Guessing is covariance-based, it only gives rough order of magnitude.
+ * @param problem estimation problem + * @return errors in estimated parameters + * @exception EstimationException if the covariances matrix cannot be computed + * or the number of degrees of freedom is not positive (number of measurements + * lesser or equal to number of parameters) + */ + public double[] guessParametersErrors(EstimationProblem problem) + throws EstimationException { + int m = problem.getMeasurements().length; + int p = problem.getAllParameters().length; + if (m <= p) { + throw new EstimationException("no degrees of freedom ({0} measurements, {1} parameters)", + new Object[] { new Integer(m), new Integer(p)}); + } + double[] errors = new double[problem.getAllParameters().length]; + final double c = Math.sqrt(getChiSquare(problem) / (m - p)); + double[][] covar = getCovariances(problem); + for (int i = 0; i < errors.length; ++i) { + errors[i] = Math.sqrt(covar[i][i]) * c; + } + return errors; + } + + /** + * Initialization of the common parts of the estimation. + *This method must be called at the start + * of the {@link #estimate(EstimationProblem) estimate} + * method.
+ * @param problem estimation problem to solve + */ + protected void initializeEstimate(EstimationProblem problem) { + + // reset counters + costEvaluations = 0; + jacobianEvaluations = 0; + + // retrieve the equations and the parameters + measurements = problem.getMeasurements(); + parameters = problem.getUnboundParameters(); + + // arrays shared with the other private methods + rows = measurements.length; + cols = parameters.length; + jacobian = new double[rows * cols]; + residuals = new double[rows]; + + cost = Double.POSITIVE_INFINITY; + + } + + public abstract void estimate(EstimationProblem problem) + throws EstimationException; + + /** Array of measurements. */ + protected WeightedMeasurement[] measurements; + + /** Array of parameters. */ + protected EstimatedParameter[] parameters; + + /** + * Jacobian matrix. + *This matrix is in canonical form just after the calls to + * {@link #updateJacobian()}, but may be modified by the solver + * in the derived class (the {@link LevenbergMarquardtEstimator + * Levenberg-Marquardt estimator} does this).
+ */ + protected double[] jacobian; + + /** Number of columns of the jacobian matrix. */ + protected int cols; + + /** Number of rows of the jacobian matrix. */ + protected int rows; + + /** Residuals array. + *This array is in canonical form just after the calls to + * {@link #updateJacobian()}, but may be modified by the solver + * in the derived class (the {@link LevenbergMarquardtEstimator + * Levenberg-Marquardt estimator} does this).
+ */ + protected double[] residuals; + + /** Cost value (square root of the sum of the residuals). */ + protected double cost; + + /** Maximal allowed number of cost evaluations. */ + protected int maxCostEval; + + /** Number of cost evaluations. */ + protected int costEvaluations; + + /** Number of jacobian evaluations. */ + protected int jacobianEvaluations; + +} \ No newline at end of file diff --git a/src/java/org/apache/commons/math/estimation/EstimationException.java b/src/java/org/apache/commons/math/estimation/EstimationException.java index 7eba1de7c..9399e8d17 100644 --- a/src/java/org/apache/commons/math/estimation/EstimationException.java +++ b/src/java/org/apache/commons/math/estimation/EstimationException.java @@ -30,7 +30,7 @@ public class EstimationException extends MathException { /** Serializable version identifier. */ - private static final long serialVersionUID = -7414806622114810487L; + private static final long serialVersionUID = -573038581493881337L; /** * Simple constructor. @@ -38,17 +38,8 @@ extends MathException { * @param specifier format specifier (to be translated) * @param parts to insert in the format (no translation) */ - public EstimationException(String specifier, String[] parts) { + public EstimationException(String specifier, Object[] parts) { super(specifier, parts); } - /** - * Simple constructor. - * Build an exception from a cause - * @param cause cause of this exception - */ - public EstimationException(Throwable cause) { - super(cause); - } - } diff --git a/src/java/org/apache/commons/math/estimation/Estimator.java b/src/java/org/apache/commons/math/estimation/Estimator.java index 5d8b4f07f..0dacd8e5d 100644 --- a/src/java/org/apache/commons/math/estimation/Estimator.java +++ b/src/java/org/apache/commons/math/estimation/Estimator.java @@ -60,10 +60,31 @@ public interface Estimator { * criterion that is minimized by the estimator as follows: if * c is the criterion, and n is the number of * measurements, then the RMS is sqrt (c/n). + * @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; + } diff --git a/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java b/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java index 3be1c4e95..80c0cba04 100644 --- a/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java +++ b/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java @@ -34,206 +34,145 @@ import org.apache.commons.math.linear.RealMatrixImpl; * */ -public class GaussNewtonEstimator - implements Estimator, Serializable { +public class GaussNewtonEstimator extends AbstractEstimator implements Serializable { - /** - * Simple constructor. - * - *This constructor builds an estimator and stores its convergence - * characteristics.
- * - *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.
- * - *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.
- * - *If neither conditions are fulfilled before a given number of - * iterations, the algorithm is considered to have failed and an - * {@link EstimationException} is thrown.
- * - * @param maxIterations maximum number of iterations 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 - *Math.abs (Jn - Jn-1) < Jn * convergence, where
- * Jn and Jn-1 are the current and
- * preceding criterion value (square sum of the weighted residuals
- * of considered measurements).
- */
- public GaussNewtonEstimator(int maxIterations,
- double convergence,
- double steadyStateThreshold) {
- this.maxIterations = maxIterations;
- this.steadyStateThreshold = steadyStateThreshold;
- this.convergence = convergence;
- }
+ /**
+ * Simple constructor.
+ *
+ * This constructor builds an estimator and stores its convergence + * characteristics.
+ * + *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.
+ * + *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.
+ * + *If neither conditions are fulfilled before a given number of + * iterations, the algorithm is considered to have failed and an + * {@link EstimationException} is thrown.
+ * + * @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 + *Math.abs (Jn - Jn-1) < Jn * convergence, where
+ * Jn and Jn-1 are the current and
+ * preceding criterion value (square sum of the weighted residuals
+ * of considered measurements).
+ */
+ public GaussNewtonEstimator(int maxCostEval,
+ double convergence,
+ double steadyStateThreshold) {
+ setMaxCostEval(maxCostEval);
+ this.steadyStateThreshold = steadyStateThreshold;
+ this.convergence = convergence;
+ }
- /**
- * Solve an estimation problem using a least squares criterion.
- *
- * 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.
- * - *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 nos reached before the maximum - * number of iterations, an {@link EstimationException} is - * thrown.
- * - * @param problem estimation problem to solve - * @exception EstimationException if the problem cannot be solved - * - * @see EstimationProblem - * - */ - public void estimate(EstimationProblem problem) - throws EstimationException { - int iterations = 0; - double previous = 0.0; - double current = 0.0; - - // iterate until convergence is reached - do { - - if (++iterations > maxIterations) { - throw new EstimationException ("unable to converge in {0} iterations", - new String[] { - Integer.toString(maxIterations) - }); - } - - // perform one iteration - linearEstimate(problem); - - previous = current; - current = evaluateCriterion(problem); - - } while ((iterations < 2) - || (Math.abs(previous - current) > (current * steadyStateThreshold) - && (Math.abs(current) > convergence))); - - } - - /** - * Estimate the solution of a linear least square problem. - * - *The Gauss-Newton algorithm is iterative. Each iteration - * consists in solving a linearized least square problem. Several - * iterations are needed for general problems since the - * linearization is only an approximation of the problem - * behaviour. However, for linear problems one iteration is enough - * to get the solution. This method is provided in the public - * interface in order to handle more efficiently these linear - * problems.
- * - * @param problem estimation problem to solve - * @exception EstimationException if the problem cannot be solved - * - */ - public void linearEstimate(EstimationProblem problem) + /** + * Solve an estimation problem using a least squares criterion. + * + *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.
+ * + *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 nos reached before the maximum + * number of iterations, an {@link EstimationException} is + * thrown.
+ * + * @param problem estimation problem to solve + * @exception EstimationException if the problem cannot be solved + * + * @see EstimationProblem + * + */ + public void estimate(EstimationProblem problem) throws EstimationException { - EstimatedParameter[] parameters = problem.getUnboundParameters(); - WeightedMeasurement[] measurements = problem.getMeasurements(); + initializeEstimate(problem); - // build the linear problem - RealMatrix b = new RealMatrixImpl(parameters.length, 1); - RealMatrix a = new RealMatrixImpl(parameters.length, parameters.length); - double[] grad = new double[parameters.length]; - RealMatrixImpl bDecrement = new RealMatrixImpl(parameters.length, 1); - double[][] bDecrementData = bDecrement.getDataRef(); - RealMatrixImpl wGradGradT = new RealMatrixImpl(parameters.length, parameters.length); - double[][] wggData = wGradGradT.getDataRef(); - for (int i = 0; i < measurements.length; ++i) { - if (! measurements [i].isIgnored()) { + // work matrices + double[] grad = new double[parameters.length]; + RealMatrixImpl bDecrement = new RealMatrixImpl(parameters.length, 1); + double[][] bDecrementData = bDecrement.getDataRef(); + RealMatrixImpl wGradGradT = new RealMatrixImpl(parameters.length, parameters.length); + double[][] wggData = wGradGradT.getDataRef(); - double weight = measurements[i].getWeight(); - double residual = measurements[i].getResidual(); + // iterate until convergence is reached + double previous = Double.POSITIVE_INFINITY; + do { - // compute the normal equation - for (int j = 0; j < parameters.length; ++j) { - grad[j] = measurements[i].getPartial(parameters[j]); - bDecrementData[j][0] = weight * residual * grad[j]; - } + // build the linear problem + ++jacobianEvaluations; + RealMatrix b = new RealMatrixImpl(parameters.length, 1); + RealMatrix a = new RealMatrixImpl(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][0] = weight * residual * grad[j]; + } + + // build the contribution matrix for measurement i + for (int k = 0; k < parameters.length; ++k) { + double[] wggRow = wggData[k]; + double gk = grad[k]; + for (int l = 0; l < parameters.length; ++l) { + wggRow[l] = weight * gk * grad[l]; + } + } + + // update the matrices + a = a.add(wGradGradT); + b = b.add(bDecrement); - // build the contribution matrix for measurement i - for (int k = 0; k < parameters.length; ++k) { - double[] wggRow = wggData[k]; - double gk = grad[k]; - for (int l = 0; l < parameters.length; ++l) { - wggRow[l] = weight * gk * grad[l]; } } - // update the matrices - a = a.add(wGradGradT); - b = b.add(bDecrement); + try { + + // solve the linearized least squares problem + RealMatrix dX = a.solve(b); + + // update the estimated parameters + for (int i = 0; i < parameters.length; ++i) { + parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i, 0)); + } + + } catch(InvalidMatrixException e) { + throw new EstimationException("unable to solve: singular problem", new Object[0]); + } + + + previous = cost; + updateResidualsAndCost(); + + } while ((getCostEvaluations() < 2) + || (Math.abs(previous - cost) > (cost * steadyStateThreshold) + && (Math.abs(cost) > convergence))); - } } - try { + private double steadyStateThreshold; + private double convergence; - // solve the linearized least squares problem - RealMatrix dX = a.solve(b); - - // update the estimated parameters - for (int i = 0; i < parameters.length; ++i) { - parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i, 0)); - } - - } catch(InvalidMatrixException e) { - throw new EstimationException(e); - } - - } - - private double evaluateCriterion(EstimationProblem problem) { - double criterion = 0.0; - WeightedMeasurement[] measurements = problem.getMeasurements(); - - for (int i = 0; i < measurements.length; ++i) { - double residual = measurements[i].getResidual(); - criterion += measurements[i].getWeight() * residual * residual; - } - - return criterion; - - } - - /** - * 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 - * c if the criterion, and n is the number of - * measurements, then the RMS is sqrt (c/n). - * @param problem estimation problem - * @return RMS value - */ - public double getRMS(EstimationProblem problem) { - double criterion = evaluateCriterion(problem); - int n = problem.getMeasurements().length; - return Math.sqrt(criterion / n); - } - - private int maxIterations; - private double steadyStateThreshold; - private double convergence; - - private static final long serialVersionUID = -7606628156644194170L; + private static final long serialVersionUID = 5485001826076289109L; } diff --git a/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java b/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java index 3eeee1576..858419e73 100644 --- a/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java +++ b/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java @@ -19,6 +19,7 @@ package org.apache.commons.math.estimation; import java.io.Serializable; import java.util.Arrays; + /** * This class solves a least squares problem. * @@ -92,7 +93,7 @@ import java.util.Arrays; * @author Kenneth E. Hillstrom (original fortran) * @author Jorge J. More (original fortran) */ -public class LevenbergMarquardtEstimator implements Serializable, Estimator { +public class LevenbergMarquardtEstimator extends AbstractEstimator implements Serializable { /** * Build an estimator for least squares problems. @@ -107,12 +108,16 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { * */ public LevenbergMarquardtEstimator() { + + // set up the superclass with a default max cost evaluations setting + setMaxCostEval(1000); + // default values for the tuning parameters setInitialStepBoundFactor(100.0); - setMaxCostEval(1000); setCostRelativeTolerance(1.0e-10); setParRelativeTolerance(1.0e-10); setOrthoTolerance(1.0e-10); + } /** @@ -128,16 +133,6 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { this.initialStepBoundFactor = initialStepBoundFactor; } - /** - * Set the maximal number of cost evaluations. - * - * @param maxCostEval maximal number of cost evaluations - * @see #estimate - */ - public void setMaxCostEval(int maxCostEval) { - this.maxCostEval = maxCostEval; - } - /** * Set the desired relative error in the sum of squares. * @@ -170,75 +165,6 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { this.orthoTolerance = orthoTolerance; } - /** - * Get the number of cost evaluations. - * - * @return number of cost evaluations - * */ - public int getCostEvaluations() { - return costEvaluations; - } - - /** - * Get the number of jacobian evaluations. - * - * @return number of jacobian evaluations - * */ - public int getJacobianEvaluations() { - return jacobianEvaluations; - } - - /** - * Update the jacobian matrix. - */ - private void updateJacobian() { - ++jacobianEvaluations; - Arrays.fill(jacobian, 0); - for (int i = 0, index = 0; i < rows; i++) { - WeightedMeasurement wm = measurements[i]; - double factor = -Math.sqrt(wm.getWeight()); - for (int j = 0; j < cols; ++j) { - jacobian[index++] = factor * wm.getPartial(parameters[j]); - } - } - } - - /** - * Update the residuals array and cost function value. - */ - private void updateResidualsAndCost() { - ++costEvaluations; - cost = 0; - for (int i = 0, index = 0; i < rows; i++, index += cols) { - WeightedMeasurement wm = measurements[i]; - double residual = wm.getResidual(); - residuals[i] = Math.sqrt(wm.getWeight()) * residual; - cost += wm.getWeight() * residual * residual; - } - cost = Math.sqrt(cost); - } - - /** - * 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 - * c if the criterion, and n is the number of - * measurements, then the RMS is sqrt (c/n). - * - * @param problem estimation problem - * @return RMS value - */ - public double getRMS(EstimationProblem problem) { - WeightedMeasurement[] wm = problem.getMeasurements(); - double criterion = 0; - for (int i = 0; i < wm.length; ++i) { - double residual = wm[i].getResidual(); - criterion += wm[i].getWeight() * residual * residual; - } - return Math.sqrt(criterion / wm.length); - } - /** * Solve an estimation problem using the Levenberg-Marquardt algorithm. *The algorithm used is a modified Levenberg-Marquardt one, based @@ -263,7 +189,6 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { * reached with the specified algorithm settings or if there are more variables * than equations * @see #setInitialStepBoundFactor - * @see #setMaxCostEval * @see #setCostRelativeTolerance * @see #setParRelativeTolerance * @see #setOrthoTolerance @@ -271,21 +196,15 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { public void estimate(EstimationProblem problem) throws EstimationException { - // retrieve the equations and the parameters - measurements = problem.getMeasurements(); - parameters = problem.getUnboundParameters(); + initializeEstimate(problem); // arrays shared with the other private methods - rows = measurements.length; - cols = parameters.length; solvedCols = Math.min(rows, cols); - jacobian = new double[rows * cols]; diagR = new double[cols]; jacNorm = new double[cols]; beta = new double[cols]; permutation = new int[cols]; lmDir = new double[cols]; - residuals = new double[rows]; // local variables double delta = 0, xNorm = 0; @@ -300,11 +219,9 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { updateResidualsAndCost(); // outer loop - lmPar = 0; - costEvaluations = 0; - jacobianEvaluations = 0; + lmPar = 0; boolean firstIteration = true; - while (costEvaluations < maxCostEval) { + while (true) { // compute the Q.R. decomposition of the jacobian matrix updateJacobian(); @@ -477,42 +394,28 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { // tests for termination and stringent tolerances // (2.2204e-16 is the machine epsilon for IEEE754) - if (costEvaluations >= maxCostEval) { - break; - } 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", - new String[] { - Double.toString(costRelativeTolerance) - }); + new Object[] { new Double(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", - new String[] { - Double.toString(parRelativeTolerance) - }); + new Object[] { new Double(parRelativeTolerance) }); } else if (maxCosine <= 2.2204e-16) { throw new EstimationException("orthogonality tolerance is too small ({0})," + " solution is orthogonal to the jacobian", - new String[] { - Double.toString(orthoTolerance) - }); + new Object[] { new Double(orthoTolerance) }); } } } - throw new EstimationException("maximal number of evaluations exceeded ({0})", - new String[] { - Integer.toString(maxCostEval) - }); - } /** @@ -919,29 +822,9 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { } } - /** Array of measurements. */ - private WeightedMeasurement[] measurements; - - /** Array of parameters. */ - private EstimatedParameter[] parameters; - - /** - * Jacobian matrix. - *
Depending on the computation phase, this matrix is either in - * canonical form (just after the calls to updateJacobian) or in - * Q.R. decomposed form (after calls to qrDecomposition)
- */ - private double[] jacobian; - - /** Number of columns of the jacobian matrix. */ - private int cols; - /** Number of solved variables. */ private int solvedCols; - /** Number of rows of the jacobian matrix. */ - private int rows; - /** Diagonal elements of the R matrix in the Q.R. decomposition. */ private double[] diagR; @@ -963,28 +846,9 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { /** Parameters evolution direction associated with lmPar. */ private double[] lmDir; - /** Residuals array. - *Depending on the computation phase, this array is either in - * canonical form (just after the calls to updateResiduals) or in - * premultiplied by Qt form (just after calls to qTy)
- */ - private double[] residuals; - - /** Cost value (square root of the sum of the residuals). */ - private double cost; - /** Positive input variable used in determining the initial step bound. */ private double initialStepBoundFactor; - /** Maximal number of cost evaluations. */ - private int maxCostEval; - - /** Number of cost evaluations. */ - private int costEvaluations; - - /** Number of jacobian evaluations. */ - private int jacobianEvaluations; - /** Desired relative error in the sum of squares. */ private double costRelativeTolerance; @@ -995,6 +859,6 @@ public class LevenbergMarquardtEstimator implements Serializable, Estimator { * and the columns of the jacobian. */ private double orthoTolerance; - private static final long serialVersionUID = 5387476316105068340L; + private static final long serialVersionUID = -5705952631533171019L; } diff --git a/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java b/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java index cd6de0cc1..dc64695a1 100644 --- a/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java +++ b/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java @@ -110,6 +110,14 @@ public class LevenbergMarquardtEstimatorTest 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); @@ -267,7 +275,15 @@ public class LevenbergMarquardtEstimatorTest estimator.estimate(problem); assertTrue(estimator.getRMS(problem) < initialCost); assertTrue(Math.sqrt(m.length) * estimator.getRMS(problem) > 0.6); - double dJ0 = 2 * (m[0].getResidual() * m[0].getPartial(p[0]) + 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]) @@ -496,7 +512,34 @@ public class LevenbergMarquardtEstimatorTest 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); diff --git a/xdocs/changes.xml b/xdocs/changes.xml index aade6532e..94f34f5b3 100644 --- a/xdocs/changes.xml +++ b/xdocs/changes.xml @@ -124,6 +124,9 @@ Commons Math Release Notes