of the weighted residuals of considered measurements). |
| */ |
| public GaussNewtonEstimator(final int maxCostEval, final double convergence, |
| final double steadyStateThreshold) { |
| setMaxCostEval(maxCostEval); |
| this.steadyStateThreshold = steadyStateThreshold; |
| this.convergence = convergence; |
| } |
| |
| /** |
| * Set the convergence criterion threshold. |
| * @param convergence criterion threshold below which we do not need |
| * to improve the criterion anymore |
| */ |
| public void setConvergence(final double convergence) { |
| this.convergence = convergence; |
| } |
| |
| /** |
| * Set the steady state detection threshold. |
| * <p> |
| * The problem has converged has reached a steady state if |
| * <code>FastMath.abs(J<sub>n</sub> - J<sub>n-1</sub>) < |
| * J<sub>n</sub> × convergence</code>, where <code>J<sub>n</sub></code> |
| * and <code>J<sub>n-1</sub></code> are the current and preceding criterion |
| * values (square sum of the weighted residuals of considered measurements). |
| * </p> |
| * @param steadyStateThreshold steady state detection threshold |
| */ |
| public void setSteadyStateThreshold(final double steadyStateThreshold) { |
| this.steadyStateThreshold = steadyStateThreshold; |
| } |
| |
| /** |
| * Solve an estimation problem using a least squares criterion. |
| * |
| * <p>This method set the unbound parameters of the given problem |
| * starting from their current values through several iterations. At |
| * each step, the unbound parameters are changed in order to |
| * minimize a weighted least square criterion based on the |
| * measurements of the problem.</p> |
| * |
| * <p>The iterations are stopped either when the criterion goes |
| * below a physical threshold under which improvement are considered |
| * useless or when the algorithm is unable to improve it (even if it |
| * is still high). The first condition that is met stops the |
| * iterations. If the convergence it not reached before the maximum |
| * number of iterations, an {@link EstimationException} is |
| * thrown.</p> |
| * |
| * @param problem estimation problem to solve |
| * @exception EstimationException if the problem cannot be solved |
| * |
| * @see EstimationProblem |
| * |
| */ |
| @Override |
| public void estimate(EstimationProblem problem) |
| throws EstimationException { |
| |
| initializeEstimate(problem); |
| |
| // work matrices |
| double[] grad = new double[parameters.length]; |
| ArrayRealVector bDecrement = new ArrayRealVector(parameters.length); |
| double[] bDecrementData = bDecrement.getDataRef(); |
| RealMatrix wGradGradT = MatrixUtils.createRealMatrix(parameters.length, parameters.length); |
| |
| // iterate until convergence is reached |
| double previous = Double.POSITIVE_INFINITY; |
| do { |
| |
| // build the linear problem |
| incrementJacobianEvaluationsCounter(); |
| RealVector b = new ArrayRealVector(parameters.length); |
| RealMatrix a = MatrixUtils.createRealMatrix(parameters.length, parameters.length); |
| for (int i = 0; i < measurements.length; ++i) { |
| if (! measurements [i].isIgnored()) { |
| |
| double weight = measurements[i].getWeight(); |
| double residual = measurements[i].getResidual(); |
| |
| // compute the normal equation |
| for (int j = 0; j < parameters.length; ++j) { |
| grad[j] = measurements[i].getPartial(parameters[j]); |
| bDecrementData[j] = weight * residual * grad[j]; |
| } |
| |
| // build the contribution matrix for measurement i |
| for (int k = 0; k < parameters.length; ++k) { |
| double gk = grad[k]; |
| for (int l = 0; l < parameters.length; ++l) { |
| wGradGradT.setEntry(k, l, weight * gk * grad[l]); |
| } |
| } |
| |
| // update the matrices |
| a = a.add(wGradGradT); |
| b = b.add(bDecrement); |
| |
| } |
| } |
| |
| try { |
| |
| // solve the linearized least squares problem |
| RealVector dX = new LUDecompositionImpl(a).getSolver().solve(b); |
| |
| // update the estimated parameters |
| for (int i = 0; i < parameters.length; ++i) { |
| parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i)); |
| } |
| |
| } catch(InvalidMatrixException e) { |
| throw new EstimationException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM); |
| } |
| |
| |
| previous = cost; |
| updateResidualsAndCost(); |
| |
| } while ((getCostEvaluations() < 2) || |
| (FastMath.abs(previous - cost) > (cost * steadyStateThreshold) && |
| (FastMath.abs(cost) > convergence))); |
| |
| } |
| |
| } |