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Posted to commits@commons.apache.org by tn...@apache.org on 2015/04/11 16:06:06 UTC
[1/5] [math] Remove deprecated classes in optim package.
Repository: commons-math
Updated Branches:
refs/heads/master 8a7645356 -> e31fde875
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizer.java
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diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizer.java
deleted file mode 100644
index b0a2ca3..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizer.java
+++ /dev/null
@@ -1,962 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.Arrays;
-
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.MathUnsupportedOperationException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.ConvergenceChecker;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.Precision;
-
-
-/**
- * This class solves a least-squares problem using the Levenberg-Marquardt
- * algorithm.
- * <br/>
- * Constraints are not supported: the call to
- * {@link #optimize(OptimizationData[]) optimize} will throw
- * {@link MathUnsupportedOperationException} if bounds are passed to it.
- *
- * <p>This implementation <em>should</em> work even for over-determined systems
- * (i.e. systems having more point than equations). Over-determined systems
- * are solved by ignoring the point which have the smallest impact according
- * to their jacobian column norm. Only the rank of the matrix and some loop bounds
- * are changed to implement this.</p>
- *
- * <p>The resolution engine is a simple translation of the MINPACK <a
- * href="http://www.netlib.org/minpack/lmder.f">lmder</a> routine with minor
- * changes. The changes include the over-determined resolution, the use of
- * inherited convergence checker 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.</p>
- * <p>The authors of the original fortran version are:
- * <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>
- * 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>
- *
- * @since 2.0
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class LevenbergMarquardtOptimizer
- extends AbstractLeastSquaresOptimizer {
- /** Twice the "epsilon machine". */
- private static final double TWO_EPS = 2 * Precision.EPSILON;
- /** Number of solved point. */
- private int solvedCols;
- /** Diagonal elements of the R matrix in the Q.R. decomposition. */
- private double[] diagR;
- /** Norms of the columns of the jacobian matrix. */
- private double[] jacNorm;
- /** Coefficients of the Householder transforms vectors. */
- private double[] beta;
- /** Columns permutation array. */
- private int[] permutation;
- /** Rank of the jacobian matrix. */
- private int rank;
- /** Levenberg-Marquardt parameter. */
- private double lmPar;
- /** Parameters evolution direction associated with lmPar. */
- private double[] lmDir;
- /** Positive input variable used in determining the initial step bound. */
- private final double initialStepBoundFactor;
- /** Desired relative error in the sum of squares. */
- private final double costRelativeTolerance;
- /** Desired relative error in the approximate solution parameters. */
- private final double parRelativeTolerance;
- /** Desired max cosine on the orthogonality between the function vector
- * and the columns of the jacobian. */
- private final double orthoTolerance;
- /** Threshold for QR ranking. */
- private final double qrRankingThreshold;
- /** Weighted residuals. */
- private double[] weightedResidual;
- /** Weighted Jacobian. */
- private double[][] weightedJacobian;
-
- /**
- * Build an optimizer for least squares problems with default values
- * for all the tuning parameters (see the {@link
- * #LevenbergMarquardtOptimizer(double,double,double,double,double)
- * other contructor}.
- * The default values for the algorithm settings are:
- * <ul>
- * <li>Initial step bound factor: 100</li>
- * <li>Cost relative tolerance: 1e-10</li>
- * <li>Parameters relative tolerance: 1e-10</li>
- * <li>Orthogonality tolerance: 1e-10</li>
- * <li>QR ranking threshold: {@link Precision#SAFE_MIN}</li>
- * </ul>
- */
- public LevenbergMarquardtOptimizer() {
- this(100, 1e-10, 1e-10, 1e-10, Precision.SAFE_MIN);
- }
-
- /**
- * Constructor that allows the specification of a custom convergence
- * checker.
- * Note that all the usual convergence checks will be <em>disabled</em>.
- * The default values for the algorithm settings are:
- * <ul>
- * <li>Initial step bound factor: 100</li>
- * <li>Cost relative tolerance: 1e-10</li>
- * <li>Parameters relative tolerance: 1e-10</li>
- * <li>Orthogonality tolerance: 1e-10</li>
- * <li>QR ranking threshold: {@link Precision#SAFE_MIN}</li>
- * </ul>
- *
- * @param checker Convergence checker.
- */
- public LevenbergMarquardtOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- this(100, checker, 1e-10, 1e-10, 1e-10, Precision.SAFE_MIN);
- }
-
- /**
- * Constructor that allows the specification of a custom convergence
- * checker, in addition to the standard ones.
- *
- * @param initialStepBoundFactor Positive input variable used in
- * determining the initial step bound. This bound is set to the
- * product of initialStepBoundFactor and the euclidean norm of
- * {@code diag * x} if non-zero, or else to {@code initialStepBoundFactor}
- * itself. In most cases factor should lie in the interval
- * {@code (0.1, 100.0)}. {@code 100} is a generally recommended value.
- * @param checker Convergence checker.
- * @param costRelativeTolerance Desired relative error in the sum of
- * squares.
- * @param parRelativeTolerance Desired relative error in the approximate
- * solution parameters.
- * @param orthoTolerance Desired max cosine on the orthogonality between
- * the function vector and the columns of the Jacobian.
- * @param threshold Desired threshold for QR ranking. If the squared norm
- * of a column vector is smaller or equal to this threshold during QR
- * decomposition, it is considered to be a zero vector and hence the rank
- * of the matrix is reduced.
- */
- public LevenbergMarquardtOptimizer(double initialStepBoundFactor,
- ConvergenceChecker<PointVectorValuePair> checker,
- double costRelativeTolerance,
- double parRelativeTolerance,
- double orthoTolerance,
- double threshold) {
- super(checker);
- this.initialStepBoundFactor = initialStepBoundFactor;
- this.costRelativeTolerance = costRelativeTolerance;
- this.parRelativeTolerance = parRelativeTolerance;
- this.orthoTolerance = orthoTolerance;
- this.qrRankingThreshold = threshold;
- }
-
- /**
- * Build an optimizer for least squares problems with default values
- * for some of the tuning parameters (see the {@link
- * #LevenbergMarquardtOptimizer(double,double,double,double,double)
- * other contructor}.
- * The default values for the algorithm settings are:
- * <ul>
- * <li>Initial step bound factor}: 100</li>
- * <li>QR ranking threshold}: {@link Precision#SAFE_MIN}</li>
- * </ul>
- *
- * @param costRelativeTolerance Desired relative error in the sum of
- * squares.
- * @param parRelativeTolerance Desired relative error in the approximate
- * solution parameters.
- * @param orthoTolerance Desired max cosine on the orthogonality between
- * the function vector and the columns of the Jacobian.
- */
- public LevenbergMarquardtOptimizer(double costRelativeTolerance,
- double parRelativeTolerance,
- double orthoTolerance) {
- this(100,
- costRelativeTolerance, parRelativeTolerance, orthoTolerance,
- Precision.SAFE_MIN);
- }
-
- /**
- * The arguments control the behaviour of the default convergence checking
- * procedure.
- * Additional criteria can defined through the setting of a {@link
- * ConvergenceChecker}.
- *
- * @param initialStepBoundFactor Positive input variable used in
- * determining the initial step bound. This bound is set to the
- * product of initialStepBoundFactor and the euclidean norm of
- * {@code diag * x} if non-zero, or else to {@code initialStepBoundFactor}
- * itself. In most cases factor should lie in the interval
- * {@code (0.1, 100.0)}. {@code 100} is a generally recommended value.
- * @param costRelativeTolerance Desired relative error in the sum of
- * squares.
- * @param parRelativeTolerance Desired relative error in the approximate
- * solution parameters.
- * @param orthoTolerance Desired max cosine on the orthogonality between
- * the function vector and the columns of the Jacobian.
- * @param threshold Desired threshold for QR ranking. If the squared norm
- * of a column vector is smaller or equal to this threshold during QR
- * decomposition, it is considered to be a zero vector and hence the rank
- * of the matrix is reduced.
- */
- public LevenbergMarquardtOptimizer(double initialStepBoundFactor,
- double costRelativeTolerance,
- double parRelativeTolerance,
- double orthoTolerance,
- double threshold) {
- super(null); // No custom convergence criterion.
- this.initialStepBoundFactor = initialStepBoundFactor;
- this.costRelativeTolerance = costRelativeTolerance;
- this.parRelativeTolerance = parRelativeTolerance;
- this.orthoTolerance = orthoTolerance;
- this.qrRankingThreshold = threshold;
- }
-
- /** {@inheritDoc} */
- @Override
- protected PointVectorValuePair doOptimize() {
- checkParameters();
-
- final int nR = getTarget().length; // Number of observed data.
- final double[] currentPoint = getStartPoint();
- final int nC = currentPoint.length; // Number of parameters.
-
- // arrays shared with the other private methods
- solvedCols = FastMath.min(nR, nC);
- diagR = new double[nC];
- jacNorm = new double[nC];
- beta = new double[nC];
- permutation = new int[nC];
- lmDir = new double[nC];
-
- // local point
- double delta = 0;
- double xNorm = 0;
- double[] diag = new double[nC];
- double[] oldX = new double[nC];
- double[] oldRes = new double[nR];
- double[] oldObj = new double[nR];
- double[] qtf = new double[nR];
- double[] work1 = new double[nC];
- double[] work2 = new double[nC];
- double[] work3 = new double[nC];
-
- final RealMatrix weightMatrixSqrt = getWeightSquareRoot();
-
- // Evaluate the function at the starting point and calculate its norm.
- double[] currentObjective = computeObjectiveValue(currentPoint);
- double[] currentResiduals = computeResiduals(currentObjective);
- PointVectorValuePair current = new PointVectorValuePair(currentPoint, currentObjective);
- double currentCost = computeCost(currentResiduals);
-
- // Outer loop.
- lmPar = 0;
- boolean firstIteration = true;
- final ConvergenceChecker<PointVectorValuePair> checker = getConvergenceChecker();
- while (true) {
- incrementIterationCount();
-
- final PointVectorValuePair previous = current;
-
- // QR decomposition of the jacobian matrix
- qrDecomposition(computeWeightedJacobian(currentPoint));
-
- weightedResidual = weightMatrixSqrt.operate(currentResiduals);
- for (int i = 0; i < nR; i++) {
- qtf[i] = weightedResidual[i];
- }
-
- // compute Qt.res
- qTy(qtf);
-
- // 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];
- weightedJacobian[k][pk] = diagR[pk];
- }
-
- if (firstIteration) {
- // scale the point according to the norms of the columns
- // of the initial jacobian
- xNorm = 0;
- for (int k = 0; k < nC; ++k) {
- double dk = jacNorm[k];
- if (dk == 0) {
- dk = 1.0;
- }
- double xk = dk * currentPoint[k];
- xNorm += xk * xk;
- diag[k] = dk;
- }
- xNorm = FastMath.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 (currentCost != 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; i <= j; ++i) {
- sum += weightedJacobian[i][pj] * qtf[i];
- }
- maxCosine = FastMath.max(maxCosine, FastMath.abs(sum) / (s * currentCost));
- }
- }
- }
- if (maxCosine <= orthoTolerance) {
- // Convergence has been reached.
- setCost(currentCost);
- return current;
- }
-
- // rescale if necessary
- for (int j = 0; j < nC; ++j) {
- diag[j] = FastMath.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] = currentPoint[pj];
- }
- final double previousCost = currentCost;
- double[] tmpVec = weightedResidual;
- weightedResidual = oldRes;
- oldRes = tmpVec;
- tmpVec = currentObjective;
- currentObjective = oldObj;
- oldObj = tmpVec;
-
- // determine the Levenberg-Marquardt parameter
- determineLMParameter(qtf, 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];
- currentPoint[pj] = oldX[pj] + lmDir[pj];
- double s = diag[pj] * lmDir[pj];
- lmNorm += s * s;
- }
- lmNorm = FastMath.sqrt(lmNorm);
- // on the first iteration, adjust the initial step bound.
- if (firstIteration) {
- delta = FastMath.min(delta, lmNorm);
- }
-
- // Evaluate the function at x + p and calculate its norm.
- currentObjective = computeObjectiveValue(currentPoint);
- currentResiduals = computeResiduals(currentObjective);
- current = new PointVectorValuePair(currentPoint, currentObjective);
- currentCost = computeCost(currentResiduals);
-
- // compute the scaled actual reduction
- double actRed = -1.0;
- if (0.1 * currentCost < previousCost) {
- double r = currentCost / 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; i <= j; ++i) {
- work1[i] += weightedJacobian[i][pj] * dirJ;
- }
- }
- double coeff1 = 0;
- for (int j = 0; j < solvedCols; ++j) {
- coeff1 += work1[j] * work1[j];
- }
- double pc2 = previousCost * previousCost;
- 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 * currentCost >= previousCost) || (tmp < 0.1)) {
- tmp = 0.1;
- }
- delta = tmp * FastMath.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 < nC; ++k) {
- double xK = diag[k] * currentPoint[k];
- xNorm += xK * xK;
- }
- xNorm = FastMath.sqrt(xNorm);
-
- // tests for convergence.
- if (checker != null && checker.converged(getIterations(), previous, current)) {
- setCost(currentCost);
- return current;
- }
- } else {
- // failed iteration, reset the previous values
- currentCost = previousCost;
- for (int j = 0; j < solvedCols; ++j) {
- int pj = permutation[j];
- currentPoint[pj] = oldX[pj];
- }
- tmpVec = weightedResidual;
- weightedResidual = oldRes;
- oldRes = tmpVec;
- tmpVec = currentObjective;
- currentObjective = oldObj;
- oldObj = tmpVec;
- // Reset "current" to previous values.
- current = new PointVectorValuePair(currentPoint, currentObjective);
- }
-
- // Default convergence criteria.
- if ((FastMath.abs(actRed) <= costRelativeTolerance &&
- preRed <= costRelativeTolerance &&
- ratio <= 2.0) ||
- delta <= parRelativeTolerance * xNorm) {
- setCost(currentCost);
- return current;
- }
-
- // tests for termination and stringent tolerances
- if (FastMath.abs(actRed) <= TWO_EPS &&
- preRed <= TWO_EPS &&
- ratio <= 2.0) {
- throw new ConvergenceException(LocalizedFormats.TOO_SMALL_COST_RELATIVE_TOLERANCE,
- costRelativeTolerance);
- } else if (delta <= TWO_EPS * xNorm) {
- throw new ConvergenceException(LocalizedFormats.TOO_SMALL_PARAMETERS_RELATIVE_TOLERANCE,
- parRelativeTolerance);
- } else if (maxCosine <= TWO_EPS) {
- throw new ConvergenceException(LocalizedFormats.TOO_SMALL_ORTHOGONALITY_TOLERANCE,
- orthoTolerance);
- }
- }
- }
- }
-
- /**
- * Determine the Levenberg-Marquardt parameter.
- * <p>This implementation is a translation in Java of the MINPACK
- * <a href="http://www.netlib.org/minpack/lmpar.f">lmpar</a>
- * routine.</p>
- * <p>This method sets the lmPar and lmDir attributes.</p>
- * <p>The authors of the original fortran function are:</p>
- * <ul>
- * <li>Argonne National Laboratory. MINPACK project. March 1980</li>
- * <li>Burton S. Garbow</li>
- * <li>Kenneth E. Hillstrom</li>
- * <li>Jorge J. More</li>
- * </ul>
- * <p>Luc Maisonobe did the Java translation.</p>
- *
- * @param qy array containing qTy
- * @param delta upper bound on the euclidean norm of diagR * lmDir
- * @param diag diagonal matrix
- * @param work1 work array
- * @param work2 work array
- * @param work3 work array
- */
- private void determineLMParameter(double[] qy, double delta, double[] diag,
- double[] work1, double[] work2, double[] work3) {
- final int nC = weightedJacobian[0].length;
-
- // 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 < nC; ++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; i < k; ++i) {
- lmDir[permutation[i]] -= ypk * weightedJacobian[i][pk];
- }
- 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 = FastMath.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;
- double 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; i < j; ++i) {
- sum += weightedJacobian[i][pj] * 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; i <= j; ++i) {
- sum += weightedJacobian[i][pj] * qy[i];
- }
- sum /= diag[pj];
- sum2 += sum * sum;
- }
- double gNorm = FastMath.sqrt(sum2);
- double paru = gNorm / delta;
- if (paru == 0) {
- paru = Precision.SAFE_MIN / FastMath.min(delta, 0.1);
- }
-
- // if the input par lies outside of the interval (parl,paru),
- // set par to the closer endpoint
- lmPar = FastMath.min(paru, FastMath.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 = FastMath.max(Precision.SAFE_MIN, 0.001 * paru);
- }
- double sPar = FastMath.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 = FastMath.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 ((FastMath.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]] -= weightedJacobian[i][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 = FastMath.max(parl, lmPar);
- } else if (fp < 0) {
- paru = FastMath.min(paru, lmPar);
- }
-
- // compute an improved estimate for lmPar
- lmPar = FastMath.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) {
- weightedJacobian[i][pj] = weightedJacobian[j][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) {
-
- final double sin;
- final double cos;
- double rkk = weightedJacobian[k][pk];
- if (FastMath.abs(rkk) < FastMath.abs(lmDiag[k])) {
- final double cotan = rkk / lmDiag[k];
- sin = 1.0 / FastMath.sqrt(1.0 + cotan * cotan);
- cos = sin * cotan;
- } else {
- final double tan = lmDiag[k] / rkk;
- cos = 1.0 / FastMath.sqrt(1.0 + tan * tan);
- sin = cos * tan;
- }
-
- // compute the modified diagonal element of R and
- // the modified element of (Qty,0)
- weightedJacobian[k][pk] = cos * rkk + sin * lmDiag[k];
- final 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 = weightedJacobian[i][pk];
- final double temp2 = cos * rik + sin * lmDiag[i];
- lmDiag[i] = -sin * rik + cos * lmDiag[i];
- weightedJacobian[i][pk] = temp2;
- }
- }
- }
-
- // store the diagonal element of s and restore
- // the corresponding diagonal element of R
- lmDiag[j] = weightedJacobian[j][permutation[j]];
- weightedJacobian[j][permutation[j]] = 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 += weightedJacobian[i][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>
- *
- * @param jacobian Weighted Jacobian matrix at the current point.
- * @exception ConvergenceException if the decomposition cannot be performed
- */
- private void qrDecomposition(RealMatrix jacobian) throws ConvergenceException {
- // Code in this class assumes that the weighted Jacobian is -(W^(1/2) J),
- // hence the multiplication by -1.
- weightedJacobian = jacobian.scalarMultiply(-1).getData();
-
- final int nR = weightedJacobian.length;
- final int nC = weightedJacobian[0].length;
-
- // initializations
- for (int k = 0; k < nC; ++k) {
- permutation[k] = k;
- double norm2 = 0;
- for (int i = 0; i < nR; ++i) {
- double akk = weightedJacobian[i][k];
- norm2 += akk * akk;
- }
- jacNorm[k] = FastMath.sqrt(norm2);
- }
-
- // transform the matrix column after column
- for (int k = 0; k < nC; ++k) {
-
- // select the column with the greatest norm on active components
- int nextColumn = -1;
- double ak2 = Double.NEGATIVE_INFINITY;
- for (int i = k; i < nC; ++i) {
- double norm2 = 0;
- for (int j = k; j < nR; ++j) {
- double aki = weightedJacobian[j][permutation[i]];
- norm2 += aki * aki;
- }
- if (Double.isInfinite(norm2) || Double.isNaN(norm2)) {
- throw new ConvergenceException(LocalizedFormats.UNABLE_TO_PERFORM_QR_DECOMPOSITION_ON_JACOBIAN,
- nR, nC);
- }
- if (norm2 > ak2) {
- nextColumn = i;
- ak2 = norm2;
- }
- }
- if (ak2 <= qrRankingThreshold) {
- rank = k;
- return;
- }
- int pk = permutation[nextColumn];
- permutation[nextColumn] = permutation[k];
- permutation[k] = pk;
-
- // choose alpha such that Hk.u = alpha ek
- double akk = weightedJacobian[k][pk];
- double alpha = (akk > 0) ? -FastMath.sqrt(ak2) : FastMath.sqrt(ak2);
- double betak = 1.0 / (ak2 - akk * alpha);
- beta[pk] = betak;
-
- // transform the current column
- diagR[pk] = alpha;
- weightedJacobian[k][pk] -= alpha;
-
- // transform the remaining columns
- for (int dk = nC - 1 - k; dk > 0; --dk) {
- double gamma = 0;
- for (int j = k; j < nR; ++j) {
- gamma += weightedJacobian[j][pk] * weightedJacobian[j][permutation[k + dk]];
- }
- gamma *= betak;
- for (int j = k; j < nR; ++j) {
- weightedJacobian[j][permutation[k + dk]] -= gamma * weightedJacobian[j][pk];
- }
- }
- }
- 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) {
- final int nR = weightedJacobian.length;
- final int nC = weightedJacobian[0].length;
-
- for (int k = 0; k < nC; ++k) {
- int pk = permutation[k];
- double gamma = 0;
- for (int i = k; i < nR; ++i) {
- gamma += weightedJacobian[i][pk] * y[i];
- }
- gamma *= beta[pk];
- for (int i = k; i < nR; ++i) {
- y[i] -= gamma * weightedJacobian[i][pk];
- }
- }
- }
-
- /**
- * @throws MathUnsupportedOperationException if bounds were passed to the
- * {@link #optimize(OptimizationData[]) optimize} method.
- */
- private void checkParameters() {
- if (getLowerBound() != null ||
- getUpperBound() != null) {
- throw new MathUnsupportedOperationException(LocalizedFormats.CONSTRAINT);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/package-info.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/package-info.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/package-info.java
deleted file mode 100644
index ab06a53..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/package-info.java
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * 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.
- */
-
-/**
- * This package provides optimization algorithms that require derivatives.
- *
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-package org.apache.commons.math4.optim.nonlinear.vector.jacobian;
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/package-info.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/package-info.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/package-info.java
deleted file mode 100644
index 91ac3ff..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/package-info.java
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * 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.
- */
-
-/**
- * Algorithms for optimizing a vector function.
- *
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-package org.apache.commons.math4.optim.nonlinear.vector;
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java b/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
index cbfa268..c8a59e9 100644
--- a/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/univariate/MultiStartUnivariateOptimizer.java
@@ -45,9 +45,9 @@ public class MultiStartUnivariateOptimizer
/** Number of evaluations already performed for all starts. */
private int totalEvaluations;
/** Number of starts to go. */
- private int starts;
+ private final int starts;
/** Random generator for multi-start. */
- private RandomGenerator generator;
+ private final RandomGenerator generator;
/** Found optima. */
private UnivariatePointValuePair[] optima;
/** Optimization data. */
@@ -211,6 +211,7 @@ public class MultiStartUnivariateOptimizer
*/
private void sortPairs(final GoalType goal) {
Arrays.sort(optima, new Comparator<UnivariatePointValuePair>() {
+ @Override
public int compare(final UnivariatePointValuePair o1,
final UnivariatePointValuePair o2) {
if (o1 == null) {
[4/5] [math] Remove deprecated classes in optim package.
Posted by tn...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizerTest.java
deleted file mode 100644
index 641f4d4..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/LevenbergMarquardtOptimizerTest.java
+++ /dev/null
@@ -1,375 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-import java.util.List;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.exception.MathUnsupportedOperationException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.linear.SingularMatrixException;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.SimpleBounds;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.Precision;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * <p>Some of the unit tests are re-implementations of the MINPACK <a
- * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
- * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
- * convenience, it is reproduced below.</p>
-
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- * Minpack Copyright Notice (1999) University of Chicago.
- * All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- * <li>Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- * must include the following acknowledgment:
- * <code>This product includes software developed by the University of
- * Chicago, as Operator of Argonne National Laboratory.</code>
- * Alternately, this acknowledgment may appear in the software itself,
- * if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- * BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
-
- * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
- * @author Burton S. Garbow (original fortran minpack tests)
- * @author Kenneth E. Hillstrom (original fortran minpack tests)
- * @author Jorge J. More (original fortran minpack tests)
- * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
- */
-@Deprecated
-public class LevenbergMarquardtOptimizerTest
- extends AbstractLeastSquaresOptimizerAbstractTest {
- @Override
- public AbstractLeastSquaresOptimizer createOptimizer() {
- return new LevenbergMarquardtOptimizer();
- }
-
- @Test(expected=MathUnsupportedOperationException.class)
- public void testConstraintsUnsupported() {
- createOptimizer().optimize(new MaxEval(100),
- new Target(new double[] { 2 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 1, 2 }),
- new SimpleBounds(new double[] { -10, 0 },
- new double[] { 20, 30 }));
- }
-
- @Override
- @Test(expected=SingularMatrixException.class)
- public void testNonInvertible() {
- /*
- * Overrides the method from parent class, since the default singularity
- * threshold (1e-14) does not trigger the expected exception.
- */
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 2, -3 },
- { 2, 1, 3 },
- { -3, 0, -9 }
- }, new double[] { 1, 1, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0 }));
- Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);
-
- optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
- }
-
- @Test
- public void testControlParameters() {
- CircleVectorial circle = new CircleVectorial();
- circle.addPoint( 30.0, 68.0);
- circle.addPoint( 50.0, -6.0);
- circle.addPoint(110.0, -20.0);
- circle.addPoint( 35.0, 15.0);
- circle.addPoint( 45.0, 97.0);
- checkEstimate(circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- 0.1, 10, 1.0e-14, 1.0e-16, 1.0e-10, false);
- checkEstimate(circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- 0.1, 10, 1.0e-15, 1.0e-17, 1.0e-10, true);
- checkEstimate(circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- 0.1, 5, 1.0e-15, 1.0e-16, 1.0e-10, true);
- circle.addPoint(300, -300);
- checkEstimate(circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- 0.1, 20, 1.0e-18, 1.0e-16, 1.0e-10, true);
- }
-
- private void checkEstimate(ModelFunction problem,
- ModelFunctionJacobian problemJacobian,
- double initialStepBoundFactor, int maxCostEval,
- double costRelativeTolerance, double parRelativeTolerance,
- double orthoTolerance, boolean shouldFail) {
- try {
- LevenbergMarquardtOptimizer optimizer
- = new LevenbergMarquardtOptimizer(initialStepBoundFactor,
- costRelativeTolerance,
- parRelativeTolerance,
- orthoTolerance,
- Precision.SAFE_MIN);
- optimizer.optimize(new MaxEval(maxCostEval),
- problem,
- problemJacobian,
- new Target(new double[] { 0, 0, 0, 0, 0 }),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- Assert.assertTrue(!shouldFail);
- } catch (DimensionMismatchException ee) {
- Assert.assertTrue(shouldFail);
- } catch (TooManyEvaluationsException ee) {
- Assert.assertTrue(shouldFail);
- }
- }
-
- /**
- * Non-linear test case: fitting of decay curve (from Chapter 8 of
- * Bevington's textbook, "Data reduction and analysis for the physical sciences").
- * XXX The expected ("reference") values may not be accurate and the tolerance too
- * relaxed for this test to be currently really useful (the issue is under
- * investigation).
- */
- @Test
- public void testBevington() {
- final double[][] dataPoints = {
- // column 1 = times
- { 15, 30, 45, 60, 75, 90, 105, 120, 135, 150,
- 165, 180, 195, 210, 225, 240, 255, 270, 285, 300,
- 315, 330, 345, 360, 375, 390, 405, 420, 435, 450,
- 465, 480, 495, 510, 525, 540, 555, 570, 585, 600,
- 615, 630, 645, 660, 675, 690, 705, 720, 735, 750,
- 765, 780, 795, 810, 825, 840, 855, 870, 885, },
- // column 2 = measured counts
- { 775, 479, 380, 302, 185, 157, 137, 119, 110, 89,
- 74, 61, 66, 68, 48, 54, 51, 46, 55, 29,
- 28, 37, 49, 26, 35, 29, 31, 24, 25, 35,
- 24, 30, 26, 28, 21, 18, 20, 27, 17, 17,
- 14, 17, 24, 11, 22, 17, 12, 10, 13, 16,
- 9, 9, 14, 21, 17, 13, 12, 18, 10, },
- };
-
- final BevingtonProblem problem = new BevingtonProblem();
-
- final int len = dataPoints[0].length;
- final double[] weights = new double[len];
- for (int i = 0; i < len; i++) {
- problem.addPoint(dataPoints[0][i],
- dataPoints[1][i]);
-
- weights[i] = 1 / dataPoints[1][i];
- }
-
- final LevenbergMarquardtOptimizer optimizer
- = new LevenbergMarquardtOptimizer();
-
- final PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(dataPoints[1]),
- new Weight(weights),
- new InitialGuess(new double[] { 10, 900, 80, 27, 225 }));
-
- final double[] solution = optimum.getPoint();
- final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };
-
- final double[][] covarMatrix = optimizer.computeCovariances(solution, 1e-14);
- final double[][] expectedCovarMatrix = {
- { 3.38, -3.69, 27.98, -2.34, -49.24 },
- { -3.69, 2492.26, 81.89, -69.21, -8.9 },
- { 27.98, 81.89, 468.99, -44.22, -615.44 },
- { -2.34, -69.21, -44.22, 6.39, 53.80 },
- { -49.24, -8.9, -615.44, 53.8, 929.45 }
- };
-
- final int numParams = expectedSolution.length;
-
- // Check that the computed solution is within the reference error range.
- for (int i = 0; i < numParams; i++) {
- final double error = FastMath.sqrt(expectedCovarMatrix[i][i]);
- Assert.assertEquals("Parameter " + i, expectedSolution[i], solution[i], error);
- }
-
- // Check that each entry of the computed covariance matrix is within 10%
- // of the reference matrix entry.
- for (int i = 0; i < numParams; i++) {
- for (int j = 0; j < numParams; j++) {
- Assert.assertEquals("Covariance matrix [" + i + "][" + j + "]",
- expectedCovarMatrix[i][j],
- covarMatrix[i][j],
- FastMath.abs(0.1 * expectedCovarMatrix[i][j]));
- }
- }
- }
-
- @Test
- public void testCircleFitting2() {
- final double xCenter = 123.456;
- final double yCenter = 654.321;
- final double xSigma = 10;
- final double ySigma = 15;
- final double radius = 111.111;
- // The test is extremely sensitive to the seed.
- final long seed = 59421061L;
- final RandomCirclePointGenerator factory
- = new RandomCirclePointGenerator(xCenter, yCenter, radius,
- xSigma, ySigma,
- seed);
- final CircleProblem circle = new CircleProblem(xSigma, ySigma);
-
- final int numPoints = 10;
- for (Vector2D p : factory.generate(numPoints)) {
- circle.addPoint(p.getX(), p.getY());
- }
-
- // First guess for the center's coordinates and radius.
- final double[] init = { 90, 659, 115 };
-
- final LevenbergMarquardtOptimizer optimizer
- = new LevenbergMarquardtOptimizer();
- final PointVectorValuePair optimum = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(circle.target()),
- new Weight(circle.weight()),
- new InitialGuess(init));
-
- final double[] paramFound = optimum.getPoint();
-
- // Retrieve errors estimation.
- final double[] asymptoticStandardErrorFound = optimizer.computeSigma(paramFound, 1e-14);
-
- // Check that the parameters are found within the assumed error bars.
- Assert.assertEquals(xCenter, paramFound[0], asymptoticStandardErrorFound[0]);
- Assert.assertEquals(yCenter, paramFound[1], asymptoticStandardErrorFound[1]);
- Assert.assertEquals(radius, paramFound[2], asymptoticStandardErrorFound[2]);
- }
-
- private static class BevingtonProblem {
- private List<Double> time;
- private List<Double> count;
-
- public BevingtonProblem() {
- time = new ArrayList<Double>();
- count = new ArrayList<Double>();
- }
-
- public void addPoint(double t, double c) {
- time.add(t);
- count.add(c);
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- double[] values = new double[time.size()];
- for (int i = 0; i < values.length; ++i) {
- final double t = time.get(i);
- values[i] = params[0] +
- params[1] * FastMath.exp(-t / params[3]) +
- params[2] * FastMath.exp(-t / params[4]);
- }
- return values;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] params) {
- double[][] jacobian = new double[time.size()][5];
-
- for (int i = 0; i < jacobian.length; ++i) {
- final double t = time.get(i);
- jacobian[i][0] = 1;
-
- final double p3 = params[3];
- final double p4 = params[4];
- final double tOp3 = t / p3;
- final double tOp4 = t / p4;
- jacobian[i][1] = FastMath.exp(-tOp3);
- jacobian[i][2] = FastMath.exp(-tOp4);
- jacobian[i][3] = params[1] * FastMath.exp(-tOp3) * tOp3 / p3;
- jacobian[i][4] = params[2] * FastMath.exp(-tOp4) * tOp4 / p4;
- }
- return jacobian;
- }
- });
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/MinpackTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/MinpackTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/MinpackTest.java
deleted file mode 100644
index c3486c0..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/MinpackTest.java
+++ /dev/null
@@ -1,1467 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.Arrays;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * <p>Some of the unit tests are re-implementations of the MINPACK <a
- * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
- * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
- * convenience, it is reproduced below.</p>
-
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- * Minpack Copyright Notice (1999) University of Chicago.
- * All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- * <li>Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- * must include the following acknowledgment:
- * <code>This product includes software developed by the University of
- * Chicago, as Operator of Argonne National Laboratory.</code>
- * Alternately, this acknowledgment may appear in the software itself,
- * if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- * BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
-
- * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
- * @author Burton S. Garbow (original fortran minpack tests)
- * @author Kenneth E. Hillstrom (original fortran minpack tests)
- * @author Jorge J. More (original fortran minpack tests)
- * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
- */
-@Deprecated
-public class MinpackTest {
-
- @Test
- public void testMinpackLinearFullRank() {
- minpackTest(new LinearFullRankFunction(10, 5, 1.0,
- 5.0, 2.23606797749979), false);
- minpackTest(new LinearFullRankFunction(50, 5, 1.0,
- 8.06225774829855, 6.70820393249937), false);
- }
-
- @Test
- public void testMinpackLinearRank1() {
- minpackTest(new LinearRank1Function(10, 5, 1.0,
- 291.521868819476, 1.4638501094228), false);
- minpackTest(new LinearRank1Function(50, 5, 1.0,
- 3101.60039334535, 3.48263016573496), false);
- }
-
- @Test
- public void testMinpackLinearRank1ZeroColsAndRows() {
- minpackTest(new LinearRank1ZeroColsAndRowsFunction(10, 5, 1.0), false);
- minpackTest(new LinearRank1ZeroColsAndRowsFunction(50, 5, 1.0), false);
- }
-
- @Test
- public void testMinpackRosenbrok() {
- minpackTest(new RosenbrockFunction(new double[] { -1.2, 1.0 },
- FastMath.sqrt(24.2)), false);
- minpackTest(new RosenbrockFunction(new double[] { -12.0, 10.0 },
- FastMath.sqrt(1795769.0)), false);
- minpackTest(new RosenbrockFunction(new double[] { -120.0, 100.0 },
- 11.0 * FastMath.sqrt(169000121.0)), false);
- }
-
- @Test
- public void testMinpackHelicalValley() {
- minpackTest(new HelicalValleyFunction(new double[] { -1.0, 0.0, 0.0 },
- 50.0), false);
- minpackTest(new HelicalValleyFunction(new double[] { -10.0, 0.0, 0.0 },
- 102.95630140987), false);
- minpackTest(new HelicalValleyFunction(new double[] { -100.0, 0.0, 0.0},
- 991.261822123701), false);
- }
-
- @Test
- public void testMinpackPowellSingular() {
- minpackTest(new PowellSingularFunction(new double[] { 3.0, -1.0, 0.0, 1.0 },
- 14.6628782986152), false);
- minpackTest(new PowellSingularFunction(new double[] { 30.0, -10.0, 0.0, 10.0 },
- 1270.9838708654), false);
- minpackTest(new PowellSingularFunction(new double[] { 300.0, -100.0, 0.0, 100.0 },
- 126887.903284750), false);
- }
-
- @Test
- public void testMinpackFreudensteinRoth() {
- minpackTest(new FreudensteinRothFunction(new double[] { 0.5, -2.0 },
- 20.0124960961895, 6.99887517584575,
- new double[] {
- 11.4124844654993,
- -0.896827913731509
- }), false);
- minpackTest(new FreudensteinRothFunction(new double[] { 5.0, -20.0 },
- 12432.833948863, 6.9988751744895,
- new double[] {
- 11.41300466147456,
- -0.896796038685959
- }), false);
- minpackTest(new FreudensteinRothFunction(new double[] { 50.0, -200.0 },
- 11426454.595762, 6.99887517242903,
- new double[] {
- 11.412781785788564,
- -0.8968051074920405
- }), false);
- }
-
- @Test
- public void testMinpackBard() {
- minpackTest(new BardFunction(1.0, 6.45613629515967, 0.0906359603390466,
- new double[] {
- 0.0824105765758334,
- 1.1330366534715,
- 2.34369463894115
- }), false);
- minpackTest(new BardFunction(10.0, 36.1418531596785, 4.17476870138539,
- new double[] {
- 0.840666673818329,
- -158848033.259565,
- -164378671.653535
- }), false);
- minpackTest(new BardFunction(100.0, 384.114678637399, 4.17476870135969,
- new double[] {
- 0.840666673867645,
- -158946167.205518,
- -164464906.857771
- }), false);
- }
-
- @Test
- public void testMinpackKowalikOsborne() {
- minpackTest(new KowalikOsborneFunction(new double[] { 0.25, 0.39, 0.415, 0.39 },
- 0.0728915102882945,
- 0.017535837721129,
- new double[] {
- 0.192807810476249,
- 0.191262653354071,
- 0.123052801046931,
- 0.136053221150517
- }), false);
- minpackTest(new KowalikOsborneFunction(new double[] { 2.5, 3.9, 4.15, 3.9 },
- 2.97937007555202,
- 0.032052192917937,
- new double[] {
- 728675.473768287,
- -14.0758803129393,
- -32977797.7841797,
- -20571594.1977912
- }), false);
- minpackTest(new KowalikOsborneFunction(new double[] { 25.0, 39.0, 41.5, 39.0 },
- 29.9590617016037,
- 0.0175364017658228,
- new double[] {
- 0.192948328597594,
- 0.188053165007911,
- 0.122430604321144,
- 0.134575665392506
- }), false);
- }
-
- @Test
- public void testMinpackMeyer() {
- minpackTest(new MeyerFunction(new double[] { 0.02, 4000.0, 250.0 },
- 41153.4665543031, 9.37794514651874,
- new double[] {
- 0.00560963647102661,
- 6181.34634628659,
- 345.223634624144
- }), false);
- minpackTest(new MeyerFunction(new double[] { 0.2, 40000.0, 2500.0 },
- 4168216.89130846, 792.917871779501,
- new double[] {
- 1.42367074157994e-11,
- 33695.7133432541,
- 901.268527953801
- }), true);
- }
-
- @Test
- public void testMinpackWatson() {
- minpackTest(new WatsonFunction(6, 0.0,
- 5.47722557505166, 0.0478295939097601,
- new double[] {
- -0.0157249615083782, 1.01243488232965,
- -0.232991722387673, 1.26043101102818,
- -1.51373031394421, 0.99299727291842
- }), false);
- minpackTest(new WatsonFunction(6, 10.0,
- 6433.12578950026, 0.0478295939096951,
- new double[] {
- -0.0157251901386677, 1.01243485860105,
- -0.232991545843829, 1.26042932089163,
- -1.51372776706575, 0.99299573426328
- }), false);
- minpackTest(new WatsonFunction(6, 100.0,
- 674256.040605213, 0.047829593911544,
- new double[] {
- -0.0157247019712586, 1.01243490925658,
- -0.232991922761641, 1.26043292929555,
- -1.51373320452707, 0.99299901922322
- }), false);
- minpackTest(new WatsonFunction(9, 0.0,
- 5.47722557505166, 0.00118311459212420,
- new double[] {
- -0.153070644166722e-4, 0.999789703934597,
- 0.0147639634910978, 0.146342330145992,
- 1.00082109454817, -2.61773112070507,
- 4.10440313943354, -3.14361226236241,
- 1.05262640378759
- }), false);
- minpackTest(new WatsonFunction(9, 10.0,
- 12088.127069307, 0.00118311459212513,
- new double[] {
- -0.153071334849279e-4, 0.999789703941234,
- 0.0147639629786217, 0.146342334818836,
- 1.00082107321386, -2.61773107084722,
- 4.10440307655564, -3.14361222178686,
- 1.05262639322589
- }), false);
- minpackTest(new WatsonFunction(9, 100.0,
- 1269109.29043834, 0.00118311459212384,
- new double[] {
- -0.153069523352176e-4, 0.999789703958371,
- 0.0147639625185392, 0.146342341096326,
- 1.00082104729164, -2.61773101573645,
- 4.10440301427286, -3.14361218602503,
- 1.05262638516774
- }), false);
- minpackTest(new WatsonFunction(12, 0.0,
- 5.47722557505166, 0.217310402535861e-4,
- new double[] {
- -0.660266001396382e-8, 1.00000164411833,
- -0.000563932146980154, 0.347820540050756,
- -0.156731500244233, 1.05281515825593,
- -3.24727109519451, 7.2884347837505,
- -10.271848098614, 9.07411353715783,
- -4.54137541918194, 1.01201187975044
- }), false);
- minpackTest(new WatsonFunction(12, 10.0,
- 19220.7589790951, 0.217310402518509e-4,
- new double[] {
- -0.663710223017410e-8, 1.00000164411787,
- -0.000563932208347327, 0.347820540486998,
- -0.156731503955652, 1.05281517654573,
- -3.2472711515214, 7.28843489430665,
- -10.2718482369638, 9.07411364383733,
- -4.54137546533666, 1.01201188830857
- }), false);
- minpackTest(new WatsonFunction(12, 100.0,
- 2018918.04462367, 0.217310402539845e-4,
- new double[] {
- -0.663806046485249e-8, 1.00000164411786,
- -0.000563932210324959, 0.347820540503588,
- -0.156731504091375, 1.05281517718031,
- -3.24727115337025, 7.28843489775302,
- -10.2718482410813, 9.07411364688464,
- -4.54137546660822, 1.0120118885369
- }), false);
- }
-
- @Test
- public void testMinpackBox3Dimensional() {
- minpackTest(new Box3DimensionalFunction(10, new double[] { 0.0, 10.0, 20.0 },
- 32.1115837449572), false);
- }
-
- @Test
- public void testMinpackJennrichSampson() {
- minpackTest(new JennrichSampsonFunction(10, new double[] { 0.3, 0.4 },
- 64.5856498144943, 11.1517793413499,
- new double[] {
-// 0.2578330049, 0.257829976764542
- 0.2578199266368004, 0.25782997676455244
- }), false);
- }
-
- @Test
- public void testMinpackBrownDennis() {
- minpackTest(new BrownDennisFunction(20,
- new double[] { 25.0, 5.0, -5.0, -1.0 },
- 2815.43839161816, 292.954288244866,
- new double[] {
- -11.59125141003, 13.2024883984741,
- -0.403574643314272, 0.236736269844604
- }), false);
- minpackTest(new BrownDennisFunction(20,
- new double[] { 250.0, 50.0, -50.0, -10.0 },
- 555073.354173069, 292.954270581415,
- new double[] {
- -11.5959274272203, 13.2041866926242,
- -0.403417362841545, 0.236771143410386
- }), false);
- minpackTest(new BrownDennisFunction(20,
- new double[] { 2500.0, 500.0, -500.0, -100.0 },
- 61211252.2338581, 292.954306151134,
- new double[] {
- -11.5902596937374, 13.2020628854665,
- -0.403688070279258, 0.236665033746463
- }), false);
- }
-
- @Test
- public void testMinpackChebyquad() {
- minpackTest(new ChebyquadFunction(1, 8, 1.0,
- 1.88623796907732, 1.88623796907732,
- new double[] { 0.5 }), false);
- minpackTest(new ChebyquadFunction(1, 8, 10.0,
- 5383344372.34005, 1.88424820499951,
- new double[] { 0.9817314924684 }), false);
- minpackTest(new ChebyquadFunction(1, 8, 100.0,
- 0.118088726698392e19, 1.88424820499347,
- new double[] { 0.9817314852934 }), false);
- minpackTest(new ChebyquadFunction(8, 8, 1.0,
- 0.196513862833975, 0.0593032355046727,
- new double[] {
- 0.0431536648587336, 0.193091637843267,
- 0.266328593812698, 0.499999334628884,
- 0.500000665371116, 0.733671406187302,
- 0.806908362156733, 0.956846335141266
- }), false);
- minpackTest(new ChebyquadFunction(9, 9, 1.0,
- 0.16994993465202, 0.0,
- new double[] {
- 0.0442053461357828, 0.199490672309881,
- 0.23561910847106, 0.416046907892598,
- 0.5, 0.583953092107402,
- 0.764380891528940, 0.800509327690119,
- 0.955794653864217
- }), false);
- minpackTest(new ChebyquadFunction(10, 10, 1.0,
- 0.183747831178711, 0.0806471004038253,
- new double[] {
- 0.0596202671753563, 0.166708783805937,
- 0.239171018813509, 0.398885290346268,
- 0.398883667870681, 0.601116332129320,
- 0.60111470965373, 0.760828981186491,
- 0.833291216194063, 0.940379732824644
- }), false);
- }
-
- @Test
- public void testMinpackBrownAlmostLinear() {
- minpackTest(new BrownAlmostLinearFunction(10, 0.5,
- 16.5302162063499, 0.0,
- new double[] {
- 0.979430303349862, 0.979430303349862,
- 0.979430303349862, 0.979430303349862,
- 0.979430303349862, 0.979430303349862,
- 0.979430303349862, 0.979430303349862,
- 0.979430303349862, 1.20569696650138
- }), false);
- minpackTest(new BrownAlmostLinearFunction(10, 5.0,
- 9765624.00089211, 0.0,
- new double[] {
- 0.979430303349865, 0.979430303349865,
- 0.979430303349865, 0.979430303349865,
- 0.979430303349865, 0.979430303349865,
- 0.979430303349865, 0.979430303349865,
- 0.979430303349865, 1.20569696650135
- }), false);
- minpackTest(new BrownAlmostLinearFunction(10, 50.0,
- 0.9765625e17, 0.0,
- new double[] {
- 1.0, 1.0, 1.0, 1.0, 1.0,
- 1.0, 1.0, 1.0, 1.0, 1.0
- }), false);
- minpackTest(new BrownAlmostLinearFunction(30, 0.5,
- 83.476044467848, 0.0,
- new double[] {
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 0.997754216442807,
- 0.997754216442807, 1.06737350671578
- }), false);
- minpackTest(new BrownAlmostLinearFunction(40, 0.5,
- 128.026364472323, 0.0,
- new double[] {
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 1.00000000000002, 1.00000000000002,
- 0.999999999999121
- }), false);
- }
-
- @Test
- public void testMinpackOsborne1() {
- minpackTest(new Osborne1Function(new double[] { 0.5, 1.5, -1.0, 0.01, 0.02, },
- 0.937564021037838, 0.00739249260904843,
- new double[] {
- 0.375410049244025, 1.93584654543108,
- -1.46468676748716, 0.0128675339110439,
- 0.0221227011813076
- }), false);
- }
-
- @Test
- public void testMinpackOsborne2() {
- minpackTest(new Osborne2Function(new double[] {
- 1.3, 0.65, 0.65, 0.7, 0.6,
- 3.0, 5.0, 7.0, 2.0, 4.5, 5.5
- },
- 1.44686540984712, 0.20034404483314,
- new double[] {
- 1.30997663810096, 0.43155248076,
- 0.633661261602859, 0.599428560991695,
- 0.754179768272449, 0.904300082378518,
- 1.36579949521007, 4.82373199748107,
- 2.39868475104871, 4.56887554791452,
- 5.67534206273052
- }), false);
- }
-
- private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
- LevenbergMarquardtOptimizer optimizer
- = new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
- FastMath.sqrt(2.22044604926e-16),
- 2.22044604926e-16);
- try {
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(400 * (function.getN() + 1)),
- function.getModelFunction(),
- function.getModelFunctionJacobian(),
- new Target(function.getTarget()),
- new Weight(function.getWeight()),
- new InitialGuess(function.getStartPoint()));
- Assert.assertFalse(exceptionExpected);
- function.checkTheoreticalMinCost(optimizer.getRMS());
- function.checkTheoreticalMinParams(optimum);
- } catch (TooManyEvaluationsException e) {
- Assert.assertTrue(exceptionExpected);
- }
- }
-
- private static abstract class MinpackFunction {
- protected int n;
- protected int m;
- protected double[] startParams;
- protected double theoreticalMinCost;
- protected double[] theoreticalMinParams;
- protected double costAccuracy;
- protected double paramsAccuracy;
-
- protected MinpackFunction(int m, double[] startParams,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- this.m = m;
- this.n = startParams.length;
- this.startParams = startParams.clone();
- this.theoreticalMinCost = theoreticalMinCost;
- this.theoreticalMinParams = theoreticalMinParams;
- this.costAccuracy = 1.0e-8;
- this.paramsAccuracy = 1.0e-5;
- }
-
- protected static double[] buildArray(int n, double x) {
- double[] array = new double[n];
- Arrays.fill(array, x);
- return array;
- }
-
- public double[] getTarget() {
- return buildArray(m, 0.0);
- }
-
- public double[] getWeight() {
- return buildArray(m, 1.0);
- }
-
- public double[] getStartPoint() {
- return startParams.clone();
- }
-
- protected void setCostAccuracy(double costAccuracy) {
- this.costAccuracy = costAccuracy;
- }
-
- protected void setParamsAccuracy(double paramsAccuracy) {
- this.paramsAccuracy = paramsAccuracy;
- }
-
- public int getN() {
- return startParams.length;
- }
-
- public void checkTheoreticalMinCost(double rms) {
- double threshold = costAccuracy * (1.0 + theoreticalMinCost);
- Assert.assertEquals(theoreticalMinCost, FastMath.sqrt(m) * rms, threshold);
- }
-
- public void checkTheoreticalMinParams(PointVectorValuePair optimum) {
- double[] params = optimum.getPointRef();
- if (theoreticalMinParams != null) {
- for (int i = 0; i < theoreticalMinParams.length; ++i) {
- double mi = theoreticalMinParams[i];
- double vi = params[i];
- Assert.assertEquals(mi, vi, paramsAccuracy * (1.0 + FastMath.abs(mi)));
- }
- }
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] point) {
- return computeValue(point);
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return computeJacobian(point);
- }
- });
- }
-
- public abstract double[][] computeJacobian(double[] variables);
- public abstract double[] computeValue(double[] variables);
- }
-
- private static class LinearFullRankFunction extends MinpackFunction {
- public LinearFullRankFunction(int m, int n, double x0,
- double theoreticalStartCost,
- double theoreticalMinCost) {
- super(m, buildArray(n, x0), theoreticalMinCost,
- buildArray(n, -1.0));
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double t = 2.0 / m;
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- jacobian[i] = new double[n];
- for (int j = 0; j < n; ++j) {
- jacobian[i][j] = (i == j) ? (1 - t) : -t;
- }
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double sum = 0;
- for (int i = 0; i < n; ++i) {
- sum += variables[i];
- }
- double t = 1 + 2 * sum / m;
- double[] f = new double[m];
- for (int i = 0; i < n; ++i) {
- f[i] = variables[i] - t;
- }
- Arrays.fill(f, n, m, -t);
- return f;
- }
- }
-
- private static class LinearRank1Function extends MinpackFunction {
- public LinearRank1Function(int m, int n, double x0,
- double theoreticalStartCost,
- double theoreticalMinCost) {
- super(m, buildArray(n, x0), theoreticalMinCost, null);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- jacobian[i] = new double[n];
- for (int j = 0; j < n; ++j) {
- jacobian[i][j] = (i + 1) * (j + 1);
- }
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double[] f = new double[m];
- double sum = 0;
- for (int i = 0; i < n; ++i) {
- sum += (i + 1) * variables[i];
- }
- for (int i = 0; i < m; ++i) {
- f[i] = (i + 1) * sum - 1;
- }
- return f;
- }
- }
-
- private static class LinearRank1ZeroColsAndRowsFunction extends MinpackFunction {
- public LinearRank1ZeroColsAndRowsFunction(int m, int n, double x0) {
- super(m, buildArray(n, x0),
- FastMath.sqrt((m * (m + 3) - 6) / (2.0 * (2 * m - 3))),
- null);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- jacobian[i] = new double[n];
- jacobian[i][0] = 0;
- for (int j = 1; j < (n - 1); ++j) {
- if (i == 0) {
- jacobian[i][j] = 0;
- } else if (i != (m - 1)) {
- jacobian[i][j] = i * (j + 1);
- } else {
- jacobian[i][j] = 0;
- }
- }
- jacobian[i][n - 1] = 0;
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double[] f = new double[m];
- double sum = 0;
- for (int i = 1; i < (n - 1); ++i) {
- sum += (i + 1) * variables[i];
- }
- for (int i = 0; i < (m - 1); ++i) {
- f[i] = i * sum - 1;
- }
- f[m - 1] = -1;
- return f;
- }
- }
-
- private static class RosenbrockFunction extends MinpackFunction {
- public RosenbrockFunction(double[] startParams, double theoreticalStartCost) {
- super(2, startParams, 0.0, buildArray(2, 1.0));
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- return new double[][] { { -20 * x1, 10 }, { -1, 0 } };
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- return new double[] { 10 * (x2 - x1 * x1), 1 - x1 };
- }
- }
-
- private static class HelicalValleyFunction extends MinpackFunction {
- public HelicalValleyFunction(double[] startParams,
- double theoreticalStartCost) {
- super(3, startParams, 0.0, new double[] { 1.0, 0.0, 0.0 });
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double tmpSquare = x1 * x1 + x2 * x2;
- double tmp1 = twoPi * tmpSquare;
- double tmp2 = FastMath.sqrt(tmpSquare);
- return new double[][] {
- { 100 * x2 / tmp1, -100 * x1 / tmp1, 10 },
- { 10 * x1 / tmp2, 10 * x2 / tmp2, 0 },
- { 0, 0, 1 }
- };
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double tmp1;
- if (x1 == 0) {
- tmp1 = (x2 >= 0) ? 0.25 : -0.25;
- } else {
- tmp1 = FastMath.atan(x2 / x1) / twoPi;
- if (x1 < 0) {
- tmp1 += 0.5;
- }
- }
- double tmp2 = FastMath.sqrt(x1 * x1 + x2 * x2);
- return new double[] {
- 10.0 * (x3 - 10 * tmp1),
- 10.0 * (tmp2 - 1),
- x3
- };
- }
-
- private static final double twoPi = 2.0 * FastMath.PI;
- }
-
- private static class PowellSingularFunction extends MinpackFunction {
- public PowellSingularFunction(double[] startParams,
- double theoreticalStartCost) {
- super(4, startParams, 0.0, buildArray(4, 0.0));
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- return new double[][] {
- { 1, 10, 0, 0 },
- { 0, 0, sqrt5, -sqrt5 },
- { 0, 2 * (x2 - 2 * x3), -4 * (x2 - 2 * x3), 0 },
- { 2 * sqrt10 * (x1 - x4), 0, 0, -2 * sqrt10 * (x1 - x4) }
- };
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- return new double[] {
- x1 + 10 * x2,
- sqrt5 * (x3 - x4),
- (x2 - 2 * x3) * (x2 - 2 * x3),
- sqrt10 * (x1 - x4) * (x1 - x4)
- };
- }
-
- private static final double sqrt5 = FastMath.sqrt( 5.0);
- private static final double sqrt10 = FastMath.sqrt(10.0);
- }
-
- private static class FreudensteinRothFunction extends MinpackFunction {
- public FreudensteinRothFunction(double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(2, startParams, theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x2 = variables[1];
- return new double[][] {
- { 1, x2 * (10 - 3 * x2) - 2 },
- { 1, x2 * ( 2 + 3 * x2) - 14, }
- };
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- return new double[] {
- -13.0 + x1 + ((5.0 - x2) * x2 - 2.0) * x2,
- -29.0 + x1 + ((1.0 + x2) * x2 - 14.0) * x2
- };
- }
- }
-
- private static class BardFunction extends MinpackFunction {
- public BardFunction(double x0,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(15, buildArray(3, x0), theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x2 = variables[1];
- double x3 = variables[2];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double tmp1 = i + 1;
- double tmp2 = 15 - i;
- double tmp3 = (i <= 7) ? tmp1 : tmp2;
- double tmp4 = x2 * tmp2 + x3 * tmp3;
- tmp4 *= tmp4;
- jacobian[i] = new double[] { -1, tmp1 * tmp2 / tmp4, tmp1 * tmp3 / tmp4 };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double tmp1 = i + 1;
- double tmp2 = 15 - i;
- double tmp3 = (i <= 7) ? tmp1 : tmp2;
- f[i] = y[i] - (x1 + tmp1 / (x2 * tmp2 + x3 * tmp3));
- }
- return f;
- }
-
- private static final double[] y = {
- 0.14, 0.18, 0.22, 0.25, 0.29,
- 0.32, 0.35, 0.39, 0.37, 0.58,
- 0.73, 0.96, 1.34, 2.10, 4.39
- };
- }
-
- private static class KowalikOsborneFunction extends MinpackFunction {
- public KowalikOsborneFunction(double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(11, startParams, theoreticalMinCost,
- theoreticalMinParams);
- if (theoreticalStartCost > 20.0) {
- setCostAccuracy(2.0e-4);
- setParamsAccuracy(5.0e-3);
- }
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double tmp = v[i] * (v[i] + x3) + x4;
- double j1 = -v[i] * (v[i] + x2) / tmp;
- double j2 = -v[i] * x1 / tmp;
- double j3 = j1 * j2;
- double j4 = j3 / v[i];
- jacobian[i] = new double[] { j1, j2, j3, j4 };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- f[i] = y[i] - x1 * (v[i] * (v[i] + x2)) / (v[i] * (v[i] + x3) + x4);
- }
- return f;
- }
-
- private static final double[] v = {
- 4.0, 2.0, 1.0, 0.5, 0.25, 0.167, 0.125, 0.1, 0.0833, 0.0714, 0.0625
- };
-
- private static final double[] y = {
- 0.1957, 0.1947, 0.1735, 0.1600, 0.0844, 0.0627,
- 0.0456, 0.0342, 0.0323, 0.0235, 0.0246
- };
- }
-
- private static class MeyerFunction extends MinpackFunction {
- public MeyerFunction(double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(16, startParams, theoreticalMinCost,
- theoreticalMinParams);
- if (theoreticalStartCost > 1.0e6) {
- setCostAccuracy(7.0e-3);
- setParamsAccuracy(2.0e-2);
- }
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double temp = 5.0 * (i + 1) + 45.0 + x3;
- double tmp1 = x2 / temp;
- double tmp2 = FastMath.exp(tmp1);
- double tmp3 = x1 * tmp2 / temp;
- jacobian[i] = new double[] { tmp2, tmp3, -tmp1 * tmp3 };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- f[i] = x1 * FastMath.exp(x2 / (5.0 * (i + 1) + 45.0 + x3)) - y[i];
- }
- return f;
- }
-
- private static final double[] y = {
- 34780.0, 28610.0, 23650.0, 19630.0,
- 16370.0, 13720.0, 11540.0, 9744.0,
- 8261.0, 7030.0, 6005.0, 5147.0,
- 4427.0, 3820.0, 3307.0, 2872.0
- };
- }
-
- private static class WatsonFunction extends MinpackFunction {
- public WatsonFunction(int n, double x0,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(31, buildArray(n, x0), theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double[][] jacobian = new double[m][];
-
- for (int i = 0; i < (m - 2); ++i) {
- double div = (i + 1) / 29.0;
- double s2 = 0.0;
- double dx = 1.0;
- for (int j = 0; j < n; ++j) {
- s2 += dx * variables[j];
- dx *= div;
- }
- double temp= 2 * div * s2;
- dx = 1.0 / div;
- jacobian[i] = new double[n];
- for (int j = 0; j < n; ++j) {
- jacobian[i][j] = dx * (j - temp);
- dx *= div;
- }
- }
-
- jacobian[m - 2] = new double[n];
- jacobian[m - 2][0] = 1;
-
- jacobian[m - 1] = new double[n];
- jacobian[m - 1][0]= -2 * variables[0];
- jacobian[m - 1][1]= 1;
-
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double[] f = new double[m];
- for (int i = 0; i < (m - 2); ++i) {
- double div = (i + 1) / 29.0;
- double s1 = 0;
- double dx = 1;
- for (int j = 1; j < n; ++j) {
- s1 += j * dx * variables[j];
- dx *= div;
- }
- double s2 = 0;
- dx = 1;
- for (int j = 0; j < n; ++j) {
- s2 += dx * variables[j];
- dx *= div;
- }
- f[i] = s1 - s2 * s2 - 1;
- }
-
- double x1 = variables[0];
- double x2 = variables[1];
- f[m - 2] = x1;
- f[m - 1] = x2 - x1 * x1 - 1;
-
- return f;
- }
- }
-
- private static class Box3DimensionalFunction extends MinpackFunction {
- public Box3DimensionalFunction(int m, double[] startParams,
- double theoreticalStartCost) {
- super(m, startParams, 0.0,
- new double[] { 1.0, 10.0, 1.0 });
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double tmp = (i + 1) / 10.0;
- jacobian[i] = new double[] {
- -tmp * FastMath.exp(-tmp * x1),
- tmp * FastMath.exp(-tmp * x2),
- FastMath.exp(-i - 1) - FastMath.exp(-tmp)
- };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double tmp = (i + 1) / 10.0;
- f[i] = FastMath.exp(-tmp * x1) - FastMath.exp(-tmp * x2)
- + (FastMath.exp(-i - 1) - FastMath.exp(-tmp)) * x3;
- }
- return f;
- }
- }
-
- private static class JennrichSampsonFunction extends MinpackFunction {
- public JennrichSampsonFunction(int m, double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(m, startParams, theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double t = i + 1;
- jacobian[i] = new double[] { -t * FastMath.exp(t * x1), -t * FastMath.exp(t * x2) };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double temp = i + 1;
- f[i] = 2 + 2 * temp - FastMath.exp(temp * x1) - FastMath.exp(temp * x2);
- }
- return f;
- }
- }
-
- private static class BrownDennisFunction extends MinpackFunction {
- public BrownDennisFunction(int m, double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(m, startParams, theoreticalMinCost,
- theoreticalMinParams);
- setCostAccuracy(2.5e-8);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double temp = (i + 1) / 5.0;
- double ti = FastMath.sin(temp);
- double tmp1 = x1 + temp * x2 - FastMath.exp(temp);
- double tmp2 = x3 + ti * x4 - FastMath.cos(temp);
- jacobian[i] = new double[] {
- 2 * tmp1, 2 * temp * tmp1, 2 * tmp2, 2 * ti * tmp2
- };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double temp = (i + 1) / 5.0;
- double tmp1 = x1 + temp * x2 - FastMath.exp(temp);
- double tmp2 = x3 + FastMath.sin(temp) * x4 - FastMath.cos(temp);
- f[i] = tmp1 * tmp1 + tmp2 * tmp2;
- }
- return f;
- }
- }
-
- private static class ChebyquadFunction extends MinpackFunction {
- private static double[] buildChebyquadArray(int n, double factor) {
- double[] array = new double[n];
- double inv = factor / (n + 1);
- for (int i = 0; i < n; ++i) {
- array[i] = (i + 1) * inv;
- }
- return array;
- }
-
- public ChebyquadFunction(int n, int m, double factor,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(m, buildChebyquadArray(n, factor), theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- jacobian[i] = new double[n];
- }
-
- double dx = 1.0 / n;
- for (int j = 0; j < n; ++j) {
- double tmp1 = 1;
- double tmp2 = 2 * variables[j] - 1;
- double temp = 2 * tmp2;
- double tmp3 = 0;
- double tmp4 = 2;
- for (int i = 0; i < m; ++i) {
- jacobian[i][j] = dx * tmp4;
- double ti = 4 * tmp2 + temp * tmp4 - tmp3;
- tmp3 = tmp4;
- tmp4 = ti;
- ti = temp * tmp2 - tmp1;
- tmp1 = tmp2;
- tmp2 = ti;
- }
- }
-
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double[] f = new double[m];
-
- for (int j = 0; j < n; ++j) {
- double tmp1 = 1;
- double tmp2 = 2 * variables[j] - 1;
- double temp = 2 * tmp2;
- for (int i = 0; i < m; ++i) {
- f[i] += tmp2;
- double ti = temp * tmp2 - tmp1;
- tmp1 = tmp2;
- tmp2 = ti;
- }
- }
-
- double dx = 1.0 / n;
- boolean iev = false;
- for (int i = 0; i < m; ++i) {
- f[i] *= dx;
- if (iev) {
- f[i] += 1.0 / (i * (i + 2));
- }
- iev = ! iev;
- }
-
- return f;
- }
- }
-
- private static class BrownAlmostLinearFunction extends MinpackFunction {
- public BrownAlmostLinearFunction(int m, double factor,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(m, buildArray(m, factor), theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- jacobian[i] = new double[n];
- }
-
- double prod = 1;
- for (int j = 0; j < n; ++j) {
- prod *= variables[j];
- for (int i = 0; i < n; ++i) {
- jacobian[i][j] = 1;
- }
- jacobian[j][j] = 2;
- }
-
- for (int j = 0; j < n; ++j) {
- double temp = variables[j];
- if (temp == 0) {
- temp = 1;
- prod = 1;
- for (int k = 0; k < n; ++k) {
- if (k != j) {
- prod *= variables[k];
- }
- }
- }
- jacobian[n - 1][j] = prod / temp;
- }
-
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double[] f = new double[m];
- double sum = -(n + 1);
- double prod = 1;
- for (int j = 0; j < n; ++j) {
- sum += variables[j];
- prod *= variables[j];
- }
- for (int i = 0; i < n; ++i) {
- f[i] = variables[i] + sum;
- }
- f[n - 1] = prod - 1;
- return f;
- }
- }
-
- private static class Osborne1Function extends MinpackFunction {
- public Osborne1Function(double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(33, startParams, theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double x5 = variables[4];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double temp = 10.0 * i;
- double tmp1 = FastMath.exp(-temp * x4);
- double tmp2 = FastMath.exp(-temp * x5);
- jacobian[i] = new double[] {
- -1, -tmp1, -tmp2, temp * x2 * tmp1, temp * x3 * tmp2
- };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x1 = variables[0];
- double x2 = variables[1];
- double x3 = variables[2];
- double x4 = variables[3];
- double x5 = variables[4];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double temp = 10.0 * i;
- double tmp1 = FastMath.exp(-temp * x4);
- double tmp2 = FastMath.exp(-temp * x5);
- f[i] = y[i] - (x1 + x2 * tmp1 + x3 * tmp2);
- }
- return f;
- }
-
- private static final double[] y = {
- 0.844, 0.908, 0.932, 0.936, 0.925, 0.908, 0.881, 0.850, 0.818, 0.784, 0.751,
- 0.718, 0.685, 0.658, 0.628, 0.603, 0.580, 0.558, 0.538, 0.522, 0.506, 0.490,
- 0.478, 0.467, 0.457, 0.448, 0.438, 0.431, 0.424, 0.420, 0.414, 0.411, 0.406
- };
- }
-
- private static class Osborne2Function extends MinpackFunction {
- public Osborne2Function(double[] startParams,
- double theoreticalStartCost,
- double theoreticalMinCost,
- double[] theoreticalMinParams) {
- super(65, startParams, theoreticalMinCost,
- theoreticalMinParams);
- }
-
- @Override
- public double[][] computeJacobian(double[] variables) {
- double x01 = variables[0];
- double x02 = variables[1];
- double x03 = variables[2];
- double x04 = variables[3];
- double x05 = variables[4];
- double x06 = variables[5];
- double x07 = variables[6];
- double x08 = variables[7];
- double x09 = variables[8];
- double x10 = variables[9];
- double x11 = variables[10];
- double[][] jacobian = new double[m][];
- for (int i = 0; i < m; ++i) {
- double temp = i / 10.0;
- double tmp1 = FastMath.exp(-x05 * temp);
- double tmp2 = FastMath.exp(-x06 * (temp - x09) * (temp - x09));
- double tmp3 = FastMath.exp(-x07 * (temp - x10) * (temp - x10));
- double tmp4 = FastMath.exp(-x08 * (temp - x11) * (temp - x11));
- jacobian[i] = new double[] {
- -tmp1,
- -tmp2,
- -tmp3,
- -tmp4,
- temp * x01 * tmp1,
- x02 * (temp - x09) * (temp - x09) * tmp2,
- x03 * (temp - x10) * (temp - x10) * tmp3,
- x04 * (temp - x11) * (temp - x11) * tmp4,
- -2 * x02 * x06 * (temp - x09) * tmp2,
- -2 * x03 * x07 * (temp - x10) * tmp3,
- -2 * x04 * x08 * (temp - x11) * tmp4
- };
- }
- return jacobian;
- }
-
- @Override
- public double[] computeValue(double[] variables) {
- double x01 = variables[0];
- double x02 = variables[1];
- double x03 = variables[2];
- double x04 = variables[3];
- double x05 = variables[4];
- double x06 = variables[5];
- double x07 = variables[6];
- double x08 = variables[7];
- double x09 = variables[8];
- double x10 = variables[9];
- double x11 = variables[10];
- double[] f = new double[m];
- for (int i = 0; i < m; ++i) {
- double temp = i / 10.0;
- double tmp1 = FastMath.exp(-x05 * temp);
- double tmp2 = FastMath.exp(-x06 * (temp - x09) * (temp - x09));
- double tmp3 = FastMath.exp(-x07 * (temp - x10) * (temp - x10));
- double tmp4 = FastMath.exp(-x08 * (temp - x11) * (temp - x11));
- f[i] = y[i] - (x01 * tmp1 + x02 * tmp2 + x03 * tmp3 + x04 * tmp4);
- }
- return f;
- }
-
- private static final double[] y = {
- 1.366, 1.191, 1.112, 1.013, 0.991,
- 0.885, 0.831, 0.847, 0.786, 0.725,
- 0.746, 0.679, 0.608, 0.655, 0.616,
- 0.606, 0.602, 0.626, 0.651, 0.724,
- 0.649, 0.649, 0.694, 0.644, 0.624,
- 0.661, 0.612, 0.558, 0.533, 0.495,
- 0.500, 0.423, 0.395, 0.375, 0.372,
- 0.391, 0.396, 0.405, 0.428, 0.429,
- 0.523, 0.562, 0.607, 0.653, 0.672,
- 0.708, 0.633, 0.668, 0.645, 0.632,
- 0.591, 0.559, 0.597, 0.625, 0.739,
- 0.710, 0.729, 0.720, 0.636, 0.581,
- 0.428, 0.292, 0.162, 0.098, 0.054
- };
- }
-}
[5/5] [math] Remove deprecated classes in optim package.
Posted by tn...@apache.org.
Remove deprecated classes in optim package.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/e31fde87
Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/e31fde87
Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/e31fde87
Branch: refs/heads/master
Commit: e31fde875c6075ae3da9572c6f910cc29ceaf6c3
Parents: 0737cf8
Author: Thomas Neidhart <th...@gmail.com>
Authored: Sat Apr 11 16:05:10 2015 +0200
Committer: Thomas Neidhart <th...@gmail.com>
Committed: Sat Apr 11 16:05:10 2015 +0200
----------------------------------------------------------------------
...ltiStartMultivariateVectorOptimizerTest.java | 253 ---
...stractLeastSquaresOptimizerAbstractTest.java | 641 --------
.../AbstractLeastSquaresOptimizerTest.java | 129 --
...ractLeastSquaresOptimizerTestValidation.java | 335 ----
.../vector/jacobian/CircleProblem.java | 179 ---
.../vector/jacobian/CircleVectorial.java | 99 --
.../jacobian/GaussNewtonOptimizerTest.java | 173 ---
.../LevenbergMarquardtOptimizerTest.java | 375 -----
.../nonlinear/vector/jacobian/MinpackTest.java | 1467 ------------------
.../jacobian/RandomCirclePointGenerator.java | 91 --
.../RandomStraightLinePointGenerator.java | 99 --
.../jacobian/StatisticalReferenceDataset.java | 385 -----
.../StatisticalReferenceDatasetFactory.java | 203 ---
.../vector/jacobian/StraightLineProblem.java | 169 --
14 files changed, 4598 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
deleted file mode 100644
index 70b3f95..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.OptimizationData;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.SimpleBounds;
-import org.apache.commons.math4.optim.SimpleVectorValueChecker;
-import org.apache.commons.math4.optim.nonlinear.vector.JacobianMultivariateVectorOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.MultiStartMultivariateVectorOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
-import org.apache.commons.math4.random.GaussianRandomGenerator;
-import org.apache.commons.math4.random.JDKRandomGenerator;
-import org.apache.commons.math4.random.RandomVectorGenerator;
-import org.apache.commons.math4.random.UncorrelatedRandomVectorGenerator;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * <p>Some of the unit tests are re-implementations of the MINPACK <a
- * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
- * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
- * convenience, it is reproduced below.</p>
- *
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- * Minpack Copyright Notice (1999) University of Chicago.
- * All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- * <li>Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- * must include the following acknowledgment:
- * <code>This product includes software developed by the University of
- * Chicago, as Operator of Argonne National Laboratory.</code>
- * Alternately, this acknowledgment may appear in the software itself,
- * if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- * BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
- *
- * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
- * @author Burton S. Garbow (original fortran minpack tests)
- * @author Kenneth E. Hillstrom (original fortran minpack tests)
- * @author Jorge J. More (original fortran minpack tests)
- * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
- */
-@Deprecated
-public class MultiStartMultivariateVectorOptimizerTest {
-
- @Test(expected=NullPointerException.class)
- public void testGetOptimaBeforeOptimize() {
-
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- optimizer.getOptima();
- }
-
- @Test
- public void testTrivial() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }));
- Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
- PointVectorValuePair[] optima = optimizer.getOptima();
- Assert.assertEquals(10, optima.length);
- for (int i = 0; i < optima.length; i++) {
- Assert.assertEquals(1.5, optima[i].getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optima[i].getValue()[0], 1e-10);
- }
- Assert.assertTrue(optimizer.getEvaluations() > 20);
- Assert.assertTrue(optimizer.getEvaluations() < 50);
- Assert.assertEquals(100, optimizer.getMaxEvaluations());
- }
-
- @Test
- public void testIssue914() {
- LinearProblem problem = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- JacobianMultivariateVectorOptimizer underlyingOptimizer =
- new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6)) {
- @Override
- public PointVectorValuePair optimize(OptimizationData... optData) {
- // filter out simple bounds, as they are not supported
- // by the underlying optimizer, and we don't really care for this test
- OptimizationData[] filtered = optData.clone();
- for (int i = 0; i < filtered.length; ++i) {
- if (filtered[i] instanceof SimpleBounds) {
- filtered[i] = null;
- }
- }
- return super.optimize(filtered);
- }
- };
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(16069223052l);
- RandomVectorGenerator generator =
- new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer =
- new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }),
- new SimpleBounds(new double[] { -1.0e-10 }, new double[] { 1.0e-10 }));
- PointVectorValuePair[] optima = optimizer.getOptima();
- // only the first start should have succeeded
- Assert.assertEquals(1, optima.length);
-
- }
-
- /**
- * Test demonstrating that the user exception is finally thrown if none
- * of the runs succeed.
- */
- @Test(expected=TestException.class)
- public void testNoOptimum() {
- JacobianMultivariateVectorOptimizer underlyingOptimizer
- = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
- JDKRandomGenerator g = new JDKRandomGenerator();
- g.setSeed(12373523445l);
- RandomVectorGenerator generator
- = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
- MultiStartMultivariateVectorOptimizer optimizer
- = new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
- optimizer.optimize(new MaxEval(100),
- new Target(new double[] { 0 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }),
- new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] point) {
- throw new TestException();
- }
- }));
- }
-
- private static class TestException extends RuntimeException {
-
- private static final long serialVersionUID = 1L;}
-
- private static class LinearProblem {
- private final RealMatrix factors;
- private final double[] target;
-
- public LinearProblem(double[][] factors,
- double[] target) {
- this.factors = new BlockRealMatrix(factors);
- this.target = target;
- }
-
- public Target getTarget() {
- return new Target(target);
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] variables) {
- return factors.operate(variables);
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return factors.getData();
- }
- });
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
deleted file mode 100644
index e2814fc..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerAbstractTest.java
+++ /dev/null
@@ -1,641 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-import java.util.Arrays;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Assert;
-import org.junit.Test;
-
-/**
- * <p>Some of the unit tests are re-implementations of the MINPACK <a
- * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
- * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
- * convenience, it is reproduced below.</p>
-
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- * Minpack Copyright Notice (1999) University of Chicago.
- * All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- * <li>Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- * must include the following acknowledgment:
- * <code>This product includes software developed by the University of
- * Chicago, as Operator of Argonne National Laboratory.</code>
- * Alternately, this acknowledgment may appear in the software itself,
- * if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- * BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
-
- * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
- * @author Burton S. Garbow (original fortran minpack tests)
- * @author Kenneth E. Hillstrom (original fortran minpack tests)
- * @author Jorge J. More (original fortran minpack tests)
- * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
- */
-@Deprecated
-public abstract class AbstractLeastSquaresOptimizerAbstractTest {
-
- public abstract AbstractLeastSquaresOptimizer createOptimizer();
-
- @Test
- public void testGetIterations() {
- AbstractLeastSquaresOptimizer optim = createOptimizer();
- optim.optimize(new MaxEval(100), new Target(new double[] { 1 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 3 }),
- new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] point) {
- return new double[] {
- FastMath.pow(point[0], 4)
- };
- }
- }),
- new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return new double[][] {
- { 0.25 * FastMath.pow(point[0], 3) }
- };
- }
- }));
-
- Assert.assertTrue(optim.getIterations() > 0);
- }
-
- @Test
- public void testTrivial() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
- }
-
- @Test
- public void testQRColumnsPermutation() {
-
- LinearProblem problem
- = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
- new double[] { 4, 6, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(7, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(4, optimum.getValue()[0], 1e-10);
- Assert.assertEquals(6, optimum.getValue()[1], 1e-10);
- Assert.assertEquals(1, optimum.getValue()[2], 1e-10);
- }
-
- @Test
- public void testNoDependency() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 2, 0, 0, 0, 0, 0 },
- { 0, 2, 0, 0, 0, 0 },
- { 0, 0, 2, 0, 0, 0 },
- { 0, 0, 0, 2, 0, 0 },
- { 0, 0, 0, 0, 2, 0 },
- { 0, 0, 0, 0, 0, 2 }
- }, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- for (int i = 0; i < problem.target.length; ++i) {
- Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1e-10);
- }
- }
-
- @Test
- public void testOneSet() {
-
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 0, 0 },
- { -1, 1, 0 },
- { 0, -1, 1 }
- }, new double[] { 1, 1, 1});
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(2, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[2], 1e-10);
- }
-
- @Test
- public void testTwoSets() {
- double epsilon = 1e-7;
- LinearProblem problem = new LinearProblem(new double[][] {
- { 2, 1, 0, 4, 0, 0 },
- { -4, -2, 3, -7, 0, 0 },
- { 4, 1, -2, 8, 0, 0 },
- { 0, -3, -12, -1, 0, 0 },
- { 0, 0, 0, 0, epsilon, 1 },
- { 0, 0, 0, 0, 1, 1 }
- }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(3, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(4, optimum.getPoint()[1], 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[2], 1e-10);
- Assert.assertEquals(-2, optimum.getPoint()[3], 1e-10);
- Assert.assertEquals(1 + epsilon, optimum.getPoint()[4], 1e-10);
- Assert.assertEquals(1 - epsilon, optimum.getPoint()[5], 1e-10);
- }
-
- @Test(expected=ConvergenceException.class)
- public void testNonInvertible() throws Exception {
-
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 2, -3 },
- { 2, 1, 3 },
- { -3, 0, -9 }
- }, new double[] { 1, 1, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 0, 0 }));
- }
-
- @Test
- public void testIllConditioned() {
- LinearProblem problem1 = new LinearProblem(new double[][] {
- { 10, 7, 8, 7 },
- { 7, 5, 6, 5 },
- { 8, 6, 10, 9 },
- { 7, 5, 9, 10 }
- }, new double[] { 32, 23, 33, 31 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum1 =
- optimizer.optimize(new MaxEval(100),
- problem1.getModelFunction(),
- problem1.getModelFunctionJacobian(),
- problem1.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 1, 2, 3 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[1], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[2], 1e-10);
- Assert.assertEquals(1, optimum1.getPoint()[3], 1e-10);
-
- LinearProblem problem2 = new LinearProblem(new double[][] {
- { 10.00, 7.00, 8.10, 7.20 },
- { 7.08, 5.04, 6.00, 5.00 },
- { 8.00, 5.98, 9.89, 9.00 },
- { 6.99, 4.99, 9.00, 9.98 }
- }, new double[] { 32, 23, 33, 31 });
- PointVectorValuePair optimum2 =
- optimizer.optimize(new MaxEval(100),
- problem2.getModelFunction(),
- problem2.getModelFunctionJacobian(),
- problem2.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 0, 1, 2, 3 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-81, optimum2.getPoint()[0], 1e-8);
- Assert.assertEquals(137, optimum2.getPoint()[1], 1e-8);
- Assert.assertEquals(-34, optimum2.getPoint()[2], 1e-8);
- Assert.assertEquals( 22, optimum2.getPoint()[3], 1e-8);
- }
-
- @Test
- public void testMoreEstimatedParametersSimple() {
-
- LinearProblem problem = new LinearProblem(new double[][] {
- { 3, 2, 0, 0 },
- { 0, 1, -1, 1 },
- { 2, 0, 1, 0 }
- }, new double[] { 7, 3, 5 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 7, 6, 5, 4 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- }
-
- @Test
- public void testMoreEstimatedParametersUnsorted() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1, 0, 0, 0, 0 },
- { 0, 0, 1, 1, 1, 0 },
- { 0, 0, 0, 0, 1, -1 },
- { 0, 0, -1, 1, 0, 1 },
- { 0, 0, 0, -1, 1, 0 }
- }, new double[] { 3, 12, -1, 7, 1 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(3, optimum.getPointRef()[2], 1e-10);
- Assert.assertEquals(4, optimum.getPointRef()[3], 1e-10);
- Assert.assertEquals(5, optimum.getPointRef()[4], 1e-10);
- Assert.assertEquals(6, optimum.getPointRef()[5], 1e-10);
- }
-
- @Test
- public void testRedundantEquations() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1 },
- { 1, -1 },
- { 1, 3 }
- }, new double[] { 3, 1, 5 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 1, 1 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(2, optimum.getPointRef()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPointRef()[1], 1e-10);
- }
-
- @Test
- public void testInconsistentEquations() {
- LinearProblem problem = new LinearProblem(new double[][] {
- { 1, 1 },
- { 1, -1 },
- { 1, 3 }
- }, new double[] { 3, 1, 4 });
-
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1, 1 }),
- new InitialGuess(new double[] { 1, 1 }));
- Assert.assertTrue(optimizer.getRMS() > 0.1);
- }
-
- @Test(expected=DimensionMismatchException.class)
- public void testInconsistentSizes1() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
- new double[] { -1, 1 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- }
-
- @Test(expected=DimensionMismatchException.class)
- public void testInconsistentSizes2() {
- LinearProblem problem
- = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
- new double[] { -1, 1 });
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- problem.getTarget(),
- new Weight(new double[] { 1, 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
- Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
- Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);
-
- optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(new double[] { 1 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 0, 0 }));
- }
-
- @Test
- public void testCircleFitting() {
- CircleVectorial circle = new CircleVectorial();
- circle.addPoint( 30, 68);
- circle.addPoint( 50, -6);
- circle.addPoint(110, -20);
- circle.addPoint( 35, 15);
- circle.addPoint( 45, 97);
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(new double[] { 0, 0, 0, 0, 0 }),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- Assert.assertTrue(optimizer.getEvaluations() < 10);
- double rms = optimizer.getRMS();
- Assert.assertEquals(1.768262623567235, FastMath.sqrt(circle.getN()) * rms, 1e-10);
- Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
- Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
- Assert.assertEquals(96.07590211815305, center.getX(), 1e-6);
- Assert.assertEquals(48.13516790438953, center.getY(), 1e-6);
- double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
- Assert.assertEquals(1.839, cov[0][0], 0.001);
- Assert.assertEquals(0.731, cov[0][1], 0.001);
- Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
- Assert.assertEquals(0.786, cov[1][1], 0.001);
-
- // add perfect measurements and check errors are reduced
- double r = circle.getRadius(center);
- for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
- circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
- }
- double[] target = new double[circle.getN()];
- Arrays.fill(target, 0);
- double[] weights = new double[circle.getN()];
- Arrays.fill(weights, 2);
- optimum = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
- Assert.assertEquals(0.0016, cov[0][0], 0.001);
- Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
- Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
- Assert.assertEquals(0.0016, cov[1][1], 0.001);
- }
-
- @Test
- public void testCircleFittingBadInit() {
- CircleVectorial circle = new CircleVectorial();
- double[][] points = circlePoints;
- double[] target = new double[points.length];
- Arrays.fill(target, 0);
- double[] weights = new double[points.length];
- Arrays.fill(weights, 2);
- for (int i = 0; i < points.length; ++i) {
- circle.addPoint(points[i][0], points[i][1]);
- }
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(new double[] { -12, -12 }));
- Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
- Assert.assertTrue(optimizer.getEvaluations() < 25);
- Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
- Assert.assertEquals( 0.292235, circle.getRadius(center), 1e-6);
- Assert.assertEquals(-0.151738, center.getX(), 1e-6);
- Assert.assertEquals( 0.2075001, center.getY(), 1e-6);
- }
-
- @Test
- public void testCircleFittingGoodInit() {
- CircleVectorial circle = new CircleVectorial();
- double[][] points = circlePoints;
- double[] target = new double[points.length];
- Arrays.fill(target, 0);
- double[] weights = new double[points.length];
- Arrays.fill(weights, 2);
- for (int i = 0; i < points.length; ++i) {
- circle.addPoint(points[i][0], points[i][1]);
- }
- AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- PointVectorValuePair optimum =
- optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(target),
- new Weight(weights),
- new InitialGuess(new double[] { 0, 0 }));
- Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
- Assert.assertEquals(0.2074999736353867, optimum.getPointRef()[1], 1e-6);
- Assert.assertEquals(0.04268731682389561, optimizer.getRMS(), 1e-8);
- }
-
- private final double[][] circlePoints = new double[][] {
- {-0.312967, 0.072366}, {-0.339248, 0.132965}, {-0.379780, 0.202724},
- {-0.390426, 0.260487}, {-0.361212, 0.328325}, {-0.346039, 0.392619},
- {-0.280579, 0.444306}, {-0.216035, 0.470009}, {-0.149127, 0.493832},
- {-0.075133, 0.483271}, {-0.007759, 0.452680}, { 0.060071, 0.410235},
- { 0.103037, 0.341076}, { 0.118438, 0.273884}, { 0.131293, 0.192201},
- { 0.115869, 0.129797}, { 0.072223, 0.058396}, { 0.022884, 0.000718},
- {-0.053355, -0.020405}, {-0.123584, -0.032451}, {-0.216248, -0.032862},
- {-0.278592, -0.005008}, {-0.337655, 0.056658}, {-0.385899, 0.112526},
- {-0.405517, 0.186957}, {-0.415374, 0.262071}, {-0.387482, 0.343398},
- {-0.347322, 0.397943}, {-0.287623, 0.458425}, {-0.223502, 0.475513},
- {-0.135352, 0.478186}, {-0.061221, 0.483371}, { 0.003711, 0.422737},
- { 0.065054, 0.375830}, { 0.108108, 0.297099}, { 0.123882, 0.222850},
- { 0.117729, 0.134382}, { 0.085195, 0.056820}, { 0.029800, -0.019138},
- {-0.027520, -0.072374}, {-0.102268, -0.091555}, {-0.200299, -0.106578},
- {-0.292731, -0.091473}, {-0.356288, -0.051108}, {-0.420561, 0.014926},
- {-0.471036, 0.074716}, {-0.488638, 0.182508}, {-0.485990, 0.254068},
- {-0.463943, 0.338438}, {-0.406453, 0.404704}, {-0.334287, 0.466119},
- {-0.254244, 0.503188}, {-0.161548, 0.495769}, {-0.075733, 0.495560},
- { 0.001375, 0.434937}, { 0.082787, 0.385806}, { 0.115490, 0.323807},
- { 0.141089, 0.223450}, { 0.138693, 0.131703}, { 0.126415, 0.049174},
- { 0.066518, -0.010217}, {-0.005184, -0.070647}, {-0.080985, -0.103635},
- {-0.177377, -0.116887}, {-0.260628, -0.100258}, {-0.335756, -0.056251},
- {-0.405195, -0.000895}, {-0.444937, 0.085456}, {-0.484357, 0.175597},
- {-0.472453, 0.248681}, {-0.438580, 0.347463}, {-0.402304, 0.422428},
- {-0.326777, 0.479438}, {-0.247797, 0.505581}, {-0.152676, 0.519380},
- {-0.071754, 0.516264}, { 0.015942, 0.472802}, { 0.076608, 0.419077},
- { 0.127673, 0.330264}, { 0.159951, 0.262150}, { 0.153530, 0.172681},
- { 0.140653, 0.089229}, { 0.078666, 0.024981}, { 0.023807, -0.037022},
- {-0.048837, -0.077056}, {-0.127729, -0.075338}, {-0.221271, -0.067526}
- };
-
- public void doTestStRD(final StatisticalReferenceDataset dataset,
- final double errParams,
- final double errParamsSd) {
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] w = new double[dataset.getNumObservations()];
- Arrays.fill(w, 1);
-
- final double[][] data = dataset.getData();
- final double[] initial = dataset.getStartingPoint(0);
- final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
- final PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(100),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(data[1]),
- new Weight(w),
- new InitialGuess(initial));
-
- final double[] actual = optimum.getPoint();
- for (int i = 0; i < actual.length; i++) {
- double expected = dataset.getParameter(i);
- double delta = FastMath.abs(errParams * expected);
- Assert.assertEquals(dataset.getName() + ", param #" + i,
- expected, actual[i], delta);
- }
- }
-
- @Test
- public void testKirby2() throws IOException {
- doTestStRD(StatisticalReferenceDatasetFactory.createKirby2(), 1E-7, 1E-7);
- }
-
- @Test
- public void testHahn1() throws IOException {
- doTestStRD(StatisticalReferenceDatasetFactory.createHahn1(), 1E-7, 1E-4);
- }
-
- static class LinearProblem {
- private final RealMatrix factors;
- private final double[] target;
-
- public LinearProblem(double[][] factors, double[] target) {
- this.factors = new BlockRealMatrix(factors);
- this.target = target;
- }
-
- public Target getTarget() {
- return new Target(target);
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- return factors.operate(params);
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] params) {
- return factors.getData();
- }
- });
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
deleted file mode 100644
index aad5e43..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTest.java
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-import java.util.Arrays;
-
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Test;
-import org.junit.Assert;
-
-@Deprecated
-public class AbstractLeastSquaresOptimizerTest {
-
- public static AbstractLeastSquaresOptimizer createOptimizer() {
- return new AbstractLeastSquaresOptimizer(null) {
-
- @Override
- protected PointVectorValuePair doOptimize() {
- final double[] params = getStartPoint();
- final double[] res = computeResiduals(computeObjectiveValue(params));
- setCost(computeCost(res));
- return new PointVectorValuePair(params, null);
- }
- };
- }
-
- @Test
- public void testGetChiSquare() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1.0);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
- final double expected = dataset.getResidualSumOfSquares();
- final double actual = optimizer.getChiSquare();
- Assert.assertEquals(dataset.getName(), expected, actual,
- 1E-11 * expected);
- }
-
- @Test
- public void testGetRMS() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
-
- final double expected = FastMath
- .sqrt(dataset.getResidualSumOfSquares() /
- dataset.getNumObservations());
- final double actual = optimizer.getRMS();
- Assert.assertEquals(dataset.getName(), expected, actual,
- 1E-11 * expected);
- }
-
- @Test
- public void testComputeSigma() throws IOException {
- final StatisticalReferenceDataset dataset
- = StatisticalReferenceDatasetFactory.createKirby2();
- final AbstractLeastSquaresOptimizer optimizer = createOptimizer();
- final double[] a = dataset.getParameters();
- final double[] y = dataset.getData()[1];
- final double[] w = new double[y.length];
- Arrays.fill(w, 1);
-
- StatisticalReferenceDataset.LeastSquaresProblem problem
- = dataset.getLeastSquaresProblem();
-
- final PointVectorValuePair optimum
- = optimizer.optimize(new MaxEval(1),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(y),
- new Weight(w),
- new InitialGuess(a));
-
- final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
-
- final int dof = y.length - a.length;
- final double[] expected = dataset.getParametersStandardDeviations();
- for (int i = 0; i < sig.length; i++) {
- final double actual = FastMath.sqrt(optimizer.getChiSquare() / dof) * sig[i];
- Assert.assertEquals(dataset.getName() + ", parameter #" + i,
- expected[i], actual, 1e-6 * expected[i]);
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
deleted file mode 100644
index 9235e6b..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizerTestValidation.java
+++ /dev/null
@@ -1,335 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.Arrays;
-import java.util.List;
-import java.util.ArrayList;
-import java.awt.geom.Point2D;
-
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.stat.descriptive.StatisticalSummary;
-import org.apache.commons.math4.stat.descriptive.SummaryStatistics;
-import org.apache.commons.math4.util.FastMath;
-import org.junit.Test;
-import org.junit.Assert;
-
-/**
- * This class demonstrates the main functionality of the
- * {@link AbstractLeastSquaresOptimizer}, common to the
- * optimizer implementations in package
- * {@link org.apache.commons.math4.optimization.general}.
- * <br/>
- * Not enabled by default, as the class name does not end with "Test".
- * <br/>
- * Invoke by running
- * <pre><code>
- * mvn test -Dtest=AbstractLeastSquaresOptimizerTestValidation
- * </code></pre>
- * or by running
- * <pre><code>
- * mvn test -Dtest=AbstractLeastSquaresOptimizerTestValidation -DargLine="-DmcRuns=1234 -server"
- * </code></pre>
- */
-@Deprecated
-public class AbstractLeastSquaresOptimizerTestValidation {
- private static final int MONTE_CARLO_RUNS = Integer.parseInt(System.getProperty("mcRuns",
- "100"));
-
- /**
- * Using a Monte-Carlo procedure, this test checks the error estimations
- * as provided by the square-root of the diagonal elements of the
- * covariance matrix.
- * <br/>
- * The test generates sets of observations, each sampled from
- * a Gaussian distribution.
- * <br/>
- * The optimization problem solved is defined in class
- * {@link StraightLineProblem}.
- * <br/>
- * The output (on stdout) will be a table summarizing the distribution
- * of parameters generated by the Monte-Carlo process and by the direct
- * estimation provided by the diagonal elements of the covariance matrix.
- */
- @Test
- public void testParametersErrorMonteCarloObservations() {
- // Error on the observations.
- final double yError = 15;
-
- // True values of the parameters.
- final double slope = 123.456;
- final double offset = -98.765;
-
- // Samples generator.
- final RandomStraightLinePointGenerator lineGenerator
- = new RandomStraightLinePointGenerator(slope, offset,
- yError,
- -1e3, 1e4,
- 138577L);
-
- // Number of observations.
- final int numObs = 100; // XXX Should be a command-line option.
- // number of parameters.
- final int numParams = 2;
-
- // Parameters found for each of Monte-Carlo run.
- final SummaryStatistics[] paramsFoundByDirectSolution = new SummaryStatistics[numParams];
- // Sigma estimations (square-root of the diagonal elements of the
- // covariance matrix), for each Monte-Carlo run.
- final SummaryStatistics[] sigmaEstimate = new SummaryStatistics[numParams];
-
- // Initialize statistics accumulators.
- for (int i = 0; i < numParams; i++) {
- paramsFoundByDirectSolution[i] = new SummaryStatistics();
- sigmaEstimate[i] = new SummaryStatistics();
- }
-
- // Dummy optimizer (to compute the covariance matrix).
- final AbstractLeastSquaresOptimizer optim = new DummyOptimizer();
- final double[] init = { slope, offset };
-
- // Monte-Carlo (generates many sets of observations).
- final int mcRepeat = MONTE_CARLO_RUNS;
- int mcCount = 0;
- while (mcCount < mcRepeat) {
- // Observations.
- final Point2D.Double[] obs = lineGenerator.generate(numObs);
-
- final StraightLineProblem problem = new StraightLineProblem(yError);
- for (int i = 0; i < numObs; i++) {
- final Point2D.Double p = obs[i];
- problem.addPoint(p.x, p.y);
- }
-
- // Direct solution (using simple regression).
- final double[] regress = problem.solve();
-
- // Estimation of the standard deviation (diagonal elements of the
- // covariance matrix).
- final PointVectorValuePair optimum
- = optim.optimize(new MaxEval(Integer.MAX_VALUE),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(problem.target()),
- new Weight(problem.weight()),
- new InitialGuess(init));
- final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);
-
- // Accumulate statistics.
- for (int i = 0; i < numParams; i++) {
- paramsFoundByDirectSolution[i].addValue(regress[i]);
- sigmaEstimate[i].addValue(sigma[i]);
- }
-
- // Next Monte-Carlo.
- ++mcCount;
- }
-
- // Print statistics.
- final String line = "--------------------------------------------------------------";
- System.out.println(" True value Mean Std deviation");
- for (int i = 0; i < numParams; i++) {
- System.out.println(line);
- System.out.println("Parameter #" + i);
-
- StatisticalSummary s = paramsFoundByDirectSolution[i].getSummary();
- System.out.printf(" %+.6e %+.6e %+.6e\n",
- init[i],
- s.getMean(),
- s.getStandardDeviation());
-
- s = sigmaEstimate[i].getSummary();
- System.out.printf("sigma: %+.6e (%+.6e)\n",
- s.getMean(),
- s.getStandardDeviation());
- }
- System.out.println(line);
-
- // Check the error estimation.
- for (int i = 0; i < numParams; i++) {
- Assert.assertEquals(paramsFoundByDirectSolution[i].getSummary().getStandardDeviation(),
- sigmaEstimate[i].getSummary().getMean(),
- 8e-2);
- }
- }
-
- /**
- * In this test, the set of observations is fixed.
- * Using a Monte-Carlo procedure, it generates sets of parameters,
- * and determine the parameter change that will result in the
- * normalized chi-square becoming larger by one than the value from
- * the best fit solution.
- * <br/>
- * The optimization problem solved is defined in class
- * {@link StraightLineProblem}.
- * <br/>
- * The output (on stdout) will be a list of lines containing:
- * <ul>
- * <li>slope of the straight line,</li>
- * <li>intercept of the straight line,</li>
- * <li>chi-square of the solution defined by the above two values.</li>
- * </ul>
- * The output is separated into two blocks (with a blank line between
- * them); the first block will contain all parameter sets for which
- * {@code chi2 < chi2_b + 1}
- * and the second block, all sets for which
- * {@code chi2 >= chi2_b + 1}
- * where {@code chi2_b} is the lowest chi-square (corresponding to the
- * best solution).
- */
- @Test
- public void testParametersErrorMonteCarloParameters() {
- // Error on the observations.
- final double yError = 15;
-
- // True values of the parameters.
- final double slope = 123.456;
- final double offset = -98.765;
-
- // Samples generator.
- final RandomStraightLinePointGenerator lineGenerator
- = new RandomStraightLinePointGenerator(slope, offset,
- yError,
- -1e3, 1e4,
- 13839013L);
-
- // Number of observations.
- final int numObs = 10;
-
- // Create a single set of observations.
- final Point2D.Double[] obs = lineGenerator.generate(numObs);
-
- final StraightLineProblem problem = new StraightLineProblem(yError);
- for (int i = 0; i < numObs; i++) {
- final Point2D.Double p = obs[i];
- problem.addPoint(p.x, p.y);
- }
-
- // Direct solution (using simple regression).
- final double[] regress = problem.solve();
-
- // Dummy optimizer (to compute the chi-square).
- final AbstractLeastSquaresOptimizer optim = new DummyOptimizer();
- // Get chi-square of the best parameters set for the given set of
- // observations.
- final double bestChi2N = getChi2N(optim, problem, regress);
- final double[] sigma = optim.computeSigma(regress, 1e-14);
-
- // Monte-Carlo (generates a grid of parameters).
- final int mcRepeat = MONTE_CARLO_RUNS;
- final int gridSize = (int) FastMath.sqrt(mcRepeat);
-
- // Parameters found for each of Monte-Carlo run.
- // Index 0 = slope
- // Index 1 = offset
- // Index 2 = normalized chi2
- final List<double[]> paramsAndChi2 = new ArrayList<double[]>(gridSize * gridSize);
-
- final double slopeRange = 10 * sigma[0];
- final double offsetRange = 10 * sigma[1];
- final double minSlope = slope - 0.5 * slopeRange;
- final double minOffset = offset - 0.5 * offsetRange;
- final double deltaSlope = slopeRange/ gridSize;
- final double deltaOffset = offsetRange / gridSize;
- for (int i = 0; i < gridSize; i++) {
- final double s = minSlope + i * deltaSlope;
- for (int j = 0; j < gridSize; j++) {
- final double o = minOffset + j * deltaOffset;
- final double chi2N = getChi2N(optim, problem, new double[] {s, o});
-
- paramsAndChi2.add(new double[] {s, o, chi2N});
- }
- }
-
- // Output (for use with "gnuplot").
-
- // Some info.
-
- // For plotting separately sets of parameters that have a large chi2.
- final double chi2NPlusOne = bestChi2N + 1;
- int numLarger = 0;
-
- final String lineFmt = "%+.10e %+.10e %.8e\n";
-
- // Point with smallest chi-square.
- System.out.printf(lineFmt, regress[0], regress[1], bestChi2N);
- System.out.println(); // Empty line.
-
- // Points within the confidence interval.
- for (double[] d : paramsAndChi2) {
- if (d[2] <= chi2NPlusOne) {
- System.out.printf(lineFmt, d[0], d[1], d[2]);
- }
- }
- System.out.println(); // Empty line.
-
- // Points outside the confidence interval.
- for (double[] d : paramsAndChi2) {
- if (d[2] > chi2NPlusOne) {
- ++numLarger;
- System.out.printf(lineFmt, d[0], d[1], d[2]);
- }
- }
- System.out.println(); // Empty line.
-
- System.out.println("# sigma=" + Arrays.toString(sigma));
- System.out.println("# " + numLarger + " sets filtered out");
- }
-
- /**
- * @return the normalized chi-square.
- */
- private double getChi2N(AbstractLeastSquaresOptimizer optim,
- StraightLineProblem problem,
- double[] params) {
- final double[] t = problem.target();
- final double[] w = problem.weight();
-
- optim.optimize(new MaxEval(Integer.MAX_VALUE),
- problem.getModelFunction(),
- problem.getModelFunctionJacobian(),
- new Target(t),
- new Weight(w),
- new InitialGuess(params));
-
- return optim.getChiSquare() / (t.length - params.length);
- }
-}
-
-/**
- * A dummy optimizer.
- * Used for computing the covariance matrix.
- */
-@Deprecated
-class DummyOptimizer extends AbstractLeastSquaresOptimizer {
- public DummyOptimizer() {
- super(null);
- }
-
- /**
- * This method does nothing and returns a dummy value.
- */
- @Override
- public PointVectorValuePair doOptimize() {
- final double[] params = getStartPoint();
- final double[] res = computeResiduals(computeObjectiveValue(params));
- setCost(computeCost(res));
- return new PointVectorValuePair(params, null);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
deleted file mode 100644
index 9458fe8..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleProblem.java
+++ /dev/null
@@ -1,179 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.MathUtils;
-
-/**
- * Class that models a circle.
- * The parameters of problem are:
- * <ul>
- * <li>the x-coordinate of the circle center,</li>
- * <li>the y-coordinate of the circle center,</li>
- * <li>the radius of the circle.</li>
- * </ul>
- * The model functions are:
- * <ul>
- * <li>for each triplet (cx, cy, r), the (x, y) coordinates of a point on the
- * corresponding circle.</li>
- * </ul>
- */
-@Deprecated
-class CircleProblem {
- /** Cloud of points assumed to be fitted by a circle. */
- private final ArrayList<double[]> points;
- /** Error on the x-coordinate of the points. */
- private final double xSigma;
- /** Error on the y-coordinate of the points. */
- private final double ySigma;
- /** Number of points on the circumference (when searching which
- model point is closest to a given "observation". */
- private final int resolution;
-
- /**
- * @param xError Assumed error for the x-coordinate of the circle points.
- * @param yError Assumed error for the y-coordinate of the circle points.
- * @param searchResolution Number of points to try when searching the one
- * that is closest to a given "observed" point.
- */
- public CircleProblem(double xError,
- double yError,
- int searchResolution) {
- points = new ArrayList<double[]>();
- xSigma = xError;
- ySigma = yError;
- resolution = searchResolution;
- }
-
- /**
- * @param xError Assumed error for the x-coordinate of the circle points.
- * @param yError Assumed error for the y-coordinate of the circle points.
- */
- public CircleProblem(double xError,
- double yError) {
- this(xError, yError, 500);
- }
-
- public void addPoint(double px, double py) {
- points.add(new double[] { px, py });
- }
-
- public double[] target() {
- final double[] t = new double[points.size() * 2];
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- final int index = i * 2;
- t[index] = p[0];
- t[index + 1] = p[1];
- }
-
- return t;
- }
-
- public double[] weight() {
- final double wX = 1 / (xSigma * xSigma);
- final double wY = 1 / (ySigma * ySigma);
- final double[] w = new double[points.size() * 2];
- for (int i = 0; i < points.size(); i++) {
- final int index = i * 2;
- w[index] = wX;
- w[index + 1] = wY;
- }
-
- return w;
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- final double cx = params[0];
- final double cy = params[1];
- final double r = params[2];
-
- final double[] model = new double[points.size() * 2];
-
- final double deltaTheta = MathUtils.TWO_PI / resolution;
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- final double px = p[0];
- final double py = p[1];
-
- double bestX = 0;
- double bestY = 0;
- double dMin = Double.POSITIVE_INFINITY;
-
- // Find the angle for which the circle passes closest to the
- // current point (using a resolution of 100 points along the
- // circumference).
- for (double theta = 0; theta <= MathUtils.TWO_PI; theta += deltaTheta) {
- final double currentX = cx + r * FastMath.cos(theta);
- final double currentY = cy + r * FastMath.sin(theta);
- final double dX = currentX - px;
- final double dY = currentY - py;
- final double d = dX * dX + dY * dY;
- if (d < dMin) {
- dMin = d;
- bestX = currentX;
- bestY = currentY;
- }
- }
-
- final int index = i * 2;
- model[index] = bestX;
- model[index + 1] = bestY;
- }
-
- return model;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return jacobian(point);
- }
- });
- }
-
- private double[][] jacobian(double[] params) {
- final double[][] jacobian = new double[points.size() * 2][3];
-
- for (int i = 0; i < points.size(); i++) {
- final int index = i * 2;
- // Partial derivative wrt x-coordinate of center.
- jacobian[index][0] = 1;
- jacobian[index + 1][0] = 0;
- // Partial derivative wrt y-coordinate of center.
- jacobian[index][1] = 0;
- jacobian[index + 1][1] = 1;
- // Partial derivative wrt radius.
- final double[] p = points.get(i);
- jacobian[index][2] = (p[0] - params[0]) / params[2];
- jacobian[index + 1][2] = (p[1] - params[1]) / params[2];
- }
-
- return jacobian;
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
deleted file mode 100644
index 7b6a310..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/CircleVectorial.java
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-
-/**
- * Class used in the tests.
- */
-@Deprecated
-class CircleVectorial {
- private ArrayList<Vector2D> points;
-
- public CircleVectorial() {
- points = new ArrayList<Vector2D>();
- }
-
- public void addPoint(double px, double py) {
- points.add(new Vector2D(px, py));
- }
-
- public int getN() {
- return points.size();
- }
-
- public double getRadius(Vector2D center) {
- double r = 0;
- for (Vector2D point : points) {
- r += point.distance(center);
- }
- return r / points.size();
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- Vector2D center = new Vector2D(params[0], params[1]);
- double radius = getRadius(center);
- double[] residuals = new double[points.size()];
- for (int i = 0; i < residuals.length; i++) {
- residuals[i] = points.get(i).distance(center) - radius;
- }
-
- return residuals;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] params) {
- final int n = points.size();
- final Vector2D center = new Vector2D(params[0], params[1]);
-
- double dRdX = 0;
- double dRdY = 0;
- for (Vector2D pk : points) {
- double dk = pk.distance(center);
- dRdX += (center.getX() - pk.getX()) / dk;
- dRdY += (center.getY() - pk.getY()) / dk;
- }
- dRdX /= n;
- dRdY /= n;
-
- // Jacobian of the radius residuals.
- double[][] jacobian = new double[n][2];
- for (int i = 0; i < n; i++) {
- final Vector2D pi = points.get(i);
- final double di = pi.distance(center);
- jacobian[i][0] = (center.getX() - pi.getX()) / di - dRdX;
- jacobian[i][1] = (center.getY() - pi.getY()) / di - dRdY;
- }
-
- return jacobian;
- }
- });
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
deleted file mode 100644
index 7e73a9a..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizerTest.java
+++ /dev/null
@@ -1,173 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.IOException;
-
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.MathUnsupportedOperationException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.optim.InitialGuess;
-import org.apache.commons.math4.optim.MaxEval;
-import org.apache.commons.math4.optim.SimpleBounds;
-import org.apache.commons.math4.optim.SimpleVectorValueChecker;
-import org.apache.commons.math4.optim.nonlinear.vector.Target;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
-import org.junit.Test;
-
-/**
- * <p>Some of the unit tests are re-implementations of the MINPACK <a
- * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
- * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
- * The redistribution policy for MINPACK is available <a
- * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
- * convenience, it is reproduced below.</p>
-
- * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
- * <tr><td>
- * Minpack Copyright Notice (1999) University of Chicago.
- * All rights reserved
- * </td></tr>
- * <tr><td>
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * <ol>
- * <li>Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.</li>
- * <li>Redistributions in binary form must reproduce the above
- * copyright notice, this list of conditions and the following
- * disclaimer in the documentation and/or other materials provided
- * with the distribution.</li>
- * <li>The end-user documentation included with the redistribution, if any,
- * must include the following acknowledgment:
- * <code>This product includes software developed by the University of
- * Chicago, as Operator of Argonne National Laboratory.</code>
- * Alternately, this acknowledgment may appear in the software itself,
- * if and wherever such third-party acknowledgments normally appear.</li>
- * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
- * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
- * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
- * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
- * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
- * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
- * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
- * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
- * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
- * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
- * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
- * BE CORRECTED.</strong></li>
- * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
- * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
- * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
- * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
- * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
- * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
- * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
- * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
- * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
- * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
- * <ol></td></tr>
- * </table>
-
- * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
- * @author Burton S. Garbow (original fortran minpack tests)
- * @author Kenneth E. Hillstrom (original fortran minpack tests)
- * @author Jorge J. More (original fortran minpack tests)
- * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
- */
-@Deprecated
-public class GaussNewtonOptimizerTest
- extends AbstractLeastSquaresOptimizerAbstractTest {
-
- @Override
- public AbstractLeastSquaresOptimizer createOptimizer() {
- return new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
- }
-
- @Test(expected=MathUnsupportedOperationException.class)
- public void testConstraintsUnsupported() {
- createOptimizer().optimize(new MaxEval(100),
- new Target(new double[] { 2 }),
- new Weight(new double[] { 1 }),
- new InitialGuess(new double[] { 1, 2 }),
- new SimpleBounds(new double[] { -10, 0 },
- new double[] { 20, 30 }));
- }
-
- @Override
- @Test(expected = ConvergenceException.class)
- public void testMoreEstimatedParametersSimple() {
- /*
- * Exception is expected with this optimizer
- */
- super.testMoreEstimatedParametersSimple();
- }
-
- @Override
- @Test(expected=ConvergenceException.class)
- public void testMoreEstimatedParametersUnsorted() {
- /*
- * Exception is expected with this optimizer
- */
- super.testMoreEstimatedParametersUnsorted();
- }
-
- @Test(expected=TooManyEvaluationsException.class)
- public void testMaxEvaluations() throws Exception {
- CircleVectorial circle = new CircleVectorial();
- circle.addPoint( 30.0, 68.0);
- circle.addPoint( 50.0, -6.0);
- circle.addPoint(110.0, -20.0);
- circle.addPoint( 35.0, 15.0);
- circle.addPoint( 45.0, 97.0);
-
- GaussNewtonOptimizer optimizer
- = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));
-
- optimizer.optimize(new MaxEval(100),
- circle.getModelFunction(),
- circle.getModelFunctionJacobian(),
- new Target(new double[] { 0, 0, 0, 0, 0 }),
- new Weight(new double[] { 1, 1, 1, 1, 1 }),
- new InitialGuess(new double[] { 98.680, 47.345 }));
- }
-
- @Override
- @Test(expected=ConvergenceException.class)
- public void testCircleFittingBadInit() {
- /*
- * This test does not converge with this optimizer.
- */
- super.testCircleFittingBadInit();
- }
-
- @Override
- @Test(expected = ConvergenceException.class)
- public void testHahn1()
- throws IOException {
- /*
- * TODO This test leads to a singular problem with the Gauss-Newton
- * optimizer. This should be inquired.
- */
- super.testHahn1();
- }
-}
[3/5] [math] Remove deprecated classes in optim package.
Posted by tn...@apache.org.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomCirclePointGenerator.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomCirclePointGenerator.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomCirclePointGenerator.java
deleted file mode 100644
index d969b57..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomCirclePointGenerator.java
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import org.apache.commons.math4.distribution.NormalDistribution;
-import org.apache.commons.math4.distribution.RealDistribution;
-import org.apache.commons.math4.distribution.UniformRealDistribution;
-import org.apache.commons.math4.geometry.euclidean.twod.Vector2D;
-import org.apache.commons.math4.random.RandomGenerator;
-import org.apache.commons.math4.random.Well44497b;
-import org.apache.commons.math4.util.FastMath;
-import org.apache.commons.math4.util.MathUtils;
-
-/**
- * Factory for generating a cloud of points that approximate a circle.
- */
-@Deprecated
-public class RandomCirclePointGenerator {
- /** RNG for the x-coordinate of the center. */
- private final RealDistribution cX;
- /** RNG for the y-coordinate of the center. */
- private final RealDistribution cY;
- /** RNG for the parametric position of the point. */
- private final RealDistribution tP;
- /** Radius of the circle. */
- private final double radius;
-
- /**
- * @param x Abscissa of the circle center.
- * @param y Ordinate of the circle center.
- * @param radius Radius of the circle.
- * @param xSigma Error on the x-coordinate of the circumference points.
- * @param ySigma Error on the y-coordinate of the circumference points.
- * @param seed RNG seed.
- */
- public RandomCirclePointGenerator(double x,
- double y,
- double radius,
- double xSigma,
- double ySigma,
- long seed) {
- final RandomGenerator rng = new Well44497b(seed);
- this.radius = radius;
- cX = new NormalDistribution(rng, x, xSigma,
- NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
- cY = new NormalDistribution(rng, y, ySigma,
- NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
- tP = new UniformRealDistribution(rng, 0, MathUtils.TWO_PI);
- }
-
- /**
- * Point generator.
- *
- * @param n Number of points to create.
- * @return the cloud of {@code n} points.
- */
- public Vector2D[] generate(int n) {
- final Vector2D[] cloud = new Vector2D[n];
- for (int i = 0; i < n; i++) {
- cloud[i] = create();
- }
- return cloud;
- }
-
- /**
- * Create one point.
- *
- * @return a point.
- */
- private Vector2D create() {
- final double t = tP.sample();
- final double pX = cX.sample() + radius * FastMath.cos(t);
- final double pY = cY.sample() + radius * FastMath.sin(t);
-
- return new Vector2D(pX, pY);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomStraightLinePointGenerator.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomStraightLinePointGenerator.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomStraightLinePointGenerator.java
deleted file mode 100644
index 41def57..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/RandomStraightLinePointGenerator.java
+++ /dev/null
@@ -1,99 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.awt.geom.Point2D;
-
-import org.apache.commons.math4.distribution.NormalDistribution;
-import org.apache.commons.math4.distribution.RealDistribution;
-import org.apache.commons.math4.distribution.UniformRealDistribution;
-import org.apache.commons.math4.random.RandomGenerator;
-import org.apache.commons.math4.random.Well44497b;
-
-/**
- * Factory for generating a cloud of points that approximate a straight line.
- */
-@Deprecated
-public class RandomStraightLinePointGenerator {
- /** Slope. */
- private final double slope;
- /** Intercept. */
- private final double intercept;
- /** RNG for the x-coordinate. */
- private final RealDistribution x;
- /** RNG for the error on the y-coordinate. */
- private final RealDistribution error;
-
- /**
- * The generator will create a cloud of points whose x-coordinates
- * will be randomly sampled between {@code xLo} and {@code xHi}, and
- * the corresponding y-coordinates will be computed as
- * <pre><code>
- * y = a x + b + N(0, error)
- * </code></pre>
- * where {@code N(mean, sigma)} is a Gaussian distribution with the
- * given mean and standard deviation.
- *
- * @param a Slope.
- * @param b Intercept.
- * @param sigma Standard deviation on the y-coordinate of the point.
- * @param lo Lowest value of the x-coordinate.
- * @param hi Highest value of the x-coordinate.
- * @param seed RNG seed.
- */
- public RandomStraightLinePointGenerator(double a,
- double b,
- double sigma,
- double lo,
- double hi,
- long seed) {
- final RandomGenerator rng = new Well44497b(seed);
- slope = a;
- intercept = b;
- error = new NormalDistribution(rng, 0, sigma,
- NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
- x = new UniformRealDistribution(rng, lo, hi);
- }
-
- /**
- * Point generator.
- *
- * @param n Number of points to create.
- * @return the cloud of {@code n} points.
- */
- public Point2D.Double[] generate(int n) {
- final Point2D.Double[] cloud = new Point2D.Double[n];
- for (int i = 0; i < n; i++) {
- cloud[i] = create();
- }
- return cloud;
- }
-
- /**
- * Create one point.
- *
- * @return a point.
- */
- private Point2D.Double create() {
- final double abscissa = x.sample();
- final double yModel = slope * abscissa + intercept;
- final double ordinate = yModel + error.sample();
-
- return new Point2D.Double(abscissa, ordinate);
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDataset.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDataset.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDataset.java
deleted file mode 100644
index 8265215..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDataset.java
+++ /dev/null
@@ -1,385 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.util.MathArrays;
-
-/**
- * This class gives access to the statistical reference datasets provided by the
- * NIST (available
- * <a href="http://www.itl.nist.gov/div898/strd/general/dataarchive.html">here</a>).
- * Instances of this class can be created by invocation of the
- * {@link StatisticalReferenceDatasetFactory}.
- */
-@Deprecated
-public abstract class StatisticalReferenceDataset {
-
- /** The name of this dataset. */
- private final String name;
-
- /** The total number of observations (data points). */
- private final int numObservations;
-
- /** The total number of parameters. */
- private final int numParameters;
-
- /** The total number of starting points for the optimizations. */
- private final int numStartingPoints;
-
- /** The values of the predictor. */
- private final double[] x;
-
- /** The values of the response. */
- private final double[] y;
-
- /**
- * The starting values. {@code startingValues[j][i]} is the value of the
- * {@code i}-th parameter in the {@code j}-th set of starting values.
- */
- private final double[][] startingValues;
-
- /** The certified values of the parameters. */
- private final double[] a;
-
- /** The certified values of the standard deviation of the parameters. */
- private final double[] sigA;
-
- /** The certified value of the residual sum of squares. */
- private double residualSumOfSquares;
-
- /** The least-squares problem. */
- private final LeastSquaresProblem problem;
-
- /**
- * Creates a new instance of this class from the specified data file. The
- * file must follow the StRD format.
- *
- * @param in the data file
- * @throws IOException if an I/O error occurs
- */
- public StatisticalReferenceDataset(final BufferedReader in)
- throws IOException {
-
- final ArrayList<String> lines = new ArrayList<String>();
- for (String line = in.readLine(); line != null; line = in.readLine()) {
- lines.add(line);
- }
- int[] index = findLineNumbers("Data", lines);
- if (index == null) {
- throw new AssertionError("could not find line indices for data");
- }
- this.numObservations = index[1] - index[0] + 1;
- this.x = new double[this.numObservations];
- this.y = new double[this.numObservations];
- for (int i = 0; i < this.numObservations; i++) {
- final String line = lines.get(index[0] + i - 1);
- final String[] tokens = line.trim().split(" ++");
- // Data columns are in reverse order!!!
- this.y[i] = Double.parseDouble(tokens[0]);
- this.x[i] = Double.parseDouble(tokens[1]);
- }
-
- index = findLineNumbers("Starting Values", lines);
- if (index == null) {
- throw new AssertionError(
- "could not find line indices for starting values");
- }
- this.numParameters = index[1] - index[0] + 1;
-
- double[][] start = null;
- this.a = new double[numParameters];
- this.sigA = new double[numParameters];
- for (int i = 0; i < numParameters; i++) {
- final String line = lines.get(index[0] + i - 1);
- final String[] tokens = line.trim().split(" ++");
- if (start == null) {
- start = new double[tokens.length - 4][numParameters];
- }
- for (int j = 2; j < tokens.length - 2; j++) {
- start[j - 2][i] = Double.parseDouble(tokens[j]);
- }
- this.a[i] = Double.parseDouble(tokens[tokens.length - 2]);
- this.sigA[i] = Double.parseDouble(tokens[tokens.length - 1]);
- }
- if (start == null) {
- throw new IOException("could not find starting values");
- }
- this.numStartingPoints = start.length;
- this.startingValues = start;
-
- double dummyDouble = Double.NaN;
- String dummyString = null;
- for (String line : lines) {
- if (line.contains("Dataset Name:")) {
- dummyString = line
- .substring(line.indexOf("Dataset Name:") + 13,
- line.indexOf("(")).trim();
- }
- if (line.contains("Residual Sum of Squares")) {
- final String[] tokens = line.split(" ++");
- dummyDouble = Double.parseDouble(tokens[4].trim());
- }
- }
- if (Double.isNaN(dummyDouble)) {
- throw new IOException(
- "could not find certified value of residual sum of squares");
- }
- this.residualSumOfSquares = dummyDouble;
-
- if (dummyString == null) {
- throw new IOException("could not find dataset name");
- }
- this.name = dummyString;
-
- this.problem = new LeastSquaresProblem();
- }
-
- class LeastSquaresProblem {
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(final double[] a) {
- final int n = getNumObservations();
- final double[] yhat = new double[n];
- for (int i = 0; i < n; i++) {
- yhat[i] = getModelValue(getX(i), a);
- }
- return yhat;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(final double[] a)
- throws IllegalArgumentException {
- final int n = getNumObservations();
- final double[][] j = new double[n][];
- for (int i = 0; i < n; i++) {
- j[i] = getModelDerivatives(getX(i), a);
- }
- return j;
- }
- });
- }
- }
-
- /**
- * Returns the name of this dataset.
- *
- * @return the name of the dataset
- */
- public String getName() {
- return name;
- }
-
- /**
- * Returns the total number of observations (data points).
- *
- * @return the number of observations
- */
- public int getNumObservations() {
- return numObservations;
- }
-
- /**
- * Returns a copy of the data arrays. The data is laid out as follows <li>
- * {@code data[0][i] = x[i]},</li> <li>{@code data[1][i] = y[i]},</li>
- *
- * @return the array of data points.
- */
- public double[][] getData() {
- return new double[][] {
- MathArrays.copyOf(x), MathArrays.copyOf(y)
- };
- }
-
- /**
- * Returns the x-value of the {@code i}-th data point.
- *
- * @param i the index of the data point
- * @return the x-value
- */
- public double getX(final int i) {
- return x[i];
- }
-
- /**
- * Returns the y-value of the {@code i}-th data point.
- *
- * @param i the index of the data point
- * @return the y-value
- */
- public double getY(final int i) {
- return y[i];
- }
-
- /**
- * Returns the total number of parameters.
- *
- * @return the number of parameters
- */
- public int getNumParameters() {
- return numParameters;
- }
-
- /**
- * Returns the certified values of the paramters.
- *
- * @return the values of the parameters
- */
- public double[] getParameters() {
- return MathArrays.copyOf(a);
- }
-
- /**
- * Returns the certified value of the {@code i}-th parameter.
- *
- * @param i the index of the parameter
- * @return the value of the parameter
- */
- public double getParameter(final int i) {
- return a[i];
- }
-
- /**
- * Reurns the certified values of the standard deviations of the parameters.
- *
- * @return the standard deviations of the parameters
- */
- public double[] getParametersStandardDeviations() {
- return MathArrays.copyOf(sigA);
- }
-
- /**
- * Returns the certified value of the standard deviation of the {@code i}-th
- * parameter.
- *
- * @param i the index of the parameter
- * @return the standard deviation of the parameter
- */
- public double getParameterStandardDeviation(final int i) {
- return sigA[i];
- }
-
- /**
- * Returns the certified value of the residual sum of squares.
- *
- * @return the residual sum of squares
- */
- public double getResidualSumOfSquares() {
- return residualSumOfSquares;
- }
-
- /**
- * Returns the total number of starting points (initial guesses for the
- * optimization process).
- *
- * @return the number of starting points
- */
- public int getNumStartingPoints() {
- return numStartingPoints;
- }
-
- /**
- * Returns the {@code i}-th set of initial values of the parameters.
- *
- * @param i the index of the starting point
- * @return the starting point
- */
- public double[] getStartingPoint(final int i) {
- return MathArrays.copyOf(startingValues[i]);
- }
-
- /**
- * Returns the least-squares problem corresponding to fitting the model to
- * the specified data.
- *
- * @return the least-squares problem
- */
- public LeastSquaresProblem getLeastSquaresProblem() {
- return problem;
- }
-
- /**
- * Returns the value of the model for the specified values of the predictor
- * variable and the parameters.
- *
- * @param x the predictor variable
- * @param a the parameters
- * @return the value of the model
- */
- public abstract double getModelValue(final double x, final double[] a);
-
- /**
- * Returns the values of the partial derivatives of the model with respect
- * to the parameters.
- *
- * @param x the predictor variable
- * @param a the parameters
- * @return the partial derivatives
- */
- public abstract double[] getModelDerivatives(final double x,
- final double[] a);
-
- /**
- * <p>
- * Parses the specified text lines, and extracts the indices of the first
- * and last lines of the data defined by the specified {@code key}. This key
- * must be one of
- * </p>
- * <ul>
- * <li>{@code "Starting Values"},</li>
- * <li>{@code "Certified Values"},</li>
- * <li>{@code "Data"}.</li>
- * </ul>
- * <p>
- * In the NIST data files, the line indices are separated by the keywords
- * {@code "lines"} and {@code "to"}.
- * </p>
- *
- * @param lines the line of text to be parsed
- * @return an array of two {@code int}s. First value is the index of the
- * first line, second value is the index of the last line.
- * {@code null} if the line could not be parsed.
- */
- private static int[] findLineNumbers(final String key,
- final Iterable<String> lines) {
- for (String text : lines) {
- boolean flag = text.contains(key) && text.contains("lines") &&
- text.contains("to") && text.contains(")");
- if (flag) {
- final int[] numbers = new int[2];
- final String from = text.substring(text.indexOf("lines") + 5,
- text.indexOf("to"));
- numbers[0] = Integer.parseInt(from.trim());
- final String to = text.substring(text.indexOf("to") + 2,
- text.indexOf(")"));
- numbers[1] = Integer.parseInt(to.trim());
- return numbers;
- }
- }
- return null;
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDatasetFactory.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDatasetFactory.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDatasetFactory.java
deleted file mode 100644
index 99995e8..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StatisticalReferenceDatasetFactory.java
+++ /dev/null
@@ -1,203 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-
-import org.apache.commons.math4.util.FastMath;
-
-/**
- * A factory to create instances of {@link StatisticalReferenceDataset} from
- * available resources.
- */
-@Deprecated
-public class StatisticalReferenceDatasetFactory {
-
- private StatisticalReferenceDatasetFactory() {
- // Do nothing
- }
-
- /**
- * Creates a new buffered reader from the specified resource name.
- *
- * @param name the name of the resource
- * @return a buffered reader
- * @throws IOException if an I/O error occurred
- */
- public static BufferedReader createBufferedReaderFromResource(final String name)
- throws IOException {
- final InputStream resourceAsStream;
- resourceAsStream = StatisticalReferenceDatasetFactory.class
- .getResourceAsStream(name);
- if (resourceAsStream == null) {
- throw new IOException("could not find resource " + name);
- }
- return new BufferedReader(new InputStreamReader(resourceAsStream));
- }
-
- public static StatisticalReferenceDataset createKirby2()
- throws IOException {
- final BufferedReader in = createBufferedReaderFromResource("Kirby2.dat");
- StatisticalReferenceDataset dataset = null;
- try {
- dataset = new StatisticalReferenceDataset(in) {
-
- @Override
- public double getModelValue(final double x, final double[] a) {
- final double p = a[0] + x * (a[1] + x * a[2]);
- final double q = 1.0 + x * (a[3] + x * a[4]);
- return p / q;
- }
-
- @Override
- public double[] getModelDerivatives(final double x,
- final double[] a) {
- final double[] dy = new double[5];
- final double p = a[0] + x * (a[1] + x * a[2]);
- final double q = 1.0 + x * (a[3] + x * a[4]);
- dy[0] = 1.0 / q;
- dy[1] = x / q;
- dy[2] = x * dy[1];
- dy[3] = -x * p / (q * q);
- dy[4] = x * dy[3];
- return dy;
- }
- };
- } finally {
- in.close();
- }
- return dataset;
- }
-
- public static StatisticalReferenceDataset createHahn1()
- throws IOException {
- final BufferedReader in = createBufferedReaderFromResource("Hahn1.dat");
- StatisticalReferenceDataset dataset = null;
- try {
- dataset = new StatisticalReferenceDataset(in) {
-
- @Override
- public double getModelValue(final double x, final double[] a) {
- final double p = a[0] + x * (a[1] + x * (a[2] + x * a[3]));
- final double q = 1.0 + x * (a[4] + x * (a[5] + x * a[6]));
- return p / q;
- }
-
- @Override
- public double[] getModelDerivatives(final double x,
- final double[] a) {
- final double[] dy = new double[7];
- final double p = a[0] + x * (a[1] + x * (a[2] + x * a[3]));
- final double q = 1.0 + x * (a[4] + x * (a[5] + x * a[6]));
- dy[0] = 1.0 / q;
- dy[1] = x * dy[0];
- dy[2] = x * dy[1];
- dy[3] = x * dy[2];
- dy[4] = -x * p / (q * q);
- dy[5] = x * dy[4];
- dy[6] = x * dy[5];
- return dy;
- }
- };
- } finally {
- in.close();
- }
- return dataset;
- }
-
- public static StatisticalReferenceDataset createMGH17()
- throws IOException {
- final BufferedReader in = createBufferedReaderFromResource("MGH17.dat");
- StatisticalReferenceDataset dataset = null;
- try {
- dataset = new StatisticalReferenceDataset(in) {
-
- @Override
- public double getModelValue(final double x, final double[] a) {
- return a[0] + a[1] * FastMath.exp(-a[3] * x) + a[2] *
- FastMath.exp(-a[4] * x);
- }
-
- @Override
- public double[] getModelDerivatives(final double x,
- final double[] a) {
- final double[] dy = new double[5];
- dy[0] = 1.0;
- dy[1] = FastMath.exp(-x * a[3]);
- dy[2] = FastMath.exp(-x * a[4]);
- dy[3] = -x * a[1] * dy[1];
- dy[4] = -x * a[2] * dy[2];
- return dy;
- }
- };
- } finally {
- in.close();
- }
- return dataset;
- }
-
- public static StatisticalReferenceDataset createLanczos1()
- throws IOException {
- final BufferedReader in =
- createBufferedReaderFromResource("Lanczos1.dat");
- StatisticalReferenceDataset dataset = null;
- try {
- dataset = new StatisticalReferenceDataset(in) {
-
- @Override
- public double getModelValue(final double x, final double[] a) {
- System.out.println(a[0]+", "+a[1]+", "+a[2]+", "+a[3]+", "+a[4]+", "+a[5]);
- return a[0] * FastMath.exp(-a[3] * x) +
- a[1] * FastMath.exp(-a[4] * x) +
- a[2] * FastMath.exp(-a[5] * x);
- }
-
- @Override
- public double[] getModelDerivatives(final double x,
- final double[] a) {
- final double[] dy = new double[6];
- dy[0] = FastMath.exp(-x * a[3]);
- dy[1] = FastMath.exp(-x * a[4]);
- dy[2] = FastMath.exp(-x * a[5]);
- dy[3] = -x * a[0] * dy[0];
- dy[4] = -x * a[1] * dy[1];
- dy[5] = -x * a[2] * dy[2];
- return dy;
- }
- };
- } finally {
- in.close();
- }
- return dataset;
- }
-
- /**
- * Returns an array with all available reference datasets.
- *
- * @return the array of datasets
- * @throws IOException if an I/O error occurs
- */
- public StatisticalReferenceDataset[] createAll()
- throws IOException {
- return new StatisticalReferenceDataset[] {
- createKirby2(), createMGH17()
- };
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/e31fde87/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StraightLineProblem.java
----------------------------------------------------------------------
diff --git a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StraightLineProblem.java b/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StraightLineProblem.java
deleted file mode 100644
index e93c604..0000000
--- a/src/test/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/StraightLineProblem.java
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import java.util.ArrayList;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.analysis.UnivariateFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunction;
-import org.apache.commons.math4.optim.nonlinear.vector.ModelFunctionJacobian;
-import org.apache.commons.math4.stat.regression.SimpleRegression;
-
-/**
- * Class that models a straight line defined as {@code y = a x + b}.
- * The parameters of problem are:
- * <ul>
- * <li>{@code a}</li>
- * <li>{@code b}</li>
- * </ul>
- * The model functions are:
- * <ul>
- * <li>for each pair (a, b), the y-coordinate of the line.</li>
- * </ul>
- */
-@Deprecated
-class StraightLineProblem {
- /** Cloud of points assumed to be fitted by a straight line. */
- private final ArrayList<double[]> points;
- /** Error (on the y-coordinate of the points). */
- private final double sigma;
-
- /**
- * @param error Assumed error for the y-coordinate.
- */
- public StraightLineProblem(double error) {
- points = new ArrayList<double[]>();
- sigma = error;
- }
-
- public void addPoint(double px, double py) {
- points.add(new double[] { px, py });
- }
-
- /**
- * @return the list of x-coordinates.
- */
- public double[] x() {
- final double[] v = new double[points.size()];
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- v[i] = p[0]; // x-coordinate.
- }
-
- return v;
- }
-
- /**
- * @return the list of y-coordinates.
- */
- public double[] y() {
- final double[] v = new double[points.size()];
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- v[i] = p[1]; // y-coordinate.
- }
-
- return v;
- }
-
- public double[] target() {
- return y();
- }
-
- public double[] weight() {
- final double weight = 1 / (sigma * sigma);
- final double[] w = new double[points.size()];
- for (int i = 0; i < points.size(); i++) {
- w[i] = weight;
- }
-
- return w;
- }
-
- public ModelFunction getModelFunction() {
- return new ModelFunction(new MultivariateVectorFunction() {
- public double[] value(double[] params) {
- final Model line = new Model(params[0], params[1]);
-
- final double[] model = new double[points.size()];
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- model[i] = line.value(p[0]);
- }
-
- return model;
- }
- });
- }
-
- public ModelFunctionJacobian getModelFunctionJacobian() {
- return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
- public double[][] value(double[] point) {
- return jacobian(point);
- }
- });
- }
-
- /**
- * Directly solve the linear problem, using the {@link SimpleRegression}
- * class.
- */
- public double[] solve() {
- final SimpleRegression regress = new SimpleRegression(true);
- for (double[] d : points) {
- regress.addData(d[0], d[1]);
- }
-
- final double[] result = { regress.getSlope(), regress.getIntercept() };
- return result;
- }
-
- private double[][] jacobian(double[] params) {
- final double[][] jacobian = new double[points.size()][2];
-
- for (int i = 0; i < points.size(); i++) {
- final double[] p = points.get(i);
- // Partial derivative wrt "a".
- jacobian[i][0] = p[0];
- // Partial derivative wrt "b".
- jacobian[i][1] = 1;
- }
-
- return jacobian;
- }
-
- /**
- * Linear function.
- */
- public static class Model implements UnivariateFunction {
- final double a;
- final double b;
-
- public Model(double a,
- double b) {
- this.a = a;
- this.b = b;
- }
-
- public double value(double x) {
- return a * x + b;
- }
- }
-}
[2/5] [math] Remove deprecated classes in optim package.
Posted by tn...@apache.org.
Remove deprecated classes in optim package.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/0737cf82
Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/0737cf82
Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/0737cf82
Branch: refs/heads/master
Commit: 0737cf82db33f55cdfcb68e8f02f0b8fed40fa15
Parents: 8a76453
Author: Thomas Neidhart <th...@gmail.com>
Authored: Sat Apr 11 16:04:53 2015 +0200
Committer: Thomas Neidhart <th...@gmail.com>
Committed: Sat Apr 11 16:04:53 2015 +0200
----------------------------------------------------------------------
.../optim/linear/LinearObjectiveFunction.java | 1 +
.../optim/nonlinear/scalar/LineSearch.java | 17 +-
.../scalar/MultiStartMultivariateOptimizer.java | 5 +-
.../MultivariateFunctionMappingAdapter.java | 1 +
.../MultivariateFunctionPenaltyAdapter.java | 1 +
.../NonLinearConjugateGradientOptimizer.java | 79 --
.../scalar/noderiv/SimplexOptimizer.java | 2 +
.../JacobianMultivariateVectorOptimizer.java | 116 ---
.../optim/nonlinear/vector/ModelFunction.java | 51 -
.../nonlinear/vector/ModelFunctionJacobian.java | 51 -
.../MultiStartMultivariateVectorOptimizer.java | 122 ---
.../vector/MultivariateVectorOptimizer.java | 167 ----
.../math4/optim/nonlinear/vector/Target.java | 54 --
.../math4/optim/nonlinear/vector/Weight.java | 71 --
.../jacobian/AbstractLeastSquaresOptimizer.java | 281 ------
.../vector/jacobian/GaussNewtonOptimizer.java | 183 ----
.../jacobian/LevenbergMarquardtOptimizer.java | 962 -------------------
.../nonlinear/vector/jacobian/package-info.java | 26 -
.../optim/nonlinear/vector/package-info.java | 26 -
.../MultiStartUnivariateOptimizer.java | 5 +-
20 files changed, 20 insertions(+), 2201 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/linear/LinearObjectiveFunction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/linear/LinearObjectiveFunction.java b/src/main/java/org/apache/commons/math4/optim/linear/LinearObjectiveFunction.java
index 7b63872..f47b8cc 100644
--- a/src/main/java/org/apache/commons/math4/optim/linear/LinearObjectiveFunction.java
+++ b/src/main/java/org/apache/commons/math4/optim/linear/LinearObjectiveFunction.java
@@ -92,6 +92,7 @@ public class LinearObjectiveFunction
* @param point Point at which linear equation must be evaluated.
* @return the value of the linear equation at the current point.
*/
+ @Override
public double value(final double[] point) {
return value(new ArrayRealVector(point, false));
}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/LineSearch.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/LineSearch.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/LineSearch.java
index dae251f..558b98a 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/LineSearch.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/LineSearch.java
@@ -112,15 +112,16 @@ public class LineSearch {
final double[] direction) {
final int n = startPoint.length;
final UnivariateFunction f = new UnivariateFunction() {
- public double value(double alpha) {
- final double[] x = new double[n];
- for (int i = 0; i < n; i++) {
- x[i] = startPoint[i] + alpha * direction[i];
- }
- final double obj = mainOptimizer.computeObjectiveValue(x);
- return obj;
+ @Override
+ public double value(double alpha) {
+ final double[] x = new double[n];
+ for (int i = 0; i < n; i++) {
+ x[i] = startPoint[i] + alpha * direction[i];
}
- };
+ final double obj = mainOptimizer.computeObjectiveValue(x);
+ return obj;
+ }
+ };
final GoalType goal = mainOptimizer.getGoalType();
bracket.search(f, goal, 0, initialBracketingRange);
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java
index 11ec5df..0bfa63f 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java
@@ -16,10 +16,10 @@
*/
package org.apache.commons.math4.optim.nonlinear.scalar;
-import java.util.Collections;
-import java.util.List;
import java.util.ArrayList;
+import java.util.Collections;
import java.util.Comparator;
+import java.util.List;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.exception.NullArgumentException;
@@ -94,6 +94,7 @@ public class MultiStartMultivariateOptimizer
*/
private Comparator<PointValuePair> getPairComparator() {
return new Comparator<PointValuePair>() {
+ @Override
public int compare(final PointValuePair o1,
final PointValuePair o2) {
if (o1 == null) {
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionMappingAdapter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionMappingAdapter.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionMappingAdapter.java
index a34f02d..dc01a2f 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionMappingAdapter.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionMappingAdapter.java
@@ -175,6 +175,7 @@ public class MultivariateFunctionMappingAdapter
* @return underlying function value
* @see #unboundedToBounded(double[])
*/
+ @Override
public double value(double[] point) {
return bounded.value(unboundedToBounded(point));
}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapter.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapter.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapter.java
index 04ca7f5..33bb852 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapter.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/MultivariateFunctionPenaltyAdapter.java
@@ -158,6 +158,7 @@ public class MultivariateFunctionPenaltyAdapter
* @param point unbounded point
* @return either underlying function value or penalty function value
*/
+ @Override
public double value(double[] point) {
for (int i = 0; i < scale.length; ++i) {
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
index 078c6ec..8ef98fc 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/gradient/NonLinearConjugateGradientOptimizer.java
@@ -17,7 +17,6 @@
package org.apache.commons.math4.optim.nonlinear.scalar.gradient;
-import org.apache.commons.math4.analysis.solvers.UnivariateSolver;
import org.apache.commons.math4.exception.MathInternalError;
import org.apache.commons.math4.exception.MathUnsupportedOperationException;
import org.apache.commons.math4.exception.TooManyEvaluationsException;
@@ -78,40 +77,6 @@ public class NonLinearConjugateGradientOptimizer
}
/**
- * The initial step is a factor with respect to the search direction
- * (which itself is roughly related to the gradient of the function).
- * <br/>
- * It is used to find an interval that brackets the optimum in line
- * search.
- *
- * @since 3.1
- * @deprecated As of v3.3, this class is not used anymore.
- * This setting is replaced by the {@code initialBracketingRange}
- * argument to the new constructors.
- */
- @Deprecated
- public static class BracketingStep implements OptimizationData {
- /** Initial step. */
- private final double initialStep;
-
- /**
- * @param step Initial step for the bracket search.
- */
- public BracketingStep(double step) {
- initialStep = step;
- }
-
- /**
- * Gets the initial step.
- *
- * @return the initial step.
- */
- public double getBracketingStep() {
- return initialStep;
- }
- }
-
- /**
* Constructor with default tolerances for the line search (1e-8) and
* {@link IdentityPreconditioner preconditioner}.
*
@@ -137,27 +102,6 @@ public class NonLinearConjugateGradientOptimizer
* must be one of {@link Formula#FLETCHER_REEVES} or
* {@link Formula#POLAK_RIBIERE}.
* @param checker Convergence checker.
- * @param lineSearchSolver Solver to use during line search.
- * @deprecated as of 3.3. Please use
- * {@link #NonLinearConjugateGradientOptimizer(Formula,ConvergenceChecker,double,double,double)} instead.
- */
- @Deprecated
- public NonLinearConjugateGradientOptimizer(final Formula updateFormula,
- ConvergenceChecker<PointValuePair> checker,
- final UnivariateSolver lineSearchSolver) {
- this(updateFormula,
- checker,
- lineSearchSolver,
- new IdentityPreconditioner());
- }
-
- /**
- * Constructor with default {@link IdentityPreconditioner preconditioner}.
- *
- * @param updateFormula formula to use for updating the β parameter,
- * must be one of {@link Formula#FLETCHER_REEVES} or
- * {@link Formula#POLAK_RIBIERE}.
- * @param checker Convergence checker.
* @param relativeTolerance Relative threshold for line search.
* @param absoluteTolerance Absolute threshold for line search.
* @param initialBracketingRange Extent of the initial interval used to
@@ -185,29 +129,6 @@ public class NonLinearConjugateGradientOptimizer
* must be one of {@link Formula#FLETCHER_REEVES} or
* {@link Formula#POLAK_RIBIERE}.
* @param checker Convergence checker.
- * @param lineSearchSolver Solver to use during line search.
- * @param preconditioner Preconditioner.
- * @deprecated as of 3.3. Please use
- * {@link #NonLinearConjugateGradientOptimizer(Formula,ConvergenceChecker,double,double,double,Preconditioner)} instead.
- */
- @Deprecated
- public NonLinearConjugateGradientOptimizer(final Formula updateFormula,
- ConvergenceChecker<PointValuePair> checker,
- final UnivariateSolver lineSearchSolver,
- final Preconditioner preconditioner) {
- this(updateFormula,
- checker,
- lineSearchSolver.getRelativeAccuracy(),
- lineSearchSolver.getAbsoluteAccuracy(),
- lineSearchSolver.getAbsoluteAccuracy(),
- preconditioner);
- }
-
- /**
- * @param updateFormula formula to use for updating the β parameter,
- * must be one of {@link Formula#FLETCHER_REEVES} or
- * {@link Formula#POLAK_RIBIERE}.
- * @param checker Convergence checker.
* @param preconditioner Preconditioner.
* @param relativeTolerance Relative threshold for line search.
* @param absoluteTolerance Absolute threshold for line search.
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
index f6eee2f..ad39a05 100644
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
+++ b/src/main/java/org/apache/commons/math4/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
@@ -131,6 +131,7 @@ public class SimplexOptimizer extends MultivariateOptimizer {
// evaluations counter.
final MultivariateFunction evalFunc
= new MultivariateFunction() {
+ @Override
public double value(double[] point) {
return computeObjectiveValue(point);
}
@@ -139,6 +140,7 @@ public class SimplexOptimizer extends MultivariateOptimizer {
final boolean isMinim = getGoalType() == GoalType.MINIMIZE;
final Comparator<PointValuePair> comparator
= new Comparator<PointValuePair>() {
+ @Override
public int compare(final PointValuePair o1,
final PointValuePair o2) {
final double v1 = o1.getValue();
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/JacobianMultivariateVectorOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/JacobianMultivariateVectorOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/JacobianMultivariateVectorOptimizer.java
deleted file mode 100644
index da9ad86..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/JacobianMultivariateVectorOptimizer.java
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.optim.ConvergenceChecker;
-import org.apache.commons.math4.optim.OptimizationData;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-
-/**
- * Base class for implementing optimizers for multivariate vector
- * differentiable functions.
- * It contains boiler-plate code for dealing with Jacobian evaluation.
- * It assumes that the rows of the Jacobian matrix iterate on the model
- * functions while the columns iterate on the parameters; thus, the numbers
- * of rows is equal to the dimension of the {@link Target} while the
- * number of columns is equal to the dimension of the
- * {@link org.apache.commons.math4.optim.InitialGuess InitialGuess}.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public abstract class JacobianMultivariateVectorOptimizer
- extends MultivariateVectorOptimizer {
- /**
- * Jacobian of the model function.
- */
- private MultivariateMatrixFunction jacobian;
-
- /**
- * @param checker Convergence checker.
- */
- protected JacobianMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- }
-
- /**
- * Computes the Jacobian matrix.
- *
- * @param params Point at which the Jacobian must be evaluated.
- * @return the Jacobian at the specified point.
- */
- protected double[][] computeJacobian(final double[] params) {
- return jacobian.value(params);
- }
-
- /**
- * {@inheritDoc}
- *
- * @param optData Optimization data. In addition to those documented in
- * {@link MultivariateVectorOptimizer#optimize(OptimizationData...)}
- * MultivariateOptimizer}, this method will register the following data:
- * <ul>
- * <li>{@link ModelFunctionJacobian}</li>
- * </ul>
- * @return {@inheritDoc}
- * @throws TooManyEvaluationsException if the maximal number of
- * evaluations is exceeded.
- * @throws DimensionMismatchException if the initial guess, target, and weight
- * arguments have inconsistent dimensions.
- */
- @Override
- public PointVectorValuePair optimize(OptimizationData... optData)
- throws TooManyEvaluationsException,
- DimensionMismatchException {
- // Set up base class and perform computation.
- return super.optimize(optData);
- }
-
- /**
- * Scans the list of (required and optional) optimization data that
- * characterize the problem.
- *
- * @param optData Optimization data.
- * The following data will be looked for:
- * <ul>
- * <li>{@link ModelFunctionJacobian}</li>
- * </ul>
- */
- @Override
- protected void parseOptimizationData(OptimizationData... optData) {
- // Allow base class to register its own data.
- super.parseOptimizationData(optData);
-
- // The existing values (as set by the previous call) are reused if
- // not provided in the argument list.
- for (OptimizationData data : optData) {
- if (data instanceof ModelFunctionJacobian) {
- jacobian = ((ModelFunctionJacobian) data).getModelFunctionJacobian();
- // If more data must be parsed, this statement _must_ be
- // changed to "continue".
- break;
- }
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunction.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunction.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunction.java
deleted file mode 100644
index b371f13..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunction.java
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.optim.OptimizationData;
-
-/**
- * Model (vector) function to be optimized.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class ModelFunction implements OptimizationData {
- /** Function to be optimized. */
- private final MultivariateVectorFunction model;
-
- /**
- * @param m Model function to be optimized.
- */
- public ModelFunction(MultivariateVectorFunction m) {
- model = m;
- }
-
- /**
- * Gets the model function to be optimized.
- *
- * @return the model function.
- */
- public MultivariateVectorFunction getModelFunction() {
- return model;
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunctionJacobian.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunctionJacobian.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunctionJacobian.java
deleted file mode 100644
index 69f7860..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/ModelFunctionJacobian.java
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateMatrixFunction;
-import org.apache.commons.math4.optim.OptimizationData;
-
-/**
- * Jacobian of the model (vector) function to be optimized.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class ModelFunctionJacobian implements OptimizationData {
- /** Function to be optimized. */
- private final MultivariateMatrixFunction jacobian;
-
- /**
- * @param j Jacobian of the model function to be optimized.
- */
- public ModelFunctionJacobian(MultivariateMatrixFunction j) {
- jacobian = j;
- }
-
- /**
- * Gets the Jacobian of the model function to be optimized.
- *
- * @return the model function Jacobian.
- */
- public MultivariateMatrixFunction getModelFunctionJacobian() {
- return jacobian;
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizer.java
deleted file mode 100644
index 2b17932..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizer.java
+++ /dev/null
@@ -1,122 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import java.util.Collections;
-import java.util.List;
-import java.util.ArrayList;
-import java.util.Comparator;
-
-import org.apache.commons.math4.exception.NotStrictlyPositiveException;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.linear.ArrayRealVector;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.linear.RealVector;
-import org.apache.commons.math4.optim.BaseMultiStartMultivariateOptimizer;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.random.RandomVectorGenerator;
-
-/**
- * Multi-start optimizer for a (vector) model function.
- *
- * This class wraps an optimizer in order to use it several times in
- * turn with different starting points (trying to avoid being trapped
- * in a local extremum when looking for a global one).
- *
- * @since 3.0
- */
-@Deprecated
-public class MultiStartMultivariateVectorOptimizer
- extends BaseMultiStartMultivariateOptimizer<PointVectorValuePair> {
- /** Underlying optimizer. */
- private final MultivariateVectorOptimizer optimizer;
- /** Found optima. */
- private final List<PointVectorValuePair> optima = new ArrayList<PointVectorValuePair>();
-
- /**
- * Create a multi-start optimizer from a single-start optimizer.
- *
- * @param optimizer Single-start optimizer to wrap.
- * @param starts Number of starts to perform.
- * If {@code starts == 1}, the result will be same as if {@code optimizer}
- * is called directly.
- * @param generator Random vector generator to use for restarts.
- * @throws NullArgumentException if {@code optimizer} or {@code generator}
- * is {@code null}.
- * @throws NotStrictlyPositiveException if {@code starts < 1}.
- */
- public MultiStartMultivariateVectorOptimizer(final MultivariateVectorOptimizer optimizer,
- final int starts,
- final RandomVectorGenerator generator)
- throws NullArgumentException,
- NotStrictlyPositiveException {
- super(optimizer, starts, generator);
- this.optimizer = optimizer;
- }
-
- /**
- * {@inheritDoc}
- */
- @Override
- public PointVectorValuePair[] getOptima() {
- Collections.sort(optima, getPairComparator());
- return optima.toArray(new PointVectorValuePair[0]);
- }
-
- /**
- * {@inheritDoc}
- */
- @Override
- protected void store(PointVectorValuePair optimum) {
- optima.add(optimum);
- }
-
- /**
- * {@inheritDoc}
- */
- @Override
- protected void clear() {
- optima.clear();
- }
-
- /**
- * @return a comparator for sorting the optima.
- */
- private Comparator<PointVectorValuePair> getPairComparator() {
- return new Comparator<PointVectorValuePair>() {
- private final RealVector target = new ArrayRealVector(optimizer.getTarget(), false);
- private final RealMatrix weight = optimizer.getWeight();
-
- public int compare(final PointVectorValuePair o1,
- final PointVectorValuePair o2) {
- if (o1 == null) {
- return (o2 == null) ? 0 : 1;
- } else if (o2 == null) {
- return -1;
- }
- return Double.compare(weightedResidual(o1),
- weightedResidual(o2));
- }
-
- private double weightedResidual(final PointVectorValuePair pv) {
- final RealVector v = new ArrayRealVector(pv.getValueRef(), false);
- final RealVector r = target.subtract(v);
- return r.dotProduct(weight.operate(r));
- }
- };
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultivariateVectorOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultivariateVectorOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultivariateVectorOptimizer.java
deleted file mode 100644
index 8485ef3..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/MultivariateVectorOptimizer.java
+++ /dev/null
@@ -1,167 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.analysis.MultivariateVectorFunction;
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.BaseMultivariateOptimizer;
-import org.apache.commons.math4.optim.ConvergenceChecker;
-import org.apache.commons.math4.optim.OptimizationData;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-
-/**
- * Base class for a multivariate vector function optimizer.
- *
- * @since 3.1
- */
-@Deprecated
-public abstract class MultivariateVectorOptimizer
- extends BaseMultivariateOptimizer<PointVectorValuePair> {
- /** Target values for the model function at optimum. */
- private double[] target;
- /** Weight matrix. */
- private RealMatrix weightMatrix;
- /** Model function. */
- private MultivariateVectorFunction model;
-
- /**
- * @param checker Convergence checker.
- */
- protected MultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- }
-
- /**
- * Computes the objective function value.
- * This method <em>must</em> be called by subclasses to enforce the
- * evaluation counter limit.
- *
- * @param params Point at which the objective function must be evaluated.
- * @return the objective function value at the specified point.
- * @throws TooManyEvaluationsException if the maximal number of evaluations
- * (of the model vector function) is exceeded.
- */
- protected double[] computeObjectiveValue(double[] params) {
- super.incrementEvaluationCount();
- return model.value(params);
- }
-
- /**
- * {@inheritDoc}
- *
- * @param optData Optimization data. In addition to those documented in
- * {@link BaseMultivariateOptimizer#parseOptimizationData(OptimizationData[])
- * BaseMultivariateOptimizer}, this method will register the following data:
- * <ul>
- * <li>{@link Target}</li>
- * <li>{@link Weight}</li>
- * <li>{@link ModelFunction}</li>
- * </ul>
- * @return {@inheritDoc}
- * @throws TooManyEvaluationsException if the maximal number of
- * evaluations is exceeded.
- * @throws DimensionMismatchException if the initial guess, target, and weight
- * arguments have inconsistent dimensions.
- */
- @Override
- public PointVectorValuePair optimize(OptimizationData... optData)
- throws TooManyEvaluationsException,
- DimensionMismatchException {
- // Set up base class and perform computation.
- return super.optimize(optData);
- }
-
- /**
- * Gets the weight matrix of the observations.
- *
- * @return the weight matrix.
- */
- public RealMatrix getWeight() {
- return weightMatrix.copy();
- }
- /**
- * Gets the observed values to be matched by the objective vector
- * function.
- *
- * @return the target values.
- */
- public double[] getTarget() {
- return target.clone();
- }
-
- /**
- * Gets the number of observed values.
- *
- * @return the length of the target vector.
- */
- public int getTargetSize() {
- return target.length;
- }
-
- /**
- * Scans the list of (required and optional) optimization data that
- * characterize the problem.
- *
- * @param optData Optimization data. The following data will be looked for:
- * <ul>
- * <li>{@link Target}</li>
- * <li>{@link Weight}</li>
- * <li>{@link ModelFunction}</li>
- * </ul>
- */
- @Override
- protected void parseOptimizationData(OptimizationData... optData) {
- // Allow base class to register its own data.
- super.parseOptimizationData(optData);
-
- // The existing values (as set by the previous call) are reused if
- // not provided in the argument list.
- for (OptimizationData data : optData) {
- if (data instanceof ModelFunction) {
- model = ((ModelFunction) data).getModelFunction();
- continue;
- }
- if (data instanceof Target) {
- target = ((Target) data).getTarget();
- continue;
- }
- if (data instanceof Weight) {
- weightMatrix = ((Weight) data).getWeight();
- continue;
- }
- }
-
- // Check input consistency.
- checkParameters();
- }
-
- /**
- * Check parameters consistency.
- *
- * @throws DimensionMismatchException if {@link #target} and
- * {@link #weightMatrix} have inconsistent dimensions.
- */
- private void checkParameters() {
- if (target.length != weightMatrix.getColumnDimension()) {
- throw new DimensionMismatchException(target.length,
- weightMatrix.getColumnDimension());
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Target.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Target.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Target.java
deleted file mode 100644
index 2937888..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Target.java
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.optim.OptimizationData;
-
-/**
- * Target of the optimization procedure.
- * They are the values which the objective vector function must reproduce
- * When the parameters of the model have been optimized.
- * <br/>
- * Immutable class.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class Target implements OptimizationData {
- /** Target values (of the objective vector function). */
- private final double[] target;
-
- /**
- * @param observations Target values.
- */
- public Target(double[] observations) {
- target = observations.clone();
- }
-
- /**
- * Gets the initial guess.
- *
- * @return the initial guess.
- */
- public double[] getTarget() {
- return target.clone();
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Weight.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Weight.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Weight.java
deleted file mode 100644
index a723ae2..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/Weight.java
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector;
-
-import org.apache.commons.math4.linear.DiagonalMatrix;
-import org.apache.commons.math4.linear.NonSquareMatrixException;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.OptimizationData;
-
-/**
- * Weight matrix of the residuals between model and observations.
- * <br/>
- * Immutable class.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class Weight implements OptimizationData {
- /** Weight matrix. */
- private final RealMatrix weightMatrix;
-
- /**
- * Creates a diagonal weight matrix.
- *
- * @param weight List of the values of the diagonal.
- */
- public Weight(double[] weight) {
- weightMatrix = new DiagonalMatrix(weight);
- }
-
- /**
- * @param weight Weight matrix.
- * @throws NonSquareMatrixException if the argument is not
- * a square matrix.
- */
- public Weight(RealMatrix weight) {
- if (weight.getColumnDimension() != weight.getRowDimension()) {
- throw new NonSquareMatrixException(weight.getColumnDimension(),
- weight.getRowDimension());
- }
-
- weightMatrix = weight.copy();
- }
-
- /**
- * Gets the initial guess.
- *
- * @return the initial guess.
- */
- public RealMatrix getWeight() {
- return weightMatrix.copy();
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizer.java
deleted file mode 100644
index 6c4a2d9..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/AbstractLeastSquaresOptimizer.java
+++ /dev/null
@@ -1,281 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import org.apache.commons.math4.exception.DimensionMismatchException;
-import org.apache.commons.math4.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.linear.ArrayRealVector;
-import org.apache.commons.math4.linear.DecompositionSolver;
-import org.apache.commons.math4.linear.DiagonalMatrix;
-import org.apache.commons.math4.linear.EigenDecomposition;
-import org.apache.commons.math4.linear.MatrixUtils;
-import org.apache.commons.math4.linear.QRDecomposition;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.optim.ConvergenceChecker;
-import org.apache.commons.math4.optim.OptimizationData;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-import org.apache.commons.math4.optim.nonlinear.vector.JacobianMultivariateVectorOptimizer;
-import org.apache.commons.math4.optim.nonlinear.vector.Weight;
-import org.apache.commons.math4.util.FastMath;
-
-/**
- * Base class for implementing least-squares optimizers.
- * It provides methods for error estimation.
- *
- * @since 3.1
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public abstract class AbstractLeastSquaresOptimizer
- extends JacobianMultivariateVectorOptimizer {
- /** Square-root of the weight matrix. */
- private RealMatrix weightMatrixSqrt;
- /** Cost value (square root of the sum of the residuals). */
- private double cost;
-
- /**
- * @param checker Convergence checker.
- */
- protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- }
-
- /**
- * Computes the weighted Jacobian matrix.
- *
- * @param params Model parameters at which to compute the Jacobian.
- * @return the weighted Jacobian: W<sup>1/2</sup> J.
- * @throws DimensionMismatchException if the Jacobian dimension does not
- * match problem dimension.
- */
- protected RealMatrix computeWeightedJacobian(double[] params) {
- return weightMatrixSqrt.multiply(MatrixUtils.createRealMatrix(computeJacobian(params)));
- }
-
- /**
- * Computes the cost.
- *
- * @param residuals Residuals.
- * @return the cost.
- * @see #computeResiduals(double[])
- */
- protected double computeCost(double[] residuals) {
- final ArrayRealVector r = new ArrayRealVector(residuals);
- return FastMath.sqrt(r.dotProduct(getWeight().operate(r)));
- }
-
- /**
- * Gets the root-mean-square (RMS) value.
- *
- * The RMS the root of the arithmetic mean of the square of all weighted
- * residuals.
- * This is related to the criterion that is minimized by the optimizer
- * 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>.
- *
- * @return the RMS value.
- */
- public double getRMS() {
- return FastMath.sqrt(getChiSquare() / getTargetSize());
- }
-
- /**
- * Get a Chi-Square-like value assuming the N residuals follow N
- * distinct normal distributions centered on 0 and whose variances are
- * the reciprocal of the weights.
- * @return chi-square value
- */
- public double getChiSquare() {
- return cost * cost;
- }
-
- /**
- * Gets the square-root of the weight matrix.
- *
- * @return the square-root of the weight matrix.
- */
- public RealMatrix getWeightSquareRoot() {
- return weightMatrixSqrt.copy();
- }
-
- /**
- * Sets the cost.
- *
- * @param cost Cost value.
- */
- protected void setCost(double cost) {
- this.cost = cost;
- }
-
- /**
- * Get the covariance matrix of the optimized parameters.
- * <br/>
- * Note that this operation involves the inversion of the
- * <code>J<sup>T</sup>J</code> matrix, where {@code J} is the
- * Jacobian matrix.
- * The {@code threshold} parameter is a way for the caller to specify
- * that the result of this computation should be considered meaningless,
- * and thus trigger an exception.
- *
- * @param params Model parameters.
- * @param threshold Singularity threshold.
- * @return the covariance matrix.
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed (singular problem).
- */
- public double[][] computeCovariances(double[] params,
- double threshold) {
- // Set up the Jacobian.
- final RealMatrix j = computeWeightedJacobian(params);
-
- // Compute transpose(J)J.
- final RealMatrix jTj = j.transpose().multiply(j);
-
- // Compute the covariances matrix.
- final DecompositionSolver solver
- = new QRDecomposition(jTj, threshold).getSolver();
- return solver.getInverse().getData();
- }
-
- /**
- * Computes an estimate of the standard deviation of the parameters. The
- * returned values are the square root of the diagonal coefficients of the
- * covariance matrix, {@code sd(a[i]) ~= sqrt(C[i][i])}, where {@code a[i]}
- * is the optimized value of the {@code i}-th parameter, and {@code C} is
- * the covariance matrix.
- *
- * @param params Model parameters.
- * @param covarianceSingularityThreshold Singularity threshold (see
- * {@link #computeCovariances(double[],double) computeCovariances}).
- * @return an estimate of the standard deviation of the optimized parameters
- * @throws org.apache.commons.math4.linear.SingularMatrixException
- * if the covariance matrix cannot be computed.
- */
- public double[] computeSigma(double[] params,
- double covarianceSingularityThreshold) {
- final int nC = params.length;
- final double[] sig = new double[nC];
- final double[][] cov = computeCovariances(params, covarianceSingularityThreshold);
- for (int i = 0; i < nC; ++i) {
- sig[i] = FastMath.sqrt(cov[i][i]);
- }
- return sig;
- }
-
- /**
- * {@inheritDoc}
- *
- * @param optData Optimization data. In addition to those documented in
- * {@link JacobianMultivariateVectorOptimizer#parseOptimizationData(OptimizationData[])
- * JacobianMultivariateVectorOptimizer}, this method will register the following data:
- * <ul>
- * <li>{@link org.apache.commons.math4.optim.nonlinear.vector.Weight}</li>
- * </ul>
- * @return {@inheritDoc}
- * @throws TooManyEvaluationsException if the maximal number of
- * evaluations is exceeded.
- * @throws DimensionMismatchException if the initial guess, target, and weight
- * arguments have inconsistent dimensions.
- */
- @Override
- public PointVectorValuePair optimize(OptimizationData... optData)
- throws TooManyEvaluationsException {
- // Set up base class and perform computation.
- return super.optimize(optData);
- }
-
- /**
- * Computes the residuals.
- * The residual is the difference between the observed (target)
- * values and the model (objective function) value.
- * There is one residual for each element of the vector-valued
- * function.
- *
- * @param objectiveValue Value of the the objective function. This is
- * the value returned from a call to
- * {@link #computeObjectiveValue(double[]) computeObjectiveValue}
- * (whose array argument contains the model parameters).
- * @return the residuals.
- * @throws DimensionMismatchException if {@code params} has a wrong
- * length.
- */
- protected double[] computeResiduals(double[] objectiveValue) {
- final double[] target = getTarget();
- if (objectiveValue.length != target.length) {
- throw new DimensionMismatchException(target.length,
- objectiveValue.length);
- }
-
- final double[] residuals = new double[target.length];
- for (int i = 0; i < target.length; i++) {
- residuals[i] = target[i] - objectiveValue[i];
- }
-
- return residuals;
- }
-
- /**
- * Scans the list of (required and optional) optimization data that
- * characterize the problem.
- * If the weight matrix is specified, the {@link #weightMatrixSqrt}
- * field is recomputed.
- *
- * @param optData Optimization data. The following data will be looked for:
- * <ul>
- * <li>{@link Weight}</li>
- * </ul>
- */
- @Override
- protected void parseOptimizationData(OptimizationData... optData) {
- // Allow base class to register its own data.
- super.parseOptimizationData(optData);
-
- // The existing values (as set by the previous call) are reused if
- // not provided in the argument list.
- for (OptimizationData data : optData) {
- if (data instanceof Weight) {
- weightMatrixSqrt = squareRoot(((Weight) data).getWeight());
- // If more data must be parsed, this statement _must_ be
- // changed to "continue".
- break;
- }
- }
- }
-
- /**
- * Computes the square-root of the weight matrix.
- *
- * @param m Symmetric, positive-definite (weight) matrix.
- * @return the square-root of the weight matrix.
- */
- private RealMatrix squareRoot(RealMatrix m) {
- if (m instanceof DiagonalMatrix) {
- final int dim = m.getRowDimension();
- final RealMatrix sqrtM = new DiagonalMatrix(dim);
- for (int i = 0; i < dim; i++) {
- sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
- }
- return sqrtM;
- } else {
- final EigenDecomposition dec = new EigenDecomposition(m);
- return dec.getSquareRoot();
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/commons-math/blob/0737cf82/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizer.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizer.java b/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizer.java
deleted file mode 100644
index 34fb988..0000000
--- a/src/main/java/org/apache/commons/math4/optim/nonlinear/vector/jacobian/GaussNewtonOptimizer.java
+++ /dev/null
@@ -1,183 +0,0 @@
-/*
- * 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.math4.optim.nonlinear.vector.jacobian;
-
-import org.apache.commons.math4.exception.ConvergenceException;
-import org.apache.commons.math4.exception.MathInternalError;
-import org.apache.commons.math4.exception.MathUnsupportedOperationException;
-import org.apache.commons.math4.exception.NullArgumentException;
-import org.apache.commons.math4.exception.util.LocalizedFormats;
-import org.apache.commons.math4.linear.ArrayRealVector;
-import org.apache.commons.math4.linear.BlockRealMatrix;
-import org.apache.commons.math4.linear.DecompositionSolver;
-import org.apache.commons.math4.linear.LUDecomposition;
-import org.apache.commons.math4.linear.QRDecomposition;
-import org.apache.commons.math4.linear.RealMatrix;
-import org.apache.commons.math4.linear.SingularMatrixException;
-import org.apache.commons.math4.optim.ConvergenceChecker;
-import org.apache.commons.math4.optim.PointVectorValuePair;
-
-/**
- * Gauss-Newton least-squares solver.
- * <br/>
- * Constraints are not supported: the call to
- * {@link #optimize(OptimizationData[]) optimize} will throw
- * {@link MathUnsupportedOperationException} if bounds are passed to it.
- *
- * <p>
- * This class solve a least-square problem by solving the normal equations
- * of the linearized problem at each iteration. Either LU decomposition or
- * QR decomposition can be used to solve the normal equations. LU decomposition
- * is faster but QR decomposition is more robust for difficult problems.
- * </p>
- *
- * @since 2.0
- * @deprecated All classes and interfaces in this package are deprecated.
- * The optimizers that were provided here were moved to the
- * {@link org.apache.commons.math4.fitting.leastsquares} package
- * (cf. MATH-1008).
- */
-@Deprecated
-public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
- /** Indicator for using LU decomposition. */
- private final boolean useLU;
-
- /**
- * Simple constructor with default settings.
- * The normal equations will be solved using LU decomposition.
- *
- * @param checker Convergence checker.
- */
- public GaussNewtonOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
- this(true, checker);
- }
-
- /**
- * @param useLU If {@code true}, the normal equations will be solved
- * using LU decomposition, otherwise they will be solved using QR
- * decomposition.
- * @param checker Convergence checker.
- */
- public GaussNewtonOptimizer(final boolean useLU,
- ConvergenceChecker<PointVectorValuePair> checker) {
- super(checker);
- this.useLU = useLU;
- }
-
- /** {@inheritDoc} */
- @Override
- public PointVectorValuePair doOptimize() {
- checkParameters();
-
- final ConvergenceChecker<PointVectorValuePair> checker
- = getConvergenceChecker();
-
- // Computation will be useless without a checker (see "for-loop").
- if (checker == null) {
- throw new NullArgumentException();
- }
-
- final double[] targetValues = getTarget();
- final int nR = targetValues.length; // Number of observed data.
-
- final RealMatrix weightMatrix = getWeight();
- // Diagonal of the weight matrix.
- final double[] residualsWeights = new double[nR];
- for (int i = 0; i < nR; i++) {
- residualsWeights[i] = weightMatrix.getEntry(i, i);
- }
-
- final double[] currentPoint = getStartPoint();
- final int nC = currentPoint.length;
-
- // iterate until convergence is reached
- PointVectorValuePair current = null;
- for (boolean converged = false; !converged;) {
- incrementIterationCount();
-
- // evaluate the objective function and its jacobian
- PointVectorValuePair previous = current;
- // Value of the objective function at "currentPoint".
- final double[] currentObjective = computeObjectiveValue(currentPoint);
- final double[] currentResiduals = computeResiduals(currentObjective);
- final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint);
- current = new PointVectorValuePair(currentPoint, currentObjective);
-
- // build the linear problem
- final double[] b = new double[nC];
- final double[][] a = new double[nC][nC];
- for (int i = 0; i < nR; ++i) {
-
- final double[] grad = weightedJacobian.getRow(i);
- final double weight = residualsWeights[i];
- final double residual = currentResiduals[i];
-
- // compute the normal equation
- final double wr = weight * residual;
- for (int j = 0; j < nC; ++j) {
- b[j] += wr * grad[j];
- }
-
- // build the contribution matrix for measurement i
- for (int k = 0; k < nC; ++k) {
- double[] ak = a[k];
- double wgk = weight * grad[k];
- for (int l = 0; l < nC; ++l) {
- ak[l] += wgk * grad[l];
- }
- }
- }
-
- // Check convergence.
- if (previous != null) {
- converged = checker.converged(getIterations(), previous, current);
- if (converged) {
- setCost(computeCost(currentResiduals));
- return current;
- }
- }
-
- try {
- // solve the linearized least squares problem
- RealMatrix mA = new BlockRealMatrix(a);
- DecompositionSolver solver = useLU ?
- new LUDecomposition(mA).getSolver() :
- new QRDecomposition(mA).getSolver();
- final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray();
- // update the estimated parameters
- for (int i = 0; i < nC; ++i) {
- currentPoint[i] += dX[i];
- }
- } catch (SingularMatrixException e) {
- throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM);
- }
- }
- // Must never happen.
- throw new MathInternalError();
- }
-
- /**
- * @throws MathUnsupportedOperationException if bounds were passed to the
- * {@link #optimize(OptimizationData[]) optimize} method.
- */
- private void checkParameters() {
- if (getLowerBound() != null ||
- getUpperBound() != null) {
- throw new MathUnsupportedOperationException(LocalizedFormats.CONSTRAINT);
- }
- }
-}