resurrected the estimation package but with all interfaces and classes deprecated
this will help users switch smoothly to the new optimization.general package git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@754732 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe
parent
c37f06ed3a
commit
61ca8c04df
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@ -0,0 +1,314 @@
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/*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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*/
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package org.apache.commons.math.estimation;
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import java.util.Arrays;
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import org.apache.commons.math.linear.InvalidMatrixException;
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import org.apache.commons.math.linear.MatrixUtils;
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import org.apache.commons.math.linear.RealMatrix;
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import org.apache.commons.math.linear.decomposition.LUDecompositionImpl;
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/**
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* Base class for implementing estimators.
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* <p>This base class handles the boilerplates methods associated to thresholds
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* settings, jacobian and error estimation.</p>
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* @version $Revision$ $Date$
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* @since 1.2
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* @deprecated as of 2.0, everything in package org.apache.commons.math.estimation has
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* been deprecated and replaced by package org.apache.commons.math.optimization.general
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*
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*/
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@Deprecated
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public abstract class AbstractEstimator implements Estimator {
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/** Default maximal number of cost evaluations allowed. */
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public static final int DEFAULT_MAX_COST_EVALUATIONS = 100;
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/**
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* Build an abstract estimator for least squares problems.
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* <p>The maximal number of cost evaluations allowed is set
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* to its default value {@link #DEFAULT_MAX_COST_EVALUATIONS}.</p>
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*/
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protected AbstractEstimator() {
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setMaxCostEval(DEFAULT_MAX_COST_EVALUATIONS);
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}
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/**
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* Set the maximal number of cost evaluations allowed.
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*
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* @param maxCostEval maximal number of cost evaluations allowed
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* @see #estimate
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*/
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public final void setMaxCostEval(int maxCostEval) {
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this.maxCostEval = maxCostEval;
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}
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/**
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* Get the number of cost evaluations.
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*
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* @return number of cost evaluations
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* */
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public final int getCostEvaluations() {
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return costEvaluations;
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}
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/**
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* Get the number of jacobian evaluations.
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*
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* @return number of jacobian evaluations
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* */
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public final int getJacobianEvaluations() {
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return jacobianEvaluations;
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}
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/**
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* Update the jacobian matrix.
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*/
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protected void updateJacobian() {
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incrementJacobianEvaluationsCounter();
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Arrays.fill(jacobian, 0);
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for (int i = 0, index = 0; i < rows; i++) {
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WeightedMeasurement wm = measurements[i];
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double factor = -Math.sqrt(wm.getWeight());
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for (int j = 0; j < cols; ++j) {
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jacobian[index++] = factor * wm.getPartial(parameters[j]);
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}
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}
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}
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/**
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* Increment the jacobian evaluations counter.
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*/
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protected final void incrementJacobianEvaluationsCounter() {
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++jacobianEvaluations;
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}
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/**
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* Update the residuals array and cost function value.
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* @exception EstimationException if the number of cost evaluations
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* exceeds the maximum allowed
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*/
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protected void updateResidualsAndCost()
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throws EstimationException {
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if (++costEvaluations > maxCostEval) {
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throw new EstimationException("maximal number of evaluations exceeded ({0})",
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maxCostEval);
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}
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cost = 0;
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for (int i = 0, index = 0; i < rows; i++, index += cols) {
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WeightedMeasurement wm = measurements[i];
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double residual = wm.getResidual();
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residuals[i] = Math.sqrt(wm.getWeight()) * residual;
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cost += wm.getWeight() * residual * residual;
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}
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cost = Math.sqrt(cost);
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}
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/**
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* Get the Root Mean Square value.
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* Get the Root Mean Square value, i.e. the root of the arithmetic
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* mean of the square of all weighted residuals. This is related to the
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* criterion that is minimized by the estimator as follows: if
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* <em>c</em> if the criterion, and <em>n</em> is the number of
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* measurements, then the RMS is <em>sqrt (c/n)</em>.
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*
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* @param problem estimation problem
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* @return RMS value
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*/
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public double getRMS(EstimationProblem problem) {
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WeightedMeasurement[] wm = problem.getMeasurements();
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double criterion = 0;
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for (int i = 0; i < wm.length; ++i) {
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double residual = wm[i].getResidual();
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criterion += wm[i].getWeight() * residual * residual;
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}
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return Math.sqrt(criterion / wm.length);
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}
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/**
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* Get the Chi-Square value.
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* @param problem estimation problem
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* @return chi-square value
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*/
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public double getChiSquare(EstimationProblem problem) {
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WeightedMeasurement[] wm = problem.getMeasurements();
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double chiSquare = 0;
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for (int i = 0; i < wm.length; ++i) {
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double residual = wm[i].getResidual();
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chiSquare += residual * residual / wm[i].getWeight();
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}
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return chiSquare;
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}
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/**
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* Get the covariance matrix of unbound estimated parameters.
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* @param problem estimation problem
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* @return covariance matrix
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* @exception EstimationException if the covariance matrix
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* cannot be computed (singular problem)
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*/
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public double[][] getCovariances(EstimationProblem problem)
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throws EstimationException {
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// set up the jacobian
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updateJacobian();
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// compute transpose(J).J, avoiding building big intermediate matrices
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final int rows = problem.getMeasurements().length;
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final int cols = problem.getUnboundParameters().length;
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final int max = cols * rows;
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double[][] jTj = new double[cols][cols];
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for (int i = 0; i < cols; ++i) {
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for (int j = i; j < cols; ++j) {
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double sum = 0;
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for (int k = 0; k < max; k += cols) {
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sum += jacobian[k + i] * jacobian[k + j];
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}
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jTj[i][j] = sum;
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jTj[j][i] = sum;
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}
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}
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try {
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// compute the covariances matrix
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RealMatrix inverse =
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new LUDecompositionImpl(MatrixUtils.createRealMatrix(jTj)).getSolver().getInverse();
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return inverse.getData();
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} catch (InvalidMatrixException ime) {
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throw new EstimationException("unable to compute covariances: singular problem");
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}
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}
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/**
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* Guess the errors in unbound estimated parameters.
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* <p>Guessing is covariance-based, it only gives rough order of magnitude.</p>
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* @param problem estimation problem
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* @return errors in estimated parameters
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* @exception EstimationException if the covariances matrix cannot be computed
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* or the number of degrees of freedom is not positive (number of measurements
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* lesser or equal to number of parameters)
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*/
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public double[] guessParametersErrors(EstimationProblem problem)
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throws EstimationException {
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int m = problem.getMeasurements().length;
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int p = problem.getUnboundParameters().length;
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if (m <= p) {
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throw new EstimationException(
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"no degrees of freedom ({0} measurements, {1} parameters)",
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m, p);
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}
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double[] errors = new double[problem.getUnboundParameters().length];
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final double c = Math.sqrt(getChiSquare(problem) / (m - p));
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double[][] covar = getCovariances(problem);
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for (int i = 0; i < errors.length; ++i) {
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errors[i] = Math.sqrt(covar[i][i]) * c;
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}
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return errors;
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}
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/**
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* Initialization of the common parts of the estimation.
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* <p>This method <em>must</em> be called at the start
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* of the {@link #estimate(EstimationProblem) estimate}
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* method.</p>
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* @param problem estimation problem to solve
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*/
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protected void initializeEstimate(EstimationProblem problem) {
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// reset counters
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costEvaluations = 0;
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jacobianEvaluations = 0;
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// retrieve the equations and the parameters
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measurements = problem.getMeasurements();
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parameters = problem.getUnboundParameters();
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// arrays shared with the other private methods
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rows = measurements.length;
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cols = parameters.length;
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jacobian = new double[rows * cols];
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residuals = new double[rows];
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cost = Double.POSITIVE_INFINITY;
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}
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/**
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* Solve an estimation problem.
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*
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* <p>The method should set the parameters of the problem to several
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* trial values until it reaches convergence. If this method returns
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* normally (i.e. without throwing an exception), then the best
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* estimate of the parameters can be retrieved from the problem
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* itself, through the {@link EstimationProblem#getAllParameters
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* EstimationProblem.getAllParameters} method.</p>
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*
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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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*
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*/
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public abstract void estimate(EstimationProblem problem)
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throws EstimationException;
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/** Array of measurements. */
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protected WeightedMeasurement[] measurements;
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/** Array of parameters. */
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protected EstimatedParameter[] parameters;
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/**
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* Jacobian matrix.
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* <p>This matrix is in canonical form just after the calls to
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* {@link #updateJacobian()}, but may be modified by the solver
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* in the derived class (the {@link LevenbergMarquardtEstimator
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* Levenberg-Marquardt estimator} does this).</p>
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*/
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protected double[] jacobian;
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/** Number of columns of the jacobian matrix. */
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protected int cols;
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/** Number of rows of the jacobian matrix. */
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protected int rows;
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/** Residuals array.
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* <p>This array is in canonical form just after the calls to
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* {@link #updateJacobian()}, but may be modified by the solver
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* in the derived class (the {@link LevenbergMarquardtEstimator
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* Levenberg-Marquardt estimator} does this).</p>
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*/
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protected double[] residuals;
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/** Cost value (square root of the sum of the residuals). */
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protected double cost;
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/** Maximal allowed number of cost evaluations. */
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private int maxCostEval;
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/** Number of cost evaluations. */
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private int costEvaluations;
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/** Number of jacobian evaluations. */
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private int jacobianEvaluations;
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}
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@ -0,0 +1,126 @@
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/*
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* 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.
|
||||
*/
|
||||
|
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package org.apache.commons.math.estimation;
|
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import java.io.Serializable;
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/** This class represents the estimated parameters of an estimation problem.
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*
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* <p>The parameters of an estimation problem have a name, a value and
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* a bound flag. The value of bound parameters is considered trusted
|
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* and the solvers should not adjust them. On the other hand, the
|
||||
* solvers should adjust the value of unbounds parameters until they
|
||||
* satisfy convergence criterions specific to each solver.</p>
|
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*
|
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* @version $Revision$ $Date$
|
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* @since 1.2
|
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* @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
|
||||
*
|
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*/
|
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@Deprecated
|
||||
public class EstimatedParameter
|
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implements Serializable {
|
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|
||||
/** Simple constructor.
|
||||
* Build an instance from a first estimate of the parameter,
|
||||
* initially considered unbound.
|
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* @param name name of the parameter
|
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* @param firstEstimate first estimate of the parameter
|
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*/
|
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public EstimatedParameter(String name, double firstEstimate) {
|
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this.name = name;
|
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estimate = firstEstimate;
|
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bound = false;
|
||||
}
|
||||
|
||||
/** Simple constructor.
|
||||
* Build an instance from a first estimate of the parameter and a
|
||||
* bound flag
|
||||
* @param name name of the parameter
|
||||
* @param firstEstimate first estimate of the parameter
|
||||
* @param bound flag, should be true if the parameter is bound
|
||||
*/
|
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public EstimatedParameter(String name,
|
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double firstEstimate,
|
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boolean bound) {
|
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this.name = name;
|
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estimate = firstEstimate;
|
||||
this.bound = bound;
|
||||
}
|
||||
|
||||
/** Copy constructor.
|
||||
* Build a copy of a parameter
|
||||
* @param parameter instance to copy
|
||||
*/
|
||||
public EstimatedParameter(EstimatedParameter parameter) {
|
||||
name = parameter.name;
|
||||
estimate = parameter.estimate;
|
||||
bound = parameter.bound;
|
||||
}
|
||||
|
||||
/** Set a new estimated value for the parameter.
|
||||
* @param estimate new estimate for the parameter
|
||||
*/
|
||||
public void setEstimate(double estimate) {
|
||||
this.estimate = estimate;
|
||||
}
|
||||
|
||||
/** Get the current estimate of the parameter
|
||||
* @return current estimate
|
||||
*/
|
||||
public double getEstimate() {
|
||||
return estimate;
|
||||
}
|
||||
|
||||
/** get the name of the parameter
|
||||
* @return parameter name
|
||||
*/
|
||||
public String getName() {
|
||||
return name;
|
||||
}
|
||||
|
||||
/** Set the bound flag of the parameter
|
||||
* @param bound this flag should be set to true if the parameter is
|
||||
* bound (i.e. if it should not be adjusted by the solver).
|
||||
*/
|
||||
public void setBound(boolean bound) {
|
||||
this.bound = bound;
|
||||
}
|
||||
|
||||
/** Check if the parameter is bound
|
||||
* @return true if the parameter is bound */
|
||||
public boolean isBound() {
|
||||
return bound;
|
||||
}
|
||||
|
||||
/** Name of the parameter */
|
||||
private String name;
|
||||
|
||||
/** Current value of the parameter */
|
||||
protected double estimate;
|
||||
|
||||
/** Indicator for bound parameters
|
||||
* (ie parameters that should not be estimated)
|
||||
*/
|
||||
private boolean bound;
|
||||
|
||||
/** Serializable version identifier */
|
||||
private static final long serialVersionUID = -555440800213416949L;
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
/*
|
||||
* 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 org.apache.commons.math.MathException;
|
||||
|
||||
/**
|
||||
* This class represents exceptions thrown by the estimation solvers.
|
||||
*
|
||||
* @version $Revision$ $Date$
|
||||
* @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 EstimationException
|
||||
extends MathException {
|
||||
|
||||
/** Serializable version identifier. */
|
||||
private static final long serialVersionUID = -573038581493881337L;
|
||||
|
||||
/**
|
||||
* Simple constructor.
|
||||
* Build an exception by translating and formating a message
|
||||
* @param specifier format specifier (to be translated)
|
||||
* @param parts to insert in the format (no translation)
|
||||
*/
|
||||
public EstimationException(String specifier, Object ... parts) {
|
||||
super(specifier, parts);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
/*
|
||||
* 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;
|
||||
|
||||
/**
|
||||
* This interface represents an estimation problem.
|
||||
*
|
||||
* <p>This interface should be implemented by all real estimation
|
||||
* problems before they can be handled by the estimators through the
|
||||
* {@link Estimator#estimate Estimator.estimate} method.</p>
|
||||
*
|
||||
* <p>An estimation problem, as seen by a solver is a set of
|
||||
* parameters and a set of measurements. The parameters are adjusted
|
||||
* during the estimation through the {@link #getUnboundParameters
|
||||
* getUnboundParameters} and {@link EstimatedParameter#setEstimate
|
||||
* EstimatedParameter.setEstimate} methods. The measurements both have
|
||||
* a measured value which is generally fixed at construction and a
|
||||
* theoretical value which depends on the model and hence varies as
|
||||
* the parameters are adjusted. The purpose of the solver is to reduce
|
||||
* the residual between these values, it can retrieve the measurements
|
||||
* through the {@link #getMeasurements getMeasurements} method.</p>
|
||||
*
|
||||
* @see Estimator
|
||||
* @see WeightedMeasurement
|
||||
*
|
||||
* @version $Revision$ $Date$
|
||||
* @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 interface EstimationProblem {
|
||||
/**
|
||||
* Get the measurements of an estimation problem.
|
||||
* @return measurements
|
||||
*/
|
||||
public WeightedMeasurement[] getMeasurements();
|
||||
|
||||
/**
|
||||
* Get the unbound parameters of the problem.
|
||||
* @return unbound parameters
|
||||
*/
|
||||
public EstimatedParameter[] getUnboundParameters();
|
||||
|
||||
/**
|
||||
* Get all the parameters of the problem.
|
||||
* @return parameters
|
||||
*/
|
||||
public EstimatedParameter[] getAllParameters();
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
/*
|
||||
* 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;
|
||||
|
||||
/**
|
||||
* This interface represents solvers for estimation problems.
|
||||
*
|
||||
* <p>The classes which are devoted to solve estimation problems
|
||||
* should implement this interface. The problems which can be handled
|
||||
* should implement the {@link EstimationProblem} interface which
|
||||
* gather all the information needed by the solver.</p>
|
||||
*
|
||||
* <p>The interface is composed only of the {@link #estimate estimate}
|
||||
* method.</p>
|
||||
*
|
||||
* @see EstimationProblem
|
||||
*
|
||||
* @version $Revision$ $Date$
|
||||
* @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 interface Estimator {
|
||||
|
||||
/**
|
||||
* Solve an estimation problem.
|
||||
*
|
||||
* <p>The method should set the parameters of the problem to several
|
||||
* trial values until it reaches convergence. If this method returns
|
||||
* normally (i.e. without throwing an exception), then the best
|
||||
* estimate of the parameters can be retrieved from the problem
|
||||
* itself, through the {@link EstimationProblem#getAllParameters
|
||||
* EstimationProblem.getAllParameters} method.</p>
|
||||
*
|
||||
* @param problem estimation problem to solve
|
||||
* @exception EstimationException if the problem cannot be solved
|
||||
*
|
||||
*/
|
||||
public void estimate(EstimationProblem problem)
|
||||
throws EstimationException;
|
||||
|
||||
/**
|
||||
* Get the Root Mean Square value.
|
||||
* Get the Root Mean Square value, i.e. the root of the arithmetic
|
||||
* mean of the square of all weighted residuals. This is related to the
|
||||
* criterion that is minimized by the estimator as follows: if
|
||||
* <em>c</em> is the criterion, and <em>n</em> is the number of
|
||||
* measurements, then the RMS is <em>sqrt (c/n)</em>.
|
||||
* @see #guessParametersErrors(EstimationProblem)
|
||||
*
|
||||
* @param problem estimation problem
|
||||
* @return RMS value
|
||||
*/
|
||||
public double getRMS(EstimationProblem problem);
|
||||
|
||||
/**
|
||||
* Get the covariance matrix of estimated parameters.
|
||||
* @param problem estimation problem
|
||||
* @return covariance matrix
|
||||
* @exception EstimationException if the covariance matrix
|
||||
* cannot be computed (singular problem)
|
||||
*/
|
||||
public double[][] getCovariances(EstimationProblem problem)
|
||||
throws EstimationException;
|
||||
|
||||
/**
|
||||
* Guess the errors in estimated parameters.
|
||||
* @see #getRMS(EstimationProblem)
|
||||
* @param problem estimation problem
|
||||
* @return errors in estimated parameters
|
||||
* @exception EstimationException if the error cannot be guessed
|
||||
*/
|
||||
public double[] guessParametersErrors(EstimationProblem problem)
|
||||
throws EstimationException;
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,228 @@
|
|||
/*
|
||||
* 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.linear.InvalidMatrixException;
|
||||
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.RealVectorImpl;
|
||||
import org.apache.commons.math.linear.decomposition.LUDecompositionImpl;
|
||||
|
||||
/**
|
||||
* 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 $Revision$ $Date$
|
||||
* @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>Math.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>Math.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
|
||||
*
|
||||
*/
|
||||
public void estimate(EstimationProblem problem)
|
||||
throws EstimationException {
|
||||
|
||||
initializeEstimate(problem);
|
||||
|
||||
// work matrices
|
||||
double[] grad = new double[parameters.length];
|
||||
RealVectorImpl bDecrement = new RealVectorImpl(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 RealVectorImpl(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("unable to solve: singular problem");
|
||||
}
|
||||
|
||||
|
||||
previous = cost;
|
||||
updateResidualsAndCost();
|
||||
|
||||
} while ((getCostEvaluations() < 2) ||
|
||||
(Math.abs(previous - cost) > (cost * steadyStateThreshold) &&
|
||||
(Math.abs(cost) > convergence)));
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,874 @@
|
|||
/*
|
||||
* 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 java.util.Arrays;
|
||||
|
||||
|
||||
/**
|
||||
* This class solves a least squares problem.
|
||||
*
|
||||
* <p>This implementation <em>should</em> work even for over-determined systems
|
||||
* (i.e. systems having more variables than equations). Over-determined systems
|
||||
* are solved by ignoring the variables which have the smallest impact according
|
||||
* to their jacobian column norm. Only the rank of the matrix and some loop bounds
|
||||
* are changed to implement this.</p>
|
||||
*
|
||||
* <p>The resolution engine is a simple translation of the MINPACK <a
|
||||
* href="http://www.netlib.org/minpack/lmder.f">lmder</a> routine with minor
|
||||
* changes. The changes include the over-determined resolution and the Q.R.
|
||||
* decomposition which has been rewritten following the algorithm described in the
|
||||
* P. Lascaux and R. Theodor book <i>Analyse numérique matricielle
|
||||
* appliquée à l'art de l'ingénieur</i>, Masson 1986. The
|
||||
* redistribution policy for MINPACK is available <a
|
||||
* href="http://www.netlib.org/minpack/disclaimer">here</a>, for convenience, it
|
||||
* is reproduced below.</p>
|
||||
*
|
||||
* <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
|
||||
* <tr><td>
|
||||
* Minpack Copyright Notice (1999) University of Chicago.
|
||||
* All rights reserved
|
||||
* </td></tr>
|
||||
* <tr><td>
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
* <ol>
|
||||
* <li>Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.</li>
|
||||
* <li>Redistributions in binary form must reproduce the above
|
||||
* copyright notice, this list of conditions and the following
|
||||
* disclaimer in the documentation and/or other materials provided
|
||||
* with the distribution.</li>
|
||||
* <li>The end-user documentation included with the redistribution, if any,
|
||||
* must include the following acknowledgment:
|
||||
* <code>This product includes software developed by the University of
|
||||
* Chicago, as Operator of Argonne National Laboratory.</code>
|
||||
* Alternately, this acknowledgment may appear in the software itself,
|
||||
* if and wherever such third-party acknowledgments normally appear.</li>
|
||||
* <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
|
||||
* WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
|
||||
* UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
|
||||
* THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
|
||||
* IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
|
||||
* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
|
||||
* OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
|
||||
* OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
|
||||
* USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
|
||||
* THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
|
||||
* DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
|
||||
* UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
|
||||
* BE CORRECTED.</strong></li>
|
||||
* <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
|
||||
* HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
|
||||
* ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
|
||||
* INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
|
||||
* ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
* PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
|
||||
* SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
|
||||
* (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
|
||||
* EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
|
||||
* POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
|
||||
* <ol></td></tr>
|
||||
* </table>
|
||||
|
||||
* @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran)
|
||||
* @author Burton S. Garbow (original fortran)
|
||||
* @author Kenneth E. Hillstrom (original fortran)
|
||||
* @author Jorge J. More (original fortran)
|
||||
|
||||
* @version $Revision$ $Date$
|
||||
* @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 LevenbergMarquardtEstimator extends AbstractEstimator implements Serializable {
|
||||
|
||||
/**
|
||||
* Build an estimator for least squares problems.
|
||||
* <p>The default values for the algorithm settings are:
|
||||
* <ul>
|
||||
* <li>{@link #setInitialStepBoundFactor initial step bound factor}: 100.0</li>
|
||||
* <li>{@link #setMaxCostEval maximal cost evaluations}: 1000</li>
|
||||
* <li>{@link #setCostRelativeTolerance cost relative tolerance}: 1.0e-10</li>
|
||||
* <li>{@link #setParRelativeTolerance parameters relative tolerance}: 1.0e-10</li>
|
||||
* <li>{@link #setOrthoTolerance orthogonality tolerance}: 1.0e-10</li>
|
||||
* </ul>
|
||||
* </p>
|
||||
*/
|
||||
public LevenbergMarquardtEstimator() {
|
||||
|
||||
// set up the superclass with a default max cost evaluations setting
|
||||
setMaxCostEval(1000);
|
||||
|
||||
// default values for the tuning parameters
|
||||
setInitialStepBoundFactor(100.0);
|
||||
setCostRelativeTolerance(1.0e-10);
|
||||
setParRelativeTolerance(1.0e-10);
|
||||
setOrthoTolerance(1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Set the positive input variable used in determining the initial step bound.
|
||||
* This bound is set to the product of initialStepBoundFactor and the euclidean norm of diag*x if nonzero,
|
||||
* or else to initialStepBoundFactor itself. In most cases factor should lie
|
||||
* in the interval (0.1, 100.0). 100.0 is a generally recommended value
|
||||
*
|
||||
* @param initialStepBoundFactor initial step bound factor
|
||||
* @see #estimate
|
||||
*/
|
||||
public void setInitialStepBoundFactor(double initialStepBoundFactor) {
|
||||
this.initialStepBoundFactor = initialStepBoundFactor;
|
||||
}
|
||||
|
||||
/**
|
||||
* Set the desired relative error in the sum of squares.
|
||||
*
|
||||
* @param costRelativeTolerance desired relative error in the sum of squares
|
||||
* @see #estimate
|
||||
*/
|
||||
public void setCostRelativeTolerance(double costRelativeTolerance) {
|
||||
this.costRelativeTolerance = costRelativeTolerance;
|
||||
}
|
||||
|
||||
/**
|
||||
* Set the desired relative error in the approximate solution parameters.
|
||||
*
|
||||
* @param parRelativeTolerance desired relative error
|
||||
* in the approximate solution parameters
|
||||
* @see #estimate
|
||||
*/
|
||||
public void setParRelativeTolerance(double parRelativeTolerance) {
|
||||
this.parRelativeTolerance = parRelativeTolerance;
|
||||
}
|
||||
|
||||
/**
|
||||
* Set the desired max cosine on the orthogonality.
|
||||
*
|
||||
* @param orthoTolerance desired max cosine on the orthogonality
|
||||
* between the function vector and the columns of the jacobian
|
||||
* @see #estimate
|
||||
*/
|
||||
public void setOrthoTolerance(double orthoTolerance) {
|
||||
this.orthoTolerance = orthoTolerance;
|
||||
}
|
||||
|
||||
/**
|
||||
* Solve an estimation problem using the Levenberg-Marquardt algorithm.
|
||||
* <p>The algorithm used is a modified Levenberg-Marquardt one, based
|
||||
* on the MINPACK <a href="http://www.netlib.org/minpack/lmder.f">lmder</a>
|
||||
* routine. The algorithm settings must have been set up before this method
|
||||
* is called with the {@link #setInitialStepBoundFactor},
|
||||
* {@link #setMaxCostEval}, {@link #setCostRelativeTolerance},
|
||||
* {@link #setParRelativeTolerance} and {@link #setOrthoTolerance} methods.
|
||||
* If these methods have not been called, the default values set up by the
|
||||
* {@link #LevenbergMarquardtEstimator() constructor} will be used.</p>
|
||||
* <p>The authors of the original fortran function are:</p>
|
||||
* <ul>
|
||||
* <li>Argonne National Laboratory. MINPACK project. March 1980</li>
|
||||
* <li>Burton S. Garbow</li>
|
||||
* <li>Kenneth E. Hillstrom</li>
|
||||
* <li>Jorge J. More</li>
|
||||
* </ul>
|
||||
* <p>Luc Maisonobe did the Java translation.</p>
|
||||
*
|
||||
* @param problem estimation problem to solve
|
||||
* @exception EstimationException if convergence cannot be
|
||||
* reached with the specified algorithm settings or if there are more variables
|
||||
* than equations
|
||||
* @see #setInitialStepBoundFactor
|
||||
* @see #setCostRelativeTolerance
|
||||
* @see #setParRelativeTolerance
|
||||
* @see #setOrthoTolerance
|
||||
*/
|
||||
public void estimate(EstimationProblem problem)
|
||||
throws EstimationException {
|
||||
|
||||
initializeEstimate(problem);
|
||||
|
||||
// arrays shared with the other private methods
|
||||
solvedCols = Math.min(rows, cols);
|
||||
diagR = new double[cols];
|
||||
jacNorm = new double[cols];
|
||||
beta = new double[cols];
|
||||
permutation = new int[cols];
|
||||
lmDir = new double[cols];
|
||||
|
||||
// local variables
|
||||
double delta = 0, xNorm = 0;
|
||||
double[] diag = new double[cols];
|
||||
double[] oldX = new double[cols];
|
||||
double[] oldRes = new double[rows];
|
||||
double[] work1 = new double[cols];
|
||||
double[] work2 = new double[cols];
|
||||
double[] work3 = new double[cols];
|
||||
|
||||
// evaluate the function at the starting point and calculate its norm
|
||||
updateResidualsAndCost();
|
||||
|
||||
// outer loop
|
||||
lmPar = 0;
|
||||
boolean firstIteration = true;
|
||||
while (true) {
|
||||
|
||||
// compute the Q.R. decomposition of the jacobian matrix
|
||||
updateJacobian();
|
||||
qrDecomposition();
|
||||
|
||||
// compute Qt.res
|
||||
qTy(residuals);
|
||||
|
||||
// now we don't need Q anymore,
|
||||
// so let jacobian contain the R matrix with its diagonal elements
|
||||
for (int k = 0; k < solvedCols; ++k) {
|
||||
int pk = permutation[k];
|
||||
jacobian[k * cols + pk] = diagR[pk];
|
||||
}
|
||||
|
||||
if (firstIteration) {
|
||||
|
||||
// scale the variables according to the norms of the columns
|
||||
// of the initial jacobian
|
||||
xNorm = 0;
|
||||
for (int k = 0; k < cols; ++k) {
|
||||
double dk = jacNorm[k];
|
||||
if (dk == 0) {
|
||||
dk = 1.0;
|
||||
}
|
||||
double xk = dk * parameters[k].getEstimate();
|
||||
xNorm += xk * xk;
|
||||
diag[k] = dk;
|
||||
}
|
||||
xNorm = Math.sqrt(xNorm);
|
||||
|
||||
// initialize the step bound delta
|
||||
delta = (xNorm == 0) ? initialStepBoundFactor : (initialStepBoundFactor * xNorm);
|
||||
|
||||
}
|
||||
|
||||
// check orthogonality between function vector and jacobian columns
|
||||
double maxCosine = 0;
|
||||
if (cost != 0) {
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double s = jacNorm[pj];
|
||||
if (s != 0) {
|
||||
double sum = 0;
|
||||
for (int i = 0, index = pj; i <= j; ++i, index += cols) {
|
||||
sum += jacobian[index] * residuals[i];
|
||||
}
|
||||
maxCosine = Math.max(maxCosine, Math.abs(sum) / (s * cost));
|
||||
}
|
||||
}
|
||||
}
|
||||
if (maxCosine <= orthoTolerance) {
|
||||
return;
|
||||
}
|
||||
|
||||
// rescale if necessary
|
||||
for (int j = 0; j < cols; ++j) {
|
||||
diag[j] = Math.max(diag[j], jacNorm[j]);
|
||||
}
|
||||
|
||||
// inner loop
|
||||
for (double ratio = 0; ratio < 1.0e-4;) {
|
||||
|
||||
// save the state
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
oldX[pj] = parameters[pj].getEstimate();
|
||||
}
|
||||
double previousCost = cost;
|
||||
double[] tmpVec = residuals;
|
||||
residuals = oldRes;
|
||||
oldRes = tmpVec;
|
||||
|
||||
// determine the Levenberg-Marquardt parameter
|
||||
determineLMParameter(oldRes, delta, diag, work1, work2, work3);
|
||||
|
||||
// compute the new point and the norm of the evolution direction
|
||||
double lmNorm = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
lmDir[pj] = -lmDir[pj];
|
||||
parameters[pj].setEstimate(oldX[pj] + lmDir[pj]);
|
||||
double s = diag[pj] * lmDir[pj];
|
||||
lmNorm += s * s;
|
||||
}
|
||||
lmNorm = Math.sqrt(lmNorm);
|
||||
|
||||
// on the first iteration, adjust the initial step bound.
|
||||
if (firstIteration) {
|
||||
delta = Math.min(delta, lmNorm);
|
||||
}
|
||||
|
||||
// evaluate the function at x + p and calculate its norm
|
||||
updateResidualsAndCost();
|
||||
|
||||
// compute the scaled actual reduction
|
||||
double actRed = -1.0;
|
||||
if (0.1 * cost < previousCost) {
|
||||
double r = cost / previousCost;
|
||||
actRed = 1.0 - r * r;
|
||||
}
|
||||
|
||||
// compute the scaled predicted reduction
|
||||
// and the scaled directional derivative
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double dirJ = lmDir[pj];
|
||||
work1[j] = 0;
|
||||
for (int i = 0, index = pj; i <= j; ++i, index += cols) {
|
||||
work1[i] += jacobian[index] * dirJ;
|
||||
}
|
||||
}
|
||||
double coeff1 = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
coeff1 += work1[j] * work1[j];
|
||||
}
|
||||
double pc2 = previousCost * previousCost;
|
||||
coeff1 = coeff1 / pc2;
|
||||
double coeff2 = lmPar * lmNorm * lmNorm / pc2;
|
||||
double preRed = coeff1 + 2 * coeff2;
|
||||
double dirDer = -(coeff1 + coeff2);
|
||||
|
||||
// ratio of the actual to the predicted reduction
|
||||
ratio = (preRed == 0) ? 0 : (actRed / preRed);
|
||||
|
||||
// update the step bound
|
||||
if (ratio <= 0.25) {
|
||||
double tmp =
|
||||
(actRed < 0) ? (0.5 * dirDer / (dirDer + 0.5 * actRed)) : 0.5;
|
||||
if ((0.1 * cost >= previousCost) || (tmp < 0.1)) {
|
||||
tmp = 0.1;
|
||||
}
|
||||
delta = tmp * Math.min(delta, 10.0 * lmNorm);
|
||||
lmPar /= tmp;
|
||||
} else if ((lmPar == 0) || (ratio >= 0.75)) {
|
||||
delta = 2 * lmNorm;
|
||||
lmPar *= 0.5;
|
||||
}
|
||||
|
||||
// test for successful iteration.
|
||||
if (ratio >= 1.0e-4) {
|
||||
// successful iteration, update the norm
|
||||
firstIteration = false;
|
||||
xNorm = 0;
|
||||
for (int k = 0; k < cols; ++k) {
|
||||
double xK = diag[k] * parameters[k].getEstimate();
|
||||
xNorm += xK * xK;
|
||||
}
|
||||
xNorm = Math.sqrt(xNorm);
|
||||
} else {
|
||||
// failed iteration, reset the previous values
|
||||
cost = previousCost;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
parameters[pj].setEstimate(oldX[pj]);
|
||||
}
|
||||
tmpVec = residuals;
|
||||
residuals = oldRes;
|
||||
oldRes = tmpVec;
|
||||
}
|
||||
|
||||
// tests for convergence.
|
||||
if (((Math.abs(actRed) <= costRelativeTolerance) &&
|
||||
(preRed <= costRelativeTolerance) &&
|
||||
(ratio <= 2.0)) ||
|
||||
(delta <= parRelativeTolerance * xNorm)) {
|
||||
return;
|
||||
}
|
||||
|
||||
// tests for termination and stringent tolerances
|
||||
// (2.2204e-16 is the machine epsilon for IEEE754)
|
||||
if ((Math.abs(actRed) <= 2.2204e-16) && (preRed <= 2.2204e-16) && (ratio <= 2.0)) {
|
||||
throw new EstimationException("cost relative tolerance is too small ({0})," +
|
||||
" no further reduction in the" +
|
||||
" sum of squares is possible",
|
||||
costRelativeTolerance);
|
||||
} else if (delta <= 2.2204e-16 * xNorm) {
|
||||
throw new EstimationException("parameters relative tolerance is too small" +
|
||||
" ({0}), no further improvement in" +
|
||||
" the approximate solution is possible",
|
||||
parRelativeTolerance);
|
||||
} else if (maxCosine <= 2.2204e-16) {
|
||||
throw new EstimationException("orthogonality tolerance is too small ({0})," +
|
||||
" solution is orthogonal to the jacobian",
|
||||
orthoTolerance);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Determine the Levenberg-Marquardt parameter.
|
||||
* <p>This implementation is a translation in Java of the MINPACK
|
||||
* <a href="http://www.netlib.org/minpack/lmpar.f">lmpar</a>
|
||||
* routine.</p>
|
||||
* <p>This method sets the lmPar and lmDir attributes.</p>
|
||||
* <p>The authors of the original fortran function are:</p>
|
||||
* <ul>
|
||||
* <li>Argonne National Laboratory. MINPACK project. March 1980</li>
|
||||
* <li>Burton S. Garbow</li>
|
||||
* <li>Kenneth E. Hillstrom</li>
|
||||
* <li>Jorge J. More</li>
|
||||
* </ul>
|
||||
* <p>Luc Maisonobe did the Java translation.</p>
|
||||
*
|
||||
* @param qy array containing qTy
|
||||
* @param delta upper bound on the euclidean norm of diagR * lmDir
|
||||
* @param diag diagonal matrix
|
||||
* @param work1 work array
|
||||
* @param work2 work array
|
||||
* @param work3 work array
|
||||
*/
|
||||
private void determineLMParameter(double[] qy, double delta, double[] diag,
|
||||
double[] work1, double[] work2, double[] work3) {
|
||||
|
||||
// compute and store in x the gauss-newton direction, if the
|
||||
// jacobian is rank-deficient, obtain a least squares solution
|
||||
for (int j = 0; j < rank; ++j) {
|
||||
lmDir[permutation[j]] = qy[j];
|
||||
}
|
||||
for (int j = rank; j < cols; ++j) {
|
||||
lmDir[permutation[j]] = 0;
|
||||
}
|
||||
for (int k = rank - 1; k >= 0; --k) {
|
||||
int pk = permutation[k];
|
||||
double ypk = lmDir[pk] / diagR[pk];
|
||||
for (int i = 0, index = pk; i < k; ++i, index += cols) {
|
||||
lmDir[permutation[i]] -= ypk * jacobian[index];
|
||||
}
|
||||
lmDir[pk] = ypk;
|
||||
}
|
||||
|
||||
// evaluate the function at the origin, and test
|
||||
// for acceptance of the Gauss-Newton direction
|
||||
double dxNorm = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double s = diag[pj] * lmDir[pj];
|
||||
work1[pj] = s;
|
||||
dxNorm += s * s;
|
||||
}
|
||||
dxNorm = Math.sqrt(dxNorm);
|
||||
double fp = dxNorm - delta;
|
||||
if (fp <= 0.1 * delta) {
|
||||
lmPar = 0;
|
||||
return;
|
||||
}
|
||||
|
||||
// if the jacobian is not rank deficient, the Newton step provides
|
||||
// a lower bound, parl, for the zero of the function,
|
||||
// otherwise set this bound to zero
|
||||
double sum2, parl = 0;
|
||||
if (rank == solvedCols) {
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
work1[pj] *= diag[pj] / dxNorm;
|
||||
}
|
||||
sum2 = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double sum = 0;
|
||||
for (int i = 0, index = pj; i < j; ++i, index += cols) {
|
||||
sum += jacobian[index] * work1[permutation[i]];
|
||||
}
|
||||
double s = (work1[pj] - sum) / diagR[pj];
|
||||
work1[pj] = s;
|
||||
sum2 += s * s;
|
||||
}
|
||||
parl = fp / (delta * sum2);
|
||||
}
|
||||
|
||||
// calculate an upper bound, paru, for the zero of the function
|
||||
sum2 = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double sum = 0;
|
||||
for (int i = 0, index = pj; i <= j; ++i, index += cols) {
|
||||
sum += jacobian[index] * qy[i];
|
||||
}
|
||||
sum /= diag[pj];
|
||||
sum2 += sum * sum;
|
||||
}
|
||||
double gNorm = Math.sqrt(sum2);
|
||||
double paru = gNorm / delta;
|
||||
if (paru == 0) {
|
||||
// 2.2251e-308 is the smallest positive real for IEE754
|
||||
paru = 2.2251e-308 / Math.min(delta, 0.1);
|
||||
}
|
||||
|
||||
// if the input par lies outside of the interval (parl,paru),
|
||||
// set par to the closer endpoint
|
||||
lmPar = Math.min(paru, Math.max(lmPar, parl));
|
||||
if (lmPar == 0) {
|
||||
lmPar = gNorm / dxNorm;
|
||||
}
|
||||
|
||||
for (int countdown = 10; countdown >= 0; --countdown) {
|
||||
|
||||
// evaluate the function at the current value of lmPar
|
||||
if (lmPar == 0) {
|
||||
lmPar = Math.max(2.2251e-308, 0.001 * paru);
|
||||
}
|
||||
double sPar = Math.sqrt(lmPar);
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
work1[pj] = sPar * diag[pj];
|
||||
}
|
||||
determineLMDirection(qy, work1, work2, work3);
|
||||
|
||||
dxNorm = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
double s = diag[pj] * lmDir[pj];
|
||||
work3[pj] = s;
|
||||
dxNorm += s * s;
|
||||
}
|
||||
dxNorm = Math.sqrt(dxNorm);
|
||||
double previousFP = fp;
|
||||
fp = dxNorm - delta;
|
||||
|
||||
// if the function is small enough, accept the current value
|
||||
// of lmPar, also test for the exceptional cases where parl is zero
|
||||
if ((Math.abs(fp) <= 0.1 * delta) ||
|
||||
((parl == 0) && (fp <= previousFP) && (previousFP < 0))) {
|
||||
return;
|
||||
}
|
||||
|
||||
// compute the Newton correction
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
work1[pj] = work3[pj] * diag[pj] / dxNorm;
|
||||
}
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
work1[pj] /= work2[j];
|
||||
double tmp = work1[pj];
|
||||
for (int i = j + 1; i < solvedCols; ++i) {
|
||||
work1[permutation[i]] -= jacobian[i * cols + pj] * tmp;
|
||||
}
|
||||
}
|
||||
sum2 = 0;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
double s = work1[permutation[j]];
|
||||
sum2 += s * s;
|
||||
}
|
||||
double correction = fp / (delta * sum2);
|
||||
|
||||
// depending on the sign of the function, update parl or paru.
|
||||
if (fp > 0) {
|
||||
parl = Math.max(parl, lmPar);
|
||||
} else if (fp < 0) {
|
||||
paru = Math.min(paru, lmPar);
|
||||
}
|
||||
|
||||
// compute an improved estimate for lmPar
|
||||
lmPar = Math.max(parl, lmPar + correction);
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Solve a*x = b and d*x = 0 in the least squares sense.
|
||||
* <p>This implementation is a translation in Java of the MINPACK
|
||||
* <a href="http://www.netlib.org/minpack/qrsolv.f">qrsolv</a>
|
||||
* routine.</p>
|
||||
* <p>This method sets the lmDir and lmDiag attributes.</p>
|
||||
* <p>The authors of the original fortran function are:</p>
|
||||
* <ul>
|
||||
* <li>Argonne National Laboratory. MINPACK project. March 1980</li>
|
||||
* <li>Burton S. Garbow</li>
|
||||
* <li>Kenneth E. Hillstrom</li>
|
||||
* <li>Jorge J. More</li>
|
||||
* </ul>
|
||||
* <p>Luc Maisonobe did the Java translation.</p>
|
||||
*
|
||||
* @param qy array containing qTy
|
||||
* @param diag diagonal matrix
|
||||
* @param lmDiag diagonal elements associated with lmDir
|
||||
* @param work work array
|
||||
*/
|
||||
private void determineLMDirection(double[] qy, double[] diag,
|
||||
double[] lmDiag, double[] work) {
|
||||
|
||||
// copy R and Qty to preserve input and initialize s
|
||||
// in particular, save the diagonal elements of R in lmDir
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
int pj = permutation[j];
|
||||
for (int i = j + 1; i < solvedCols; ++i) {
|
||||
jacobian[i * cols + pj] = jacobian[j * cols + permutation[i]];
|
||||
}
|
||||
lmDir[j] = diagR[pj];
|
||||
work[j] = qy[j];
|
||||
}
|
||||
|
||||
// eliminate the diagonal matrix d using a Givens rotation
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
|
||||
// prepare the row of d to be eliminated, locating the
|
||||
// diagonal element using p from the Q.R. factorization
|
||||
int pj = permutation[j];
|
||||
double dpj = diag[pj];
|
||||
if (dpj != 0) {
|
||||
Arrays.fill(lmDiag, j + 1, lmDiag.length, 0);
|
||||
}
|
||||
lmDiag[j] = dpj;
|
||||
|
||||
// the transformations to eliminate the row of d
|
||||
// modify only a single element of Qty
|
||||
// beyond the first n, which is initially zero.
|
||||
double qtbpj = 0;
|
||||
for (int k = j; k < solvedCols; ++k) {
|
||||
int pk = permutation[k];
|
||||
|
||||
// determine a Givens rotation which eliminates the
|
||||
// appropriate element in the current row of d
|
||||
if (lmDiag[k] != 0) {
|
||||
|
||||
double sin, cos;
|
||||
double rkk = jacobian[k * cols + pk];
|
||||
if (Math.abs(rkk) < Math.abs(lmDiag[k])) {
|
||||
double cotan = rkk / lmDiag[k];
|
||||
sin = 1.0 / Math.sqrt(1.0 + cotan * cotan);
|
||||
cos = sin * cotan;
|
||||
} else {
|
||||
double tan = lmDiag[k] / rkk;
|
||||
cos = 1.0 / Math.sqrt(1.0 + tan * tan);
|
||||
sin = cos * tan;
|
||||
}
|
||||
|
||||
// compute the modified diagonal element of R and
|
||||
// the modified element of (Qty,0)
|
||||
jacobian[k * cols + pk] = cos * rkk + sin * lmDiag[k];
|
||||
double temp = cos * work[k] + sin * qtbpj;
|
||||
qtbpj = -sin * work[k] + cos * qtbpj;
|
||||
work[k] = temp;
|
||||
|
||||
// accumulate the tranformation in the row of s
|
||||
for (int i = k + 1; i < solvedCols; ++i) {
|
||||
double rik = jacobian[i * cols + pk];
|
||||
temp = cos * rik + sin * lmDiag[i];
|
||||
lmDiag[i] = -sin * rik + cos * lmDiag[i];
|
||||
jacobian[i * cols + pk] = temp;
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
// store the diagonal element of s and restore
|
||||
// the corresponding diagonal element of R
|
||||
int index = j * cols + permutation[j];
|
||||
lmDiag[j] = jacobian[index];
|
||||
jacobian[index] = lmDir[j];
|
||||
|
||||
}
|
||||
|
||||
// solve the triangular system for z, if the system is
|
||||
// singular, then obtain a least squares solution
|
||||
int nSing = solvedCols;
|
||||
for (int j = 0; j < solvedCols; ++j) {
|
||||
if ((lmDiag[j] == 0) && (nSing == solvedCols)) {
|
||||
nSing = j;
|
||||
}
|
||||
if (nSing < solvedCols) {
|
||||
work[j] = 0;
|
||||
}
|
||||
}
|
||||
if (nSing > 0) {
|
||||
for (int j = nSing - 1; j >= 0; --j) {
|
||||
int pj = permutation[j];
|
||||
double sum = 0;
|
||||
for (int i = j + 1; i < nSing; ++i) {
|
||||
sum += jacobian[i * cols + pj] * work[i];
|
||||
}
|
||||
work[j] = (work[j] - sum) / lmDiag[j];
|
||||
}
|
||||
}
|
||||
|
||||
// permute the components of z back to components of lmDir
|
||||
for (int j = 0; j < lmDir.length; ++j) {
|
||||
lmDir[permutation[j]] = work[j];
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Decompose a matrix A as A.P = Q.R using Householder transforms.
|
||||
* <p>As suggested in the P. Lascaux and R. Theodor book
|
||||
* <i>Analyse numérique matricielle appliquée à
|
||||
* l'art de l'ingénieur</i> (Masson, 1986), instead of representing
|
||||
* the Householder transforms with u<sub>k</sub> unit vectors such that:
|
||||
* <pre>
|
||||
* H<sub>k</sub> = I - 2u<sub>k</sub>.u<sub>k</sub><sup>t</sup>
|
||||
* </pre>
|
||||
* we use <sub>k</sub> non-unit vectors such that:
|
||||
* <pre>
|
||||
* H<sub>k</sub> = I - beta<sub>k</sub>v<sub>k</sub>.v<sub>k</sub><sup>t</sup>
|
||||
* </pre>
|
||||
* where v<sub>k</sub> = a<sub>k</sub> - alpha<sub>k</sub> e<sub>k</sub>.
|
||||
* The beta<sub>k</sub> coefficients are provided upon exit as recomputing
|
||||
* them from the v<sub>k</sub> vectors would be costly.</p>
|
||||
* <p>This decomposition handles rank deficient cases since the tranformations
|
||||
* are performed in non-increasing columns norms order thanks to columns
|
||||
* pivoting. The diagonal elements of the R matrix are therefore also in
|
||||
* non-increasing absolute values order.</p>
|
||||
* @exception EstimationException if the decomposition cannot be performed
|
||||
*/
|
||||
private void qrDecomposition() throws EstimationException {
|
||||
|
||||
// initializations
|
||||
for (int k = 0; k < cols; ++k) {
|
||||
permutation[k] = k;
|
||||
double norm2 = 0;
|
||||
for (int index = k; index < jacobian.length; index += cols) {
|
||||
double akk = jacobian[index];
|
||||
norm2 += akk * akk;
|
||||
}
|
||||
jacNorm[k] = Math.sqrt(norm2);
|
||||
}
|
||||
|
||||
// transform the matrix column after column
|
||||
for (int k = 0; k < cols; ++k) {
|
||||
|
||||
// select the column with the greatest norm on active components
|
||||
int nextColumn = -1;
|
||||
double ak2 = Double.NEGATIVE_INFINITY;
|
||||
for (int i = k; i < cols; ++i) {
|
||||
double norm2 = 0;
|
||||
int iDiag = k * cols + permutation[i];
|
||||
for (int index = iDiag; index < jacobian.length; index += cols) {
|
||||
double aki = jacobian[index];
|
||||
norm2 += aki * aki;
|
||||
}
|
||||
if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
|
||||
throw new EstimationException(
|
||||
"unable to perform Q.R decomposition on the {0}x{1} jacobian matrix",
|
||||
rows, cols);
|
||||
}
|
||||
if (norm2 > ak2) {
|
||||
nextColumn = i;
|
||||
ak2 = norm2;
|
||||
}
|
||||
}
|
||||
if (ak2 == 0) {
|
||||
rank = k;
|
||||
return;
|
||||
}
|
||||
int pk = permutation[nextColumn];
|
||||
permutation[nextColumn] = permutation[k];
|
||||
permutation[k] = pk;
|
||||
|
||||
// choose alpha such that Hk.u = alpha ek
|
||||
int kDiag = k * cols + pk;
|
||||
double akk = jacobian[kDiag];
|
||||
double alpha = (akk > 0) ? -Math.sqrt(ak2) : Math.sqrt(ak2);
|
||||
double betak = 1.0 / (ak2 - akk * alpha);
|
||||
beta[pk] = betak;
|
||||
|
||||
// transform the current column
|
||||
diagR[pk] = alpha;
|
||||
jacobian[kDiag] -= alpha;
|
||||
|
||||
// transform the remaining columns
|
||||
for (int dk = cols - 1 - k; dk > 0; --dk) {
|
||||
int dkp = permutation[k + dk] - pk;
|
||||
double gamma = 0;
|
||||
for (int index = kDiag; index < jacobian.length; index += cols) {
|
||||
gamma += jacobian[index] * jacobian[index + dkp];
|
||||
}
|
||||
gamma *= betak;
|
||||
for (int index = kDiag; index < jacobian.length; index += cols) {
|
||||
jacobian[index + dkp] -= gamma * jacobian[index];
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
rank = solvedCols;
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute the product Qt.y for some Q.R. decomposition.
|
||||
*
|
||||
* @param y vector to multiply (will be overwritten with the result)
|
||||
*/
|
||||
private void qTy(double[] y) {
|
||||
for (int k = 0; k < cols; ++k) {
|
||||
int pk = permutation[k];
|
||||
int kDiag = k * cols + pk;
|
||||
double gamma = 0;
|
||||
for (int i = k, index = kDiag; i < rows; ++i, index += cols) {
|
||||
gamma += jacobian[index] * y[i];
|
||||
}
|
||||
gamma *= beta[pk];
|
||||
for (int i = k, index = kDiag; i < rows; ++i, index += cols) {
|
||||
y[i] -= gamma * jacobian[index];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/** Number of solved variables. */
|
||||
private int solvedCols;
|
||||
|
||||
/** Diagonal elements of the R matrix in the Q.R. decomposition. */
|
||||
private double[] diagR;
|
||||
|
||||
/** Norms of the columns of the jacobian matrix. */
|
||||
private double[] jacNorm;
|
||||
|
||||
/** Coefficients of the Householder transforms vectors. */
|
||||
private double[] beta;
|
||||
|
||||
/** Columns permutation array. */
|
||||
private int[] permutation;
|
||||
|
||||
/** Rank of the jacobian matrix. */
|
||||
private int rank;
|
||||
|
||||
/** Levenberg-Marquardt parameter. */
|
||||
private double lmPar;
|
||||
|
||||
/** Parameters evolution direction associated with lmPar. */
|
||||
private double[] lmDir;
|
||||
|
||||
/** Positive input variable used in determining the initial step bound. */
|
||||
private double initialStepBoundFactor;
|
||||
|
||||
/** Desired relative error in the sum of squares. */
|
||||
private double costRelativeTolerance;
|
||||
|
||||
/** Desired relative error in the approximate solution parameters. */
|
||||
private double parRelativeTolerance;
|
||||
|
||||
/** Desired max cosine on the orthogonality between the function vector
|
||||
* and the columns of the jacobian. */
|
||||
private double orthoTolerance;
|
||||
|
||||
/** Serializable version identifier */
|
||||
private static final long serialVersionUID = -5705952631533171019L;
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
/*
|
||||
* 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.util.ArrayList;
|
||||
import java.util.List;
|
||||
|
||||
/**
|
||||
* Simple implementation of the {@link EstimationProblem
|
||||
* EstimationProblem} interface for boilerplate data handling.
|
||||
* <p>This class <em>only</em> handles parameters and measurements
|
||||
* storage and unbound parameters filtering. It does not compute
|
||||
* anything by itself. It should either be used with measurements
|
||||
* implementation that are smart enough to know about the
|
||||
* various parameters in order to compute the partial derivatives
|
||||
* appropriately. Since the problem-specific logic is mainly related to
|
||||
* the various measurements models, the simplest way to use this class
|
||||
* is by extending it and using one internal class extending
|
||||
* {@link WeightedMeasurement WeightedMeasurement} for each measurement
|
||||
* type. The instances of the internal classes would have access to the
|
||||
* various parameters and their current estimate.</p>
|
||||
|
||||
* @version $Revision$ $Date$
|
||||
* @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 SimpleEstimationProblem implements EstimationProblem {
|
||||
|
||||
/**
|
||||
* Build an empty instance without parameters nor measurements.
|
||||
*/
|
||||
public SimpleEstimationProblem() {
|
||||
parameters = new ArrayList<EstimatedParameter>();
|
||||
measurements = new ArrayList<WeightedMeasurement>();
|
||||
}
|
||||
|
||||
/**
|
||||
* Get all the parameters of the problem.
|
||||
* @return parameters
|
||||
*/
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
return (EstimatedParameter[]) parameters.toArray(new EstimatedParameter[parameters.size()]);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the unbound parameters of the problem.
|
||||
* @return unbound parameters
|
||||
*/
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
|
||||
// filter the unbound parameters
|
||||
List<EstimatedParameter> unbound = new ArrayList<EstimatedParameter>(parameters.size());
|
||||
for (EstimatedParameter p : parameters) {
|
||||
if (! p.isBound()) {
|
||||
unbound.add(p);
|
||||
}
|
||||
}
|
||||
|
||||
// convert to an array
|
||||
return (EstimatedParameter[]) unbound.toArray(new EstimatedParameter[unbound.size()]);
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the measurements of an estimation problem.
|
||||
* @return measurements
|
||||
*/
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return (WeightedMeasurement[]) measurements.toArray(new WeightedMeasurement[measurements.size()]);
|
||||
}
|
||||
|
||||
/** Add a parameter to the problem.
|
||||
* @param p parameter to add
|
||||
*/
|
||||
protected void addParameter(EstimatedParameter p) {
|
||||
parameters.add(p);
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a new measurement to the set.
|
||||
* @param m measurement to add
|
||||
*/
|
||||
protected void addMeasurement(WeightedMeasurement m) {
|
||||
measurements.add(m);
|
||||
}
|
||||
|
||||
/** Estimated parameters. */
|
||||
private final List<EstimatedParameter> parameters;
|
||||
|
||||
/** Measurements. */
|
||||
private final List<WeightedMeasurement> measurements;
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
/*
|
||||
* 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;
|
||||
|
||||
/**
|
||||
* This class represents measurements in estimation problems.
|
||||
*
|
||||
* <p>This abstract class implements all the methods needed to handle
|
||||
* measurements in a general way. It defines neither the {@link
|
||||
* #getTheoreticalValue getTheoreticalValue} nor the {@link
|
||||
* #getPartial getPartial} methods, which should be defined by
|
||||
* sub-classes according to the specific problem.</p>
|
||||
*
|
||||
* <p>The {@link #getTheoreticalValue getTheoreticalValue} and {@link
|
||||
* #getPartial getPartial} methods must always use the current
|
||||
* estimate of the parameters set by the solver in the problem. These
|
||||
* parameters can be retrieved through the {@link
|
||||
* EstimationProblem#getAllParameters
|
||||
* EstimationProblem.getAllParameters} method if the measurements are
|
||||
* independent of the problem, or directly if they are implemented as
|
||||
* inner classes of the problem.</p>
|
||||
*
|
||||
* <p>The instances for which the <code>ignored</code> flag is set
|
||||
* through the {@link #setIgnored setIgnored} method are ignored by the
|
||||
* solvers. This can be used to reject wrong measurements at some
|
||||
* steps of the estimation.</p>
|
||||
*
|
||||
* @see EstimationProblem
|
||||
*
|
||||
* @version $Revision$ $Date$
|
||||
* @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 abstract class WeightedMeasurement implements Serializable {
|
||||
|
||||
/** Serializable version identifier. */
|
||||
private static final long serialVersionUID = 4360046376796901941L;
|
||||
|
||||
/**
|
||||
* Simple constructor.
|
||||
* Build a measurement with the given parameters, and set its ignore
|
||||
* flag to false.
|
||||
* @param weight weight of the measurement in the least squares problem
|
||||
* (two common choices are either to use 1.0 for all measurements, or to
|
||||
* use a value proportional to the inverse of the variance of the measurement
|
||||
* type)
|
||||
*
|
||||
* @param measuredValue measured value
|
||||
*/
|
||||
public WeightedMeasurement(double weight, double measuredValue) {
|
||||
this.weight = weight;
|
||||
this.measuredValue = measuredValue;
|
||||
ignored = false;
|
||||
}
|
||||
|
||||
/** Simple constructor.
|
||||
*
|
||||
* Build a measurement with the given parameters
|
||||
*
|
||||
* @param weight weight of the measurement in the least squares problem
|
||||
* @param measuredValue measured value
|
||||
* @param ignored true if the measurement should be ignored
|
||||
*/
|
||||
public WeightedMeasurement(double weight, double measuredValue,
|
||||
boolean ignored) {
|
||||
this.weight = weight;
|
||||
this.measuredValue = measuredValue;
|
||||
this.ignored = ignored;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the weight of the measurement in the least squares problem
|
||||
*
|
||||
* @return weight
|
||||
*/
|
||||
public double getWeight() {
|
||||
return weight;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the measured value
|
||||
*
|
||||
* @return measured value
|
||||
*/
|
||||
public double getMeasuredValue() {
|
||||
return measuredValue;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the residual for this measurement
|
||||
* The residual is the measured value minus the theoretical value.
|
||||
*
|
||||
* @return residual
|
||||
*/
|
||||
public double getResidual() {
|
||||
return measuredValue - getTheoreticalValue();
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the theoretical value expected for this measurement
|
||||
* <p>The theoretical value is the value expected for this measurement
|
||||
* if the model and its parameter were all perfectly known.</p>
|
||||
* <p>The value must be computed using the current estimate of the parameters
|
||||
* set by the solver in the problem.</p>
|
||||
*
|
||||
* @return theoretical value
|
||||
*/
|
||||
public abstract double getTheoreticalValue();
|
||||
|
||||
/**
|
||||
* Get the partial derivative of the {@link #getTheoreticalValue
|
||||
* theoretical value} according to the parameter.
|
||||
* <p>The value must be computed using the current estimate of the parameters
|
||||
* set by the solver in the problem.</p>
|
||||
*
|
||||
* @param parameter parameter against which the partial derivative
|
||||
* should be computed
|
||||
* @return partial derivative of the {@link #getTheoreticalValue
|
||||
* theoretical value}
|
||||
*/
|
||||
public abstract double getPartial(EstimatedParameter parameter);
|
||||
|
||||
/**
|
||||
* Set the ignore flag to the specified value
|
||||
* Setting the ignore flag to true allow to reject wrong
|
||||
* measurements, which sometimes can be detected only rather late.
|
||||
*
|
||||
* @param ignored value for the ignore flag
|
||||
*/
|
||||
public void setIgnored(boolean ignored) {
|
||||
this.ignored = ignored;
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if this measurement should be ignored
|
||||
*
|
||||
* @return true if the measurement should be ignored
|
||||
*/
|
||||
public boolean isIgnored() {
|
||||
return ignored;
|
||||
}
|
||||
|
||||
/** Measurement weight. */
|
||||
private final double weight;
|
||||
|
||||
/** Value of the measurements. */
|
||||
private final double measuredValue;
|
||||
|
||||
/** Ignore measurement indicator. */
|
||||
private boolean ignored;
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
<html>
|
||||
<!--
|
||||
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.
|
||||
-->
|
||||
<!-- $Revision$ -->
|
||||
<body>
|
||||
This package provided classes to solve estimation problems, it is deprecated since 2.0.
|
||||
|
||||
<p>This package has been deprecated as of 2.0. It is replaced by the optimization.general package.</p>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
/*
|
||||
* 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 org.apache.commons.math.estimation.EstimatedParameter;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
@Deprecated
|
||||
public class EstimatedParameterTest
|
||||
extends TestCase {
|
||||
|
||||
public EstimatedParameterTest(String name) {
|
||||
super(name);
|
||||
}
|
||||
|
||||
public void testConstruction() {
|
||||
|
||||
EstimatedParameter p1 = new EstimatedParameter("p1", 1.0);
|
||||
assertTrue(p1.getName().equals("p1"));
|
||||
checkValue(p1.getEstimate(), 1.0);
|
||||
assertTrue(! p1.isBound());
|
||||
|
||||
EstimatedParameter p2 = new EstimatedParameter("p2", 2.0, true);
|
||||
assertTrue(p2.getName().equals("p2"));
|
||||
checkValue(p2.getEstimate(), 2.0);
|
||||
assertTrue(p2.isBound());
|
||||
|
||||
}
|
||||
|
||||
public void testBound() {
|
||||
|
||||
EstimatedParameter p = new EstimatedParameter("p", 0.0);
|
||||
assertTrue(! p.isBound());
|
||||
p.setBound(true);
|
||||
assertTrue(p.isBound());
|
||||
p.setBound(false);
|
||||
assertTrue(! p.isBound());
|
||||
|
||||
}
|
||||
|
||||
public void testEstimate() {
|
||||
|
||||
EstimatedParameter p = new EstimatedParameter("p", 0.0);
|
||||
checkValue(p.getEstimate(), 0.0);
|
||||
|
||||
for (double e = 0.0; e < 10.0; e += 0.5) {
|
||||
p.setEstimate(e);
|
||||
checkValue(p.getEstimate(), e);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
return new TestSuite(EstimatedParameterTest.class);
|
||||
}
|
||||
|
||||
private void checkValue(double value, double expected) {
|
||||
assertTrue(Math.abs(value - expected) < 1.0e-10);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,732 @@
|
|||
/*
|
||||
* 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.util.ArrayList;
|
||||
import java.util.HashSet;
|
||||
|
||||
import junit.framework.Test;
|
||||
import junit.framework.TestCase;
|
||||
import junit.framework.TestSuite;
|
||||
|
||||
/**
|
||||
* <p>Some of the unit tests are re-implementations of the MINPACK <a
|
||||
* href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
|
||||
* href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
|
||||
* The redistribution policy for MINPACK is available <a
|
||||
* href="http://www.netlib.org/minpack/disclaimer">here</a>, for
|
||||
* convenience, it is reproduced below.</p>
|
||||
|
||||
* <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
|
||||
* <tr><td>
|
||||
* Minpack Copyright Notice (1999) University of Chicago.
|
||||
* All rights reserved
|
||||
* </td></tr>
|
||||
* <tr><td>
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
* <ol>
|
||||
* <li>Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.</li>
|
||||
* <li>Redistributions in binary form must reproduce the above
|
||||
* copyright notice, this list of conditions and the following
|
||||
* disclaimer in the documentation and/or other materials provided
|
||||
* with the distribution.</li>
|
||||
* <li>The end-user documentation included with the redistribution, if any,
|
||||
* must include the following acknowledgment:
|
||||
* <code>This product includes software developed by the University of
|
||||
* Chicago, as Operator of Argonne National Laboratory.</code>
|
||||
* Alternately, this acknowledgment may appear in the software itself,
|
||||
* if and wherever such third-party acknowledgments normally appear.</li>
|
||||
* <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
|
||||
* WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
|
||||
* UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
|
||||
* THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
|
||||
* IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
|
||||
* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
|
||||
* OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
|
||||
* OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
|
||||
* USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
|
||||
* THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
|
||||
* DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
|
||||
* UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
|
||||
* BE CORRECTED.</strong></li>
|
||||
* <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
|
||||
* HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
|
||||
* ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
|
||||
* INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
|
||||
* ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
* PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
|
||||
* SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
|
||||
* (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
|
||||
* EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
|
||||
* POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
|
||||
* <ol></td></tr>
|
||||
* </table>
|
||||
|
||||
* @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
|
||||
* @author Burton S. Garbow (original fortran minpack tests)
|
||||
* @author Kenneth E. Hillstrom (original fortran minpack tests)
|
||||
* @author Jorge J. More (original fortran minpack tests)
|
||||
* @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
|
||||
*/
|
||||
@Deprecated
|
||||
public class GaussNewtonEstimatorTest
|
||||
extends TestCase {
|
||||
|
||||
public GaussNewtonEstimatorTest(String name) {
|
||||
super(name);
|
||||
}
|
||||
|
||||
public void testTrivial() throws EstimationException {
|
||||
LinearProblem problem =
|
||||
new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] {2},
|
||||
new EstimatedParameter[] {
|
||||
new EstimatedParameter("p0", 0)
|
||||
}, 3.0)
|
||||
});
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(1.5,
|
||||
problem.getUnboundParameters()[0].getEstimate(),
|
||||
1.0e-10);
|
||||
}
|
||||
|
||||
public void testQRColumnsPermutation() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] x = {
|
||||
new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { x[0], x[1] },
|
||||
4.0),
|
||||
new LinearMeasurement(new double[] { 2.0 },
|
||||
new EstimatedParameter[] { x[1] },
|
||||
6.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -2.0 },
|
||||
new EstimatedParameter[] { x[0], x[1] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(7.0, x[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(3.0, x[1].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testNoDependency() throws EstimationException {
|
||||
EstimatedParameter[] p = new EstimatedParameter[] {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0),
|
||||
new EstimatedParameter("p3", 0),
|
||||
new EstimatedParameter("p4", 0),
|
||||
new EstimatedParameter("p5", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0] }, 0.0),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1] }, 1.1),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2] }, 2.2),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[3] }, 3.3),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[4] }, 4.4),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[5] }, 5.5)
|
||||
});
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
for (int i = 0; i < p.length; ++i) {
|
||||
assertEquals(0.55 * i, p[i].getEstimate(), 1.0e-10);
|
||||
}
|
||||
}
|
||||
|
||||
public void testOneSet() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0 },
|
||||
new EstimatedParameter[] { p[0] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(2.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(3.0, p[2].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testTwoSets() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 1),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 3),
|
||||
new EstimatedParameter("p4", 4),
|
||||
new EstimatedParameter("p5", 5)
|
||||
};
|
||||
|
||||
double epsilon = 1.0e-7;
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
|
||||
// 4 elements sub-problem
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0, 4.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[3] },
|
||||
2.0),
|
||||
new LinearMeasurement(new double[] { -4.0, -2.0, 3.0, -7.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
-9.0),
|
||||
new LinearMeasurement(new double[] { 4.0, 1.0, -2.0, 8.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
2.0),
|
||||
new LinearMeasurement(new double[] { -3.0, -12.0, -1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2], p[3] },
|
||||
2.0),
|
||||
|
||||
// 2 elements sub-problem
|
||||
new LinearMeasurement(new double[] { epsilon, 1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
1.0 + epsilon * epsilon),
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
2.0)
|
||||
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals( 3.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals( 4.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(-1.0, p[2].getEstimate(), 1.0e-10);
|
||||
assertEquals(-2.0, p[3].getEstimate(), 1.0e-10);
|
||||
assertEquals( 1.0 + epsilon, p[4].getEstimate(), 1.0e-10);
|
||||
assertEquals( 1.0 - epsilon, p[5].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testNonInversible() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0)
|
||||
};
|
||||
LinearMeasurement[] m = new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 2.0, -3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -3.0, -9.0 },
|
||||
new EstimatedParameter[] { p[0], p[2] },
|
||||
1.0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(m);
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
try {
|
||||
estimator.estimate(problem);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
}
|
||||
|
||||
public void testIllConditioned() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 1),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 3)
|
||||
};
|
||||
|
||||
LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 10.0, 7.0, 8.0, 7.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
32.0),
|
||||
new LinearMeasurement(new double[] { 7.0, 5.0, 6.0, 5.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
23.0),
|
||||
new LinearMeasurement(new double[] { 8.0, 6.0, 10.0, 9.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
33.0),
|
||||
new LinearMeasurement(new double[] { 7.0, 5.0, 9.0, 10.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
31.0)
|
||||
});
|
||||
GaussNewtonEstimator estimator1 = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator1.estimate(problem1);
|
||||
assertEquals(0, estimator1.getRMS(problem1), 1.0e-10);
|
||||
assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[2].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[3].getEstimate(), 1.0e-10);
|
||||
|
||||
LinearProblem problem2 = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 10.0, 7.0, 8.1, 7.2 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
32.0),
|
||||
new LinearMeasurement(new double[] { 7.08, 5.04, 6.0, 5.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
23.0),
|
||||
new LinearMeasurement(new double[] { 8.0, 5.98, 9.89, 9.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
33.0),
|
||||
new LinearMeasurement(new double[] { 6.99, 4.99, 9.0, 9.98 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
31.0)
|
||||
});
|
||||
GaussNewtonEstimator estimator2 = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator2.estimate(problem2);
|
||||
assertEquals(0, estimator2.getRMS(problem2), 1.0e-10);
|
||||
assertEquals(-81.0, p[0].getEstimate(), 1.0e-8);
|
||||
assertEquals(137.0, p[1].getEstimate(), 1.0e-8);
|
||||
assertEquals(-34.0, p[2].getEstimate(), 1.0e-8);
|
||||
assertEquals( 22.0, p[3].getEstimate(), 1.0e-8);
|
||||
|
||||
}
|
||||
|
||||
public void testMoreEstimatedParametersSimple() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 7),
|
||||
new EstimatedParameter("p1", 6),
|
||||
new EstimatedParameter("p2", 5),
|
||||
new EstimatedParameter("p3", 4)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 3.0, 2.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
7.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2], p[3] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[2] },
|
||||
5.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
try {
|
||||
estimator.estimate(problem);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
public void testMoreEstimatedParametersUnsorted() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 2),
|
||||
new EstimatedParameter("p1", 2),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 2),
|
||||
new EstimatedParameter("p4", 2),
|
||||
new EstimatedParameter("p5", 2)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[2], p[3], p[4] },
|
||||
12.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
-1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[3], p[2], p[5] },
|
||||
7.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[4], p[3] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
try {
|
||||
estimator.estimate(problem);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
public void testRedundantEquations() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
5.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
EstimatedParameter[] all = problem.getAllParameters();
|
||||
for (int i = 0; i < all.length; ++i) {
|
||||
assertEquals(all[i].getName().equals("p0") ? 2.0 : 1.0,
|
||||
all[i].getEstimate(), 1.0e-10);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
public void testInconsistentEquations() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
4.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertTrue(estimator.getRMS(problem) > 0.1);
|
||||
|
||||
}
|
||||
|
||||
public void testBoundParameters() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("unbound0", 2, false),
|
||||
new EstimatedParameter("unbound1", 2, false),
|
||||
new EstimatedParameter("bound", 2, true)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0, 2.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
7.0)
|
||||
});
|
||||
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
estimator.estimate(problem);
|
||||
assertTrue(estimator.getRMS(problem) < 1.0e-10);
|
||||
double[][] covariances = estimator.getCovariances(problem);
|
||||
int i0 = 0, i1 = 1;
|
||||
if (problem.getUnboundParameters()[0].getName().endsWith("1")) {
|
||||
i0 = 1;
|
||||
i1 = 0;
|
||||
}
|
||||
assertEquals(11.0 / 24, covariances[i0][i0], 1.0e-10);
|
||||
assertEquals(-3.0 / 24, covariances[i0][i1], 1.0e-10);
|
||||
assertEquals(-3.0 / 24, covariances[i1][i0], 1.0e-10);
|
||||
assertEquals( 3.0 / 24, covariances[i1][i1], 1.0e-10);
|
||||
|
||||
double[] errors = estimator.guessParametersErrors(problem);
|
||||
assertEquals(0, errors[i0], 1.0e-10);
|
||||
assertEquals(0, errors[i1], 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testMaxIterations() {
|
||||
Circle circle = new Circle(98.680, 47.345);
|
||||
circle.addPoint( 30.0, 68.0);
|
||||
circle.addPoint( 50.0, -6.0);
|
||||
circle.addPoint(110.0, -20.0);
|
||||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
try {
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(4, 1.0e-14, 1.0e-14);
|
||||
estimator.estimate(circle);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
}
|
||||
|
||||
public void testCircleFitting() throws EstimationException {
|
||||
Circle circle = new Circle(98.680, 47.345);
|
||||
circle.addPoint( 30.0, 68.0);
|
||||
circle.addPoint( 50.0, -6.0);
|
||||
circle.addPoint(110.0, -20.0);
|
||||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-10, 1.0e-10);
|
||||
estimator.estimate(circle);
|
||||
double rms = estimator.getRMS(circle);
|
||||
assertEquals(1.768262623567235, Math.sqrt(circle.getM()) * rms, 1.0e-10);
|
||||
assertEquals(69.96016176931406, circle.getRadius(), 1.0e-10);
|
||||
assertEquals(96.07590211815305, circle.getX(), 1.0e-10);
|
||||
assertEquals(48.13516790438953, circle.getY(), 1.0e-10);
|
||||
}
|
||||
|
||||
public void testCircleFittingBadInit() throws EstimationException {
|
||||
Circle circle = new Circle(-12, -12);
|
||||
double[][] points = new double[][] {
|
||||
{-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724},
|
||||
{-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619},
|
||||
{-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832},
|
||||
{-0.075133, 0.483271}, {-0.007759, 0.452680}, { 0.060071, 0.410235},
|
||||
{ 0.103037, 0.341076}, { 0.118438, 0.273884}, { 0.131293, 0.192201},
|
||||
{ 0.115869, 0.129797}, { 0.072223, 0.058396}, { 0.022884, 0.000718},
|
||||
{-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
|
||||
{-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526},
|
||||
{-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398},
|
||||
{-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513},
|
||||
{-0.135352, 0.478186}, {-0.061221, 0.483371}, { 0.003711, 0.422737},
|
||||
{ 0.065054, 0.375830}, { 0.108108, 0.297099}, { 0.123882, 0.222850},
|
||||
{ 0.117729, 0.134382}, { 0.085195, 0.056820}, { 0.029800, -0.019138},
|
||||
{-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
|
||||
{-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926},
|
||||
{-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068},
|
||||
{-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119},
|
||||
{-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560},
|
||||
{ 0.001375, 0.434937}, { 0.082787, 0.385806}, { 0.115490, 0.323807},
|
||||
{ 0.141089, 0.223450}, { 0.138693, 0.131703}, { 0.126415, 0.049174},
|
||||
{ 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
|
||||
{-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
|
||||
{-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597},
|
||||
{-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428},
|
||||
{-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380},
|
||||
{-0.071754, 0.516264}, { 0.015942, 0.472802}, { 0.076608, 0.419077},
|
||||
{ 0.127673, 0.330264}, { 0.159951, 0.262150}, { 0.153530, 0.172681},
|
||||
{ 0.140653, 0.089229}, { 0.078666, 0.024981}, { 0.023807, -0.037022},
|
||||
{-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
|
||||
};
|
||||
for (int i = 0; i < points.length; ++i) {
|
||||
circle.addPoint(points[i][0], points[i][1]);
|
||||
}
|
||||
GaussNewtonEstimator estimator = new GaussNewtonEstimator(100, 1.0e-6, 1.0e-6);
|
||||
try {
|
||||
estimator.estimate(circle);
|
||||
fail("an exception should have been caught");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
}
|
||||
|
||||
private static class LinearProblem extends SimpleEstimationProblem {
|
||||
|
||||
public LinearProblem(LinearMeasurement[] measurements) {
|
||||
HashSet<EstimatedParameter> set = new HashSet<EstimatedParameter>();
|
||||
for (int i = 0; i < measurements.length; ++i) {
|
||||
addMeasurement(measurements[i]);
|
||||
EstimatedParameter[] parameters = measurements[i].getParameters();
|
||||
for (int j = 0; j < parameters.length; ++j) {
|
||||
set.add(parameters[j]);
|
||||
}
|
||||
}
|
||||
for (EstimatedParameter p : set) {
|
||||
addParameter(p);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private static class LinearMeasurement extends WeightedMeasurement {
|
||||
|
||||
public LinearMeasurement(double[] factors, EstimatedParameter[] parameters,
|
||||
double setPoint) {
|
||||
super(1.0, setPoint, true);
|
||||
this.factors = factors;
|
||||
this.parameters = parameters;
|
||||
setIgnored(false);
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
double v = 0;
|
||||
for (int i = 0; i < factors.length; ++i) {
|
||||
v += factors[i] * parameters[i].getEstimate();
|
||||
}
|
||||
return v;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
for (int i = 0; i < parameters.length; ++i) {
|
||||
if (parameters[i] == parameter) {
|
||||
return factors[i];
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getParameters() {
|
||||
return parameters;
|
||||
}
|
||||
|
||||
private double[] factors;
|
||||
private EstimatedParameter[] parameters;
|
||||
private static final long serialVersionUID = -3922448707008868580L;
|
||||
|
||||
}
|
||||
|
||||
private static class Circle implements EstimationProblem {
|
||||
|
||||
public Circle(double cx, double cy) {
|
||||
this.cx = new EstimatedParameter("cx", cx);
|
||||
this.cy = new EstimatedParameter(new EstimatedParameter("cy", cy));
|
||||
points = new ArrayList<PointModel>();
|
||||
}
|
||||
|
||||
public void addPoint(double px, double py) {
|
||||
points.add(new PointModel(px, py));
|
||||
}
|
||||
|
||||
public int getM() {
|
||||
return points.size();
|
||||
}
|
||||
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return (WeightedMeasurement[]) points.toArray(new PointModel[points.size()]);
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public double getPartialRadiusX() {
|
||||
double dRdX = 0;
|
||||
for (PointModel point : points) {
|
||||
dRdX += point.getPartialDiX();
|
||||
}
|
||||
return dRdX / points.size();
|
||||
}
|
||||
|
||||
public double getPartialRadiusY() {
|
||||
double dRdY = 0;
|
||||
for (PointModel point : points) {
|
||||
dRdY += point.getPartialDiY();
|
||||
}
|
||||
return dRdY / points.size();
|
||||
}
|
||||
|
||||
public double getRadius() {
|
||||
double r = 0;
|
||||
for (PointModel point : points) {
|
||||
r += point.getCenterDistance();
|
||||
}
|
||||
return r / points.size();
|
||||
}
|
||||
|
||||
public double getX() {
|
||||
return cx.getEstimate();
|
||||
}
|
||||
|
||||
public double getY() {
|
||||
return cy.getEstimate();
|
||||
}
|
||||
|
||||
private class PointModel extends WeightedMeasurement {
|
||||
|
||||
public PointModel(double px, double py) {
|
||||
super(1.0, 0.0);
|
||||
this.px = px;
|
||||
this.py = py;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
if (parameter == cx) {
|
||||
return getPartialDiX() - getPartialRadiusX();
|
||||
} else if (parameter == cy) {
|
||||
return getPartialDiY() - getPartialRadiusY();
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public double getCenterDistance() {
|
||||
double dx = px - cx.getEstimate();
|
||||
double dy = py - cy.getEstimate();
|
||||
return Math.sqrt(dx * dx + dy * dy);
|
||||
}
|
||||
|
||||
public double getPartialDiX() {
|
||||
return (cx.getEstimate() - px) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getPartialDiY() {
|
||||
return (cy.getEstimate() - py) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
return getCenterDistance() - getRadius();
|
||||
}
|
||||
|
||||
private double px;
|
||||
private double py;
|
||||
private static final long serialVersionUID = 1L;
|
||||
|
||||
}
|
||||
|
||||
private EstimatedParameter cx;
|
||||
private EstimatedParameter cy;
|
||||
private ArrayList<PointModel> points;
|
||||
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
return new TestSuite(GaussNewtonEstimatorTest.class);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,844 @@
|
|||
/*
|
||||
* 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.util.ArrayList;
|
||||
import java.util.HashSet;
|
||||
|
||||
import junit.framework.Test;
|
||||
import junit.framework.TestCase;
|
||||
import junit.framework.TestSuite;
|
||||
|
||||
/**
|
||||
* <p>Some of the unit tests are re-implementations of the MINPACK <a
|
||||
* href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
|
||||
* href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
|
||||
* The redistribution policy for MINPACK is available <a
|
||||
* href="http://www.netlib.org/minpack/disclaimer">here</a>, for
|
||||
* convenience, it is reproduced below.</p>
|
||||
|
||||
* <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
|
||||
* <tr><td>
|
||||
* Minpack Copyright Notice (1999) University of Chicago.
|
||||
* All rights reserved
|
||||
* </td></tr>
|
||||
* <tr><td>
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
* <ol>
|
||||
* <li>Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.</li>
|
||||
* <li>Redistributions in binary form must reproduce the above
|
||||
* copyright notice, this list of conditions and the following
|
||||
* disclaimer in the documentation and/or other materials provided
|
||||
* with the distribution.</li>
|
||||
* <li>The end-user documentation included with the redistribution, if any,
|
||||
* must include the following acknowledgment:
|
||||
* <code>This product includes software developed by the University of
|
||||
* Chicago, as Operator of Argonne National Laboratory.</code>
|
||||
* Alternately, this acknowledgment may appear in the software itself,
|
||||
* if and wherever such third-party acknowledgments normally appear.</li>
|
||||
* <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
|
||||
* WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
|
||||
* UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
|
||||
* THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
|
||||
* IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
|
||||
* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
|
||||
* OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
|
||||
* OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
|
||||
* USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
|
||||
* THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
|
||||
* DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
|
||||
* UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
|
||||
* BE CORRECTED.</strong></li>
|
||||
* <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
|
||||
* HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
|
||||
* ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
|
||||
* INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
|
||||
* ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||
* PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
|
||||
* SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
|
||||
* (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
|
||||
* EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
|
||||
* POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
|
||||
* <ol></td></tr>
|
||||
* </table>
|
||||
|
||||
* @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
|
||||
* @author Burton S. Garbow (original fortran minpack tests)
|
||||
* @author Kenneth E. Hillstrom (original fortran minpack tests)
|
||||
* @author Jorge J. More (original fortran minpack tests)
|
||||
* @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
|
||||
*/
|
||||
@Deprecated
|
||||
public class LevenbergMarquardtEstimatorTest
|
||||
extends TestCase {
|
||||
|
||||
public LevenbergMarquardtEstimatorTest(String name) {
|
||||
super(name);
|
||||
}
|
||||
|
||||
public void testTrivial() throws EstimationException {
|
||||
LinearProblem problem =
|
||||
new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] {2},
|
||||
new EstimatedParameter[] {
|
||||
new EstimatedParameter("p0", 0)
|
||||
}, 3.0)
|
||||
});
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
try {
|
||||
estimator.guessParametersErrors(problem);
|
||||
fail("an exception should have been thrown");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception caught");
|
||||
}
|
||||
assertEquals(1.5,
|
||||
problem.getUnboundParameters()[0].getEstimate(),
|
||||
1.0e-10);
|
||||
}
|
||||
|
||||
public void testQRColumnsPermutation() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] x = {
|
||||
new EstimatedParameter("p0", 0), new EstimatedParameter("p1", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { x[0], x[1] },
|
||||
4.0),
|
||||
new LinearMeasurement(new double[] { 2.0 },
|
||||
new EstimatedParameter[] { x[1] },
|
||||
6.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -2.0 },
|
||||
new EstimatedParameter[] { x[0], x[1] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(7.0, x[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(3.0, x[1].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testNoDependency() throws EstimationException {
|
||||
EstimatedParameter[] p = new EstimatedParameter[] {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0),
|
||||
new EstimatedParameter("p3", 0),
|
||||
new EstimatedParameter("p4", 0),
|
||||
new EstimatedParameter("p5", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0] }, 0.0),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1] }, 1.1),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2] }, 2.2),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[3] }, 3.3),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[4] }, 4.4),
|
||||
new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[5] }, 5.5)
|
||||
});
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
for (int i = 0; i < p.length; ++i) {
|
||||
assertEquals(0.55 * i, p[i].getEstimate(), 1.0e-10);
|
||||
}
|
||||
}
|
||||
|
||||
public void testOneSet() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0 },
|
||||
new EstimatedParameter[] { p[0] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(2.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(3.0, p[2].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testTwoSets() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 1),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 3),
|
||||
new EstimatedParameter("p4", 4),
|
||||
new EstimatedParameter("p5", 5)
|
||||
};
|
||||
|
||||
double epsilon = 1.0e-7;
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
|
||||
// 4 elements sub-problem
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0, 4.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[3] },
|
||||
2.0),
|
||||
new LinearMeasurement(new double[] { -4.0, -2.0, 3.0, -7.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
-9.0),
|
||||
new LinearMeasurement(new double[] { 4.0, 1.0, -2.0, 8.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
2.0),
|
||||
new LinearMeasurement(new double[] { -3.0, -12.0, -1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2], p[3] },
|
||||
2.0),
|
||||
|
||||
// 2 elements sub-problem
|
||||
new LinearMeasurement(new double[] { epsilon, 1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
1.0 + epsilon * epsilon),
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
2.0)
|
||||
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals( 3.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals( 4.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(-1.0, p[2].getEstimate(), 1.0e-10);
|
||||
assertEquals(-2.0, p[3].getEstimate(), 1.0e-10);
|
||||
assertEquals( 1.0 + epsilon, p[4].getEstimate(), 1.0e-10);
|
||||
assertEquals( 1.0 - epsilon, p[5].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testNonInversible() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 0),
|
||||
new EstimatedParameter("p2", 0)
|
||||
};
|
||||
LinearMeasurement[] m = new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 2.0, -3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { -3.0, -9.0 },
|
||||
new EstimatedParameter[] { p[0], p[2] },
|
||||
1.0)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(m);
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
double initialCost = estimator.getRMS(problem);
|
||||
estimator.estimate(problem);
|
||||
assertTrue(estimator.getRMS(problem) < initialCost);
|
||||
assertTrue(Math.sqrt(m.length) * estimator.getRMS(problem) > 0.6);
|
||||
try {
|
||||
estimator.getCovariances(problem);
|
||||
fail("an exception should have been thrown");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception caught");
|
||||
}
|
||||
double dJ0 = 2 * (m[0].getResidual() * m[0].getPartial(p[0])
|
||||
+ m[1].getResidual() * m[1].getPartial(p[0])
|
||||
+ m[2].getResidual() * m[2].getPartial(p[0]));
|
||||
double dJ1 = 2 * (m[0].getResidual() * m[0].getPartial(p[1])
|
||||
+ m[1].getResidual() * m[1].getPartial(p[1]));
|
||||
double dJ2 = 2 * (m[0].getResidual() * m[0].getPartial(p[2])
|
||||
+ m[1].getResidual() * m[1].getPartial(p[2])
|
||||
+ m[2].getResidual() * m[2].getPartial(p[2]));
|
||||
assertEquals(0, dJ0, 1.0e-10);
|
||||
assertEquals(0, dJ1, 1.0e-10);
|
||||
assertEquals(0, dJ2, 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testIllConditioned() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 0),
|
||||
new EstimatedParameter("p1", 1),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 3)
|
||||
};
|
||||
|
||||
LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 10.0, 7.0, 8.0, 7.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
32.0),
|
||||
new LinearMeasurement(new double[] { 7.0, 5.0, 6.0, 5.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
23.0),
|
||||
new LinearMeasurement(new double[] { 8.0, 6.0, 10.0, 9.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
33.0),
|
||||
new LinearMeasurement(new double[] { 7.0, 5.0, 9.0, 10.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
31.0)
|
||||
});
|
||||
LevenbergMarquardtEstimator estimator1 = new LevenbergMarquardtEstimator();
|
||||
estimator1.estimate(problem1);
|
||||
assertEquals(0, estimator1.getRMS(problem1), 1.0e-10);
|
||||
assertEquals(1.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[2].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[3].getEstimate(), 1.0e-10);
|
||||
|
||||
LinearProblem problem2 = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 10.0, 7.0, 8.1, 7.2 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
32.0),
|
||||
new LinearMeasurement(new double[] { 7.08, 5.04, 6.0, 5.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
23.0),
|
||||
new LinearMeasurement(new double[] { 8.0, 5.98, 9.89, 9.0 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
33.0),
|
||||
new LinearMeasurement(new double[] { 6.99, 4.99, 9.0, 9.98 },
|
||||
new EstimatedParameter[] { p[0], p[1], p[2], p[3] },
|
||||
31.0)
|
||||
});
|
||||
LevenbergMarquardtEstimator estimator2 = new LevenbergMarquardtEstimator();
|
||||
estimator2.estimate(problem2);
|
||||
assertEquals(0, estimator2.getRMS(problem2), 1.0e-10);
|
||||
assertEquals(-81.0, p[0].getEstimate(), 1.0e-8);
|
||||
assertEquals(137.0, p[1].getEstimate(), 1.0e-8);
|
||||
assertEquals(-34.0, p[2].getEstimate(), 1.0e-8);
|
||||
assertEquals( 22.0, p[3].getEstimate(), 1.0e-8);
|
||||
|
||||
}
|
||||
|
||||
public void testMoreEstimatedParametersSimple() throws EstimationException {
|
||||
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 7),
|
||||
new EstimatedParameter("p1", 6),
|
||||
new EstimatedParameter("p2", 5),
|
||||
new EstimatedParameter("p3", 4)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 3.0, 2.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
7.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[1], p[2], p[3] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 2.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[2] },
|
||||
5.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testMoreEstimatedParametersUnsorted() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 2),
|
||||
new EstimatedParameter("p1", 2),
|
||||
new EstimatedParameter("p2", 2),
|
||||
new EstimatedParameter("p3", 2),
|
||||
new EstimatedParameter("p4", 2),
|
||||
new EstimatedParameter("p5", 2)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[2], p[3], p[4] },
|
||||
12.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[4], p[5] },
|
||||
-1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[3], p[2], p[5] },
|
||||
7.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[4], p[3] },
|
||||
1.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(3.0, p[2].getEstimate(), 1.0e-10);
|
||||
assertEquals(4.0, p[3].getEstimate(), 1.0e-10);
|
||||
assertEquals(5.0, p[4].getEstimate(), 1.0e-10);
|
||||
assertEquals(6.0, p[5].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testRedundantEquations() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
5.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertEquals(0, estimator.getRMS(problem), 1.0e-10);
|
||||
assertEquals(2.0, p[0].getEstimate(), 1.0e-10);
|
||||
assertEquals(1.0, p[1].getEstimate(), 1.0e-10);
|
||||
|
||||
}
|
||||
|
||||
public void testInconsistentEquations() throws EstimationException {
|
||||
EstimatedParameter[] p = {
|
||||
new EstimatedParameter("p0", 1),
|
||||
new EstimatedParameter("p1", 1)
|
||||
};
|
||||
LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
|
||||
new LinearMeasurement(new double[] { 1.0, 1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
3.0),
|
||||
new LinearMeasurement(new double[] { 1.0, -1.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
1.0),
|
||||
new LinearMeasurement(new double[] { 1.0, 3.0 },
|
||||
new EstimatedParameter[] { p[0], p[1] },
|
||||
4.0)
|
||||
});
|
||||
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(problem);
|
||||
assertTrue(estimator.getRMS(problem) > 0.1);
|
||||
|
||||
}
|
||||
|
||||
public void testControlParameters() throws EstimationException {
|
||||
Circle circle = new Circle(98.680, 47.345);
|
||||
circle.addPoint( 30.0, 68.0);
|
||||
circle.addPoint( 50.0, -6.0);
|
||||
circle.addPoint(110.0, -20.0);
|
||||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
checkEstimate(circle, 0.1, 10, 1.0e-14, 1.0e-16, 1.0e-10, false);
|
||||
checkEstimate(circle, 0.1, 10, 1.0e-15, 1.0e-17, 1.0e-10, true);
|
||||
checkEstimate(circle, 0.1, 5, 1.0e-15, 1.0e-16, 1.0e-10, true);
|
||||
circle.addPoint(300, -300);
|
||||
checkEstimate(circle, 0.1, 20, 1.0e-18, 1.0e-16, 1.0e-10, true);
|
||||
}
|
||||
|
||||
private void checkEstimate(EstimationProblem problem,
|
||||
double initialStepBoundFactor, int maxCostEval,
|
||||
double costRelativeTolerance, double parRelativeTolerance,
|
||||
double orthoTolerance, boolean shouldFail) {
|
||||
try {
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.setInitialStepBoundFactor(initialStepBoundFactor);
|
||||
estimator.setMaxCostEval(maxCostEval);
|
||||
estimator.setCostRelativeTolerance(costRelativeTolerance);
|
||||
estimator.setParRelativeTolerance(parRelativeTolerance);
|
||||
estimator.setOrthoTolerance(orthoTolerance);
|
||||
estimator.estimate(problem);
|
||||
assertTrue(! shouldFail);
|
||||
} catch (EstimationException ee) {
|
||||
assertTrue(shouldFail);
|
||||
} catch (Exception e) {
|
||||
fail("wrong exception type caught");
|
||||
}
|
||||
}
|
||||
|
||||
public void testCircleFitting() throws EstimationException {
|
||||
Circle circle = new Circle(98.680, 47.345);
|
||||
circle.addPoint( 30.0, 68.0);
|
||||
circle.addPoint( 50.0, -6.0);
|
||||
circle.addPoint(110.0, -20.0);
|
||||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(circle);
|
||||
assertTrue(estimator.getCostEvaluations() < 10);
|
||||
assertTrue(estimator.getJacobianEvaluations() < 10);
|
||||
double rms = estimator.getRMS(circle);
|
||||
assertEquals(1.768262623567235, Math.sqrt(circle.getM()) * rms, 1.0e-10);
|
||||
assertEquals(69.96016176931406, circle.getRadius(), 1.0e-10);
|
||||
assertEquals(96.07590211815305, circle.getX(), 1.0e-10);
|
||||
assertEquals(48.13516790438953, circle.getY(), 1.0e-10);
|
||||
double[][] cov = estimator.getCovariances(circle);
|
||||
assertEquals(1.839, cov[0][0], 0.001);
|
||||
assertEquals(0.731, cov[0][1], 0.001);
|
||||
assertEquals(cov[0][1], cov[1][0], 1.0e-14);
|
||||
assertEquals(0.786, cov[1][1], 0.001);
|
||||
double[] errors = estimator.guessParametersErrors(circle);
|
||||
assertEquals(1.384, errors[0], 0.001);
|
||||
assertEquals(0.905, errors[1], 0.001);
|
||||
|
||||
// add perfect measurements and check errors are reduced
|
||||
double cx = circle.getX();
|
||||
double cy = circle.getY();
|
||||
double r = circle.getRadius();
|
||||
for (double d= 0; d < 2 * Math.PI; d += 0.01) {
|
||||
circle.addPoint(cx + r * Math.cos(d), cy + r * Math.sin(d));
|
||||
}
|
||||
estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(circle);
|
||||
cov = estimator.getCovariances(circle);
|
||||
assertEquals(0.004, cov[0][0], 0.001);
|
||||
assertEquals(6.40e-7, cov[0][1], 1.0e-9);
|
||||
assertEquals(cov[0][1], cov[1][0], 1.0e-14);
|
||||
assertEquals(0.003, cov[1][1], 0.001);
|
||||
errors = estimator.guessParametersErrors(circle);
|
||||
assertEquals(0.004, errors[0], 0.001);
|
||||
assertEquals(0.004, errors[1], 0.001);
|
||||
|
||||
}
|
||||
|
||||
public void testCircleFittingBadInit() throws EstimationException {
|
||||
Circle circle = new Circle(-12, -12);
|
||||
double[][] points = new double[][] {
|
||||
{-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724},
|
||||
{-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619},
|
||||
{-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832},
|
||||
{-0.075133, 0.483271}, {-0.007759, 0.452680}, { 0.060071, 0.410235},
|
||||
{ 0.103037, 0.341076}, { 0.118438, 0.273884}, { 0.131293, 0.192201},
|
||||
{ 0.115869, 0.129797}, { 0.072223, 0.058396}, { 0.022884, 0.000718},
|
||||
{-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
|
||||
{-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526},
|
||||
{-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398},
|
||||
{-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513},
|
||||
{-0.135352, 0.478186}, {-0.061221, 0.483371}, { 0.003711, 0.422737},
|
||||
{ 0.065054, 0.375830}, { 0.108108, 0.297099}, { 0.123882, 0.222850},
|
||||
{ 0.117729, 0.134382}, { 0.085195, 0.056820}, { 0.029800, -0.019138},
|
||||
{-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
|
||||
{-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926},
|
||||
{-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068},
|
||||
{-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119},
|
||||
{-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560},
|
||||
{ 0.001375, 0.434937}, { 0.082787, 0.385806}, { 0.115490, 0.323807},
|
||||
{ 0.141089, 0.223450}, { 0.138693, 0.131703}, { 0.126415, 0.049174},
|
||||
{ 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
|
||||
{-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
|
||||
{-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597},
|
||||
{-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428},
|
||||
{-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380},
|
||||
{-0.071754, 0.516264}, { 0.015942, 0.472802}, { 0.076608, 0.419077},
|
||||
{ 0.127673, 0.330264}, { 0.159951, 0.262150}, { 0.153530, 0.172681},
|
||||
{ 0.140653, 0.089229}, { 0.078666, 0.024981}, { 0.023807, -0.037022},
|
||||
{-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
|
||||
};
|
||||
for (int i = 0; i < points.length; ++i) {
|
||||
circle.addPoint(points[i][0], points[i][1]);
|
||||
}
|
||||
LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
|
||||
estimator.estimate(circle);
|
||||
assertTrue(estimator.getCostEvaluations() < 15);
|
||||
assertTrue(estimator.getJacobianEvaluations() < 10);
|
||||
assertEquals( 0.030184491196225207, estimator.getRMS(circle), 1.0e-9);
|
||||
assertEquals( 0.2922350065939634, circle.getRadius(), 1.0e-9);
|
||||
assertEquals(-0.15173845023862165, circle.getX(), 1.0e-8);
|
||||
assertEquals( 0.20750021499570379, circle.getY(), 1.0e-8);
|
||||
}
|
||||
|
||||
public void testMath199() {
|
||||
try {
|
||||
QuadraticProblem problem = new QuadraticProblem();
|
||||
problem.addPoint (0, -3.182591015485607, 0.0);
|
||||
problem.addPoint (1, -2.5581184967730577, 4.4E-323);
|
||||
problem.addPoint (2, -2.1488478161387325, 1.0);
|
||||
problem.addPoint (3, -1.9122489313410047, 4.4E-323);
|
||||
problem.addPoint (4, 1.7785661310051026, 0.0);
|
||||
new LevenbergMarquardtEstimator().estimate(problem);
|
||||
fail("an exception should have been thrown");
|
||||
} catch (EstimationException ee) {
|
||||
// expected behavior
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private static class LinearProblem implements EstimationProblem {
|
||||
|
||||
public LinearProblem(LinearMeasurement[] measurements) {
|
||||
this.measurements = measurements;
|
||||
}
|
||||
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return measurements;
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
return getAllParameters();
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
HashSet<EstimatedParameter> set = new HashSet<EstimatedParameter>();
|
||||
for (int i = 0; i < measurements.length; ++i) {
|
||||
EstimatedParameter[] parameters = measurements[i].getParameters();
|
||||
for (int j = 0; j < parameters.length; ++j) {
|
||||
set.add(parameters[j]);
|
||||
}
|
||||
}
|
||||
return (EstimatedParameter[]) set.toArray(new EstimatedParameter[set.size()]);
|
||||
}
|
||||
|
||||
private LinearMeasurement[] measurements;
|
||||
|
||||
}
|
||||
|
||||
private static class LinearMeasurement extends WeightedMeasurement {
|
||||
|
||||
public LinearMeasurement(double[] factors, EstimatedParameter[] parameters,
|
||||
double setPoint) {
|
||||
super(1.0, setPoint);
|
||||
this.factors = factors;
|
||||
this.parameters = parameters;
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
double v = 0;
|
||||
for (int i = 0; i < factors.length; ++i) {
|
||||
v += factors[i] * parameters[i].getEstimate();
|
||||
}
|
||||
return v;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
for (int i = 0; i < parameters.length; ++i) {
|
||||
if (parameters[i] == parameter) {
|
||||
return factors[i];
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getParameters() {
|
||||
return parameters;
|
||||
}
|
||||
|
||||
private double[] factors;
|
||||
private EstimatedParameter[] parameters;
|
||||
private static final long serialVersionUID = -3922448707008868580L;
|
||||
|
||||
}
|
||||
|
||||
private static class Circle implements EstimationProblem {
|
||||
|
||||
public Circle(double cx, double cy) {
|
||||
this.cx = new EstimatedParameter("cx", cx);
|
||||
this.cy = new EstimatedParameter("cy", cy);
|
||||
points = new ArrayList<PointModel>();
|
||||
}
|
||||
|
||||
public void addPoint(double px, double py) {
|
||||
points.add(new PointModel(px, py));
|
||||
}
|
||||
|
||||
public int getM() {
|
||||
return points.size();
|
||||
}
|
||||
|
||||
public WeightedMeasurement[] getMeasurements() {
|
||||
return (WeightedMeasurement[]) points.toArray(new PointModel[points.size()]);
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getAllParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public EstimatedParameter[] getUnboundParameters() {
|
||||
return new EstimatedParameter[] { cx, cy };
|
||||
}
|
||||
|
||||
public double getPartialRadiusX() {
|
||||
double dRdX = 0;
|
||||
for (PointModel point : points) {
|
||||
dRdX += point.getPartialDiX();
|
||||
}
|
||||
return dRdX / points.size();
|
||||
}
|
||||
|
||||
public double getPartialRadiusY() {
|
||||
double dRdY = 0;
|
||||
for (PointModel point : points) {
|
||||
dRdY += point.getPartialDiY();
|
||||
}
|
||||
return dRdY / points.size();
|
||||
}
|
||||
|
||||
public double getRadius() {
|
||||
double r = 0;
|
||||
for (PointModel point : points) {
|
||||
r += point.getCenterDistance();
|
||||
}
|
||||
return r / points.size();
|
||||
}
|
||||
|
||||
public double getX() {
|
||||
return cx.getEstimate();
|
||||
}
|
||||
|
||||
public double getY() {
|
||||
return cy.getEstimate();
|
||||
}
|
||||
|
||||
private class PointModel extends WeightedMeasurement {
|
||||
|
||||
public PointModel(double px, double py) {
|
||||
super(1.0, 0.0);
|
||||
this.px = px;
|
||||
this.py = py;
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
if (parameter == cx) {
|
||||
return getPartialDiX() - getPartialRadiusX();
|
||||
} else if (parameter == cy) {
|
||||
return getPartialDiY() - getPartialRadiusY();
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
public double getCenterDistance() {
|
||||
double dx = px - cx.getEstimate();
|
||||
double dy = py - cy.getEstimate();
|
||||
return Math.sqrt(dx * dx + dy * dy);
|
||||
}
|
||||
|
||||
public double getPartialDiX() {
|
||||
return (cx.getEstimate() - px) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getPartialDiY() {
|
||||
return (cy.getEstimate() - py) / getCenterDistance();
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
return getCenterDistance() - getRadius();
|
||||
}
|
||||
|
||||
private double px;
|
||||
private double py;
|
||||
private static final long serialVersionUID = 1L;
|
||||
|
||||
}
|
||||
|
||||
private EstimatedParameter cx;
|
||||
private EstimatedParameter cy;
|
||||
private ArrayList<PointModel> points;
|
||||
|
||||
}
|
||||
|
||||
private static class QuadraticProblem extends SimpleEstimationProblem {
|
||||
|
||||
private EstimatedParameter a;
|
||||
private EstimatedParameter b;
|
||||
private EstimatedParameter c;
|
||||
|
||||
public QuadraticProblem() {
|
||||
a = new EstimatedParameter("a", 0.0);
|
||||
b = new EstimatedParameter("b", 0.0);
|
||||
c = new EstimatedParameter("c", 0.0);
|
||||
addParameter(a);
|
||||
addParameter(b);
|
||||
addParameter(c);
|
||||
}
|
||||
|
||||
public void addPoint(double x, double y, double w) {
|
||||
addMeasurement(new LocalMeasurement(x, y, w));
|
||||
}
|
||||
|
||||
public double getA() {
|
||||
return a.getEstimate();
|
||||
}
|
||||
|
||||
public double getB() {
|
||||
return b.getEstimate();
|
||||
}
|
||||
|
||||
public double getC() {
|
||||
return c.getEstimate();
|
||||
}
|
||||
|
||||
public double theoreticalValue(double x) {
|
||||
return ( (a.getEstimate() * x + b.getEstimate() ) * x + c.getEstimate());
|
||||
}
|
||||
|
||||
private double partial(double x, EstimatedParameter parameter) {
|
||||
if (parameter == a) {
|
||||
return x * x;
|
||||
} else if (parameter == b) {
|
||||
return x;
|
||||
} else {
|
||||
return 1.0;
|
||||
}
|
||||
}
|
||||
|
||||
private class LocalMeasurement extends WeightedMeasurement {
|
||||
|
||||
private static final long serialVersionUID = 1555043155023729130L;
|
||||
private final double x;
|
||||
|
||||
// constructor
|
||||
public LocalMeasurement(double x, double y, double w) {
|
||||
super(w, y);
|
||||
this.x = x;
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
return theoreticalValue(x);
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter parameter) {
|
||||
return partial(x, parameter);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
return new TestSuite(LevenbergMarquardtEstimatorTest.class);
|
||||
}
|
||||
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,127 @@
|
|||
/*
|
||||
* 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 org.apache.commons.math.estimation.EstimatedParameter;
|
||||
import org.apache.commons.math.estimation.WeightedMeasurement;
|
||||
|
||||
import junit.framework.*;
|
||||
|
||||
@Deprecated
|
||||
public class WeightedMeasurementTest
|
||||
extends TestCase {
|
||||
|
||||
public WeightedMeasurementTest(String name) {
|
||||
super(name);
|
||||
p1 = null;
|
||||
p2 = null;
|
||||
}
|
||||
|
||||
public void testConstruction() {
|
||||
WeightedMeasurement m = new MyMeasurement(3.0, theoretical() + 0.1, this);
|
||||
checkValue(m.getWeight(), 3.0);
|
||||
checkValue(m.getMeasuredValue(), theoretical() + 0.1);
|
||||
}
|
||||
|
||||
public void testIgnored() {
|
||||
WeightedMeasurement m = new MyMeasurement(3.0, theoretical() + 0.1, this);
|
||||
assertTrue(!m.isIgnored());
|
||||
m.setIgnored(true);
|
||||
assertTrue(m.isIgnored());
|
||||
m.setIgnored(false);
|
||||
assertTrue(!m.isIgnored());
|
||||
}
|
||||
|
||||
public void testTheory() {
|
||||
WeightedMeasurement m = new MyMeasurement(3.0, theoretical() + 0.1, this);
|
||||
checkValue(m.getTheoreticalValue(), theoretical());
|
||||
checkValue(m.getResidual(), 0.1);
|
||||
|
||||
double oldP1 = p1.getEstimate();
|
||||
p1.setEstimate(oldP1 + m.getResidual() / m.getPartial(p1));
|
||||
checkValue(m.getResidual(), 0.0);
|
||||
p1.setEstimate(oldP1);
|
||||
checkValue(m.getResidual(), 0.1);
|
||||
|
||||
double oldP2 = p2.getEstimate();
|
||||
p2.setEstimate(oldP2 + m.getResidual() / m.getPartial(p2));
|
||||
checkValue(m.getResidual(), 0.0);
|
||||
p2.setEstimate(oldP2);
|
||||
checkValue(m.getResidual(), 0.1);
|
||||
|
||||
}
|
||||
|
||||
public static Test suite() {
|
||||
return new TestSuite(WeightedMeasurementTest.class);
|
||||
}
|
||||
|
||||
public void setUp() {
|
||||
p1 = new EstimatedParameter("p1", 1.0);
|
||||
p2 = new EstimatedParameter("p2", 2.0);
|
||||
}
|
||||
|
||||
public void tearDown() {
|
||||
p1 = null;
|
||||
p2 = null;
|
||||
}
|
||||
|
||||
private void checkValue(double value, double expected) {
|
||||
assertTrue(Math.abs(value - expected) < 1.0e-10);
|
||||
}
|
||||
|
||||
private double theoretical() {
|
||||
return 3 * p1.getEstimate() - p2.getEstimate();
|
||||
}
|
||||
|
||||
private double partial(EstimatedParameter p) {
|
||||
if (p == p1) {
|
||||
return 3.0;
|
||||
} else if (p == p2) {
|
||||
return -1.0;
|
||||
} else {
|
||||
return 0.0;
|
||||
}
|
||||
}
|
||||
|
||||
private static class MyMeasurement
|
||||
extends WeightedMeasurement {
|
||||
|
||||
public MyMeasurement(double weight, double measuredValue,
|
||||
WeightedMeasurementTest testInstance) {
|
||||
super(weight, measuredValue);
|
||||
this.testInstance = testInstance;
|
||||
}
|
||||
|
||||
public double getTheoreticalValue() {
|
||||
return testInstance.theoretical();
|
||||
}
|
||||
|
||||
public double getPartial(EstimatedParameter p) {
|
||||
return testInstance.partial(p);
|
||||
}
|
||||
|
||||
private transient WeightedMeasurementTest testInstance;
|
||||
|
||||
private static final long serialVersionUID = -246712922500792332L;
|
||||
|
||||
}
|
||||
|
||||
private EstimatedParameter p1;
|
||||
private EstimatedParameter p2;
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue