http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java b/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java deleted file mode 100644 index 5c2c6d2..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/fitting/WeightedObservedPoint.java +++ /dev/null @@ -1,76 +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.optimization.fitting; - -import java.io.Serializable; - -/** This class is a simple container for weighted observed point in - * {@link CurveFitter curve fitting}. - * <p>Instances of this class are guaranteed to be immutable.</p> - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 2.0 - */ -@Deprecated -public class WeightedObservedPoint implements Serializable { - - /** Serializable version id. */ - private static final long serialVersionUID = 5306874947404636157L; - - /** Weight of the measurement in the fitting process. */ - private final double weight; - - /** Abscissa of the point. */ - private final double x; - - /** Observed value of the function at x. */ - private final double y; - - /** Simple constructor. - * @param weight weight of the measurement in the fitting process - * @param x abscissa of the measurement - * @param y ordinate of the measurement - */ - public WeightedObservedPoint(final double weight, final double x, final double y) { - this.weight = weight; - this.x = x; - this.y = y; - } - - /** Get the weight of the measurement in the fitting process. - * @return weight of the measurement in the fitting process - */ - public double getWeight() { - return weight; - } - - /** Get the abscissa of the point. - * @return abscissa of the point - */ - public double getX() { - return x; - } - - /** Get the observed value of the function at x. - * @return observed value of the function at x - */ - public double getY() { - return y; - } - -} -
http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java b/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java deleted file mode 100644 index 98683a8..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/fitting/package-info.java +++ /dev/null @@ -1,30 +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 classes to perform curve fitting. - * - * <p>Curve fitting is a special case of a least squares problem - * were the parameters are the coefficients of a function <code>f</code> - * whose graph <code>y=f(x)</code> should pass through sample points, and - * were the objective function is the squared sum of residuals - * <code>f(x<sub>i</sub>)-y<sub>i</sub></code> for observed points - * (x<sub>i</sub>, y<sub>i</sub>).</p> - * - * - */ -package org.apache.commons.math4.optimization.fitting; http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java deleted file mode 100644 index 1fbcbcb..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractDifferentiableOptimizer.java +++ /dev/null @@ -1,90 +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.optimization.general; - -import org.apache.commons.math4.analysis.MultivariateVectorFunction; -import org.apache.commons.math4.analysis.differentiation.GradientFunction; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableFunction; -import org.apache.commons.math4.optimization.ConvergenceChecker; -import org.apache.commons.math4.optimization.GoalType; -import org.apache.commons.math4.optimization.InitialGuess; -import org.apache.commons.math4.optimization.OptimizationData; -import org.apache.commons.math4.optimization.PointValuePair; -import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateOptimizer; - -/** - * Base class for implementing optimizers for multivariate scalar - * differentiable functions. - * It contains boiler-plate code for dealing with gradient evaluation. - * - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 3.1 - */ -@Deprecated -public abstract class AbstractDifferentiableOptimizer - extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction> { - /** - * Objective function gradient. - */ - private MultivariateVectorFunction gradient; - - /** - * @param checker Convergence checker. - */ - protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) { - super(checker); - } - - /** - * Compute the gradient vector. - * - * @param evaluationPoint Point at which the gradient must be evaluated. - * @return the gradient at the specified point. - */ - protected double[] computeObjectiveGradient(final double[] evaluationPoint) { - return gradient.value(evaluationPoint); - } - - /** - * {@inheritDoc} - * - * @deprecated In 3.1. Please use - * {@link #optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])} - * instead. - */ - @Override@Deprecated - protected PointValuePair optimizeInternal(final int maxEval, - final MultivariateDifferentiableFunction f, - final GoalType goalType, - final double[] startPoint) { - return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint)); - } - - /** {@inheritDoc} */ - @Override - protected PointValuePair optimizeInternal(final int maxEval, - final MultivariateDifferentiableFunction f, - final GoalType goalType, - final OptimizationData... optData) { - // Store optimization problem characteristics. - gradient = new GradientFunction(f); - - // Perform optimization. - return super.optimizeInternal(maxEval, f, goalType, optData); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java deleted file mode 100644 index 3bc9a05..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractLeastSquaresOptimizer.java +++ /dev/null @@ -1,577 +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.optimization.general; - -import org.apache.commons.math4.analysis.DifferentiableMultivariateVectorFunction; -import org.apache.commons.math4.analysis.FunctionUtils; -import org.apache.commons.math4.analysis.differentiation.DerivativeStructure; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableVectorFunction; -import org.apache.commons.math4.exception.DimensionMismatchException; -import org.apache.commons.math4.exception.NumberIsTooSmallException; -import org.apache.commons.math4.exception.util.LocalizedFormats; -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.optimization.ConvergenceChecker; -import org.apache.commons.math4.optimization.DifferentiableMultivariateVectorOptimizer; -import org.apache.commons.math4.optimization.InitialGuess; -import org.apache.commons.math4.optimization.OptimizationData; -import org.apache.commons.math4.optimization.PointVectorValuePair; -import org.apache.commons.math4.optimization.Target; -import org.apache.commons.math4.optimization.Weight; -import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateVectorOptimizer; -import org.apache.commons.math4.util.FastMath; - -/** - * Base class for implementing least squares optimizers. - * It handles the boilerplate methods associated to thresholds settings, - * Jacobian and error estimation. - * <br/> - * This class constructs the Jacobian matrix of the function argument in method - * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int, - * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[]) - * optimize} and assumes that the rows of that 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 org.apache.commons.math4.optimization.Target Target} while - * the number of columns is equal to the dimension of the - * {@link org.apache.commons.math4.optimization.InitialGuess InitialGuess}. - * - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 1.2 - */ -@Deprecated -public abstract class AbstractLeastSquaresOptimizer - extends BaseAbstractMultivariateVectorOptimizer<DifferentiableMultivariateVectorFunction> - implements DifferentiableMultivariateVectorOptimizer { - /** - * Singularity threshold (cf. {@link #getCovariances(double)}). - * @deprecated As of 3.1. - */ - @Deprecated - private static final double DEFAULT_SINGULARITY_THRESHOLD = 1e-14; - /** - * Jacobian matrix of the weighted residuals. - * This matrix is in canonical form just after the calls to - * {@link #updateJacobian()}, but may be modified by the solver - * in the derived class (the {@link LevenbergMarquardtOptimizer - * Levenberg-Marquardt optimizer} does this). - * @deprecated As of 3.1. To be removed in 4.0. Please use - * {@link #computeWeightedJacobian(double[])} instead. - */ - @Deprecated - protected double[][] weightedResidualJacobian; - /** Number of columns of the jacobian matrix. - * @deprecated As of 3.1. - */ - @Deprecated - protected int cols; - /** Number of rows of the jacobian matrix. - * @deprecated As of 3.1. - */ - @Deprecated - protected int rows; - /** Current point. - * @deprecated As of 3.1. - */ - @Deprecated - protected double[] point; - /** Current objective function value. - * @deprecated As of 3.1. - */ - @Deprecated - protected double[] objective; - /** Weighted residuals - * @deprecated As of 3.1. - */ - @Deprecated - protected double[] weightedResiduals; - /** Cost value (square root of the sum of the residuals). - * @deprecated As of 3.1. Field to become "private" in 4.0. - * Please use {@link #setCost(double)}. - */ - @Deprecated - protected double cost; - /** Objective function derivatives. */ - private MultivariateDifferentiableVectorFunction jF; - /** Number of evaluations of the Jacobian. */ - private int jacobianEvaluations; - /** Square-root of the weight matrix. */ - private RealMatrix weightMatrixSqrt; - - /** - * Simple constructor with default settings. - * The convergence check is set to a {@link - * org.apache.commons.math4.optimization.SimpleVectorValueChecker}. - * @deprecated See {@link org.apache.commons.math4.optimization.SimpleValueChecker#SimpleValueChecker()} - */ - @Deprecated - protected AbstractLeastSquaresOptimizer() {} - - /** - * @param checker Convergence checker. - */ - protected AbstractLeastSquaresOptimizer(ConvergenceChecker<PointVectorValuePair> checker) { - super(checker); - } - - /** - * @return the number of evaluations of the Jacobian function. - */ - public int getJacobianEvaluations() { - return jacobianEvaluations; - } - - /** - * Update the jacobian matrix. - * - * @throws DimensionMismatchException if the Jacobian dimension does not - * match problem dimension. - * @deprecated As of 3.1. Please use {@link #computeWeightedJacobian(double[])} - * instead. - */ - @Deprecated - protected void updateJacobian() { - final RealMatrix weightedJacobian = computeWeightedJacobian(point); - weightedResidualJacobian = weightedJacobian.scalarMultiply(-1).getData(); - } - - /** - * Computes the 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. - * @since 3.1 - */ - protected RealMatrix computeWeightedJacobian(double[] params) { - ++jacobianEvaluations; - - final DerivativeStructure[] dsPoint = new DerivativeStructure[params.length]; - final int nC = params.length; - for (int i = 0; i < nC; ++i) { - dsPoint[i] = new DerivativeStructure(nC, 1, i, params[i]); - } - final DerivativeStructure[] dsValue = jF.value(dsPoint); - final int nR = getTarget().length; - if (dsValue.length != nR) { - throw new DimensionMismatchException(dsValue.length, nR); - } - final double[][] jacobianData = new double[nR][nC]; - for (int i = 0; i < nR; ++i) { - int[] orders = new int[nC]; - for (int j = 0; j < nC; ++j) { - orders[j] = 1; - jacobianData[i][j] = dsValue[i].getPartialDerivative(orders); - orders[j] = 0; - } - } - - return weightMatrixSqrt.multiply(MatrixUtils.createRealMatrix(jacobianData)); - } - - /** - * Update the residuals array and cost function value. - * @throws DimensionMismatchException if the dimension does not match the - * problem dimension. - * @throws org.apache.commons.math4.exception.TooManyEvaluationsException - * if the maximal number of evaluations is exceeded. - * @deprecated As of 3.1. Please use {@link #computeResiduals(double[])}, - * {@link #computeObjectiveValue(double[])}, {@link #computeCost(double[])} - * and {@link #setCost(double)} instead. - */ - @Deprecated - protected void updateResidualsAndCost() { - objective = computeObjectiveValue(point); - final double[] res = computeResiduals(objective); - - // Compute cost. - cost = computeCost(res); - - // Compute weighted residuals. - final ArrayRealVector residuals = new ArrayRealVector(res); - weightedResiduals = weightMatrixSqrt.operate(residuals).toArray(); - } - - /** - * Computes the cost. - * - * @param residuals Residuals. - * @return the cost. - * @see #computeResiduals(double[]) - * @since 3.1 - */ - protected double computeCost(double[] residuals) { - final ArrayRealVector r = new ArrayRealVector(residuals); - return FastMath.sqrt(r.dotProduct(getWeight().operate(r))); - } - - /** - * Get the Root Mean Square value. - * Get the Root Mean Square value, i.e. the root of the arithmetic - * mean of the square of all weighted residuals. This is related to the - * criterion that is minimized by the 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 RMS value - */ - public double getRMS() { - return FastMath.sqrt(getChiSquare() / rows); - } - - /** - * 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. - * @since 3.1 - */ - public RealMatrix getWeightSquareRoot() { - return weightMatrixSqrt.copy(); - } - - /** - * Sets the cost. - * - * @param cost Cost value. - * @since 3.1 - */ - protected void setCost(double cost) { - this.cost = cost; - } - - /** - * Get the covariance matrix of the optimized parameters. - * - * @return the covariance matrix. - * @throws org.apache.commons.math4.linear.SingularMatrixException - * if the covariance matrix cannot be computed (singular problem). - * @see #getCovariances(double) - * @deprecated As of 3.1. Please use {@link #computeCovariances(double[],double)} - * instead. - */ - @Deprecated - public double[][] getCovariances() { - return getCovariances(DEFAULT_SINGULARITY_THRESHOLD); - } - - /** - * 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 threshold Singularity threshold. - * @return the covariance matrix. - * @throws org.apache.commons.math4.linear.SingularMatrixException - * if the covariance matrix cannot be computed (singular problem). - * @deprecated As of 3.1. Please use {@link #computeCovariances(double[],double)} - * instead. - */ - @Deprecated - public double[][] getCovariances(double threshold) { - return computeCovariances(point, threshold); - } - - /** - * 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). - * @since 3.1 - */ - 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(); - } - - /** - * <p> - * Returns an estimate of the standard deviation of each parameter. The - * returned values are the so-called (asymptotic) standard errors on the - * parameters, defined as {@code sd(a[i]) = sqrt(S / (n - m) * C[i][i])}, - * where {@code a[i]} is the optimized value of the {@code i}-th parameter, - * {@code S} is the minimized value of the sum of squares objective function - * (as returned by {@link #getChiSquare()}), {@code n} is the number of - * observations, {@code m} is the number of parameters and {@code C} is the - * covariance matrix. - * </p> - * <p> - * See also - * <a href="http://en.wikipedia.org/wiki/Least_squares";>Wikipedia</a>, - * or - * <a href="http://mathworld.wolfram.com/LeastSquaresFitting.html";>MathWorld</a>, - * equations (34) and (35) for a particular case. - * </p> - * - * @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. - * @throws NumberIsTooSmallException if the number of degrees of freedom is not - * positive, i.e. the number of measurements is less or equal to the number of - * parameters. - * @deprecated as of version 3.1, {@link #computeSigma(double[],double)} should be used - * instead. It should be emphasized that {@code guessParametersErrors} and - * {@code computeSigma} are <em>not</em> strictly equivalent. - */ - @Deprecated - public double[] guessParametersErrors() { - if (rows <= cols) { - throw new NumberIsTooSmallException(LocalizedFormats.NO_DEGREES_OF_FREEDOM, - rows, cols, false); - } - double[] errors = new double[cols]; - final double c = FastMath.sqrt(getChiSquare() / (rows - cols)); - double[][] covar = computeCovariances(point, 1e-14); - for (int i = 0; i < errors.length; ++i) { - errors[i] = FastMath.sqrt(covar[i][i]) * c; - } - return errors; - } - - /** - * 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. - * @since 3.1 - */ - 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} - * @deprecated As of 3.1. Please use - * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int, - * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[]) - * optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)} - * instead. - */ - @Override - @Deprecated - public PointVectorValuePair optimize(int maxEval, - final DifferentiableMultivariateVectorFunction f, - final double[] target, final double[] weights, - final double[] startPoint) { - return optimizeInternal(maxEval, - FunctionUtils.toMultivariateDifferentiableVectorFunction(f), - new Target(target), - new Weight(weights), - new InitialGuess(startPoint)); - } - - /** - * Optimize an objective function. - * Optimization is considered to be a weighted least-squares minimization. - * The cost function to be minimized is - * <code>∑weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code> - * - * @param f Objective function. - * @param target Target value for the objective functions at optimum. - * @param weights Weights for the least squares cost computation. - * @param startPoint Start point for optimization. - * @return the point/value pair giving the optimal value for objective - * function. - * @param maxEval Maximum number of function evaluations. - * @throws org.apache.commons.math4.exception.DimensionMismatchException - * if the start point dimension is wrong. - * @throws org.apache.commons.math4.exception.TooManyEvaluationsException - * if the maximal number of evaluations is exceeded. - * @throws org.apache.commons.math4.exception.NullArgumentException if - * any argument is {@code null}. - * @deprecated As of 3.1. Please use - * {@link BaseAbstractMultivariateVectorOptimizer#optimize(int, - * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[]) - * optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)} - * instead. - */ - @Deprecated - public PointVectorValuePair optimize(final int maxEval, - final MultivariateDifferentiableVectorFunction f, - final double[] target, final double[] weights, - final double[] startPoint) { - return optimizeInternal(maxEval, f, - new Target(target), - new Weight(weights), - new InitialGuess(startPoint)); - } - - /** - * Optimize an objective function. - * Optimization is considered to be a weighted least-squares minimization. - * The cost function to be minimized is - * <code>∑weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code> - * - * @param maxEval Allowed number of evaluations of the objective function. - * @param f Objective function. - * @param optData Optimization data. The following data will be looked for: - * <ul> - * <li>{@link Target}</li> - * <li>{@link Weight}</li> - * <li>{@link InitialGuess}</li> - * </ul> - * @return the point/value pair giving the optimal value of the objective - * function. - * @throws org.apache.commons.math4.exception.TooManyEvaluationsException if - * the maximal number of evaluations is exceeded. - * @throws DimensionMismatchException if the target, and weight arguments - * have inconsistent dimensions. - * @see BaseAbstractMultivariateVectorOptimizer#optimizeInternal(int, - * org.apache.commons.math4.analysis.MultivariateVectorFunction,OptimizationData[]) - * @since 3.1 - * @deprecated As of 3.1. Override is necessary only until this class's generic - * argument is changed to {@code MultivariateDifferentiableVectorFunction}. - */ - @Deprecated - protected PointVectorValuePair optimizeInternal(final int maxEval, - final MultivariateDifferentiableVectorFunction f, - OptimizationData... optData) { - // XXX Conversion will be removed when the generic argument of the - // base class becomes "MultivariateDifferentiableVectorFunction". - return super.optimizeInternal(maxEval, FunctionUtils.toDifferentiableMultivariateVectorFunction(f), optData); - } - - /** {@inheritDoc} */ - @Override - protected void setUp() { - super.setUp(); - - // Reset counter. - jacobianEvaluations = 0; - - // Square-root of the weight matrix. - weightMatrixSqrt = squareRoot(getWeight()); - - // Store least squares problem characteristics. - // XXX The conversion won't be necessary when the generic argument of - // the base class becomes "MultivariateDifferentiableVectorFunction". - // XXX "jF" is not strictly necessary anymore but is currently more - // efficient than converting the value returned from "getObjectiveFunction()" - // every time it is used. - jF = FunctionUtils.toMultivariateDifferentiableVectorFunction((DifferentiableMultivariateVectorFunction) getObjectiveFunction()); - - // Arrays shared with "private" and "protected" methods. - point = getStartPoint(); - rows = getTarget().length; - cols = point.length; - } - - /** - * 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. - * @since 3.1 - */ - 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; - } - - /** - * 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/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java deleted file mode 100644 index 1bb8cc0..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/general/AbstractScalarDifferentiableOptimizer.java +++ /dev/null @@ -1,114 +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.optimization.general; - -import org.apache.commons.math4.analysis.DifferentiableMultivariateFunction; -import org.apache.commons.math4.analysis.FunctionUtils; -import org.apache.commons.math4.analysis.MultivariateVectorFunction; -import org.apache.commons.math4.analysis.differentiation.MultivariateDifferentiableFunction; -import org.apache.commons.math4.optimization.ConvergenceChecker; -import org.apache.commons.math4.optimization.DifferentiableMultivariateOptimizer; -import org.apache.commons.math4.optimization.GoalType; -import org.apache.commons.math4.optimization.PointValuePair; -import org.apache.commons.math4.optimization.direct.BaseAbstractMultivariateOptimizer; - -/** - * Base class for implementing optimizers for multivariate scalar - * differentiable functions. - * It contains boiler-plate code for dealing with gradient evaluation. - * - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 2.0 - */ -@Deprecated -public abstract class AbstractScalarDifferentiableOptimizer - extends BaseAbstractMultivariateOptimizer<DifferentiableMultivariateFunction> - implements DifferentiableMultivariateOptimizer { - /** - * Objective function gradient. - */ - private MultivariateVectorFunction gradient; - - /** - * Simple constructor with default settings. - * The convergence check is set to a - * {@link org.apache.commons.math4.optimization.SimpleValueChecker - * SimpleValueChecker}. - * @deprecated See {@link org.apache.commons.math4.optimization.SimpleValueChecker#SimpleValueChecker()} - */ - @Deprecated - protected AbstractScalarDifferentiableOptimizer() {} - - /** - * @param checker Convergence checker. - */ - protected AbstractScalarDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) { - super(checker); - } - - /** - * Compute the gradient vector. - * - * @param evaluationPoint Point at which the gradient must be evaluated. - * @return the gradient at the specified point. - * @throws org.apache.commons.math4.exception.TooManyEvaluationsException - * if the allowed number of evaluations is exceeded. - */ - protected double[] computeObjectiveGradient(final double[] evaluationPoint) { - return gradient.value(evaluationPoint); - } - - /** {@inheritDoc} */ - @Override - protected PointValuePair optimizeInternal(int maxEval, - final DifferentiableMultivariateFunction f, - final GoalType goalType, - final double[] startPoint) { - // Store optimization problem characteristics. - gradient = f.gradient(); - - return super.optimizeInternal(maxEval, f, goalType, startPoint); - } - - /** - * Optimize an objective function. - * - * @param f Objective function. - * @param goalType Type of optimization goal: either - * {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}. - * @param startPoint Start point for optimization. - * @param maxEval Maximum number of function evaluations. - * @return the point/value pair giving the optimal value for objective - * function. - * @throws org.apache.commons.math4.exception.DimensionMismatchException - * if the start point dimension is wrong. - * @throws org.apache.commons.math4.exception.TooManyEvaluationsException - * if the maximal number of evaluations is exceeded. - * @throws org.apache.commons.math4.exception.NullArgumentException if - * any argument is {@code null}. - */ - public PointValuePair optimize(final int maxEval, - final MultivariateDifferentiableFunction f, - final GoalType goalType, - final double[] startPoint) { - return optimizeInternal(maxEval, - FunctionUtils.toDifferentiableMultivariateFunction(f), - goalType, - startPoint); - } -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java b/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java deleted file mode 100644 index fae7419..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/general/ConjugateGradientFormula.java +++ /dev/null @@ -1,50 +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.optimization.general; - -/** - * Available choices of update formulas for the β parameter - * in {@link NonLinearConjugateGradientOptimizer}. - * <p> - * The β parameter is used to compute the successive conjugate - * search directions. For non-linear conjugate gradients, there are - * two formulas to compute β: - * <ul> - * <li>Fletcher-Reeves formula</li> - * <li>Polak-Ribière formula</li> - * </ul> - * On the one hand, the Fletcher-Reeves formula is guaranteed to converge - * if the start point is close enough of the optimum whether the - * Polak-Ribière formula may not converge in rare cases. On the - * other hand, the Polak-Ribière formula is often faster when it - * does converge. Polak-Ribière is often used. - * <p> - * @see NonLinearConjugateGradientOptimizer - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 2.0 - */ -@Deprecated -public enum ConjugateGradientFormula { - - /** Fletcher-Reeves formula. */ - FLETCHER_REEVES, - - /** Polak-Ribière formula. */ - POLAK_RIBIERE - -} http://git-wip-us.apache.org/repos/asf/commons-math/blob/b4669aad/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java ---------------------------------------------------------------------- diff --git a/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java b/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java deleted file mode 100644 index 9a44084..0000000 --- a/src/main/java/org/apache/commons/math4/optimization/general/GaussNewtonOptimizer.java +++ /dev/null @@ -1,194 +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.optimization.general; - -import org.apache.commons.math4.exception.ConvergenceException; -import org.apache.commons.math4.exception.MathInternalError; -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.optimization.ConvergenceChecker; -import org.apache.commons.math4.optimization.PointVectorValuePair; -import org.apache.commons.math4.optimization.SimpleVectorValueChecker; - -/** - * Gauss-Newton least-squares solver. - * <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> - * - * @deprecated As of 3.1 (to be removed in 4.0). - * @since 2.0 - * - */ -@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 and the - * convergence check is set to a {@link SimpleVectorValueChecker} - * with default tolerances. - * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()} - */ - @Deprecated - public GaussNewtonOptimizer() { - this(true); - } - - /** - * 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); - } - - /** - * Simple constructor with default settings. - * The convergence check is set to a {@link SimpleVectorValueChecker} - * with default tolerances. - * - * @param useLU If {@code true}, the normal equations will be solved - * using LU decomposition, otherwise they will be solved using QR - * decomposition. - * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()} - */ - @Deprecated - public GaussNewtonOptimizer(final boolean useLU) { - this(useLU, new SimpleVectorValueChecker()); - } - - /** - * @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() { - 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; - int iter = 0; - for (boolean converged = false; !converged;) { - ++iter; - - // 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]; - } - } - } - - 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); - } - - // Check convergence. - if (previous != null) { - converged = checker.converged(iter, previous, current); - if (converged) { - cost = computeCost(currentResiduals); - // Update (deprecated) "point" field. - point = current.getPoint(); - return current; - } - } - } - // Must never happen. - throw new MathInternalError(); - } -}
