You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@commons.apache.org by lu...@apache.org on 2007/02/26 23:59:47 UTC
svn commit: r512061 - in /jakarta/commons/proper/math/trunk/src:
java/org/apache/commons/math/estimation/
mantissa/src/org/spaceroots/mantissa/estimation/
mantissa/src/org/spaceroots/mantissa/estimation/doc-files/
mantissa/tests-src/org/spaceroots/mant...
Author: luc
Date: Mon Feb 26 14:59:45 2007
New Revision: 512061
URL: http://svn.apache.org/viewvc?view=rev&rev=512061
Log:
added the estimation package from Mantissa
Added:
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java (with props)
jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html (with props)
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/
- copied from r511516, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/EstimatedParameterTest.java
- copied, changed from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/EstimatedParameterTest.java
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java
- copied, changed from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/LevenbergMarquardtEstimatorTest.java
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/MinpackTest.java
- copied, changed from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/MinpackTest.java
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/WeightedMeasurementTest.java
- copied, changed from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/WeightedMeasurementTest.java
Removed:
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/EstimatedParameter.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/EstimationException.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/EstimationProblem.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/Estimator.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/GaussNewtonEstimator.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/LeastSquaresEstimator.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/LevenbergMarquardtEstimator.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/WeightedMeasurement.java
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/doc-files/
jakarta/commons/proper/math/trunk/src/mantissa/src/org/spaceroots/mantissa/estimation/package.html
jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/AllTests.java
jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/GaussNewtonEstimatorTest.java
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,123 @@
+// 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 represent the estimated parameters of an estimation problem.
+
+ * <p>The parameters of an estimation problem have a name, a value and
+ * a bound flag. The value of bound parameters is considered trusted
+ * 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>
+
+ * @version $Id: EstimatedParameter.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+public class EstimatedParameter
+ implements Serializable {
+
+ /** Simple constructor.
+ * Build an instance from a first estimate of the parameter,
+ * initially considered unbound.
+ * @param name name of the parameter
+ * @param firstEstimate first estimate of the parameter
+ */
+ public EstimatedParameter(String name, double firstEstimate) {
+ this.name = name;
+ estimate = firstEstimate;
+ 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
+ */
+ public EstimatedParameter(String name,
+ double firstEstimate,
+ boolean bound) {
+ this.name = name;
+ 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;
+
+ private static final long serialVersionUID = -555440800213416949L;
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimatedParameter.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,52 @@
+// 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 $Id: EstimationException.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+public class EstimationException
+extends MathException {
+
+ /** Serializable version identifier. */
+ private static final long serialVersionUID = -7414806622114810487L;
+
+ /** 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, String[] parts) {
+ super(specifier, parts);
+ }
+
+ /** Simple constructor.
+ * Build an exception from a cause
+ * @param cause cause of this exception
+ */
+ public EstimationException(Throwable cause) {
+ super(cause);
+ }
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationException.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,61 @@
+// 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 $Id: EstimationProblem.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+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();
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/EstimationProblem.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,66 @@
+// 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 $Id: Estimator.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+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> if the criterion, and <em>n</em> is the number of
+ * measurements, the the RMS is <em>sqrt (c/n)</em>.
+ * @param problem estimation problem
+ * @return RMS value
+ */
+ public double getRMS(EstimationProblem problem);
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/Estimator.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,235 @@
+// 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.RealMatrix;
+import org.apache.commons.math.linear.RealMatrixImpl;
+
+/** 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 $Id: GaussNewtonEstimator.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+public class GaussNewtonEstimator
+ implements Estimator, Serializable {
+
+ /** Simple constructor.
+
+ * <p>This constructor build an estimator and store 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 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
+ * <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
+ * <code>Jn</code> and <code>Jn-1</code> 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;
+ }
+
+ /** 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 nos 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 {
+ 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.
+
+ * <p>The Gauss-Newton algorithm is iterative. Each iteration
+ * consist 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.</p>
+
+ * @param problem estimation problem to solve
+ * @exception EstimationException if the problem cannot be solved
+
+ */
+ public void linearEstimate(EstimationProblem problem)
+ throws EstimationException {
+
+ EstimatedParameter[] parameters = problem.getUnboundParameters();
+ WeightedMeasurement[] measurements = problem.getMeasurements();
+
+ // 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()) {
+
+ 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);
+
+ }
+ }
+
+ 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(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
+ * <em>c</em> if the criterion, and <em>n</em> is the number of
+ * measurements, then the RMS is <em>sqrt (c/n)</em>.
+ * @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;
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/GaussNewtonEstimator.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,971 @@
+/*
+ * 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. This feature has undergone only basic testing
+ * for now and should still be considered experimental.</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)
+ * @author Luc Maisonobe (Java translation)
+ */
+public class LevenbergMarquardtEstimator implements Serializable, Estimator {
+
+ /** 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() {
+ // default values for the tuning parameters
+ setInitialStepBoundFactor(100.0);
+ setMaxCostEval(1000);
+ 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 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.
+ * @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;
+ }
+
+ /** 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
+ * <em>c</em> if the criterion, and <em>n</em> is the number of
+ * measurements, then the RMS is <em>sqrt (c/n)</em>.
+ * @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.
+ * <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 #setMaxCostEval
+ * @see #setCostRelativeTolerance
+ * @see #setParRelativeTolerance
+ * @see #setOrthoTolerance
+ */
+ public void estimate(EstimationProblem problem)
+ throws EstimationException {
+
+ // 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;
+ 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;
+ 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;
+ costEvaluations = 0;
+ jacobianEvaluations = 0;
+ boolean firstIteration = true;
+ while (costEvaluations < maxCostEval) {
+
+ // 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 (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)
+ });
+ } 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)
+ });
+ } 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)
+ });
+ }
+
+ }
+
+ }
+
+ throw new EstimationException("maximal number of evaluations exceeded ({0})",
+ new String[] {
+ Integer.toString(maxCostEval)
+ });
+
+ }
+
+ /** 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>
+ */
+ private void qrDecomposition() {
+
+ // 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 (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];
+ }
+ }
+ }
+
+ /** Array of measurements. */
+ private WeightedMeasurement[] measurements;
+
+ /** Array of parameters. */
+ private EstimatedParameter[] parameters;
+
+ /** Jacobian matrix.
+ * <p>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)</p>
+ */
+ 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;
+
+ /** 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;
+
+ /** Residuals array.
+ * <p>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)</p>
+ */
+ 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;
+
+ /** 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;
+
+ private static final long serialVersionUID = 5387476316105068340L;
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java Mon Feb 26 14:59:45 2007
@@ -0,0 +1,143 @@
+// 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
+ * independant 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 $Id: WeightedMeasurement.java 1705 2006-09-17 19:57:39Z luc $
+ * @author L. Maisonobe
+
+ */
+
+public abstract class WeightedMeasurement implements Serializable {
+
+ /** 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;
+ }
+
+ private final double weight;
+ private final double measuredValue;
+ private boolean ignored;
+
+}
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/WeightedMeasurement.java
------------------------------------------------------------------------------
svn:eol-style = native
Added: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html?view=auto&rev=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html (added)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html Mon Feb 26 14:59:45 2007
@@ -0,0 +1,52 @@
+<html>
+<body>
+This package provides classes to solve estimation problems.
+
+<p>The estimation problems considered here are parametric problems where a user model
+depends on initially unknown scalar parameters and several measurements made on
+values that depend on the model are available. As an example, one can consider the
+flow rate of a river given rain data on its vicinity, or the center and radius of a
+circle given points on a ring.</p>
+
+<p>One important class of estimation problems is weighted least squares problems.
+They basically consist in finding the values for some parameters p<sub>k</sub> such
+that a cost function J = sum(w<sub>i</sub> r<sub>i</sub><sup>2</sup>) is minimized.
+The various r<sub>i</sub> terms represent the deviation r<sub>i</sub> =
+mes<sub>i</sub> - mod<sub>i</sub> between the measurements and the parameterized
+models. The w<sub>i</sub> factors are the measurements weights, they are often chosen
+either all equal to 1.0 or proportional to the inverse of the variance of the
+measurement type. The solver adjusts the values of the estimated parameters
+p<sub>k</sub> which are not bound. It does not touch the parameters which have been
+put in a bound state by the user.</p>
+
+<p>This package provides the {@link
+org.spaceroots.mantissa.estimation.EstimatedParameter EstimatedParameter} class to
+represent each estimated parameter, and the {@link
+org.spaceroots.mantissa.estimation.WeightedMeasurement WeightedMeasurement} abstract
+class the user can extend to define its measurements. All parameters and measurements
+are then provided to some {@link org.spaceroots.mantissa.estimation.Estimator
+Estimator} packed together in an {@link
+org.spaceroots.mantissa.estimation.EstimationProblem EstimationProblem} instance
+which acts only as a container. The package provides two common estimators for
+weighted least squares problems, one based on the {@link
+org.spaceroots.mantissa.estimation.GaussNewtonEstimator Gauss-Newton} method and the
+other one based on the {@link
+org.spaceroots.mantissa.estimation.LevenbergMarquardtEstimator Levenberg-Marquardt}
+method.</p>
+
+<p>The class diagram for the public classes of this package is displayed below. The
+orange boxes <code>UserProblem</code>, <code>UserFirstMeasurementType</code> and
+<code>UserSecondMeasurementType</code> are exemple of what the user should create to
+use this package: implement his own problem and measurement types using the {@link
+org.spaceroots.mantissa.estimation.EstimationProblem} interface and {@link
+org.spaceroots.mantissa.estimation.WeightedMeasurement} abstract class, and then use
+one of the provided estimators (for example {@link
+org.spaceroots.mantissa.estimation.GaussNewtonEstimator} or {@link
+org.spaceroots.mantissa.estimation.LevenbergMarquardtEstimator}) to solve it. The
+white boxes are the interfaces and classes already provided by the library.</p>
+
+<img src="doc-files/org_spaceroots_mantissa_estimation_classes.png" />
+
+@author L. Maisonobe
+</body>
+</html>
\ No newline at end of file
Propchange: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/estimation/package.html
------------------------------------------------------------------------------
svn:eol-style = native
Copied: jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/EstimatedParameterTest.java (from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/EstimatedParameterTest.java)
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/EstimatedParameterTest.java?view=diff&rev=512061&p1=jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/EstimatedParameterTest.java&r1=512056&p2=jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/EstimatedParameterTest.java&r2=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/EstimatedParameterTest.java (original)
+++ jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/EstimatedParameterTest.java Mon Feb 26 14:59:45 2007
@@ -15,7 +15,9 @@
// specific language governing permissions and limitations
// under the License.
-package org.spaceroots.mantissa.estimation;
+package org.apache.commons.math.estimation;
+
+import org.apache.commons.math.estimation.EstimatedParameter;
import junit.framework.*;
Copied: jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java (from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/LevenbergMarquardtEstimatorTest.java)
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java?view=diff&rev=512061&p1=jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/LevenbergMarquardtEstimatorTest.java&r1=512056&p2=jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java&r2=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/LevenbergMarquardtEstimatorTest.java (original)
+++ jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/LevenbergMarquardtEstimatorTest.java Mon Feb 26 14:59:45 2007
@@ -15,13 +15,19 @@
* limitations under the License.
*/
-package org.spaceroots.mantissa.estimation;
+package org.apache.commons.math.estimation;
import java.util.ArrayList;
-import java.util.IdentityHashMap;
+import java.util.HashMap;
import java.util.Iterator;
import java.util.Set;
+import org.apache.commons.math.estimation.EstimatedParameter;
+import org.apache.commons.math.estimation.EstimationException;
+import org.apache.commons.math.estimation.EstimationProblem;
+import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
+import org.apache.commons.math.estimation.WeightedMeasurement;
+
import junit.framework.*;
/**
@@ -519,7 +525,7 @@
}
public EstimatedParameter[] getAllParameters() {
- IdentityHashMap map = new IdentityHashMap();
+ HashMap map = new HashMap();
for (int i = 0; i < measurements.length; ++i) {
EstimatedParameter[] parameters = measurements[i].getParameters();
for (int j = 0; j < parameters.length; ++j) {
Copied: jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/MinpackTest.java (from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/MinpackTest.java)
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/MinpackTest.java?view=diff&rev=512061&p1=jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/MinpackTest.java&r1=512056&p2=jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/MinpackTest.java&r2=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/MinpackTest.java (original)
+++ jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/MinpackTest.java Mon Feb 26 14:59:45 2007
@@ -1,6 +1,12 @@
-package org.spaceroots.mantissa.estimation;
+package org.apache.commons.math.estimation;
import java.util.Arrays;
+
+import org.apache.commons.math.estimation.EstimatedParameter;
+import org.apache.commons.math.estimation.EstimationException;
+import org.apache.commons.math.estimation.EstimationProblem;
+import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
+import org.apache.commons.math.estimation.WeightedMeasurement;
import junit.framework.*;
Copied: jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/WeightedMeasurementTest.java (from r512056, jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/WeightedMeasurementTest.java)
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/WeightedMeasurementTest.java?view=diff&rev=512061&p1=jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/WeightedMeasurementTest.java&r1=512056&p2=jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/WeightedMeasurementTest.java&r2=512061
==============================================================================
--- jakarta/commons/proper/math/trunk/src/mantissa/tests-src/org/spaceroots/mantissa/estimation/WeightedMeasurementTest.java (original)
+++ jakarta/commons/proper/math/trunk/src/test/org/apache/commons/math/estimation/WeightedMeasurementTest.java Mon Feb 26 14:59:45 2007
@@ -15,7 +15,10 @@
// specific language governing permissions and limitations
// under the License.
-package org.spaceroots.mantissa.estimation;
+package org.apache.commons.math.estimation;
+
+import org.apache.commons.math.estimation.EstimatedParameter;
+import org.apache.commons.math.estimation.WeightedMeasurement;
import junit.framework.*;
---------------------------------------------------------------------
To unsubscribe, e-mail: commons-dev-unsubscribe@jakarta.apache.org
For additional commands, e-mail: commons-dev-help@jakarta.apache.org